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| author | SoloDShelby | 2024-07-19 14:41:40 +0300 |
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| committer | SoloDShelby | 2024-07-19 14:41:40 +0300 |
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Evaluation code for paper 1
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diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/01.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/01.json new file mode 100644 index 0000000..5b9cc0f --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/01.json @@ -0,0 +1,402 @@ +{ + "created_at": "2024-05-31T23:02:19.686688Z", + "data": { + "amplify": false, + "answer": "The genes associated with diabetes mentioned in the text include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, HNFA4, LOC387761, KCNQ1, MTNR1B, GCKR, BACH2, CTNNB1, CREB5, PARD3B, PARD6G, PRKCZ, EBF1, GIGYF1, ADCY5, PROX1, DGKB-TMEM195, CRY2, FADS1, GLIS3, and C2CD4B.", + "context": { + "2bef9608-4bd6-4252-9fbd-2413b2cad4f8": [ + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "\n\nTo see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p ≤ 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14)." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3)." + } + ], + "2dade65a-5d31-4839-b2c9-4c6cd3056f58": [ + { + "document_id": "2dade65a-5d31-4839-b2c9-4c6cd3056f58", + "text": "\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4]." + } + ], + "31588831-61b3-4018-9962-bd6985c3061b": [ + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + } + ], + "3c35547c-eb9b-470d-b74b-0f9a0529e965": [ + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "text": "\n\nAmong the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287)." + } + ], + "45c14654-f263-4031-9941-206d7b6a97f3": [ + { + "document_id": "45c14654-f263-4031-9941-206d7b6a97f3", + "text": "\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c ." + } + ], + "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c": [ + { + "document_id": "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c", + "text": "\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting β-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the β-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the β-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype." + } + ], + "5d7a863d-1811-4eea-9fb0-fbc3067aa664": [ + { + "document_id": "5d7a863d-1811-4eea-9fb0-fbc3067aa664", + "text": "\n\nDespite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 ." + } + ], + "7bd7a98f-955a-4988-8981-a0ff7ab6f7df": [ + { + "document_id": "7bd7a98f-955a-4988-8981-a0ff7ab6f7df", + "text": "\n\nSimilar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Type 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5×10 −13 ), PPARG (odds ratio, 1.20; P = 4.0×10 −4 ), FTO (odds ratio, 1.14; P = 9.2×10 −5 ), KCNJ11 (odds ratio, 1.13; P = 3.6×10 −4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic β cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through β cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic β cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47]." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Most Relevant T2DM Susceptibility Genes\n\nGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "\n\nOne of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009)." + } + ], + "a579db95-2a40-43ff-b237-d47f90aaf64f": [ + { + "document_id": "a579db95-2a40-43ff-b237-d47f90aaf64f", + "text": "Genes boosted in type 2 diabetes\n\nBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes." + } + ], + "b978a189-6fbd-4791-8072-7db79f43746a": [ + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "RESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "\nOBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two" + } + ], + "bbb4af44-2659-4207-b9a1-0ff85d379a9f": [ + { + "document_id": "bbb4af44-2659-4207-b9a1-0ff85d379a9f", + "text": "\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies." + } + ], + "d9564b3c-efac-42ae-8e15-bf962c0a7a3c": [ + { + "document_id": "d9564b3c-efac-42ae-8e15-bf962c0a7a3c", + "text": "Introduction\n\nMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + } + ] + }, + "data_source": [ + { + "document_id": "bbb4af44-2659-4207-b9a1-0ff85d379a9f", + "section_type": "main", + "text": "\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies." + }, + { + "document_id": "5d7a863d-1811-4eea-9fb0-fbc3067aa664", + "section_type": "main", + "text": "\n\nDespite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 ." + }, + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "section_type": "main", + "text": "Relation to human and rodent association and linkage studies\n\nRecently, a total of nine candidate genes for T2DM have been identified and replicated in humans through multi- [5][6][7][8][9][10][11].Interestingly, none of these genes shows a high score in our meta-analysis, although Pparg and Tcf7l2 are significant on the less restrictive 0.01 level.On the other hand, from the data we could infer that Fto and Hhex act in pancreatic islets indicated by the T2DM-GeneMiner result for these genes.Cdkal1 and Cdkn2a are not expressed in the transcriptional studies.These genes show very low expression levels or might be active in tissues not included in our study.Since our meta-analysis approach takes into account several data sets from DNA microarrays, our candidate genes have a bias towards transcripts whose expression is changed in the context of T2DM.Moreover, the gene variants from association studies may not result in altered gene expression and, for most SNPs found in association studies, there is a lack of functional information since the variation mostly occurs in non-coding regions of the genes.In order to correlate the T2DM genes with genetic variation we plotted the number of known SNPs for the genes [see Figure 2 in Additional file 1].No general tendency to highly variable genes is observable.Two genes of the candidate list show high variation, Pgcp (9,098 SNPs) and Sorbs1 (4,130).Particularly interesting is Pgcp, because it has not been related to T2DM before and its functional role is also undetermined." + }, + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "section_type": "main", + "text": "\n\nOne of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009)." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nAmong the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287)." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Type 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5×10 −13 ), PPARG (odds ratio, 1.20; P = 4.0×10 −4 ), FTO (odds ratio, 1.14; P = 9.2×10 −5 ), KCNJ11 (odds ratio, 1.13; P = 3.6×10 −4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "\n\nIn 2010, a meta-analysis of 21 genome-wide association studies performed by Dupuis and colleagues identified ADCY5, PROX1, GCK, GCKR, and DGKB/TMEM195 as new genetic loci for T2D susceptibility [22].Among these loci, DGKB/TMEM195, GCK, PROX1, and ADCY5 mainly affect -cell functions, whereas the locus mapped in GCKR shows a primary effect on insulin action [22].In the same year, another genome-wide association study by Qi and colleagues discovered new variants near RBMS1 and ITGB6 genes at 2q24, and these variants were found to affect glucose metabolism and insulin resistance [23].In addition, an expanded meta-analysis of existing GWAS by Voight and colleagues identified 12 new signals with a combined < 5 × 10 −8 , including BCL11A, ZBED3, KLF14, TP53INP1, TLE4, CENTD2, HMGA2, HNF1A, PRC1, ZFAND6, DUSP9, and KCNQ1 [24].HNF1A was previously recognized as the causal gene of MODY3 [62] and also harbored the common variant (G319S) that contributes to early-onset T2D [63,64].DUSP9, mapped on chromosome X, encodes a member of the family of mitogen-activated protein kinase phosphatase 4, MKP4, which is important in cell cycle regulation and plays pivotal roles in regulating insulin action [65][66][67]." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "section_type": "main", + "text": "RESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "\n\nBecause obesity is linked to the development of T2D, we adjusted for body mass index (BMI) in the regression and found that the association of these genes with diabetes-related traits remained significant (Supplementary Tables 17 and 18).We used the generalized linear mixed model implemented by SAIGE-Gene which accounts for relatedness and adjusts for unbalanced case-control ratios 16 to verify association of our variant sets of interest with glucose, HbA1c, and T2D diagnosis.SAIGE-Gene was run in the European ancestry population including related individuals (n = 398,574).Using the p-value thresholds previously employed, all associations were statistically significant using this method apart from the associations of TNRC6B pLOF with HbA1c (p = 6.85 × 10 -6 ) and T2D diagnosis (p = 4.77 × 10 -5 ) which were less significant (Supplementary Table 19)." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic β cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through β cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic β cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47]." + }, + { + "document_id": "6b7c6ac7-208d-4942-af31-cc3c37252751", + "section_type": "main", + "text": "\n\nImportantly, our findings demonstrate that more than 50% of the genes in which genetic variants have been known to increase risk of T2DM showed altered expression in different tissues.The perturbation was highest, as expected, in pancreatic islets, where eight genes i.e.HHEX, HNF1B, KCNQ1, NOTCH2, TCF7L2, THADA, TSPAN8 and WFS1, showed aberrant expression.All of these genetic loci, apart from the less studied TSPAN8, have been implicated in pathways primarily involved in insulin secretion, cell proliferation and regeneration [30].Of note, genetic variants in the THADA and WFS1 have recently been shown to impair glucagon-like peptide-1stimulated insulin secretion [31,32].Furthermore, many of these loci have also shown effects on insulin sensitivity [33].In line with this, five genes, i.e.HNF1B, IRS1, KCNJ11, NOTCH2 and WFS1, were also differentially expressed in skeletal muscle.Of all T2DM genes, IRS1 seems to have a clear effect on insulin sensitivity; the T2DM-associated allele was associated with decreased IRS1 protein expression as well as reduced phosphatidylinositol-3-kinase-activity and insulin-stimulated glucose uptake in humans [12]." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "section_type": "abstract", + "text": "\nOBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two" + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "\n\nTo see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p ≤ 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14)." + }, + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "section_type": "main", + "text": "Box 1: Genes nearest to loci associated with fasting diabetes-related quantitative traits\n\nThe DGKB-TMEM195 locus was recently reported to be associated with fasting glucose 24 ; here we report genome-wide significant replication of that finding and evaluate the genes mapping closest to the lead SNP in further detail.DGKB encodes the β (1 of 10) isotype of the catalytic domain of diacylglycerol kinase, which regulates the intracellular concentration of the second messenger diacylglycerol.In rat pancreatic islets, glucose increases diacylglycerol 49 , which activates protein kinase C (PKC) and thus potentiates insulin secretion 50 .TMEM195 encodes transmembrane protein 195, an integral membrane phosphoprotein highly expressed in liver.ADCY5 encodes adenylate cyclase 5, which catalyzes the generation of cAMP.Upon binding to its receptor in pancreatic beta cells, glucagon-like peptide 1 (GLP-1) induces cAMP-mediated activation of protein kinase A, transcription of the proinsulin gene and stimulation of insulin secretory processes 51 ." + }, + { + "document_id": "16e272af-f687-4261-99cf-8125a9e7cdc7", + "section_type": "main", + "text": "\n\nFigure2| effect sizes of the 11 common variants confirmed to be involved in type 2 diabetes risk.The x axis gives the year that published evidence reached the levels of statistical confidence that are now accepted as necessary for genetic association studies.CDKAL1, CDK5 regulatory subunitassociated protein 1-like 1; CDKN2, cyclin-dependent kinase inhibitor 2A; FTO, fat mass and obesity-associated; HHEX, haematopoietically expressed homeobox; IDE, insulin-degrading enzyme; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; KCNJ11, potassium inwardly-rectifying channel, subfamily J, member 11; PPARG, peroxisome proliferator-activated receptor-γ gene; SLC30A8, solute carrier family 30 (zinc transporter), member 8; TCF2, transcription factor 2, hepatic; TCF7L2, transcription factor 7-like 2 (T-cell specific, HMg-box); WFS1, Wolfram syndrome 1." + }, + { + "document_id": "5564cfa4-6a5c-4328-a0b6-5cd1cc0b2338", + "section_type": "main", + "text": "Box 1: Genes nearest to loci associated with fasting diabetes-related quantitative traits\n\nThe DGKB-TMEM195 locus was recently reported to be associated with fasting glucose 24 ; here we report genome-wide significant replication of that finding and evaluate the genes mapping closest to the lead SNP in further detail.DGKB encodes the β (1 of 10) isotype of the catalytic domain of diacylglycerol kinase, which regulates the intracellular concentration of the second messenger diacylglycerol.In rat pancreatic islets, glucose increases diacylglycerol 49 , which activates protein kinase C (PKC) and thus potentiates insulin secretion 50 .TMEM195 encodes transmembrane protein 195, an integral membrane phosphoprotein highly expressed in liver.ADCY5 encodes adenylate cyclase 5, which catalyzes the generation of cAMP.Upon binding to its receptor in pancreatic beta cells, glucagon-like peptide 1 (GLP-1) induces cAMP-mediated activation of protein kinase A, transcription of the proinsulin gene and stimulation of insulin secretory processes 51 ." + }, + { + "document_id": "9e3a4f4a-24d6-4a12-a798-ca654e225e7e", + "section_type": "main", + "text": "\n\nWhile the above findings show no evidence of association between relevant mitochondrial gene sets and T2D, these genes could still display causal associations with specific intermediate phenotypes linked to the disease.Support for this comes from reported mitochondrial dysfunction in insulin-resistant individuals [8].Therefore, we tested the same three gene sets described above for enrichment of associations with seven different glucose and insulin-related traits characteristic of T2D, using GWA metaanalyses of up to 46,186 non-diabetic individuals [37,38] (Soranzo N. et al., unpublished data).The quantitative traits analyzed include fasting levels of glucose and insulin, glucose and insulin levels 2 hours following a 75-gram oral glucose tolerance test, indices of b-cell function (HOMA-B) and insulin resistance (HOMA-IR) [49], and glycated hemoglobin levels (HbA 1C ), which reflect long-term plasma glucose concentrations (see Materials and Methods)." + }, + { + "document_id": "7bd7a98f-955a-4988-8981-a0ff7ab6f7df", + "section_type": "main", + "text": "\n\nSimilar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits." + }, + { + "document_id": "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c", + "section_type": "main", + "text": "\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "section_type": "main", + "text": "\n\nIn addition, these analyses highlighted notable biological connections between sets of genes within confirmed T2D-association regions.For example, HMGA2 emerges as a key transcriptional regulator of IGF2BP2 (refs.53,54).However, because Hmga/Hmg1c knockout mice are deficient in adipocyte differentiation 45 , and the IGF2BP2 risk allele is associated with reduced beta-cell function 55 , further work is required to establish the relevance of this regulatory Each point refers to a single T2D association signal, with colors denoting the strength of the association to either the x-axis variable (lefthand of each pair of plots) or y-axis variable (right-hand of each pair) (red, P < 10 −3 ; orange, 10 −3 < P < 10 −2 ; yellow, 0.01 < P < 0.05; green, 0.05 < P < 0.20; blue, P > 0.20).The two KCNQ1 associations are distinguished by the notation KCNQ1 for rs163184 and KCNQ1* for rs231362.The gene names associated with each signal have been chosen on the basis of proximity to the index SNP and should not be presumed to indicate causality." + }, + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "section_type": "main", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + }, + { + "document_id": "5564cfa4-6a5c-4328-a0b6-5cd1cc0b2338", + "section_type": "main", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nIn another important study, 12 loci, previously identified by GWAS as predictors of coronary heart disease (CHD) in the general population, were investigated in three CHD case-control studies of diabetic patients.Among them, five variants, rs4977574 (CDKN2A/2B), rs12526453 (PHACTR1), rs646776 (CELSR2-PSRC1-SORT1), rs2259816 (HNF1A), and rs11206510 (PCSK9), showed a significant association with the risk for CHD also in type 2 DM (43).Among the type 2 DM susceptibility genes investigated by GWAS, the transcription factor 7-like 2 gene (TCF7L2) has been identified as one of the most significant (73).TCF7L2 variants have been found to be associated with CVD in some (40,53), but not in all (74) reports, although the association between TCF7L2 risk alleles and CAD was not higher in diabetic individuals.Subsequent studies analyzed the association of three TCF7L2 variants (rs7903146, rs12255372, and rs11196205) with CAD in 1,650 patients that underwent coronary angiography, and found that these variants were more strongly associated with CAD in diabetic patients than in non-diabetics (54)." + }, + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "section_type": "main", + "text": "Other Association Studies of T2D\n\nAnother strong candidate gene for T2D is ABCC8, which encodes the sulfonylurea receptor (SUR1).This protein is the drug target for a widely used class of hypoglycemic medications, and the ABCC8 gene is also mutated in the monogenic disorder familial hyperinsulinism (168).ABCC8 carries a silent C → T polymorphism in exon 18 (T759T; also reported as \"exon 22\" or T761T), which has been associated with T2D in several populations (3,70,73,92), though not in others (3,63,64,77,103,149).The same gene also harbors an intronic cag → tag polymorphism at the -3 position (variably reported as \"intron 24\" or \"exon 16,\" depending on the gene orientation), with the preponderance of the evidence favoring the c allele as the one conferring risk (92,121), although other groups disagree (3, 70,77,135,149)." + }, + { + "document_id": "45c14654-f263-4031-9941-206d7b6a97f3", + "section_type": "main", + "text": "\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c ." + }, + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting β-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the β-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the β-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Most Relevant T2DM Susceptibility Genes\n\nGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM." + }, + { + "document_id": "2dade65a-5d31-4839-b2c9-4c6cd3056f58", + "section_type": "main", + "text": "\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4]." + }, + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "section_type": "main", + "text": "\n\nFor eighteen genes only limited functional information is available as a basis for assessing a possible relationship to T2DM: Ccrn4l, Serpina12, Htatip2, Mest, Pgcp, Tmsb4x, Angptl4, Mrpl33, Ndfip1, Yipf5, Tmem30a, Asnsd1, Oact5, Larp5, Thrsp, 1810015C04Rik, 2310003F16Rik, and 2610002J02Rik.High genetic variation is known for Pgcp in mouse.Serpina12, a target of Hnf4a, is massively changed in liver and 1810015C04Rik in pancreatic islets." + }, + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "section_type": "main", + "text": "\n\nGlucagon receptor.The G 40 S variant has been associated with T2D in some but not all populations. 56sulin.Case-control studies have suggested an association between T2D and variation at a regulatory minisatellite upstream of the insulin gene.Unlike type 1 diabetes, susceptibility to T2D is associated with the larger class III alleles. 30To rule out the possibility of latent population substructure, Huxtable et al applied family-based association methods (using parent ± ospring trios ascertained via individuals with early-onset T2D) to con®rm this class III association and to show that the susceptibility eect is preferentially transmitted via the paternal allele. 31This ®ts neatly with evidence of maternal imprinting in this region during early development." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "\n\nWe also examined whether we detect associations for the 8 genes encoding T2D drug targets (GLP1R, IGF1R, PPARG, INSR, SLC5A2, DPP4, KCNJ11, ABCC8).Variant sets in three of these genes, DPP4, GLP1R and KCNJ11 significantly associated with either T2D diagnosis or HbA1c levels (p ≤ 0.003 correcting for 15 variant sets tested) and an additional 4 genes had a nominally significant association with T2D and/or HbA1c (Supplementary Figure 5 and Supplementary Table 27).Table 3. Genes and variant sets associated with multiple diabetes-related traits.Variant sets significant for at least one trait in our primary analysis that are also associated with additional diabetes traits (p ≤ 0.0016, 32 sets tested) are shown.Effect is shown in SD of transformed values or as an odds ratio (OR).www.nature.com/scientificreports/PheWAS of GIGYF1 pLOF reveals associations with cholesterol levels, hypothyroidism and complications of diabetes.The most significant novel associations were seen for GIGYF1 pLOF which associated with increased glucose and HbA1c levels as well as increased incidence of T2D diagnosis.To give additional insight into the biological roles of GIGYF1 we performed a phenome-wide association study (PheWAS) testing GIGYF1 pLOF for association with 142 quantitative traits and 262 ICD10-coded diagnoses (Fig. 3).GIGYF1 pLOF strongly associated with decreased levels of total cholesterol (p = 2.44 × 10 -12 , effect = − 0.61 SD) which was, in large part, driven by LDL cholesterol (p = 2.40 × 10 -10 , effect = − 0.56 SD) although an effect on HDL cholesterol was also observed (Table 4).To understand the extent to which this is influenced by the use of cholesterol-lowering medication in diabetics, we adjusted for medication use in the regression and also performed a separate analysis excluding those on cholesterol-lowering medication.The association between GIGYF1 pLOF and LDL cholesterol levels was significant in both analyses (Supplementary Table 28).GIGYF1 pLOF also associated with decreased grip strength and decreased peak expiratory flow.Notably, GIGYF1 pLOF also associated with increased levels of the kidney injury biomarker cystatin c (p = 6.65 × 10 -6 , effect = 0.36 SD) and increased diagnosis of urinary system disorders (p = 7.32 × 10 -5 , OR = 2.71) (Tables 4 and 5)." + }, + { + "document_id": "553ae95d-0a2b-4f2a-8123-da9a9e9e7a77", + "section_type": "main", + "text": "\n\nMinor susceptibility might operate in some populations from other genes, including insulin receptor substrate 1 ( IRS -1 ), adiponectin ( ACDC ) or ectonucleotide pyrophosphatase/phosphodiesterase 1 enzyme ( ENPP1 ) in a context of obesity or diabesity.• In genome scans of diabetic families, loci for T2DM have been found at several sites, including chromosomes 1q, 2q ( NIDDM1 ), 2p, 3q, 12q, 11q, 10q and 20.NIDDM1 has been identifi ed as coding for calpain 10, a non -lysosomal cysteine protease with actions at the mitochondria and plasma membrane, and also in pancreatic β -cell apoptosis.• In 2007, fi ve large genome -wide association studies in European descent populations have identifi ed new potential T2DM genes, including the Wnt signaling related transcription factors TCF7L2 and HHEX , the zinc transporter ZnT8 ( SLC30A8 ), the CDK5 regulatory subunit -associated protein 1 -like 1 ( CDKAL1 ) and a regulatory protein for IGF2 ( IGF2BP2 ).A consensus of close to 20 confi rmed T2DMsusceptibility loci to date provided novel insights into the biology of T2DM and glucose homeostasis, but individually with a relatively small genetic effect.Importantly, these genes implicate several pathways involved in β -cell development and function.• Compared with clinical risk factors alone, the inclusion of common genetic variants (at least those identifi ed to date) associated with the risk of T2DM has a small effect on the ability to predict future development of T2DM.At the individual level, however, a combined genotype score based on 15 risk alleles confers a 5 -8 fold increased risk of developing T2DM.Identifying the subgroups of individuals at higher risk is important to target these subjects with more effective preventative measures." + }, + { + "document_id": "752b2413-8c90-4af7-b65b-db429145b3bb", + "section_type": "abstract", + "text": "\nThe intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis.However, the specific genes through which these regulatory variants act remain poorly characterized.We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and highdensity genotyping.We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations.At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5.At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1.At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis.Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci." + }, + { + "document_id": "d9564b3c-efac-42ae-8e15-bf962c0a7a3c", + "section_type": "main", + "text": "Introduction\n\nMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + }, + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "section_type": "main", + "text": "\n\nMost (71%) of the 1895 genes had minimal evidence linking them to a causal role in T2D pathogenesis (PCS < 0.05) (Additional file 4: Figure S3).However, 95% of T2D loci included at least one gene (median, 3) with PCS > 0.10, and at 70% of loci, there was at least one gene with PCS > 0.20 (Additional file 4: Figure S3).The top-scoring genes across the 101 loci (such as IRS1 [PCS = 0.69], SLC30A8 [PCS = 0.77], HNF1B [PCS = 0.54]) include many of the genes with the strongest prior claims for involvement in T2D risk, prior claims which arise in part from data used to generate the PCSs.For example, these genes each contain rare coding variants directly implicated in the development of T2D (or related conditions): these rare variants are independent of the common variant GWAS signals, but their relationship to diabetes is likely to have been captured through the semantic mapping.The PCS also highlighted several other highly scoring candidates with known causal roles in relation to diabetes and obesity such as MC4R (PCS = 0.43), WFS1 (0.41), ABCC8 (0.37), LEP (0.27), GCK (0.24) and HNF1A (0.23).At other loci, these analyses highlighted candidates that have received scant attention to date; for example, CENPW (PCS = 0.83) scored highly both in terms of semantic links to T2D-relevant processes and an adipose cis-eQTL linking the T2D GWAS SNP to CENPW expression [21]." + }, + { + "document_id": "a579db95-2a40-43ff-b237-d47f90aaf64f", + "section_type": "main", + "text": "Genes boosted in type 2 diabetes\n\nBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3)." + }, + { + "document_id": "b1d09a6d-334a-48f4-b4ed-4754f398d046", + "section_type": "main", + "text": "\n\nThrough genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05).Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance.Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations.This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies.Functional analysis of these newly discovered loci will further improve our understanding of glycemic control." + }, + { + "document_id": "752b2413-8c90-4af7-b65b-db429145b3bb", + "section_type": "main", + "text": "\n\nThe intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis.However, the specific genes through which these regulatory variants act remain poorly characterized.We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and highdensity genotyping.We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations.At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5.At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1.At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis.Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci." + } + ], + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2&gene", + "PPARG&gene", + "KCNJ11&gene", + "SLC30A8&gene", + "HHEX&gene", + "CDKAL1&gene", + "CDKN2A&gene", + "IGF2BP2&gene", + "FTO&gene", + "WFS1&gene" + ], + "metadata": [ + { + "object": "he aim of this study was to ascertain the polymorphic markers profile of ADIPOQ, KCNJ11 and TCF7L2 genes in Kyrgyz population and to analyze the association of polymorphic markers and combinations of ADIPOQ gene's G276T locus, KCNJ11 gene's Glu23Lys locus and TCF7L2 gene's VS3C>T locus with type two diabetes T2D in Kyrgyz population", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab334669" + }, + { + "object": "TCF7L2 gene expression was determined using quantitative real-time RT-PCR. Treatment with curcumin significantly increased TCF7L2 gene expression while treatment with LPS decreased TCF7L2 gene expression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab767034" + }, + { + "object": "Novel mutations were detected in ABCC8 and KCNJ11 gene in Chinese patients with congenital hyperinsulinism CHI. Hotspot mutations such as T1042Qfs*75, I1511K, E501K, G111R in ABCC8 gene, and R34H in KCNJ11 gene are predominantly responsible for Chinese CHI patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab535847" + }, + { + "object": "Description of a novel missense mutation of the WFS1 gene in exon 4 of WFS1 gene in two Italian siblings with Wolfram syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab225713" + }, + { + "object": "Our results indicate that the genetic variation in the FTO gene might be related to single metabolic disturbances. However, the FTO gene polymorphisms are not associated with the risk of MetS [metabolic syndrome ].", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560870" + }, + { + "object": "Common variations in the FTO gene are associated with hip fracture risk in women and that FTO gene may help improve the predictive value of hip fracture risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab150499" + }, + { + "object": "CDKAL1 gene rs7756992 A/G polymorphism was significantly associated with T2DM. The person with G allele of CDKAL1 gene rs7756992 A/G polymorphism might be predisposed to T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab131436" + }, + { + "object": "This FTO gene variation might influence the baseline lipid oxidation in PCOS patients and might explain the impact of the FTO gene on body weight in PCOS.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab929792" + }, + { + "object": "functionally distinct alleles of the PPARG gene are positioned in different parts of the cell nucleus. This confirms the importance of nuclear architecture to the regulation of PPARG gene transcription, and thus to the fate of the adipose cell.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab307597" + }, + { + "object": "The PPARG gene has been reported to be strongly associated with type-2 diabetes, but the present study did not support the hypothesis that the PPARG gene may also play an important role in the development of schizophrenia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab847159" + } + ], + "question": "what genes are associated with diabetes?", + "subquestions": null, + "task_id": "8909D2606E33C312F2ECC705FAF65CA2", + "usage": { + "chatgpt": 10960, + "gpt-4": 6551, + "gpt-4-turbo-preview": 5611 + }, + "user_id": 2 + }, + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "task_id": "8909D2606E33C312F2ECC705FAF65CA2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/02.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/02.json new file mode 100644 index 0000000..d6e1df5 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/02.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-05-31T23:04:51.896429Z", + "data": { + "amplify": false, + "answer": "Genomics provides a comprehensive understanding of the genetic factors influencing the development and progression of diabetes. Nutritional genomics focuses on the interaction between genes and dietary patterns, which plays a crucial role in diabetes. By studying gene-nutrient interactions and genetic variations, researchers can develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Furthermore, genomics can help identify how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake, potentially leading to diabetes. Thus, genomics offers a promising approach to understanding the nutritional factors of diabetes and developing personalized dietary interventions.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nenetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "2a71b781-89fe-4055-bbb1-15aa226e1e3a": [ + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "789097da-e961-4486-8c83-816626556b16": [ + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "text": "\n\nNonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da": [ + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + } + ] + }, + "data_source": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "abstract", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "section_type": "main", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "abstract", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "section_type": "main", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nenetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes." + }, + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "section_type": "main", + "text": "\n\nNonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "abstract", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "main", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "main", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "section_type": "main", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "abstract", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "INTRODUCTION\n\nThis research project grows out of interest in the genetics and genomics of complex diseases, particularly Type 1 Diabetes (T1D).The field of genomics has provided the first systematic approaches to discovering genes and cellular pathways underlying a number of diseases (Lander, 2011. ).My research is focused on SNP variants that occur in susceptibility regions for T1D." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "Conclusions\n\nIn view of the overwhelming inconsistency observed in the results of genetic association studies of type 2 diabetes across the globe, it is pertinent to design the future studies in a way that neutralizes the confounding factors and provides useful results.It is equally important to curate the existing data and reanalyze it through advanced computational methods in the era of systems biology.Further, we need functional studies that complement the pace of genomic research.The post-genomic strategies are perplexed with practical difficulties; yet it is imperative to overcome those and conduct integrated genomic-metabolomic studies to derive meaningful outcomes of practical utility.These approaches may provide better insights into understanding the molecular mechanisms operating in the manifestation of the disease and may help in devising methods for prevention and/or treatment." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "section_type": "main", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "41bc85bc-314f-4d92-9007-5d1571506ef3", + "section_type": "main", + "text": "Discussion\n\nThe goal of the present study was to understand whether metabolic factors affect the expression of the genes recently implicated in the development of type 2 diabetes for which there was little prior evidence of their potential role(s) in this disease.Although many additional SNPs have been identified in subsequent GWAS and meta-analyses [18], we focussed these studies on the genes identified in the first waves of GWAS, as these have been the subject of most follow-up studies to date.Specifically, we examined acute changes in expression of these genes in response to feeding and fasting and longer term changes in the expression of these genes in response to a diet high in fat and sugar, recognized as a critical environmental risk factor for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "section_type": "main", + "text": "\n\nDiabetes is caused due to complex interaction between genetic and environmental factors, like poor life style, diet, physical inactivity and overweight.Genetic factors play a major role in causal of T2DM; however, identification and understanding of genetic factors were of great challenge.Genetic variation in the human genome exists in different forms; from single base pair to large structural variation.In recent times, as the technology has improved; SNP studies, large scale association studies, and next generation sequencing were carried out which helped in the better understanding of T2DM [3].Comparative genomic hybridization (CGH) technique has helped us know about copy number variation (CNVs) and its effect on human genome [4].Understanding the CNVs is critical for the proper study of disease-associated changes because segmental CNVs have been demonstrated in developmental disorders and susceptibility to disease [5,6].Therefore, analysis of CNVs at the whole-genome level is required to create a baseline of human genomic variation [7]." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "\n\nThis perspective changed with the success of the first genome-wide association studies for Type 2 diabetes in 2007 [15,16].These studies were made possible by: (i) the completion of first drafts of the human genome; (ii) the description of haplotypes ('hapmap'); (iii) the development of suitable technology (notably oligonucleotide arrays) to identify variants (single nucleotide polymorphisms); and (iv) the ability to obtain DNA from large populations (often tens of thousands) of healthy people and people with Type 2 diabetes.Given the central dogma of molecular biology, i.e. that information flows from genomic DNA through mRNA to proteins, and providing that robust account is taken of confounding factors, for example through population stratification and multiple testing, variants found more frequently in the Type 2 diabetes-affected population could reasonably be assumed to play a direct role in the disease process." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nAs estimated from the currently achieved genome coverage, the next generation of high-density SNP arrays is expected to provide about half a dozen novel type 2 diabetes risk loci in the near future using the same case-control setting.Alternative settings, such as correlational analyses with state-of-the-art measures for glucose-and incretin-stimulated insulin secretion, whole-body and tissue-specific insulin sensitivity, will probably further increase this number.Moreover, future studies on the role of copy number variants, with their obvious impact on gene dosage, could once more extend our appreciation of the genetic component of type 2 diabetes.Finally, taking into account that gene-environment interactions contribute to the development of type 2 diabetes (393, 394), well-de-fined intervention studies have a good potential to discover risk variants that remain cryptic in cross-sectional settings.The current emergence of diabetes-relevant genes susceptible to persistent and partly inheritable epigenetic regulations, i.e., DNA methylation and histone modifications, further underscores the importance of gene-environment interactions and the complexity of type 2 diabetes genetics (198,395,396).Because epigenetic modifications clearly affect gene expression, the establishment of diabetes-related gene expression profiles of metabolically relevant tissues or easily available surrogate \"tissues\", such as lymphocytes, could help identify novel candidate genes for type 2 diabetes." + } + ], + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mellitus", + "genomics", + "nutritional", + "factors", + "gene-nutrient", + "interactions", + "type&2", + "genetic", + "variants" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "genotypes of methylenetetrahydrofolate reductase gene may be a risk factor for type 2 diabetes mellitus. interaction between genetic polymorphism and environmental factors increases the risk of type 2 diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320805" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "genetic association/nutrigenomic studies in population in South Korea: Data suggest that an SNP in BDNF rs6265 is negatively associated with type 2 diabetes; BDNF Val/Met and Met/Met variants rs6265 decrease risk for glucose intolerance and type 2 diabetes. Middle-aged individuals with BDNF Val/Val are prone to developing type 2 diabetes even with low energy intake and low protein intake.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316682" + }, + { + "object": "show that ER and GR both have the ability to alter the genomic distribution of the FoxA1 pioneer factor. Single-molecule tracking experiments reveal a highly dynamic interaction of FoxA1 with chromatin in vivo; FoxA1 factor is not associated with footprints at its binding sites throughout the genome; findings support a model wherein interactions between transcription factors and pioneer factors are highly dynamic.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab704238" + }, + { + "object": "APOE and CETP TaqIB polymorphisms might not be the genetic risk factors for type 2 diabetes mellitus in Southern Thai population, however, APOE and CETP TaqIB polymorphisms were associated with serum lipids in healthy controls and type 2 diabetes mellitus, respectively.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77338" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "The results of this meta-analysis support the hypothesis that RBP4 is a modest independent risk factor for gestational diabetes mellitus i.e., nonobese patients with gestational diabetes mellitus might express RBP4 at abnormal levels.The association between RBP4 rs3758539 polymorphism and gestational diabetes mellitus risk was not confirmed.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab860992" + }, + { + "object": "Study reports new variants, 1 near exon splice variant and 9 deep-intronic variants in ABCA4 and identifies splicing defects for 12 out of 19 variants. 4 deep-intronic variants create pseudo-exons or elongate the upstream exon. 8 noncanonical splice site NCSS variants cause a partial deletion or skipping of one or more exons in messenger RNAs. Among the 12 variants, 9 lead to stop codons predicting truncated proteins.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab747508" + }, + { + "object": "genetic association studies: Data suggest that an SNP in IGF2BP2 rs4402960 is associated with type 2 diabetes; IGF2BP2 may have genetic interactions with insulin-like growth factor II with a protective effect in male patients with type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316531" + } + ], + "question": "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "usage": { + "chatgpt": 5995, + "gpt-4": 4151, + "gpt-4-turbo-preview": 3211 + }, + "user_id": 2 + }, + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/03.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/03.json new file mode 100644 index 0000000..1a43ffb --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/03.json @@ -0,0 +1,399 @@ +{ + "created_at": "2024-05-31T23:07:32.253629Z", + "data": { + "amplify": false, + "answer": "Genomics can be used to understand the nutritional factors of diabetes by studying nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This approach can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Additionally, genomics can identify gene variants that interact with specific nutrients, providing insights into how diet and genetics interact to influence diabetes risk.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "e9b48e14-aa0c-4331-a17d-82a7f424233c": [ + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + } + ], + "f9b65334-56b7-43e9-9fda-b778c18c1c67": [ + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "text": "Genomics for Type 2 Diabetes\n\nMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + } + ] + }, + "data_source": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "abstract", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "abstract", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "section_type": "main", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "abstract", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "section_type": "main", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + }, + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "section_type": "main", + "text": "Genomics for Type 2 Diabetes\n\nMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "main", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "section_type": "main", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "main", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "section_type": "main", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "8f74252a-5ce1-4109-86b6-5b0228b23bba", + "section_type": "main", + "text": "\n\nThe clinical benefits of genomics: lessons from monogenic obesity and diabetes Thanks to their high penetrance, the alleles responsible for rare, monogenic forms of non-autoimmune diabetes and obesity were relatively easily identified through linkage analysis (reviewed in Owen and Hattersley 2001;O'Rahilly and Farooqi 2006).These discoveries have led to molecular classifications of disease with demonstrable prognostic and therapeutic relevance.For example, individuals with maturity onset diabetes of the young (MODY) due to mutations in HNF1A respond particularly well to treatment with sulfonylureas, whilst those with mutations in glucokinase (GCK) can often come off medication entirely given their relatively benign prognosis (Schnyder et al. 2005;Pearson et al. 2003)." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "abstract", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nGenome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions." + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "main", + "text": "Conclusions\n\nHow will sequencing genomes influence the health of people at risk for or affected with diabetes?The more complete understanding of the biological mechanisms underlying diabetes derived from these studies may lead to identification of novel drug targets.Individuals with variants in genes responsible for MODY or neonatal diabetes respond better to specific drugs [50,51], and sequencing may identify small numbers of individuals with combinations of rarer, more highly penetrant variants that respond better to specific therapeutic options.Although sets of known variants for type 2 diabetes do not add substantially to prediction of type 2 diabetes development in the overall population [52,53], identification of individuals at greater or lower genetic risk for diabetes within the overall population or in specific subgroups, such as younger onset or leaner individuals [54,55], could lead to better targeted health information and also allow identification of higher risk individuals leading to more efficient design of clinical trials for disease prevention." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "Future prospects\n\nWhilst the examples above provide interesting insights, it is clear that we are only at the beginning of mining the information generated by genome-wide association studies for Type 2 diabetes and other complex traits.work in human genetics, involving ever larger cohorts, meta-analyses and the search for rarer and more penetrant variants will in future be important to identify all of the heritable elements that control Type 2 diabetes risk; however, the useful deployment of this information for either disease prediction or the development of new therapies will require considerable further efforts at the cellular and molecular level to understand the function of the identified genes.Moreover, and although not the subject of this particular review, actions of single nucleotide polymorphisms through non-coding genes, e.g.mi-croRNAs and long non-coding RNAs, will require deeper investigation." + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + }, + { + "document_id": "41bc85bc-314f-4d92-9007-5d1571506ef3", + "section_type": "main", + "text": "\n\nIn summary, we have identified nutritional regulation of many of the newly found type 2 diabetes-associated genes.As these studies were performed with a relatively small number of samples, it should be noted that smaller changes in expression may also exist that we had insufficient power to detect.These data provide support for the involvement of these newly identified type 2 diabetes susceptibility genes in β-cell function and also suggest potential roles for many of them in peripheral tissues, notably in the brain and hypothalamus, highlighting the potential importance of neuronal regulation of metabolism and islet function to type 2 diabetes [38][39][40][41].Our study also highlights the tissue-specific regulation of these genes (changes in one or more tissues where the gene is expressed but not in all tissues), suggesting that the SNPs identified in the GWAS studies may need to be examined in the appropriate tissues and under several metabolic contexts [37].Indeed, recent studies aimed at identifying genetic variants that affect gene expression (eQTLs) have found varying effects of these SNPs on gene expression in different tissues, particularly for SNPs located within not between genes, and notably that the SNPs were more associated with expression of diabetesassociated genes in metabolically relevant tissues such as liver, adipose and muscle than in lymphocytes, which are sometimes used as a surrogate because they are easily accessible [80][81][82].The abundant regulation of these genes by nutritional status found in our study also suggests there are likely gene-diet interactions involving these SNPs [83] that may be a complicating factor in future human studies to assess the functional implications of the associated SNPs." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nWhat will be the clinical benefit of all this genetic knowledge beyond its use for prediction of the individual's type 2 diabetes risk?One major advantage of knowing an at-risk person's genotype could be to offer an individually tailored lifestyle intervention program to prevent or, at least, to significantly retard the onset of overt diabetes.This aim requires extensive future work to understand the interaction between risk genes and lifestyle modifications, such as diet (this research area is called nutrigenomics) and exercise regimens (this research area is called physiogenomics).In this regard, data from the Diabetes Prevention Program provided evidence that behavioral intervention can mitigate or even abolish the diabetes risk conferred by TCF7L2 or ENPP1, respectively (127,129).In the Finnish Diabetes Prevention Study, physical activity was shown to reduce the type 2 diabetes risk of PPARG risk allele carriers (387).Another advantage of the genetic knowledge could be to offer type 2 diabetic patients an individually tailored pharmacological therapy with currently available or newly developed, e.g., risk gene-targeting, antidiabetic drugs.Thus, future pharmacogenomic studies have to thoroughly investigate the interaction between risk genes and drugs.Understanding these interactions appears important also because it could help to reduce the therapeutical use of drugs (with their side effects) that are ineffective in certain genotypes." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "THE GENETICS OF TYPE 1 DIABETES\n\nThe study of the genome to map disease-susceptibility regions for T1D and other multifactorial diseases has been facilitated by recent advances in next generation DNA sequencing methods." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "INTRODUCTION\n\nThis research project grows out of interest in the genetics and genomics of complex diseases, particularly Type 1 Diabetes (T1D).The field of genomics has provided the first systematic approaches to discovering genes and cellular pathways underlying a number of diseases (Lander, 2011. ).My research is focused on SNP variants that occur in susceptibility regions for T1D." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nAs estimated from the currently achieved genome coverage, the next generation of high-density SNP arrays is expected to provide about half a dozen novel type 2 diabetes risk loci in the near future using the same case-control setting.Alternative settings, such as correlational analyses with state-of-the-art measures for glucose-and incretin-stimulated insulin secretion, whole-body and tissue-specific insulin sensitivity, will probably further increase this number.Moreover, future studies on the role of copy number variants, with their obvious impact on gene dosage, could once more extend our appreciation of the genetic component of type 2 diabetes.Finally, taking into account that gene-environment interactions contribute to the development of type 2 diabetes (393, 394), well-de-fined intervention studies have a good potential to discover risk variants that remain cryptic in cross-sectional settings.The current emergence of diabetes-relevant genes susceptible to persistent and partly inheritable epigenetic regulations, i.e., DNA methylation and histone modifications, further underscores the importance of gene-environment interactions and the complexity of type 2 diabetes genetics (198,395,396).Because epigenetic modifications clearly affect gene expression, the establishment of diabetes-related gene expression profiles of metabolically relevant tissues or easily available surrogate \"tissues\", such as lymphocytes, could help identify novel candidate genes for type 2 diabetes." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + } + ], + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&genomics", + "nutrition", + "nutrient-gene&interactions", + "diabetes&mellitus", + "nutritional&genomics", + "gene&variants", + "epigenetic&modifications", + "GWAS", + "pharmacogenomics", + "personalized&medicine", + "machine&learning" + ], + "metadata": [ + { + "object": "Three loci with high mutation frequencies, the 138665410 FOXL2 gene variant, the 23862952 MYH6 gene variant, and the 71098693 HYDIN gene variant were found to be significantly associated with sporadic Atrial Septal Defect P<0.05; variants in FOXL2 and MYH6 were found in patients with isolated, sporadic Atrial Septal Defect P<5x10-4.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab953981" + }, + { + "object": "The results of this meta-analysis support the hypothesis that RBP4 is a modest independent risk factor for gestational diabetes mellitus i.e., nonobese patients with gestational diabetes mellitus might express RBP4 at abnormal levels.The association between RBP4 rs3758539 polymorphism and gestational diabetes mellitus risk was not confirmed.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab860992" + }, + { + "object": "We studied the association between retinoic acid receptor responder 2 rs17173608 and rs4721 gene polymorphisms and gestational diabetes mellitus. We found that RARRES2 rs4721 polymorphism increased the risk of gestational diabetes mellitus. RARRES2 rs17173608 polymorphism is not associated with gestational diabetes mellitus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1013771" + }, + { + "object": "Data show that circulating ghrelin is high in situations of nutritional deficiency starvation and low in situations of nutritional plenty free access to food or total parenteral nutrition.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab191174" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + }, + { + "object": "meta-analysis indicated that the risk allele of the GCK -30G>A polymorphism may increase gestational diabetes mellitus and type 2 diabetes mellitus risk in whites, whereas additional studies are needed to confirm the effect of this polymorphism on both diseases in Asians and Africans", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab478385" + }, + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "The aim of this study was to examine the frequency of exocrine dysfunctions of the pancreas according to the level of fecal elastase-1 FE-1 in patients with diabetes mellitus, type 1 and diabetes mellitus, type 2.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab778488" + }, + { + "object": "Patellar tendon properties are not influenced by the MMP3 gene variants measured. Although MMP3 gene variants are associated with risk of tendon pathology, association is unlikely to be mediated via underlying tendon dimensional and functional properties.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab582593" + } + ], + "question": "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "usage": { + "chatgpt": 6443, + "gpt-4": 4073, + "gpt-4-turbo-preview": 3136 + }, + "user_id": 2 + }, + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/04.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/04.json new file mode 100644 index 0000000..ef341f0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/04.json @@ -0,0 +1,404 @@ +{ + "created_at": "2024-05-31T23:10:07.733262Z", + "data": { + "amplify": false, + "answer": "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any association between the genes SH2B3 or ERBB3 and diabetes.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "\n\nFigure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 × 10 −9 )." + } + ], + "15524ac0-da3c-4c01-8ae2-1b8c901105ad": [ + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "text": "\n\nAll the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency." + } + ], + "1ef9a72d-b9ef-4955-a351-fca0175da3d1": [ + { + "document_id": "1ef9a72d-b9ef-4955-a351-fca0175da3d1", + "text": "\n\nOne method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12)." + } + ], + "21368075-9e10-4260-b346-43b1029b3bf0": [ + { + "document_id": "21368075-9e10-4260-b346-43b1029b3bf0", + "text": "Results\n\nImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p ¼ 0.536), while DEA produced a comparably weak enrichment score (p ¼ 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes." + } + ], + "2715e261-b26c-46d6-918f-c6aa47688f0c": [ + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "text": "35\nABSTRACT 11\nA GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES\nESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE\nREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY\nMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,\nC Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S\nTurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie\nDepts." + } + ], + "4322db2f-5f43-4fc0-8968-b24438a7d6b9": [ + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "text": "\n\nSecond, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion." + } + ], + "647571cd-ff36-4be4-97c4-cd006d9bfbaf": [ + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "\n\nIn summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "DISCUSSION\n\nWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype." + } + ], + "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae": [ + { + "document_id": "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae", + "text": "\n\nThese observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 ." + } + ], + "7b7ce30c-f398-4b0e-bcb6-52f2644201fd": [ + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "text": "\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)-␥ signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73)." + } + ], + "7e816722-443f-463c-8a79-852752df28e6": [ + { + "document_id": "7e816722-443f-463c-8a79-852752df28e6", + "text": "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes\n\nFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used." + } + ], + "845adde7-823a-4bfc-9f5e-7082d2e26102": [ + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "text": "\n\nIn this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion." + } + ], + "8aee60c9-9bb4-4867-96c9-830c1e43c72e": [ + { + "document_id": "8aee60c9-9bb4-4867-96c9-830c1e43c72e", + "text": "\n\nAt present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients." + } + ], + "9fd49699-612f-48c0-b1d9-e01158472be6": [ + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "text": "\n\nIn summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets." + } + ], + "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd": [ + { + "document_id": "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd", + "text": "\n\nResults.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "The authors then used mouse liver and adipose expression\ndata from several mouse crosses to construct causal expression networks for the ERBB3 and\nRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any\nknown Type I diabetes genes whereas RPS26 is associated a network of several genes that\nare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis\ndemonstrates the power of combining human and mouse data with a network based\napproach that has been proposed for use in drug discovery (Schadt et al." + } + ], + "ebb49f39-ee30-4b32-959d-305276fd589e": [ + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nIn conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + } + ], + "faa23996-65fc-4bc6-938a-c959e981d493": [ + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "text": "\n\nFinally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + } + ] + }, + "data_source": [ + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "section_type": "main", + "text": "\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)-␥ signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73)." + }, + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "section_type": "main", + "text": "\n\nIn summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nSecond, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "\n\nAll the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "section_type": "main", + "text": "\n\nIn summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems." + }, + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "section_type": "main", + "text": "The authors then used mouse liver and adipose expression\ndata from several mouse crosses to construct causal expression networks for the ERBB3 and\nRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any\nknown Type I diabetes genes whereas RPS26 is associated a network of several genes that\nare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis\ndemonstrates the power of combining human and mouse data with a network based\napproach that has been proposed for use in drug discovery (Schadt et al." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "main", + "text": "\n\nIn conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D." + }, + { + "document_id": "1ef9a72d-b9ef-4955-a351-fca0175da3d1", + "section_type": "main", + "text": "\n\nOne method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12)." + }, + { + "document_id": "7e816722-443f-463c-8a79-852752df28e6", + "section_type": "main", + "text": "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes\n\nFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "abstract", + "text": "\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "\n\nFigure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 × 10 −9 )." + }, + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "section_type": "main", + "text": "\n\nIn this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion." + }, + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "section_type": "main", + "text": "\n\nFinally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "main", + "text": "\n\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "section_type": "main", + "text": "35\nABSTRACT 11\nA GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES\nESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE\nREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY\nMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,\nC Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S\nTurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie\nDepts." + }, + { + "document_id": "21368075-9e10-4260-b346-43b1029b3bf0", + "section_type": "main", + "text": "Results\n\nImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p ¼ 0.536), while DEA produced a comparably weak enrichment score (p ¼ 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "section_type": "main", + "text": "DISCUSSION\n\nWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype." + }, + { + "document_id": "8aee60c9-9bb4-4867-96c9-830c1e43c72e", + "section_type": "main", + "text": "\n\nAt present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients." + }, + { + "document_id": "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae", + "section_type": "main", + "text": "\n\nThese observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 ." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "Parallel transcriptional regulation in human islets\n\nTo determine whether the findings observed in mice were applicable to humans, we investigated whether the pathway identified in NOD mice also demonstrated genetic linkage to diabetes or glucose regulation traits in humans.GLIS3 polymorphisms have previously been associated with altered glucose regulation; we additionally identified nominally significant associations for MANF, XRCC4 and LIG4 polymorphisms (Supplementary Table 2).In an independent approach that takes into account environmental effects, we analyzed RNA-seq data from human pancreatic islets isolated from 119 donors, including 14 diagnosed with T2D 28 .To assess the validity of the Glis3-Manf relationship observed in mice, we investigated the relationship of these two genes in human islets.A trend toward reduced GLIS3 expression was observed in T2D islets, whereas MANF expression appeared unchanged (Supplementary Fig. 13).Critically, a significant positive relationship was observed between GLIS3 and MANF levels in human islets (Fig. 8a).Next, we investigated whether patients with T2D might exhibit reduced XRCC4 expression, analogous to the NOD polymorphisms.We found no change in XRCC4 expression in T2D islets (Fig. 8b); however, the levels of the obligate binding partner encoded by LIG4 were significantly reduced (Fig. 8c).In mice, Xrcc4 polymorphisms were associated with increased senescence; likewise, in patients with T2D, the levels of the senescence markers H2AFX (Fig. 8d) and CDKN1A (Fig. 8e) were increased.Finally, a direct relationship was observed between reduced LIG4 and increased H2AFX levels (Fig. 8f).Although the cause of coregulation cannot be assessed in ex vivo human islets, the parallel with NOD mice strongly supports a conservation of diabetes susceptibility mechanisms across species.3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 Fluorescence" + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "main", + "text": "\n\nWe previously reported that circulating levels of these cytokines were sufficient to reduce glucose-stimulated insulin release and increase cell death in islets from diabetes-prone mice but not heterozygous controls (12).To begin to identify the genes responsible for this effect, we conducted a microarray study of islets isolated from prediabetic BKS.Cg-m ϩ/ϩ Lepr db /J (db/db) mice and heterozygous controls to compare their responses to exposure to circulating levels of IL-1 and IL-6 at concentrations that mimic low-grade inflammation.The most cytokine-sensitive genes from the mouse islet microarray study were evaluated for associations with the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.GUARDIAN is a genome-wide association scan (GWAS) in Hispanic Americans, the largest US minority group and one at high risk of T2D (13).Participants in this study were monitored for glucose homeostasis measured by the frequently sampled intravenous glucose tolerance test (FSIVGTT) and the euglycemic clamp.Both FSIVGTTs and the euglycemic clamp methods yield underlying physiological, highly heritable parameters that are relevant to the risk of T2D (14,15)." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nIt has been hypothesized for a while that individual differences in insulin secretion capacity are predominantly determined by genetics (186,187).This is now clearly strengthened by the finding that, among the 27 confirmed (Table 1) and potential (Table 2) diabetes risk genes mentioned above, 18 genes affect -cell function, namely CAPN10 (188), CDC123/CAMK1D (189), CDKAL1 (166, 174, 190 -193), CDKN2A/B (34,167,193), ENPP1 (194), FOXO1 (77), HHEX (167,190,193,195,196), IGF2BP2 (34,166,167), JAZF1 (189), KCNJ11 (38,41,193), KCNQ1 (180,197), MTNR1B (181)(182)(183), PPARGC1A (198), SGK1 (79), SLC30A8 (34,166), TCF7L2 (129,134,138,160,193,199,200), TSPAN8/ LGR5 (189), and WFS1 (201)(202)(203).This was revealed by calculating fasting state-and oral glucose tolerance test (OGTT)-derived (plasma insulin-and C-peptide-based) surrogate indices for insulin secretion that do not allow further dissection of the aspects of -cell function affected, such as insulin maturation, glucose sensitivity, or incretin sensitivity.From these rough estimates of -cell function, pathomechanisms showing how these common gene variants impair -cell function were only proposed for the biological candidates KCNJ11, FOXO1, and SGK1, which have been well studied in vitro as well as in mice in vivo.KCNJ11 (potassium inwardly-rectifying channel, subfamily J, member 11; OMIM entry no.600937) encodes the pore-forming subunit Kir6.2 of the ATP-sensitive potassium channel of -cells, which couples glucose sensing with membrane depolarization and exocytosis of insulin granules.The best studied and confirmed diabetes risk variant E23K (rs5219) was shown in vitro to increase the probability of the channel's open state, to enhance its activity, and to impair its ATP sensitivity, thereby inhibiting -cell excitability and insulin release (204,205).Furthermore, the same variant was suggested to impair insulin secretion due to its enhanced response to the channel-ac-tivating effect of intracellular acyl coenzyme As, fatty acid metabolites known to be elevated in obese and type 2 diabetic subjects (206)." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nFor the first approach, we assessed whether the differentially methylated genes have any overlap or other association with known T2D risk genes.Then, we carried out an Ingenuity Pathway Analysis (IPA; Figure 6A) to identify pathways that are epigenetically affected in T2D islets according to our methylation profiling data.This was augmented by a manual search for the differentially methylated genes in scientific literature reporting on the general biology as well as T2D-related functions of these genes or the pathways they are part of (Figures 6 and 7).For the second approach, we knocked down expression of several genes by RNA interference and tested the functional consequence of their depletion in b-cells (Figure 8).For two selected genes, we explored their functional role more extensively in isolated b-cells and human islets (Figure 9)." + }, + { + "document_id": "e92427da-dee9-472f-bfa1-2e7bfa7de521", + "section_type": "main", + "text": "\n\nTo evaluate the effects of hyperglycemia or other metabolic consequences of DM per se on expression, we identified 12 genes altered in DM as compared with both nondiabetic groups but not as a function of family history (Table 4, which is published as supporting information on the PNAS web site).This included a 70-kDa heat-shock protein (HSP701A), which was decreased by 42% in DM and whose expression correlated inversely with fasting glucose for all subjects (r ϭ Ϫ0.77).Expression of a related HSP70 gene was previously found to be reduced in Caucasian diabetic subjects (20)." + }, + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "section_type": "main", + "text": "\n\nIt is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis." + }, + { + "document_id": "18d88787-096b-4fc1-ad4e-3d1b1f3a90d9", + "section_type": "main", + "text": "\n\nFigure 2: The role of type 2 diabetes genes in insulin secretion Pancreatic β-cell genes associated with type 2 diabetes are in italics.G6P=glucose-6-phosphate. Adapted from Florez JC.Newly identifi ed loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes?Diabetologia 2008; 51: 1100-10, by kind permission of the author and Springer Science + Business Media." + }, + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "section_type": "abstract", + "text": "\nThe majority of genetic risk variants for type 2 diabetes (T2D) affect insulin secretion, but the mechanisms through which they influence pancreatic islet function remain largely unknown.We functionally characterized human islets to determine secretory, biophysical, and ultrastructural features in relation to genetic risk profiles in diabetic and nondiabetic donors.Islets from donors with T2D exhibited impaired insulin secretion, which was more pronounced in lean than obese diabetic donors.We assessed the impact of 14 disease susceptibility variants on measures of glucose sensing, exocytosis, and structure.Variants near TCF7L2 and ADRA2A were associated with reduced glucose-induced insulin secretion, whereas susceptibility variants near ADRA2A, KCNJ11, KCNQ1, and TCF7L2 were associated with reduced depolarization-evoked insulin exocytosis.KCNQ1, ADRA2A, KCNJ11, HHEX/IDE, and SLC2A2 variants affected granule docking.We combined our results to create a novel genetic risk score for b-cell dysfunction that includes aberrant granule docking, decreased Ca 2+ sensitivity of exocytosis, and reduced insulin release.Individuals with a high risk score displayed an impaired response to intravenous glucose and deteriorating insulin secretion over time.Our results underscore the importance of defects in b-cell exocytosis in T2D and demonstrate the potential of cellular phenotypic characterization in the elucidation of complex genetic disorders." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nIt has been suggested that progressively occurring DNA methylation errors lead to diminished gene responsiveness to external stimuli and might thus contribute to the development of T2D (Gallou-Kabani and Junien, 2005).Our findings of prevalent promoter hypomethylation in T2D islets are indicative of active biological processes involved in adaptation to the diabetic environment as well as biological pathways associated with b-cell dysfunction and apoptosis (Figures 6B and 7).The functional relevance of some of the differentially methylated genes in b-cells was documented by screening for b-cell survival/death following RNAi and subsequent exposure to stresses relevant to T2D (Figure 8).Given the increased evidence that ER stress-induced apoptosis is one of the mechanisms of b-cell loss in T2D (Eizirik et al, 2008), it was of interest to further assess the biological functions of two putative ER stress-related genes that we found to be hypomethylated in T2D islets, namely NIBAN and CHAC1.We observed that these two genes are upregulated by synthetic ER stressors and by the more physiologically relevant saturated fatty acid palmitate in human islets, while knockdown of their expression by specific RNAi demonstrated their modulatory role in apoptosis (cf. Figure 9).While NIBAN protects against ER stress-induced apoptosis, CHAC1 seems to contribute to cell death.The hypomethylation observed at both genes could be explained by competing proapoptotic and antiapoptotic processes during ER stress response in diabetic islets.NIBAN is a negative regulator of translation initiation factor eIF2a (Sun et al, 2007).Therefore, its hypomethylation may indicate an attempt to re-establish ER homeostasis by reduction of protein synthesis (Eizirik et al, 2008).Pending the outcome of these attempts, ER stress-induced apoptosis may be triggered by CHAC1 and other proapoptotic genes." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nA recent study assessed gene expression in different islet cell types including the insulin-producing b-cells (Dorrell et al, 2011).A comparison showed that 240 of our 254 genes are covered by the microarray used by these authors.In all, 170 of these genes have a positive presence call in b-cells.This indicates that the majority of the genes we detected as differentially methylated in T2D islets are expressed in non-diabetic b-cells to a sufficient amount to be reliably detected by microarrays, that is, these are genes actively transcribed in b-cells." + }, + { + "document_id": "4a1a2496-1172-4262-8158-a3a96b80bcf4", + "section_type": "main", + "text": "\n\nStrikingly, three of the 10 candidate miRNA regulatory hubs in the T2D gene network were 59-shifted isomiRs: miR-375+1, miR-375-1, and miR-183-5p+1 (Fig. 4A).Moreover, all three of these were more significantly associated with T2D genes than their 59reference counterparts (Table S3 in File S2).This is particularly intriguing, given the already well-established role of 59-reference miR-375 in beta cell formation and function." + }, + { + "document_id": "70667239-7e12-494f-a6dd-5b1d073b5a56", + "section_type": "main", + "text": "\n\nNevertheless, taken together there is good evidence to propose that in human pancreas and in rodent pancreatic cell lines, steady state levels of insulin mRNA are lower from insulin genes linked to the class III VNTR alleles that for type 1 diabetes are dominantly protective.It is, however, difficult to explain how an approximately 30% reduction in insulin expression could explain the dominantly protective effect of class III VNTR alleles.Perhaps the pancreas is not the primary site of action of IDDM2-VNTRencoded predisposition to type 1 diabetes.In mice, the insulin gene is expressed transiently at birth in the thymus [30], presumably contributing to the normal state of non-responsiveness to insulin protein." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nThe analyses described above found only few common T2D candidate genes among the differentially methylated genes uncovered in this study.This could imply that T2D pathogenesis in islets is partially mediated by previously unappreciated genes.To decipher their roles in the context of T2D islets, as a first step we performed an IPA to determine which canonical pathways were overrepresented in our set of genes (Figure 6A).Inflammation-related processes were highly enriched, in particular the acute phase response and IL-8 signalling.Other enriched pathways, such as apoptosis and death receptor signalling, emphasise the role of b-cell loss in T2D.Enrichment for pathways involved in metabolism and internal and external cell structure (e.g., actin cytoskeleton and integrin signalling) may be indicative of altered islet function and architecture." + }, + { + "document_id": "41bc85bc-314f-4d92-9007-5d1571506ef3", + "section_type": "main", + "text": "Regulation of GWAS diabetes genes by glucose in pancreatic islets\n\nMany of the recently discovered type 2 diabetes genes have been suggested to affect the development and/or function of pancreatic islets [6].The function, growth and survival of β-cells can be regulated acutely and chronically by glucose [34].Thus, we examined whether the new type 2 diabetes susceptibility genes are regulated by overnight incubation in low (5 mM) or high (25 mM) glucose (Figure 5).Most genes were significantly or tended to be downregulated under conditions of high glucose.Cdkal1, Cdkn2a (Arf, P = 0.07), Ide, Jazf1, Camk1d, and Tspan8 (P = 0.06) expression levels were decreased ~50-60%.Meanwhile, the expression of Cdkn2b, Hhex (P = 0.10), Cdc123, Adamts9 (P = 0.09), and Thada were reduced 30-40%.To ensure the islets incubated in high glucose did not have globally decreased expression, we examined the expression of Txnip, which has been shown to be highly upregulated by glucose [35] and found that its expression was still significantly elevated in the islets cultured in high glucose (Figure 5).Mouse islets consist of β-cells and other cell types.Thus, the MIN6 β-cell line was also examined.We found that all the genes were expressed in this cell line (not shown), although this does not preclude that they also are expressed in other cell types within the islet." + }, + { + "document_id": "29d09d03-fd2f-48b3-a020-ea574d583dc4", + "section_type": "main", + "text": "\n\nThe majority of association studies has shown multiple gene loci for epigenetic regulation in these central mediators of type II diabetes, β-cells.Chen and colleagues characterized Ezh2 fl/fl mice and Cdkn2a −/− mice to reveal that an increased Ink4a and Arf expression in β-cells was linked to a reduced proliferative capacity.While Ezh2 levels declined throughout aging, INK4A levels increased.ChIP analysis uncovered that H3K27me3 occupancy regulating Ink4a and Ezh2 was declining with age, while H3K4me3 and histone acetylation at the Ink4a locus ascended in older mice.The authors concluded from their study that EZH2-dependent histone methylation and repression of the Ink4a/Arf locus are required for β-cell expansion [223,226].In a further study, the methylome of β cells was analyzed pancreatic islets from young and old mice using whole genome shotgun bisulfite sequencing (WGSBS).Overall, higher methylation rates (especially in CpGs with low methylation levels in youth), accompanied by a decline in replicative capacity, increased promoter methylation and decreased expression of cell cycle regulators were detected in \"healthy\" old β-cells.Intriguingly, this observation was associated with a functional improvement in aged murine and human islets [223,227]." + }, + { + "document_id": "787e2a2c-be24-4970-94b1-0f872a8cd684", + "section_type": "main", + "text": "\n\nWe screened our pediatric diabetes cohort with unknown etiology using Sanger sequencing.In mouse pancreatic β-cell lines (Min6 and SJ cells), we performed insulin secretion assay and quantitative RT-PCR to measure the β-cell function transfected with the detected HDAC4 variants and wild type.We carried out immunostaining and Western blot to investigate if the detected HDAC4 variants affect the cellular translocation and acetylation status of Forkhead box protein O1 (FoxO1) in the pancreatic β-cells." + }, + { + "document_id": "36858807-1395-4b2f-a3ee-e054f9b0149d", + "section_type": "main", + "text": "\n\nAs ER stress markers were not activated to potentially explain reduced insulin secretion, genes related to insulin secretion pathway were investigated using real-time-PCR, which revealed downregulation of the glucose-stimulated insulin secretion (GSIS) pathway and the glucose uptake pathway in RIN-m β-cells when compared to the control, indicating impairment of these pathways.mRNA levels by real-time PCR (Fig. 4c) showed a decrease in glucose transporter 2 (Glut2 [MIM: 138160]) to 54% compared to the control, p < 0.001.Pancreatic and duodenal homeobox 1 (Pdx1 [MIM: 600733]) was also suppressed to 85.7%, p = 0.01.On the other hand, the forkhead box protein A2 (Foxa2 [MIM: 600288]) mRNA level, which regulates PDX1, was unchanged, while the mRNA of glucokinase (Gck [MIM: 138079]), which phosphorylates glucose in the first step of the GSIS pathway in β-cells, was slightly elevated (11.5%, p = 0.008)." + }, + { + "document_id": "286480ca-0d7f-4a93-952b-2cf57292104d", + "section_type": "main", + "text": "\n\nIt is yet unclear, however, whether the decreased expression of Ica1 plays a functional role in the development (cause) or is merely an effect of diabetes.Interestingly, even though Ica1 (also known as Ica69) has been associated with diabetes in the human, mouse, and rat (4, 8 -10, 12, 16, 18, 19, 34), the Ica1 gene locus has not been previously identified as a risk locus for diabetes in either humans or in experimental models of diabetes, and this is the first time that this gene has been associated with a diabetes-related QTL." + }, + { + "document_id": "1dc0547a-1d61-4b27-b848-512875b52081", + "section_type": "main", + "text": "\n\nIt is yet unclear, however, whether the decreased expression of Ica1 plays a functional role in the development (cause) or is merely an effect of diabetes.Interestingly, even though Ica1 (also known as Ica69) has been associated with diabetes in the human, mouse, and rat (4, 8 -10, 12, 16, 18, 19, 34), the Ica1 gene locus has not been previously identified as a risk locus for diabetes in either humans or in experimental models of diabetes, and this is the first time that this gene has been associated with a diabetes-related QTL." + }, + { + "document_id": "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd", + "section_type": "main", + "text": "\n\nResults.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion." + }, + { + "document_id": "e92427da-dee9-472f-bfa1-2e7bfa7de521", + "section_type": "main", + "text": "\n\nGenes differentially expressed between control and diabetic subjects may reflect either the pathophysiology of insulin resistance (primary alterations) or secondary effects of hyperglycemia, hyperlipidemia, and other metabolic factors.To identify potentially primary expression changes associated with insulin resistance, we compared gene expression in FHϩ (nondiabetic but insulin resistant) and FHϪ controls.One hundred sixty-six genes were differentially expressed between FHϩ and FHϪ (P Ͻ 0.05) (Table 3, which is published as supporting information on the PNAS web site); 55 were common to both [FHϪ vs. DM] and [FHϪ vs. FHϩ] comparisons.No single gene remained differentially expressed after Benjamini-Hochberg multiple comparison testing.However, ontology classification analysis (17) revealed that 20S and 26S proteasome complexes were the top-ranked cellular component terms (Z 7.7 and 7.3); mitochondrion-linked genes were also overrepresented (Z 3.2).Cell structure (P ϭ 0.004), protein degradation (P ϭ 3.7 ϫ 10 Ϫ4 ), and energy generation (P ϭ 0.003) groups were represented to a greater extent than expected for random distribution; with multiple comparison testing, the protein degradation͞26S proteasome (P ϭ 1 ϫ 10 Ϫ5 ) group remained significant." + } + ], + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "type&1&diabetes", + "type&2&diabetes", + "SH2B3", + "IFIH1", + "ERBB3", + "insulin", + "pancreatic&islets", + "gene&expression", + "mutations" + ], + "metadata": [ + { + "object": "We identified 32 compound heterozygous mutations and 9 homozygous mutations in IL10 receptor subunit alpha and 1 homozygous mutation in IL10 receptor subunit beta. Among these mutations, 10 novel mutations were identified, and 6 pathogenic mutations had been previously described. In patients with IL10 receptor subunit alpha mutations, c.301C>T p.R101RW and c.537 G>A p.T179T were the most common mutations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007199" + }, + { + "object": "Data, including studies involving single-cell analysis, suggest that insulin-secreting cells exhibit 3 major states regarding unfolded protein response UPR: 1 low UPR and low insulin gene expression; 2 low UPR and high insulin gene expression; 3 high UPR and low insulin gene expression. The latter state promotes cell proliferation; UPR appears to mediate recovery from ER stress due to high insulin production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215528" + }, + { + "object": "Ten mutations were identified in five unrelated Chinese families and two sporadic patients with childhood, and adult hypophosphatasia including eight missense mutations and two frameshift mutations. Of which, four were novel: one frameshift mutation p.R138Pfsx45; three missense mutations p.C201R, p.V459A, p.C497S. No identical mutations and any other new ALPL mutations were found in unrelated 50 healthy controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab768168" + }, + { + "object": "Our aim was to identify VHL gene mutations in Argentinian patients who fulfilled the clinical criteria for type 1 VHL disease and in patients with VHL-associated manifestations. VHL mutations were detected in 16/19 84.2% patients in Group 1 and included: gross deletions 4/16; nonsense mutations 6/16; frameshift mutations 4/16; missense mutations 1/16; and splicing mutations 1/16. Three mutations were novel.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab550929" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "WFS1 and GJB2 mutations were identified in eight of 74 cases of Low-Frequency Sensorineural Hearing Loss. Four cases had heterozygous WFS1 mutations; one had a heterozygous WFS1 mutation and a heterozygous GJB2 mutation; and three cases had biallelic GJB2 mutations. Three cases with WFS1 mutations were sporadic; two of them were confirmed to be caused by a de novo mutation based on the genetic analysis of their parents.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014986" + }, + { + "object": "Two patients harbored KRAS with codon 12 mutations; one harbored the gly12val mutation with a variation of leu597val in the BRAF exon 15 codon, the other harbored mutation in the BRAF exon 15 codon. One patient harbored a codon 117 mutation with a BRAF V600E mutation. The last patient harbored a NRAS exon 2 mutation with the GGT/GAT, V600G mutation in the BRAF exon 15 codon", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab978995" + }, + { + "object": "Findings extend the phenotype of insulin mutation carriers and suggest that insulin screening is warranted not only in neonatal diabetes, but also in maturity onset diabetes of the young and in selected cases of type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab288509" + }, + { + "object": "CFTR genes from 46 African Americans and 356 Hispanic Americans were screened. 8 new mutations 1 missense mutation, 1 splice-site mutation & 6 frame-shift mutations & 21 distinct rare mutations not in the commercial mutation panels were identified.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab134892" + }, + { + "object": "4 different mutations including 1 novel mutation were identified. 4 had R225Q mutation, 3 had P224S mutation, 2 others had different frame-shift mutations. Identical gene mutations showed wide variation in supernumerary tooth formation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab10218" + } + ], + "question": "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "subquestions": null, + "task_id": "A9F5CC0D31CE591D56814F3A276760E5", + "usage": { + "chatgpt": 8842, + "gpt-4": 5247, + "gpt-4-turbo-preview": 4320 + }, + "user_id": 2 + }, + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "task_id": "A9F5CC0D31CE591D56814F3A276760E5" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/05.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/05.json new file mode 100644 index 0000000..0afbfb0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/05.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-05-31T23:11:51.586254Z", + "data": { + "amplify": false, + "answer": "Genetics plays a significant role in the emergence of diabetes. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus contribute to 50-60% of the genetic risk by affecting protein binding and antigen presentation to T cells. Other gene variants that modulate immune regulation, viral responses, environmental signals, and endocrine function also contribute to the disease. The risk for type 1 diabetes is strongly influenced by multiple genetic loci. In type 2 diabetes, many common variants, largely with small effect size, contribute to overall disease risk. Monogenic forms of diabetes, such as MODY, result from mutations in a single gene. Family history also plays a role, suggesting that genetics conveys more than heritable information; it probably includes non-genetic familial behaviors and norms.", + "context": { + "00591f6a-0d6f-4993-ae6c-e9a8109a95ec": [ + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "A. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is ϳ10 -15 times greater.Although ϳ70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3)." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "II. THE GENETICS OF TYPE 1 DIABETES\n\nA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nThe relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in -cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of -cell function and insulin action in the general population also hint at a preponderance of -cell effects (52)." + } + ], + "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01": [ + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "text": "\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared" + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "Environment\n\nThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "The genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008)." + } + ], + "76ae2f09-af4d-422a-b939-625f0fe4ae1c": [ + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + } + ], + "83a34294-d942-476f-be2f-ff8d7ec3dec4": [ + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "text": "\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al." + } + ], + "8d723c99-bd3c-43eb-9b31-14ee233c2ed4": [ + { + "document_id": "8d723c99-bd3c-43eb-9b31-14ee233c2ed4", + "text": "\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications." + } + ], + "9240ab9b-c5bb-4475-ad2b-111843cb146a": [ + { + "document_id": "9240ab9b-c5bb-4475-ad2b-111843cb146a", + "text": "\n\nThe risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population." + } + ], + "92eb0c69-5e98-41aa-9084-506e7f223b1a": [ + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "text": "Genetic Background and Environment\n\nBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Genetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin." + } + ], + "9cce7fe9-cb40-4e75-85bc-d8655c3343d6": [ + { + "document_id": "9cce7fe9-cb40-4e75-85bc-d8655c3343d6", + "text": "\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40]." + } + ], + "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a": [ + { + "document_id": "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a", + "text": "\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1α (HNF-1α), HNF-1β, HNF-4α, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 ." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + } + ] + }, + "data_source": [ + { + "document_id": "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a", + "section_type": "main", + "text": "\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1α (HNF-1α), HNF-1β, HNF-4α, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 ." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "Type 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease." + }, + { + "document_id": "8d723c99-bd3c-43eb-9b31-14ee233c2ed4", + "section_type": "main", + "text": "\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "Environment\n\nThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "abstract", + "text": "\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "section_type": "abstract", + "text": "\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared" + }, + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "section_type": "main", + "text": "Genetic Background and Environment\n\nBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Genetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + }, + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "section_type": "main", + "text": "\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nThe relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in -cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of -cell function and insulin action in the general population also hint at a preponderance of -cell effects (52)." + }, + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "section_type": "main", + "text": "The genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008)." + }, + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "section_type": "main", + "text": "\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "section_type": "main", + "text": "II. THE GENETICS OF TYPE 1 DIABETES\n\nA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology." + }, + { + "document_id": "fb7a24a3-9d72-49d7-93df-7a2f400f44c4", + "section_type": "main", + "text": "\n\nGenetics is one example of the 'other risk factors' involved in the pathogenesis of DR.Twin and epidemiological studies have strongly suggested a genetic component in the etiology of DR (6 -10), with heritability scores ranging from 27 to 52% in both type 1 and type 2 diabetes (7 -10).There is an increased risk of severe DR among family members of DR subjects (8,9) and in siblings of affected subjects (8,9).Furthermore, several studies have also shown a discrepant rate of the prevalence of DR among different racial ethnic groups in the US population, with a significantly higher prevalence observed among Hispanic, African-American and Chinese-American when compared with Caucasian populations (11).While these differences may partially be attributed to lifestyle factors, evidence from familial aggregation, ethnic differences and heritability clearly supports a genetic contribution in the etiology of DR." + }, + { + "document_id": "25481e34-2a45-4448-84f0-32c823cfcd03", + "section_type": "main", + "text": "\n\nMost cases of diabetes have multiple genetic and environmental causes and are classified according to the presumed pathophysiologic defectdautoimmune destruction of b-cells leading to insulin deficiency for type 1 diabetes and varying degrees of insulin resistance and deficiency for type 2 diabetes.In other words, the vast majority of diabetes is polygenic, and despite the growth in knowledge about the various genetic causes of diabetes in recent years, classification of individual cases into meaningful subtypes based on the underlying genetics has been difficult.On the other hand, genetic testing may be useful for the diagnosis of certain forms of diabetes caused by defects in a single gene, such as HNF1A mutations for maturityonset diabetes of the young (MODY) (39) and activating KCNJ11 mutations for neonatal diabetes (40), both of which are highly responsive to sulfonylurea therapy.These monogenic forms of diabetes account for ;1-2% of diabetes cases (41,42), and they typically present at a young age (,25 years) and follow an autosomal dominant pattern of inheritance.Targeted genotyping could also play a role in the diagnosis of type 2 diabetes in specific populations.For example, a rare missense variant in HNF1A (p.E508K) that increased the risk of diabetes fivefold was present among 2% in a study of Latinos in the southern U.S. with type 2 diabetes (20); additional studies are needed to determine whether this functional variant shares the sulfonylurearesponsiveness of the HNF1A variants that cause MODY." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "If an environmental contributor is near ubiquitous and the genetic\npredisposition common as well, interventions are most sensibly weighted towards\nenvironmental risk factor modification.\n Even here, though, there is room for further research, since the etiopathogenesis\nof type 2 diabetes may not be as well understood as some suggest. Specifically,\nChaufan implies that dietary intervention to prevent prenatal ‘programming’\nleading to susceptibility to develop type 2 diabetes (the fetal origins of adult onset\ndisease hypothesis) is as evidence-based as dietary management of the adult diabetic state. However, many questions remain in this area." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "Type 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring." + }, + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "section_type": "main", + "text": "\n\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes." + }, + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "section_type": "main", + "text": "CONCLUSION\n\nThe greatest genetic risk (both increased risk, susceptible, and decreased risk, protective) for type 1 diabetes is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes.There are currently about 50 non-HLA region loci that also affect the type 1 diabetes risk.Many of the assumed functions of the non-HLA genes of interest suggest that variants at these loci act in concert on the adaptive and innate immune systems to initiate, magnify, and perpetuate -cell destruction.The clues that genetic studies provide will eventually help lead us to identify how -cell destruction is influenced by environmental factors.While there is extensive overlap between type 1 diabetes and other immune-mediated diseases, it appears that type 1 and type 2 diabetes are genetically distinct entities.These observations may suggest ways to help identify causal gene(s) and, ultimately, a set of disease-associated variants defined on specific haplotypes.Unlike other complex human diseases, relatively little familial clustering remains to be explained for type 1 diabetes.The remaining missing heritability for type 1 diabetes is likely to be explained by as yet unmapped common variants, rare variants, structural polymorphisms, and gene-gene and/or gene-environmental interactions, in which we can expect epigenetic effects to play a role.The examination of the type 1 diabetes genes and their pathways may reveal the earliest pathogenic mechanisms that result in the engagement of the innate and adaptive immune systems to produce massive -cell destruction and clinical disease.The resources established by the international T1DGC are available to the research community and provide a basis for future discovery of genes that regulate the earliest events in type 1 diabetes etiology-potential targets for intervention or biomarkers for monitoring the effects and outcomes of potential therapeutic agents." + }, + { + "document_id": "57d91713-225c-4c04-a9e7-e275588e2a68", + "section_type": "main", + "text": "Introduction\n\nClustering in families implicates a genetic component of diabetic nephropathy, but so far the specific genes underlying diabetic nephropathy remain largely unknown [1,2].Family studies have furthermore revealed that parental type 2 diabetes mellitus is associated with diabetic nephropathy in offspring with type 1 diabetes mellitus [3,4].A positive family history of type 2 diabetes mellitus has also been associated with cardiovascular disease [5] as well as markers of cardiovascular disease [6] in offspring with type 1 diabetes mellitus.Genetic variants or single-nucleotide polymorphisms (SNPs) predisposing to type 2 diabetes mellitus in the Finnish population have recently been identified in large-scale, genome-wide association studies [7,8].The question thus arises of whether these SNPs, which predispose to type 2 diabetes mellitus, also predispose to diabetic nephropathy and related complications in patients with type 1 diabetes mellitus.We therefore assessed the impact of a set of SNPs known to influence susceptibility to type 2 diabetes mellitus on diabetic nephropathy as well as diabetic retinopathy and cardiovascular disease in patients with type 1 diabetes mellitus." + }, + { + "document_id": "977994e6-80dc-4b82-9bb1-4a89455cd4da", + "section_type": "main", + "text": "Evidence for a genetic basis: family and twin studies of Type I diabetes\n\nWhat is the evidence that Type I diabetes has a genetic basis?The simplest evidence comes from the fact that the frequency of the disorder is higher in close relatives of diabetic patients than in the general population (note: the reference population in the discussion which follows are people of European ancestry, who have the highest prevalence of Type I diabetes).For example, the frequency of Type I diabetes in siblings of diabetics is about 6 % by age 30 [1], while the frequency in the general population is about 0.4 % by age 30 [2].Thus, Type I diabetes is about 6/0.4,i. e. 15 times more common in siblings of diabetic patients than in the general population.This ratio between frequency in siblings compared with the general population is referred to as l sib [3]." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "The proportion of diabetics t h a t will result from\nmating between genetic types can be predicted with\ncertainty, since the inheritance is known to be under\nthe control of a recessive gene with complete penetrance. Offspring t h a t will exhibit the diabetic syndrome can be distinguished from those t h a t will not,\nas early as 3 weeks after birth.\n Some disadvantages are equally apparent. Diabetic\nhomozygotes do not breed, and heterozygotes cannot\nbe distinguished from normals except b y progeny\ntesting." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "section_type": "main", + "text": "A. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is ϳ10 -15 times greater.Although ϳ70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Genetics of Diabetic Complications in Humans\n\nEpidemiologic studies have clearly established that only a subgroup of individuals with diabetes are at risk of nephropathy (2).To identify genetic determinants and candidate genes that confer susceptibility or progression for DNP in individuals with type 1 and type 2 diabetes, the National Institutes of Health established the ongoing Family Investigation of Nephropathy and Diabetes study consortium.The Family Investigation of Nephropathy and Diabetes is using Mapping by Admixture Linkage Disequilibrium and traditional affected and discordant sibling pair and relative pair analyses.Previous linkage analysis studies led to the mapping of several susceptibility loci for DNP on specific regions on chromosomes 3, 7, 9, 12, and 20 (14,15)." + }, + { + "document_id": "9cce7fe9-cb40-4e75-85bc-d8655c3343d6", + "section_type": "main", + "text": "\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40]." + }, + { + "document_id": "44cfaebc-d9de-4d25-8991-4b17d524ac6e", + "section_type": "main", + "text": "Introduction\n\nIn 1962, under the title \"Diabetes mellitus: A 'thrifty' genotype rendered detrimental by 'progress'?\" one of us published the suggestion that the basic defect in diabetes mellitus was a quick insulin trigger [I].This was an asset to our tribal, hunting-and-gathering ancestors, with their intermittent, sometimes feast-or-famine alimentation, since it should have minimized renal loss of precious glucose.Currently, however, it was hypothesized, the pattern of over-alimentation in the technologically advanced nations resulted in insulin levels that elicited the insulin antagonists popularized by Vallance-Owen and colleagues [2][3][4] , and the result was diabetes mellitus.The changing dietary patterns of Western Civilization had compromised a complex homeostatic mechanism.The paper was written before the clear distinction between type I and type II diabetes had been drawn, but in retrospect was directed at type II or non-insulin dependent diabetes (NIDDM).This quick insulin trigger was under a (still) poorly defined genetic control.Since too quick an insulin trigger might be as disadvantageous as too slow a trigger, it was suggested that this genetic control might take the form of a balanced polymorphism, by analogy with the polymorphisms for the sickle cell allele (ßs) then receiving so much attention.When other laboratories could not confirm Vallance-Owen's insulin antagonists (except in rare cases), the original physiological basis for the hypothesis collapsed.Although alternative \"balance\" hypotheses came to mind [5], they were neither as simple nor as intellectually satisfactory.However, the problem remained: why is the predisposition to NIDDM so frequent?Explanations based on the \"thrifty genotype\" hypothesis continue to be frequently invoked." + }, + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "section_type": "main", + "text": "I\n\nn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "\n\nPresently, 48 other genomic regions, referred to as susceptibility regions, have been found to also confer susceptibility to T1D (Burren et al., 2011;Steck and Rewers, 2011;Yang et al., 2011;Bluestone et al. 2010;Poicot et al., 2010;Todd et al., 2010;Todd et al., 2007).But their contribution is minimal in comparison to the HLA locus (Gillespie, 2014).Also, research has shown that less than 10% of individuals with HLA-conferred diabetes susceptibility actually progress to clinical disease (Knip andSiljandera, 2008, Wenzlau et al., 2008).This implies that additional factors are needed to trigger and drive β-cell destruction in genetically predisposed persons (Knip and Siljandera, 2008).Environmental factors are believed to influence the expression of T1D.The reason being that in the case of identical twins, if one twin has T1D, the other twin only has it 30%-50% of the time, despite having the same genome.This means that other factors contribute to the prevalence or onset of this disease (Knip et al., 2005)." + }, + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "section_type": "main", + "text": "\n\nA coherent synthesis of these data has yet to emerge but will inevitably include components of several of these competing, but not mutually exclusive, hypotheses.Indeed, there is evidence that models incorporating both genetic and environmental variation best explain the observed data. 28,32The observation that the risk of diabetes in modern societies with a lower rate of fetomaternal deprivation is increased at both extremes of birthweight (i.e.producing a U-shaped curve) suggests a schema capable of accommodating the insulin gene data. 33,34As with almost all human traits, the answer to the question `nature or nurture?' is almost certainly `both'." + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nObserved increased risk in African Americans is likely to result from a combination of shared environmental and genetic factors.Although there are few published studies specifically investigating familial aggregation of type 2 diabetes in African-American families, Rotimi et al. (10) found that relatives of African-American probands with type 2 diabetes had a 2.95-fold (95% CI 1.55-5.62)higher prevalence of diabetes when compared with relatives of unaffected individuals.In the GENNID (Genetics of Noninsulin Dependent Diabetes Mellitus) African-American families, the majority of first-degree relatives of African-American individuals with type 2 diabetes had abnormal glucose tolerance (11), with 27% found to have undiagnosed diabetes and 31% impaired fasting glucose and/or impaired glucose tolerance." + }, + { + "document_id": "144c9105-3ce9-46cc-b9c6-cc14cf40e945", + "section_type": "main", + "text": "\n\nClearly genetics play an important role in the T1D disease process as both MZ and DZ twins have the same environmental exposures but different concordance rates and length to diagnosis of the second twin.Numerous genes have been associated with T1D, the most significant being the HLA region on chromosome 6 [6].More than 90% of type 1 diabetics carry HLA alleles DR3-DQ2 or DR4-DQ8 compared to no more than 40% of the general population [7].Alleles at HLA-DQB1 are known to be, in part, protective [8].Single nucleotide polymorphisms (SNPs) are also associated with T1D.A recent genome-wide association study of approximately 2,000 patients with each of 7 common, chronic diseases, including T1D, and 7,000 shared controls confirmed the association of SNPs in 5 previously identified regions with T1D and discovered 5 novel associations.However, the authors concluded that these regions, with the exception of the HLA on chromosome 6, confer only modest effects on T1D, and ''the association signals so far identified account for only a small proportion of overall familiality'' [9].These results suggest that additional genetic variants contribute to inheritance of T1D." + }, + { + "document_id": "d1f8656e-e58a-4461-b75b-89815b2c7369", + "section_type": "main", + "text": "\n\nA neat example of this kind of interplay relates to the control of birth weight (Figure 2).In developed societies, it has been shown that the relationship between birth weight and T2D risk is best described through a U-shaped curve (shown in exaggerated form in the figure), such that the future risk of T2D is highest in individuals with either low or high birth weight as compared with those of average birth weight.Both associations with the extremes of birth weight result from a mix of genetic and nongenetic effects.At the lower extreme, the association between low birth weight and later T2D risk reflects both the long-term programming effects of an adverse intrauterine environment (most likely mediated through epigenetic effects) 12 and the impact of a subset of T2D-risk variants, such as those at CDKAL1, which have a marked effect on the secretion of insulin in early life (a time at which insulin acts as a major influence on growth). 75At the other extreme, the association between high birth weight and later T2D risk is mediated, at least in part, by exposure to maternal diabetes during pregnancy 61,63 and by direct genetic effects, such as those of the T2D risk-variants at TCF7L2, where the dominant effect of allelic variation in the fetomaternal unit appears to be to promote maternal hyperglycemia (and consequent fetal macrosomia). 76his review highlights evidence to support the notion that individual predisposition to T2D and obesity reflects a complex mix of genetic, epigenetic, and environmental influences.Despite recent progress, the mechanisms driving these interactions remain poorly understood." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "Genes\n\n2][43][44][45][46][47] Twin studies need to be considered carefully, however, as the intrauterine environments of dizygotic-twin (separate placentas), monozygotic-twin (60-70% share one placenta), and singleton pregnancies (one placenta without competition for maternal nutrients) will all be diff erent, and this can be a confounder in the inter pretation of eff ects. 44A large study from Sweden on familial risk of type 2 diabetes showed that the relative risks were highest in individuals with at least two aff ected siblings, irrespective of parental diabetes status. 42This fi nding suggests that a recessive pattern of inheritance from uncommon genetic defects, the sharing of similar intrauterine, postnatal, or both environments by siblings (eg, breastfeeding or bottle feeding or childhood nutrition), or a combination of these factors is important.9][50] A greater number of these loci are associated with impaired β-cell function (KCNJ11, TCF7L2, WFS1, HNF1B, SLC30A8, CDKAL1, IGF2BP2, CDKN2A, CDKN2B, NOTCH2, CAMK1D, THADA, KCNQ1, MTNR1B, GCKR, GCK, PROX1, SLC2A2, G6PC2, GLIS3, ADRA2A, and GIPR) than impaired insulin sensitivity (PPARG, IRS1, IGF1, FTO, and KLF14) or obesity (FTO). 38,48,50Of these, TCF7L2 is the strongest susceptibility locus for type 2 diabetes, being associated with β-cell dysfunction. 48Most patients with monogenic forms of diabetes also have gene defects that aff ect islet β-cell function. 51,52Nevertheless, only around 10% of the heritability of type 2 diabetes can be explained by susceptibility loci identifi ed so far, with each locus having a low eff ect size. 36The remaining heritability might be related to a large number of less common variants (allele frequency <5%) that are diffi cult to fi nd with current approaches of genome-wide association studies, and/or epigenetic phenomena." + }, + { + "document_id": "d1f8656e-e58a-4461-b75b-89815b2c7369", + "section_type": "main", + "text": "\n\nFirst, the fetal origins hypothesis established the notion of \"metabolic programming\" whereby nutritional and other exposures during early life generate long-term changes that later predispose to T2D and cardiovascular disease. 12This hypothesis builds on strong epidemiological data linking early life events to state art state art disease risk in late life, as seen, for example, in survivors of the Dutch \"Hunger Winter.\" 60 A growing body of data, from animal as well as human studies, has established that the molecular basis of programming involves altered DNA methylation. 61 second set of observations emerges from the longstanding follow-up of members of the Pima Native American community in Arizona, a population with an extremely high prevalence of T2D and obesity.The offspring of mothers who have T2D during pregnancy are at substantially higher risk of developing both T2D (45 vs. 1.4%) and obesity (58 vs. 17%) than are those born to women who are nondiabetic during pregnancy.61,62 Crucially, this difference is unlikely to completely reflect genetic transmission, as the distinction is preserved in children born to the same mother; that is, offspring born after the mother was diagnosed with T2D have higher rates of subsequent T2D and obesity than their siblings who arrived while their mother was nondiabetic.63 These findings suggest that the intrauterine environment is an important determinant of T2D and obesity predisposition, and they are broadly consistent with reports that the transmission of T2D and obesity is greater from mothers than from fathers.12,61 The increased risk of diabetes in female offspring of diabetic mothers clearly sets up the potential for an amplification of diabetes prevalence over successive generations." + }, + { + "document_id": "903e9615-c329-48be-9547-386a00f2dd94", + "section_type": "main", + "text": "\n\nDevelopmental Origins of Diabetes.Many Asian adults who experienced great hardship during wartime or civil unrest in early life are now experiencing marked changes in lifestyle.In addition, low birth weight and exposure to undernutrition in utero are common in some Asian populations, especially in India, where 30% of infants are underweight. 115Insults or stresses during the intrauterine period can lead to permanent changes in structure, metabolism, and physiology through altered expression of the genome without changes in the DNA codes, a process called epigenetics. 116These early life events may influence later susceptibility to diabetes, the metabolic syndrome, and cardiorenal diseases.Prospective studies from India have shown the impact of fetal undernutrition (often manifested as low birth weight) as well as overnutrition (eg, the infant of a mother with diabetes) on future risk of diabetes. 115In India, thinness in infancy and overweight at age 12 years was associated with increased risk of developing IGT or diabetes in young adulthood. 117 recent meta-analysis of 30 studies found a significant graded association between low birth weight and increased risk of type 2 diabetes. 118Low birth weight has also been found to predict diabetes and the metabolic syndrome in Asian adults and children, [119][120][121] thus lending support to the notion that fetal programming with exposure to poor nutrition in utero or during early childhood can promote a fatpreserving or thrifty phenotype.These metabolic changes predispose individuals to insulin resistance and reduced beta cell function.Positive energy balance in later life, caused by rapid westernization of diet and lifestyle, may then exaggerate accumulation of adiposity, particularly in the central depots. 122he 2-to 3-fold higher risk of gestational diabetes in Asian women than in their white counterparts also may contribute to the increasing epidemic of young-onset diabetes in Asia. 123Asian women with a history of gestational diabetes have a substantially increased risk of diabetes, while their offspring exhibit early features of the metabolic syndrome, thus setting up a vicious cycle of \"diabetes begetting diabetes. \"This combination of gestational diabetes, in utero nutritional imbalance, childhood obesity, and overnutrition in adulthood will continue to fuel the epidemic in Asian countries undergoing rapid nutritional transitions. 115enetic Susceptibility.Among lean, healthy individuals matched for age, BMI, waist circumference, birth weight, and current diet, Asians (especially those of Southeast Asian descent) had higher levels of postprandial glycemia and lower insulin sensitivity than whites in response to a 75-g carbohydrate load. 124These findings raise the possibility that Asians are more genetically susceptible to insulin resistance and diabetes than whites." + }, + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "section_type": "main", + "text": "\n\nAll these speculations may be utterly demolished the moment the precise etiologies of NIDDM [Non-Insulin-Dependent Diabetes Mellitus] become known.Until that time, however, devising fanciful hypotheses based on evolutionary principles offers an intellectual sweepstakes in which I invite you all to join. [Neel 1982:290] In perhaps his last written statement on the thrifty genotype hypothesis, Neel writes that there is \"no support to the notion that high frequency of NIDDM in reservation Amerindians might be due simply to an ethnic predisposition-rather, it must predominantly reflect lifestyle changes\" (Neel 1999:S3).In spite of this, many genetic epidemiologists argue that genetic differences explain rates of diabetes between different populations.For example, drawing on research with Mexicanos/as, one diabetes consortium member writes, \"there is strong evidence that Mexican Americans living in the barrio have considerably more Native Amerindian genetic admixture and as a result may have higher genetic susceptibility to diabetes\" (Stern 1999:S67). \"It smells and tastes like a thrifty gene in terms of its metabolic function,\" remarked one molecular biologist interested in the protein implicated in a genetic study of diabetes." + }, + { + "document_id": "9240ab9b-c5bb-4475-ad2b-111843cb146a", + "section_type": "main", + "text": "\n\nThe risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population." + } + ], + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "genetic&risk", + "HLA", + "immune&function", + "environmental&factors", + "autoimmunity", + "gene&variants", + "epigenetic", + "insulin&gene", + "genetic&screening" + ], + "metadata": [ + { + "object": "The HLA-B*42, HLA-C*17, HLA-DPA1*03, and HLA-DPB1*105 genotypes were associated with allergic asthma and the HLA-B*48 genotype with the nonallergic phenotype. The presence of the haplotype HLA-DPA1*03 DQA*05 was associated with allergic asthma, and the presence of HLA-DPA1*03 and the absence of HLA-DQA*05 with nonallergic asthma.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab821120" + }, + { + "object": "In patients diagnosed with HLA-B27-related anterior uveitis cohort HLA-B27+1 and with HLA-B27- non related anterior uveitis cohort HLA-B27-, no significant differences were found regarding clinical characteristics between both cohorts with the exception of a higher frequency of recurrences in cohort HLA-B27+ and a higher frequency of chronic uveitis in cohort HLA-B27-.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab397404" + }, + { + "object": "HLA-B13:02, HLA-B38:02, HLA-B44:03, and HLA-B56:01 alleles were significantly increased in autistic subjects. HLA-B18:02 and HLA-B46:12 alleles were negatively associated with autism when compared to normal controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab356725" + }, + { + "object": "Haplotyping was done on 91 Southern Europe celiac patients. HLA-DR3-DQ2 without HLA-DR7-DQ2 was present in 62.6%, HLA-DR7-DQ2 without HLA-DR3-DQ2 was present in 16.5% and HLA-DR4-DQ8 without HLA-DQ2 was present in 3.3%.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab332478" + }, + { + "object": "The Sonora, Mexico HLA-DQ risk heterodimer proportion was 16.1% for HLA-DQ2 and 13.6% for HLA-DQ8, with an HLA-DQ2:HLA-DQ8 ratio of 1.2:1. The DQ8/DQ2 genotype represented a 1:14 risk for type 1 diabetes, whereas the DQ8/DQB1*0201 combination showed a 1:6 risk for celiac disease.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872942" + }, + { + "object": "In this study, molecular dynamics simulation was performed on the complexes of Top1 peptide with various HLA-DR subtypes divided into ATASSc-associated alleles HLA-DRB1*08:02, HLA-DRB1*11:01 and HLA-DRB1*11:04, suspected allele HLA-DRB5*01:02, and non-associated allele HLA-DRB1*01:01.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab404240" + }, + { + "object": "Data from pediatric patients with celiac disease CD in the Netherlands suggest that HLA-DQ2.2 HLA-DQA1/HLA-DQB1 is important HLA-type related to CD; the 6% of CD patients lacking 2 major diagnostic markers HLA-DQ2.5 and HLA-DQ8 carry HLA-DQ2.2.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab177145" + }, + { + "object": "The meta-analysis suggested that HLA-DRB1*15 and HLA-DRB1*15:01 polymorphisms might be associated with increased AA risk in Asians. IST might be more effective in HLA-DRB1*15+ and HLA-DRB1*15:01+ Asian patients with AA than in HLA-DRB1*15- and HLA-DRB1*15:01- Asian patients with AA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab834223" + }, + { + "object": "The association of the HLA-A*24:02, HLA-B*39:01 and HLA-B*39:06 alleles with type 1 diabetes is restricted to specific HLA-DR/HLA-DQ haplotypes in Finns.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782265" + }, + { + "object": "Data suggest HLA-DRB1*03 haplotype splits in African Americans into HLA-DRB1*03:01 which confers susceptibility to type 1 diabetes and HLA-DRB1*03:02 rarely observed in those with European ancestry which confers protection from type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203455" + } + ], + "question": "How does genetics influence the emergency of diabetes?", + "subquestions": null, + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "usage": { + "chatgpt": 8194, + "gpt-4": 4183, + "gpt-4-turbo-preview": 3246 + }, + "user_id": 2 + }, + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/06.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/06.json new file mode 100644 index 0000000..d908030 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/06.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-05-31T23:15:26.855315Z", + "data": { + "amplify": false, + "answer": "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "context": { + "1cebda9b-a727-4bfd-8521-cf61903235cc": [ + { + "document_id": "1cebda9b-a727-4bfd-8521-cf61903235cc", + "text": "\n\nThe prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic β-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 ." + } + ], + "4252d7ad-82de-480c-a801-9ed1c84fb968": [ + { + "document_id": "4252d7ad-82de-480c-a801-9ed1c84fb968", + "text": "\n\nIn the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nTable 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4]." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nThe ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "Introduction\n\nGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009)." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Animal Models\n\n9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nType 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors)." + } + ], + "7d4a197e-3774-40a4-9897-ed7c71f213b6": [ + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "text": "Introduction\n\nDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D)." + } + ], + "961f88ba-2090-4904-942c-f0e014bbe53f": [ + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "text": "Classification of Diabetes\n\nOn the basis of insulin deficiency, diabetes can be classified into the following types as follows." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "| INTRODUCTION\n\nToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Häring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "CONCLUSIONS\n\nDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9)." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12)." + } + ], + "ab32e261-658c-4a8b-94fc-857826b29f5a": [ + { + "document_id": "ab32e261-658c-4a8b-94fc-857826b29f5a", + "text": "\n\nBackground Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "\n\nDiabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some £14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass." + } + ], + "b72eb0d1-50e3-4def-94bc-abf77891f519": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3)." + } + ], + "ba7298cd-4d19-4f98-9a2a-5fb625aa0068": [ + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "text": "Introduction\n\nDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2]." + } + ], + "ceab3d6d-62ca-459a-9a97-02a16d4dd193": [ + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\n\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nIntroduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + } + ] + }, + "data_source": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "CONCLUSIONS\n\nDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions." + }, + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "section_type": "main", + "text": "Introduction\n\nDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2]." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 2 Diabetes\n\nIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9)." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "\n\nTable 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4]." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "section_type": "main", + "text": "\n\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "section_type": "abstract", + "text": "\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "\n\nThe ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis." + }, + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "section_type": "main", + "text": "| INTRODUCTION\n\nToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Häring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3)." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 1 Diabetes\n\nBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12)." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "\n\nDiabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some £14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass." + }, + { + "document_id": "1cebda9b-a727-4bfd-8521-cf61903235cc", + "section_type": "main", + "text": "\n\nThe prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic β-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 ." + }, + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "section_type": "main", + "text": "Introduction\n\nGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009)." + }, + { + "document_id": "ab32e261-658c-4a8b-94fc-857826b29f5a", + "section_type": "main", + "text": "\n\nBackground Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "Animal Models\n\n9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes." + }, + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "section_type": "main", + "text": "\n\nIntroduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + }, + { + "document_id": "4252d7ad-82de-480c-a801-9ed1c84fb968", + "section_type": "main", + "text": "\n\nIn the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nType 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors)." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Background\n\nThe past few decades have shown a marked increase in the number of patients with diabetes rising from 151 million (4.6% of the global population) in 2000 to 463 million (9.3%) in 2019 [1].The risk of type 2 diabetes (T2DM), the most common type of diabetes, is modified by a strong interaction between environmental and genetic factors [2,3].T2DM is a multifactorial disease with a population-specific heritability (26% in the European population) [4].A number of common variants implicated in the pathogenesis and genetic architecture of T2DM have been identified so far, some of them also capable of modifying the pharmacologic response to antidiabetic drugs [5,6]." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "Introduction\n\nDiabetes is one of the most prevalent complex disorders with type 2 diabetes accounting for more than 90% of all diabetic cases.Hyperglycemia is the characteristic feature of this syndrome, which results from defective insulin secretion or action.The disease itself may not lead to death of the affected individual but being the major risk factor of macrovascular complications like coronary artery disease, cerebrovascular events and peripheral vascular disease, diabetes is an indirect cause of deaths due to such diseases.It is also responsible for disabilities such as diabetic nephropathy, diabetic neuropathy, diabetic retinopathy, skin complications, eye complications as well as mental illness.The International Diabetes Federation (IDF) 2015 reported an estimate of 415 million adults (20-79 years of age) worldwide to have diabetes in the year 2015, which is projected to reach 642 million by the year 2040.Diabetes has been a major public health concern in the 21st century (IDF 2015) among the worldwide countries/territories, particularly in China, India and USA, which show the alarmingly increasing prevalence (figure 1).India, in particular, is expected to have doubled its prevalence by 2040." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nTHE GLOBAL BURDEN OF TYPE 2 DIABETES-The dynamics of the diabetes epidemic are changing rapidly.Once a disease of the West, type 2 diabetes has now spread to every country in the world.Once \"a disease of affluence,\" it is now increasingly common among the poor.Once an adult-onset disease almost unheard of in children, rising rates of childhood obesity have rendered it more common in the pediatric population, especially in certain ethnic groups.According to the International Diabetes Federation (1), diabetes affects at least 285 million people worldwide, and that number is expected to reach 438 million by the year 2030, with two-thirds of all diabetes cases occurring in low-to middle-income countries.The number of adults with impaired glucose tolerance will rise from 344 million in 2010 to an estimated 472 million by 2030." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nThere is a high degree of variability for prevalence of type 2 diabetes across the globe.East Asia, South Asia, and Australia have more adults with diabetes than any other region (153 million).North America and the Caribbean have the highest prevalence rate, with one in eight affected (8)." + }, + { + "document_id": "988d55c7-f831-4adb-94c0-6de4ebf4727b", + "section_type": "main", + "text": "\n\nIn Germany, type 2 diabetes shows increasing prevalence with 5-8 million people having some form of diabetes (prevalence: 6-10%).In an effort to identify causative genetic factors, we report here results of linkage studies in which we identified two type 2 diabetes loci.We elucidated potentially interacting regions by conditioning our sample on the positive linkage signals identified.Taken together, our results and the findings of other studies provide evidence for a complex metabolic syndrome locus on chromosome 1p36.13." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "\n\nof those initially classified may require revision [7] .The classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA [1] .Wilkin [8] proposed the accelerator hypothesis that argues \"type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds\" [9] .The difference between the two types relies on the tempo, the faster tempo reflecting the more susceptible genotype and earlier presentation in which obesity, and therefore, insulin resistance, is the center of the hypothesis.Other predictors of type 1 diabetes include increased height growth velocity [10,11] and impaired glucose sensitivity of β cells [12] .The implications of increased free radicals, oxidative stress, and many metabolic stressors in the development, pathogenesis and complications of diabetes mellitus [13-18] are very strong and well documented despite the inconsistency of the clinical trials using antioxidants in the treatment regimens of diabetes [19][20][21] .The female hormone 17-β estradiol acting through the estrogen receptor-α (ER-α) is essential for the development and preservation of pancreatic β cell function since it was clearly demonstrated that induced oxidative stress leads to β-cell destruction in ER-α knockout mouse.The ER-α receptor activity protects pancreatic islets against glucolipotoxicity and therefore prevents β-cell dysfunction [22] ." + }, + { + "document_id": "2e317f9d-c028-41b7-a99e-28da61db9970", + "section_type": "main", + "text": "Introduction\n\nDiabetes impacts approximately 200 million people worldwide [1], with microvascular and cardiovascular disease being the primary complications.Approximately 10% of cases are type 1 diabetes (T1D) sufferers, with ,3% increase in the incidence of T1D globally per year [2].It is expected that the incidence is 40% higher in 2010 than in 1998 [3].T1D is a clear example of a complex trait that results from the interplay between environmental and genetic factors.There are many lines of evidence that there is a strong genetic component to T1D, primarily due to the fact that T1D has high concordance among monozygotic twins [4] and runs strongly in families, together with a high sibling risk [5]." + }, + { + "document_id": "b9c9912f-0344-4945-adb1-fd038bed90ab", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes is a common complex disease characterised by deficient insulin secretion and decreased insulin sensitivity.In 2010, 285 million people worldwide were affected by type 2 diabetes [1], with 60% of them located in Asia [2,3].China now has the largest number of patients with diabetes in the world, with an estimated 92 million affected individuals, and an additional 150 million with impaired glucose tolerance [4]." + }, + { + "document_id": "f44149e0-d183-48c1-a937-729e7abd87f5", + "section_type": "main", + "text": "Background\n\nType 2 diabetes mellitus (T2D) is a phenotypic and genetically heterogeneous chronic disease [1] that represents 90% to 95% of all diabetes types; given its magnitude, it has become an increasingly important public health problem worldwide, occurring in ever-younger individuals [2].In México, the National Health Survey 2000 (ENSA 2000) showed a T2D prevalence of 7.5% in individuals 20 years and older [3]." + }, + { + "document_id": "15b5c53c-d153-4932-9d24-9864e92a601d", + "section_type": "main", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) is a complex disease characterized by insulin resistance and b-cell dysfunction.An estimated 630 million adults are expected to have T2D by 2045, 1 making it one of the fastest growing global health challenges of the 21st century.Genome-wide association studies (GWASs) have successfully identified more than 500 genomic loci to be associated with T2D, 2 although the majority of these are driven by common variants with small individual effects on T2D risk." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "TYPE 2 DIABETES MELLITUS\n\nThe global prevalence of diabetes in adults (20-79 years old) according to a report published in 2013 by the IDF was 8.3% (382 million people), with 14 million more men than women (198 million men vs 184 million women), the majority between the ages 40 and 59 years and the number is expected to rise beyond 592 million by 2035 with a 10.1% global prevalence.tissues.In addition to insulin resistance, the increased demand for insulin could not be met by the pancreatic β cells due to defects in the function of these cells [18] .On the contrary, insulin secretion decreases with the increased demand for insulin by time due to the gradual destruction of β cells [57] that could transform some of type 2 diabetes patients from being independent to become dependent on insulin.Most type 2 diabetes patients are not dependent on insulin where insulin secretion continues and insulin depletion rarely occurs." + }, + { + "document_id": "251d15dc-e1ec-4fea-8c29-b000f51a62cd", + "section_type": "main", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) is a complex metabolic disorder that accounts for 85%-95% of all cases of diabetes and afflicts hundreds of millions of people worldwide (http://www.diabetesatlas.org/content/diabetes).It is a leading cause of substantial morbidity and is characterized by defects in insulin sensitivity and secretion resulting from the progressive dysfunc-tion and loss of b cells in the pancreatic islets of Langerhans (Butler et al., 2007;Muoio and Newgard, 2008).Both genetic predisposition and environmental factors contribute to these islet defects.Islets constitute 1%-2% of human pancreatic mass (Joslin and Kahn, 2005) and are composed of five endocrine cell types that secrete different hormones: a cells (glucagon), b cells (insulin), d cells (somatostatin), PP cells (pancreatic polypeptide Y), and 3 cells (ghrelin).These cells sense changes in blood glucose concentration and respond by modulating the activity of multiple pathways, including insulin and glucagon secretion, to maintain glucose homeostasis (Joslin and Kahn, 2005).Several key transcription factors (TFs) that regulate these responses are known (Oliver-Krasinski and Stoffers, 2008).However, efforts to identify cis-regulatory elements upon which these and other factors act have been restricted primarily to promoter regions at specific loci (e.g., INS, PDX1) (Brink, 2003;Ohneda et al., 2000)." + }, + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "section_type": "main", + "text": "\n\nType 2 diabetes affects more than 200 million individuals worldwide, and its prevalence is continuously increasing in many countries, including Japan.Although the precise mechanisms underlying the development and progression of type 2 diabetes have not been fully elucidated, a combination of multiple genetic and environmental factors is considered to contribute to the pathogenesis of the disease 1 ." + }, + { + "document_id": "ff69cd83-ab79-4c24-8bc5-fd9009aa259b", + "section_type": "main", + "text": "Background & Summary\n\nDiabetes is one of the fastest-growing health challenges of the 21 st century.The most common form of diabetes, type 2 diabetes (T2D), is a complex multifactorial disease which can lead to further severe health consequences such as cardiovascular diseases and premature death.In 2019, 463 million people worldwide were living with diabetes according to the International Diabetes Federation, and this number is expected to rise to 700 million by 2045 1 .Genome-wide association studies (GWAS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D.A recent study performed a meta-analysis of T2D across 32 GWAS of European ancestry participants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk 2 .The summary statistics from this meta-analysis are publicly available; however, the GWAS results for each participating study, including EPIC-InterAct, cannot be acquired easily." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nDIABETES EPIDEMIC-The latest estimates from the Center for Disease Control and Prevention indicate that in 2010 approximately 26 million American adults had diabetes and 79 million had prediabetes (1).African Americans and other ethnic groups continue to suffer higher rates of diabetes than whites.Worldwide, diabetes affects 285 million adults (2).Type 2 diabetes accounts for ;95% of all cases.The exact reasons for the diabetes epidemic, and its predilection for certain ethnic groups, are unknown.However, interactions between genetic predisposition and environmental triggers (or accelerants) are generally presumed to underlie the etiology of diabetes (3-5) (Fig. 1).The best known environmental risk factors are dietary habits, physical inactivity, and obesity; interventions that ameliorate these risk factors prevent the development of type 2 diabetes (6,7)." + }, + { + "document_id": "d15b3490-241d-4766-8e3e-feb683503d1b", + "section_type": "main", + "text": "\n\nType 2 diabetes is one of the leading health problems in the United States, affecting approximately 21 million persons or almost 10% of the US adult population (1).Type 2 diabetes is nearly twice as prevalent among African Americans as among Caucasians (1)." + }, + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "section_type": "main", + "text": "Introduction\n\nDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D)." + }, + { + "document_id": "6a2d9ea5-7018-42fe-bed9-2c9c508531cb", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes mellitus (T2D) is a major chronic disease worldwide, affecting more than 300 million people.The greatest increase in the prevalence of T2D in the coming years is likely to be in Asia, home to half of the world's population with 3 billion people [1][2].It is estimated that in China alone, there are 100 million people with T2D [3]." + }, + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "section_type": "main", + "text": "Classification of Diabetes\n\nOn the basis of insulin deficiency, diabetes can be classified into the following types as follows." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) is a common disease with substantial and rapidly increasing global impact.While prevalence varies with age, sex and population, the global age-standardized adult diabetes prevalence is >9.2%, and an estimated >347 million adults have diabetes (1).Diabetes can be diagnosed based on the level of blood glucose after fasting or 2 h after an oral glucose challenge (2hGlu), or based on hemoglobin A1c (HbA1c), which provides a 3month average of blood glucose (2).In many individuals with T2D, insulin resistance coexists with obesity, adverse lipid profiles, high blood pressure and a proinflammatory state, each likely influenced by genetic and environmental factors (3).Progression to T2D is characterized by abnormalities in pancreatic islet β-cell function in the presence of insulin resistance (4), although these biological processes are only partially defined.Strong evidence for a genetic component exists for T2D risk, insulin secretion and insulin action (5,6)." + }, + { + "document_id": "ee21529b-bf7d-49ec-a21e-c52c9c7ff7e1", + "section_type": "main", + "text": "Symptomatic T1DM\n\nAccording to the International Diabetes Federation, 8.8% of the adult population worldwide has diabetes 14 .Of all individuals with diabetes, only 10-15% have T1DM; type 2 diabetes mellitus (T2DM) is the most common form.However, T1DM is the most com mon form of diabetes in children (<15 years of age), and >500,000 children are currently living with this condition globally." + }, + { + "document_id": "8857153e-a7be-45ee-84dd-14911bdd064a", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) affects at least 6% of the world's population; the worldwide prevalence is expected to double by 2025 [1].T2D is a complex disorder that is characterized by hyperglycemia, which results from impaired pancreatic b cell function, decreased insulin action at target tissues, and increased glucose output by the liver [2].Both genetic and environmental factors contribute to the pathogenesis of T2D.The disease is considered to be a polygenic disorder in which each genetic variant confers a partial and additive effect.Only 5%-10% of T2D cases are due to single gene defects; these include maturity-onset diabetes of the young (MODY), insulin resistance syndromes, mitochondrial diabetes, and neonatal diabetes [3][4][5].Inherited variations have been identified from studies of monogenic diabetes, and have provided insights into b cell physiology, insulin release, and the action of insulin on target cells [6]." + } + ], + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "type&2&diabetes", + "gestational&diabetes", + "LADA", + "MODY", + "insulin&resistance", + "pancreatic&beta&cells", + "autoimmune&destruction", + "insulin&deficiency", + "genetic&factors" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "The genotype EE/EK/KK frequencies % for the CTRL group 38.2/50.2/11.6, Type 1 Diabetes 34.3/52.0/13.7, and Type 2 Diabetes 38.2/48.9/12.9 were in Hardy-Weinberg equilibrium and there were no significant differences. The minor allele frequencies MAF; K for CTRL 37.0%, Type 1 Diabetes 39.7%, and Type 2 Diabetes 37.4% were not different among the groups", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab818180" + }, + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "Sfrp5 may be concurrently associated with COPD [ chronic obstructive pulmonary disease ] and insulin resistance; insulin resistance may be associated with airway inflammation and airflow limitation. Sfrp5 may be involved in the development of COPD and may be the key link by which insulin resistance exerts its effects on airway inflammation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702425" + }, + { + "object": "Data suggest a novel pathophysiological role of CD163 in type 2 diabetes; monocyte surface CD163 levels are significantly associated with insulin resistance in patients with type 2 diabetes; the association of insulin resistance with soluble CD163 levels is less significant. This study was conducted in Japan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab202739" + }, + { + "object": "Decreased plasma ghrelin significantly associated with abdominal adiposity, hyperinsulinemia and insulin resistance in type 2 diabetic patients. Hyperinsulinemia with insulin resistance may suppress plasma ghrelin in type 2 diabetes mellitus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab218455" + }, + { + "object": "results show an association between the AGER -374 T/A polymorphism & type 1 diabetes; the polymorphism was associated with diabetic nephropathy in both type 1 & type 2 diabetes & with sight-threatening retinopathy in type 1 diabetic patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab660185" + }, + { + "object": "polymorphism of IAPP gene in nondiabetic subjects and type 2 diabetes is associated with lower levels of LDL cholesterol; abnormalities of coding regions or 5'-UTR not associated with type 2 diabetes or gestational diabetes mellitus in Spanish population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab604559" + }, + { + "object": "The association of variants in IRS1 with type 2 diabetes and type 2 diabetes-related phenotypes and the differential expression of IRS1 in adipocytes and skeletal muscle suggest a role of this gene in the pathogenesis of type 2 diabetes in Pima Indians.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782328" + } + ], + "question": "How many types of diabetes exist?", + "subquestions": null, + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "usage": { + "chatgpt": 6403, + "gpt-4": 4251, + "gpt-4-turbo-preview": 3329 + }, + "user_id": 2 + }, + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_01.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_01.json new file mode 100644 index 0000000..b9417a9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_01.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-05-31T23:02:19.686688Z", + "data": { + "amplify": false, + "answer": "The genes associated with diabetes mentioned in the text include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, HNFA4, LOC387761, KCNQ1, MTNR1B, GCKR, BACH2, CTNNB1, CREB5, PARD3B, PARD6G, PRKCZ, EBF1, GIGYF1, ADCY5, PROX1, DGKB-TMEM195, CRY2, FADS1, GLIS3, and C2CD4B.", + "context": { + "2bef9608-4bd6-4252-9fbd-2413b2cad4f8": [ + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "\n\nTo see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p ≤ 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14)." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3)." + } + ], + "2dade65a-5d31-4839-b2c9-4c6cd3056f58": [ + { + "document_id": "2dade65a-5d31-4839-b2c9-4c6cd3056f58", + "text": "\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4]." + } + ], + "31588831-61b3-4018-9962-bd6985c3061b": [ + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + } + ], + "3c35547c-eb9b-470d-b74b-0f9a0529e965": [ + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "text": "\n\nAmong the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287)." + } + ], + "45c14654-f263-4031-9941-206d7b6a97f3": [ + { + "document_id": "45c14654-f263-4031-9941-206d7b6a97f3", + "text": "\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c ." + } + ], + "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c": [ + { + "document_id": "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c", + "text": "\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting β-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the β-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the β-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype." + } + ], + "5d7a863d-1811-4eea-9fb0-fbc3067aa664": [ + { + "document_id": "5d7a863d-1811-4eea-9fb0-fbc3067aa664", + "text": "\n\nDespite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 ." + } + ], + "7bd7a98f-955a-4988-8981-a0ff7ab6f7df": [ + { + "document_id": "7bd7a98f-955a-4988-8981-a0ff7ab6f7df", + "text": "\n\nSimilar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Type 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5×10 −13 ), PPARG (odds ratio, 1.20; P = 4.0×10 −4 ), FTO (odds ratio, 1.14; P = 9.2×10 −5 ), KCNJ11 (odds ratio, 1.13; P = 3.6×10 −4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic β cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through β cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic β cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47]." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Most Relevant T2DM Susceptibility Genes\n\nGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "\n\nOne of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009)." + } + ], + "a579db95-2a40-43ff-b237-d47f90aaf64f": [ + { + "document_id": "a579db95-2a40-43ff-b237-d47f90aaf64f", + "text": "Genes boosted in type 2 diabetes\n\nBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes." + } + ], + "b978a189-6fbd-4791-8072-7db79f43746a": [ + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "RESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "\nOBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two" + } + ], + "bbb4af44-2659-4207-b9a1-0ff85d379a9f": [ + { + "document_id": "bbb4af44-2659-4207-b9a1-0ff85d379a9f", + "text": "\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies." + } + ], + "d9564b3c-efac-42ae-8e15-bf962c0a7a3c": [ + { + "document_id": "d9564b3c-efac-42ae-8e15-bf962c0a7a3c", + "text": "Introduction\n\nMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + } + ] + }, + "data_source": [], + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2&gene", + "PPARG&gene", + "KCNJ11&gene", + "SLC30A8&gene", + "HHEX&gene", + "CDKAL1&gene", + "CDKN2A&gene", + "IGF2BP2&gene", + "FTO&gene", + "WFS1&gene" + ], + "metadata": [], + "question": "what genes are associated with diabetes?", + "subquestions": null, + "task_id": "8909D2606E33C312F2ECC705FAF65CA2", + "usage": { + "chatgpt": 10960, + "gpt-4": 6551, + "gpt-4-turbo-preview": 5611 + }, + "user_id": 2 + }, + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "task_id": "8909D2606E33C312F2ECC705FAF65CA2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_02.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_02.json new file mode 100644 index 0000000..e3021cb --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_02.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-05-31T23:04:51.896429Z", + "data": { + "amplify": false, + "answer": "Genomics provides a comprehensive understanding of the genetic factors influencing the development and progression of diabetes. Nutritional genomics focuses on the interaction between genes and dietary patterns, which plays a crucial role in diabetes. By studying gene-nutrient interactions and genetic variations, researchers can develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Furthermore, genomics can help identify how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake, potentially leading to diabetes. Thus, genomics offers a promising approach to understanding the nutritional factors of diabetes and developing personalized dietary interventions.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nenetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "2a71b781-89fe-4055-bbb1-15aa226e1e3a": [ + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "789097da-e961-4486-8c83-816626556b16": [ + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "text": "\n\nNonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da": [ + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + } + ] + }, + "data_source": [], + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mellitus", + "genomics", + "nutritional", + "factors", + "gene-nutrient", + "interactions", + "type&2", + "genetic", + "variants" + ], + "metadata": [], + "question": "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "usage": { + "chatgpt": 5995, + "gpt-4": 4151, + "gpt-4-turbo-preview": 3211 + }, + "user_id": 2 + }, + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_03.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_03.json new file mode 100644 index 0000000..becabbc --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_03.json @@ -0,0 +1,147 @@ +{ + "created_at": "2024-05-31T23:07:32.253629Z", + "data": { + "amplify": false, + "answer": "Genomics can be used to understand the nutritional factors of diabetes by studying nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This approach can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Additionally, genomics can identify gene variants that interact with specific nutrients, providing insights into how diet and genetics interact to influence diabetes risk.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "e9b48e14-aa0c-4331-a17d-82a7f424233c": [ + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + } + ], + "f9b65334-56b7-43e9-9fda-b778c18c1c67": [ + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "text": "Genomics for Type 2 Diabetes\n\nMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + } + ] + }, + "data_source": [], + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&genomics", + "nutrition", + "nutrient-gene&interactions", + "diabetes&mellitus", + "nutritional&genomics", + "gene&variants", + "epigenetic&modifications", + "GWAS", + "pharmacogenomics", + "personalized&medicine", + "machine&learning" + ], + "metadata": [], + "question": "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "usage": { + "chatgpt": 6443, + "gpt-4": 4073, + "gpt-4-turbo-preview": 3136 + }, + "user_id": 2 + }, + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_04.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_04.json new file mode 100644 index 0000000..e02ac80 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_04.json @@ -0,0 +1,152 @@ +{ + "created_at": "2024-05-31T23:10:07.733262Z", + "data": { + "amplify": false, + "answer": "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any association between the genes SH2B3 or ERBB3 and diabetes.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "\n\nFigure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 × 10 −9 )." + } + ], + "15524ac0-da3c-4c01-8ae2-1b8c901105ad": [ + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "text": "\n\nAll the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency." + } + ], + "1ef9a72d-b9ef-4955-a351-fca0175da3d1": [ + { + "document_id": "1ef9a72d-b9ef-4955-a351-fca0175da3d1", + "text": "\n\nOne method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12)." + } + ], + "21368075-9e10-4260-b346-43b1029b3bf0": [ + { + "document_id": "21368075-9e10-4260-b346-43b1029b3bf0", + "text": "Results\n\nImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p ¼ 0.536), while DEA produced a comparably weak enrichment score (p ¼ 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes." + } + ], + "2715e261-b26c-46d6-918f-c6aa47688f0c": [ + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "text": "35\nABSTRACT 11\nA GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES\nESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE\nREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY\nMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,\nC Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S\nTurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie\nDepts." + } + ], + "4322db2f-5f43-4fc0-8968-b24438a7d6b9": [ + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "text": "\n\nSecond, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion." + } + ], + "647571cd-ff36-4be4-97c4-cd006d9bfbaf": [ + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "\n\nIn summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "DISCUSSION\n\nWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype." + } + ], + "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae": [ + { + "document_id": "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae", + "text": "\n\nThese observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 ." + } + ], + "7b7ce30c-f398-4b0e-bcb6-52f2644201fd": [ + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "text": "\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)-␥ signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73)." + } + ], + "7e816722-443f-463c-8a79-852752df28e6": [ + { + "document_id": "7e816722-443f-463c-8a79-852752df28e6", + "text": "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes\n\nFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used." + } + ], + "845adde7-823a-4bfc-9f5e-7082d2e26102": [ + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "text": "\n\nIn this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion." + } + ], + "8aee60c9-9bb4-4867-96c9-830c1e43c72e": [ + { + "document_id": "8aee60c9-9bb4-4867-96c9-830c1e43c72e", + "text": "\n\nAt present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients." + } + ], + "9fd49699-612f-48c0-b1d9-e01158472be6": [ + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "text": "\n\nIn summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets." + } + ], + "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd": [ + { + "document_id": "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd", + "text": "\n\nResults.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "The authors then used mouse liver and adipose expression\ndata from several mouse crosses to construct causal expression networks for the ERBB3 and\nRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any\nknown Type I diabetes genes whereas RPS26 is associated a network of several genes that\nare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis\ndemonstrates the power of combining human and mouse data with a network based\napproach that has been proposed for use in drug discovery (Schadt et al." + } + ], + "ebb49f39-ee30-4b32-959d-305276fd589e": [ + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nIn conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + } + ], + "faa23996-65fc-4bc6-938a-c959e981d493": [ + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "text": "\n\nFinally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + } + ] + }, + "data_source": [], + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "type&1&diabetes", + "type&2&diabetes", + "SH2B3", + "IFIH1", + "ERBB3", + "insulin", + "pancreatic&islets", + "gene&expression", + "mutations" + ], + "metadata": [], + "question": "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "subquestions": null, + "task_id": "A9F5CC0D31CE591D56814F3A276760E5", + "usage": { + "chatgpt": 8842, + "gpt-4": 5247, + "gpt-4-turbo-preview": 4320 + }, + "user_id": 2 + }, + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "task_id": "A9F5CC0D31CE591D56814F3A276760E5" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_05.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_05.json new file mode 100644 index 0000000..3957957 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_05.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T23:11:51.586254Z", + "data": { + "amplify": false, + "answer": "Genetics plays a significant role in the emergence of diabetes. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus contribute to 50-60% of the genetic risk by affecting protein binding and antigen presentation to T cells. Other gene variants that modulate immune regulation, viral responses, environmental signals, and endocrine function also contribute to the disease. The risk for type 1 diabetes is strongly influenced by multiple genetic loci. In type 2 diabetes, many common variants, largely with small effect size, contribute to overall disease risk. Monogenic forms of diabetes, such as MODY, result from mutations in a single gene. Family history also plays a role, suggesting that genetics conveys more than heritable information; it probably includes non-genetic familial behaviors and norms.", + "context": { + "00591f6a-0d6f-4993-ae6c-e9a8109a95ec": [ + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "A. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is ϳ10 -15 times greater.Although ϳ70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3)." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "II. THE GENETICS OF TYPE 1 DIABETES\n\nA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nThe relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in -cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of -cell function and insulin action in the general population also hint at a preponderance of -cell effects (52)." + } + ], + "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01": [ + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "text": "\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared" + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "Environment\n\nThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "The genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008)." + } + ], + "76ae2f09-af4d-422a-b939-625f0fe4ae1c": [ + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + } + ], + "83a34294-d942-476f-be2f-ff8d7ec3dec4": [ + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "text": "\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al." + } + ], + "8d723c99-bd3c-43eb-9b31-14ee233c2ed4": [ + { + "document_id": "8d723c99-bd3c-43eb-9b31-14ee233c2ed4", + "text": "\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications." + } + ], + "9240ab9b-c5bb-4475-ad2b-111843cb146a": [ + { + "document_id": "9240ab9b-c5bb-4475-ad2b-111843cb146a", + "text": "\n\nThe risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population." + } + ], + "92eb0c69-5e98-41aa-9084-506e7f223b1a": [ + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "text": "Genetic Background and Environment\n\nBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Genetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin." + } + ], + "9cce7fe9-cb40-4e75-85bc-d8655c3343d6": [ + { + "document_id": "9cce7fe9-cb40-4e75-85bc-d8655c3343d6", + "text": "\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40]." + } + ], + "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a": [ + { + "document_id": "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a", + "text": "\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1α (HNF-1α), HNF-1β, HNF-4α, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 ." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + } + ] + }, + "data_source": [], + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "genetic&risk", + "HLA", + "immune&function", + "environmental&factors", + "autoimmunity", + "gene&variants", + "epigenetic", + "insulin&gene", + "genetic&screening" + ], + "metadata": [], + "question": "How does genetics influence the emergency of diabetes?", + "subquestions": null, + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "usage": { + "chatgpt": 8194, + "gpt-4": 4183, + "gpt-4-turbo-preview": 3246 + }, + "user_id": 2 + }, + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_06.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_06.json new file mode 100644 index 0000000..81454c1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_06.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T23:15:26.855315Z", + "data": { + "amplify": false, + "answer": "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "context": { + "1cebda9b-a727-4bfd-8521-cf61903235cc": [ + { + "document_id": "1cebda9b-a727-4bfd-8521-cf61903235cc", + "text": "\n\nThe prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic β-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 ." + } + ], + "4252d7ad-82de-480c-a801-9ed1c84fb968": [ + { + "document_id": "4252d7ad-82de-480c-a801-9ed1c84fb968", + "text": "\n\nIn the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nTable 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4]." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nThe ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "Introduction\n\nGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009)." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Animal Models\n\n9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nType 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors)." + } + ], + "7d4a197e-3774-40a4-9897-ed7c71f213b6": [ + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "text": "Introduction\n\nDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D)." + } + ], + "961f88ba-2090-4904-942c-f0e014bbe53f": [ + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "text": "Classification of Diabetes\n\nOn the basis of insulin deficiency, diabetes can be classified into the following types as follows." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "| INTRODUCTION\n\nToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Häring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "CONCLUSIONS\n\nDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9)." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12)." + } + ], + "ab32e261-658c-4a8b-94fc-857826b29f5a": [ + { + "document_id": "ab32e261-658c-4a8b-94fc-857826b29f5a", + "text": "\n\nBackground Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "\n\nDiabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some £14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass." + } + ], + "b72eb0d1-50e3-4def-94bc-abf77891f519": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3)." + } + ], + "ba7298cd-4d19-4f98-9a2a-5fb625aa0068": [ + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "text": "Introduction\n\nDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2]." + } + ], + "ceab3d6d-62ca-459a-9a97-02a16d4dd193": [ + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\n\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nIntroduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + } + ] + }, + "data_source": [], + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "type&2&diabetes", + "gestational&diabetes", + "LADA", + "MODY", + "insulin&resistance", + "pancreatic&beta&cells", + "autoimmune&destruction", + "insulin&deficiency", + "genetic&factors" + ], + "metadata": [], + "question": "How many types of diabetes exist?", + "subquestions": null, + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "usage": { + "chatgpt": 6403, + "gpt-4": 4251, + "gpt-4-turbo-preview": 3329 + }, + "user_id": 2 + }, + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_01.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_01.json new file mode 100644 index 0000000..4b91711 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_01.json @@ -0,0 +1,394 @@ +{ + "created_at": "2024-04-18T18:19:31.857649Z", + "data": { + "amplify": false, + "answer": "The onset of diabetes in mice can occur as early as 3 to 4 weeks of age, with blood sugar concentrations often elevating between 5 and 8 weeks. However, in some cases, hyperglycemia can develop at approximately 20 weeks of age.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "52990c69-609c-448e-9f2c-36e1655ca6db": [ + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control. A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + } + ], + "c802cb60-1a15-4962-8e6d-f06608c00a54": [ + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control.\n A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "section_type": "main", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33].\n Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "section_type": "main", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + }, + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "section_type": "main", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nTo investigate the effects of genetic background variation on the measured traits, we also conducted a genetic cross using CAST as the diabetes-resistant strain (CAST cross).In the male BC1 progeny of this CAST cross, the onset of the hyperglycemia was slightly delayed compared to the B6 cross; 27% vs 45% of the male BC1 mice showed Ͼ300 mg/dl plasma glucose at 20 weeks.In the CAST cross the hyperglycemia was also maintained throughout the 30-week period studied.Therefore, the mean of three glucose measurements between 22 and 28 weeks of age for each BC1 progeny was used for genetic analysis.Body weights were measured at 24 weeks.At the end of the study (28 weeks), plasma insulin levels and nasal-anal lengths were measured, and five fat pads were dissected and weighed.In total, 95 male BC1 mice were collected and genotyped individually with 69 SSLP markers spaced through out the genome." + }, + { + "document_id": "a551b815-1d9d-4dae-a194-8f77e317b506", + "section_type": "main", + "text": "Diabetes monitoring\n\nCohorts of female mice were housed in an SPF facility and tested once a week for elevated urinary glucose (>110 mmol/L) using Diastix reagent strips (Bayer Australia, Ltd.) over a 300-d time course.Three consecutive elevated readings indicated the onset of diabetes.Pairwise comparisons of the diabetes incidence between mouse strains were done using the log-rank test." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Two of the mice had\nblood sugar concentrations only slightly above normal\nat the end of the 3 month period, while two others\nstabilized at the starting blood sugar concentrations.\n Weight gains of diabetic mice on this ration, were,\non the whole, variable but somewhat smaller than\nthose seen on the chow ration. However, those diabetic\nmice that showed the greatest decrease in rate of\nweight gain did not necessarily have the lowest blood\nsugar concentrations at the end of the treatment\n\nperiod." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "The diabetic mouse on the\nright weighs 50 per cent more t h a n the control mouse on the left and shows\ntypical f a t deposition\n\nwith age and concomitant elevations of blood sugar\nconcentration have been described previously [11]\nand will not be dealt with in detail here. Although\nthere are individual variations in the age of onset of\ndiabetes and the rate of increase in weight and blood\nsugar concentration, there is a general pattern, which\nis depicted in Fig. 2." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease.\n Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths.\n After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "\n\nM16 mouse: M16 mouse is a new model for obesity and type 2 diabetes which results from long-term selection for 3 to 6 wk weight gain from an Institute of Cancer Research, London, UK (ICR) base population.M16 mice exhibit early onset of obesity and are larger at all ages characterized by increased body fat percentage, fat cell size, fat cell numbers, and organ weights.These mice also exhibit hyperphagia, accompanied by moderate obesity, and are hyperinsulinaemic, hyperleptinaemic and hypercholesterolaemic relative to ICR.Both M16 males and females are hyperglycaemic relative to ICR, with 56 and 22 per cent higher fasted blood glucose levels at 8 wk of age.M16 mice represent an outbred animal model to facilitate gene discovery and pathway regulation controlling early onset polygenic obesity and type 2 diabetic phenotypes.Phenotypes prevalent in the M16 model, with obesity and diabesity exhibited at a young age, closely mirror current trends in human populations 36 ." + }, + { + "document_id": "38be907c-70ea-45f2-a8c1-7aed203a5256", + "section_type": "main", + "text": "Mice and Intervention Protocol\n\nProtocols were approved by the Rutgers University Institutional Care and Use Committee and followed federal and state laws.Five-week-old male C57BL/6J mice (10-20 g) were purchased from The Jackson Laboratory (Bar Harbor, ME) and fed a standard chow diet ad libitum (cat.no.5015; Purina) during their 1-week acclimatization period.Animals were housed, five per cage, with free access to water in a room with a temperature of 24 6 1°C and a 12:12-h light:dark cycle (7:00 A.M.-7:00 P.M.).At 6 weeks of age, oral glucose tolerance tests (OGTTs) were performed on 45 mice.The area under the curve (AUC) corresponding to the OGTT data from each mouse was calculated, and a mean AUC for each cage of five mice was determined.The nine cages were separated into three groups based on the average AUCs calculated for each cage so that each group of 15 mice would be similar at baseline with respect to oral glucose tolerance.This method of assignment was used as a way to normalize oral glucose tolerance at baseline and also keep mice in their original cage placements, as switching the animals around can sometimes lead to aggressive behavior in the new group.Mice were fed GP-SPI diet, SPI diet, or HFD (n = 15 mice/diet group) for a total of 13 weeks.The HFD group was used mainly as a control to monitor body weight gain and food intake between groups.Various end points were measured during the intervention period as described below.A second group of 5-week-old male C57BL/6J mice (10-20 g) (n = 10) was purchased at a later time to have an LFD cohort with which to compare body weights, food intake, and microbiome samples.These LFD-fed mice were similarly housed (five per cage) in the same experimental room and space.Mice were initially fed a regular chow diet ad libitum for 1 week and then switched to the LFD for 12 weeks with OGTT performed at the same intervals." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "main", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + }, + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "section_type": "main", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nLeptin-receptor-deficient db/db mice on the C57BLKS/J background largely recapitulate the obesity phenotype of the ob/ob mouse.The nomenclature of db (that is, diabetic) stems from the original observation of marked hyperglycaemia in these mice.db/db mice are hyperphagic and have reduced energy expenditure, leading to early-onset obesity 195 .They are also hypothermic, have decreased linear growth owing to GH deficiency and are infertile 195 , and leptin levels in db/db mice are markedly elevated 205 .Hyperinsulinaemia can be detected as early as 10 days of age, and insulin levels continue to increase until 3 months of age.The hyperinsulinaemia is accompanied by hyperplasia and hypertrophy of the pancreatic β-cells.After 3 months, levels of insulin in db/db mice drop profoundly, which is concomitant with the atrophy of β-cells.Consequently, marked and sustained hyper glycaemia with blood glucose values >400 mg/dl promotes premature death around 5-8 months of age.However, the db/db model does not capture all the diabetic complications observed in the human disease.Vascular and retinal complications, for example, are rarely documented in db/db mice, likely because of the dramatically shortened lifespan.Notably, db/db mice on a C57BL/6J background exhibit only mild diabetic symptoms and a normal lifespan, despite marked obesity 78,79,195 ." + }, + { + "document_id": "7d5b12ef-7b17-4b49-8da2-1a4179601520", + "section_type": "main", + "text": "LEW.1AR1/Ztm-Iddm Rats\n\nIn this strain, type 1 diabetes develops at age 2 months as result of immune damage caused by heavy infiltration of the islets of Langerhans by B and T lymphocytes, macrophages and NK cells and beta cell destruction by apoptosis [85][86][87].The mutation in this strain resides in the Dock8 gene, which encodes a member of the DOCK180 protein superfamily of guanine nucleotide exchange factors that act as activators of Rac/Rho family GTPases [88]." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted.\n\n Fig. 8." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "\n\nALS/Lt mouse: Alloxan susceptible (ALS) new mouse model is produced by inbreeding outbred CD-1 mice (a commercial stock of ICR mice from which inbred NSY and NON mouse are developed), with selection for susceptibility to alloxan (ALX), a generator of highly reactive oxygen free radicals and a potent betacell toxin.Initially, the type 2 diabetes predisposition of ALS mouse was recognized by congenic analysis of the yellow mutation (Ay) at the agouti locus on chromosome 2. Indeed, in ALS/Lt (a substrain maintained at Jackson Laboratory, Bar Habor) mice, hyperinsulinaemia and impaired glucose tolerance develop spontaneously between 6 and 8 wk of age in alloxan-untreated males.This mouse model with reduced ability to diffuse free radical stress is of obvious interest because free radical-mediated damage is implicated in the pathogenesis and complications of both type 1 and type 2 diabetes 62 ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "I n the latter three,\nbody weights were stabilized at that seen when treatment was initiated. However, no actual weight losses\nwere seen and the relative obesity of these mice was\nstill apparent.\n Discussion\nThe marked tendency to obesity,\nactivities of several insulin-dependent\nthe degranulation of fl-cells of the islets\nobserved in the younger diabetic mice\n\nthe increased\nenzymes, and\nof Langerhans\nare quite con-\nVol. 3, 2Vo. 2, 1967\n\nD.L. COLEMAXand K.P. I-IuMM]~L:Studies with the Mutation, Diabetes\n\nsistent with the increased levels of circulating insulin\nfound in these mice." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "\n\nRodent models of diabetic retinopathy iii)" + } + ], + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db/db&mice", + "diabetes", + "onset", + "age", + "obesity", + "hyperglycemia", + "C57BL/KsJ", + "C57BL/6J", + "insulin&resistance", + "albuminuria" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data suggest that circulating IGF-1 levels are higher, insulin resistance is worse, and lean mass is higher in mice with obesity induced at earlier age modeling peripubertal-onset obesity as compared to older mice modeling adult-onset obesity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab205540" + }, + { + "object": "We used young, leptin receptor deficient Db/Db mice to mimic the effect of diet and diabetes on adolescents. Db/Db and Control mice were fed either Western or Control diets, and were sacrificed at 3 months of age. Db/Db mice were obese, while only female mice developed diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014541" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "The mean age of Parkinsonism onset among LRRK2 G2385R carriers was 42.7 years old for early-onset compared to 74.3 for late-onset patients. LRRK2 G2385R mutation appears to be as prevalent among early-onset as late-onset patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab833283" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Galectin-3 and S100A9 are overexpressed in Pancreatic cancer-associated diabetes tumors and mediate insulin resistance. Galectin-3 and S100A9 distinguish Pancreatic cancer-associated diabetes from type 2 diabetes in subjects with new-onset diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab310949" + }, + { + "object": "This study investigates the involvement of a 14-bp deletion polymorphism rs371194629 at the 3' untranslated region of HLA-G in the context of T1DM and age of onset.the deletion/deletion DEL/DEL genotype was found to be associated with an early age of onset P = 0.001, while the presence of the insertion allele INS was associated to a later age of onset of type I diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab944007" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + } + ], + "question": "What is the age range in mouse for the onset of diabetes?", + "subquestions": null, + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "usage": { + "chatgpt": 6475, + "gpt-4": 4255, + "gpt-4-turbo-preview": 3283 + }, + "user_id": 2 + }, + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_02.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_02.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_03.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_03.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_03.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_04.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_04.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_04.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_05.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_05.json new file mode 100644 index 0000000..98933ac --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_05.json @@ -0,0 +1,390 @@ +{ + "created_at": "2024-04-18T18:24:11.260410Z", + "data": { + "amplify": false, + "answer": "Yes, there are phenotypes and genotypes associated with early onset diabetes in mice. The db/db mouse strain, which carries the diabetes (db) mutation, is one such genotype. These mice develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, which is an associated phenotype. The C57BL/Ks strain also exhibits early onset diabetes. The severity of the diabetes condition in these mice depends on the interaction of the mutant gene with the inbred background.", + "context": { + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain. Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + } + ] + }, + "data_source": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33].\n Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "section_type": "main", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain.\n Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "abstract", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "main", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease.\n Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths.\n After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "main", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Better Mouse Models. A key point to bear in mind in assessing the usefulness of mouse models is the relative plasticity displayed by rodents faced with gene deletions.Thus, differences between the penetrance of mutations in human genes linked to monogenic forms of diabetes, including maturity onset diabetes of the young (MODY), between humans and mice, are usually observed [114] with the mouse equivalents showing far less marked disturbances in glycemia or changes which are seen only after deletion of both alleles.This clearly reflects the limitations of the use of mice (weight ∼25 g, life expectancy ∼3 years) for comparisons with human subjects.Nonetheless, and although the phenotypes of the above murine models are thus often more subtle than the human counterparts, they remain useful models for the study of diabetes, allowing single-targeted gene deletions which are impossible in man.For example, human populations with different genetic backgrounds have different susceptibility to the R235W ZnT8 polymorphism.We should not, therefore, find surprising the results that different genetic backgrounds and different diet reveal different phenotypes in ZnT8 knockout models." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "f54c42a7-cba6-4d2c-b5a1-484d3ab107db", + "section_type": "abstract", + "text": "\nTo elucidate the genetic factors underlying non-insulindependent diabetes mellitus (NIDDM), we performed genomewide quantitative trait locus (QTL) analysis, using the Otsuka Long-Evans Tokushima Fatty (OLETF) rat.The OLETF rat is an excellent animal model of NIDDM because the features of the disease closely resemble human NIDDM.Genetic dissection with two kinds of F2 intercross progeny, from matings between the OLETF rat and non-diabetic control rats F344 or BN, allowed us to identify on Chromosome (Chr) 1 a major QTL associated with features of NIDDM that was common to both crosses.We also mapped two additional significant loci, on Chrs 7 and 14, in the (OLETF × F344)F 2 cross alone, and designated these three loci as Diabetes mellitus, OLETF type Dmo 1, Dmo2 and Dmo3 respectively.With regard to suggestive QTLs, we found loci on Chrs 10, 11, and 16 that were common to both crosses, as well as loci on Chrs 5 and 12 in the (OLETF × F344)F 2 cross and on Chrs 4 and 13 in the (OLETF × BN)F 2 cross.Our results showed that NIDDM in the OLETF rat is polygenic and demonstrated that different genetic backgrounds could affect ''fitness'' for QTLs and produce different phenotypic effects from the same locus. Microsatellite markers. Most markers were purchased from ResearchGenetics Inc.; some were synthesized here on the basis of information in public data bases and other reports (Du et al. 1996), and some were isolated directly in the manner described elsewhere (Bihoreau et al. 1997).Phenotyping.Measurements of body weight and oral glucose tolerance test (OGTT) were performed at 30 weeks of age.Each rat was not fed for 16 h before OGTT, and blood was taken (fasting glucose).Glucose solution (2g/kg body weight) was administered orally, and successively blood was collected at 30, 60, 90, 120 min (postprandial glucose).Plasma glucose was measured by a glucose oxidase method with Glucose-B Test Kit" + }, + { + "document_id": "e14d92cf-d1ff-4a75-beee-b3312defeffd", + "section_type": "main", + "text": "\n\nExperimental studies support epidemiological observations and have provided strong evidence for transmission of the obese and diabetic phenotype from parent to offspring through non-genetic mechanisms.Numerous studies in rodents have investigated the effects of maternal obesity obtained in response to high-fat (HF) only, or high-fat/high-sugar diet, before and/or throughout pregnancy and during lactation [32].Overnutrition and obesity in the F0 dam can also yield phenotypes in F2 and F3 generations [33,34].Despite the differences in diet composition, and length of maternal overnutrition, most of the studies showed increased offspring adiposity, insulin resistance, and finally development of poor glucose tolerance and T2D, which has been attributed to a combination of beta cell dysfunction [35] and insulin resistance [36][37][38].One must not forget that abnormalities in beta cell function are critical in defining the T2D risk, because T2D installs only when beta-cell function deteriorates and fails to compensate for insulin resistance in peripheral tissues [8].Prenatal and/or early postnatal exposure to undernutrition also causes increased adiposity and glucose intolerance/diabetes in the offspring (F1) [39,40] and reduction of the number and function of pancreatic islets [41].It also increased adiposity and glucose intolerance in the next (F2) generation [42,43].Moreover, if an undernutrition insult is sustained, there can be further propagation of metabolic phenotypes across many generations.When Wistar rats were subjected to 50% caloric restriction over 50 generations, offspring had fasting hyperinsulinemia, glucose intolerance, and increased adiposity.The impaired metabolic phenotype was not reversed by restoration of nutrition for two generations [44].In rat models of spontaneous diabetes, early beta cell alterations with decreased beta cell mass have been reported in fetuses from both spontaneously diabetic BB rats (T1D model) [45] and spontaneously diabetic GK rats (T2D model) [46].On evaluating the long-term consequences for the progeny in these models, IGT was observed in the offspring of mildly streptozotocin (STZ)-induced diabetic females due to lower insulin secretion in response to glucose, while insulin resistance was reported in the offspring of severely STZ-diabetic mothers [47][48][49].Glucose tolerance was also impaired in the offspring of normal mothers receiving glucose infusions during late gestation, and was associated with decreased glucose-induced insulin secretion [50].Since most of these models of diabetes in pregnancy have drawbacks (see discussion in [51]), we have proposed that embryo transfer experiments might represent a more relevant paradigm [52].When fertilized Wistar rat oocytes were transferred into diabetic GK female rats and the neonates were suckled by non-diabetic Wistar foster mothers, beta cell mass in the F1 offspring was decreased at fetal and adult ages, and impaired glucose tolerance was present at adult age (review in [51]).Control rats originating from Wistar oocyte transfer to normal Wistar females retained normal glucose tolerance.Therefore, maternal spontaneous diabetes shapes offspring beta cell mass and insulin secretion.Such a scenario is relevant to the GK rat model of spontaneous T2D [53] since the GK mothers are mildly hyperglycemic through their gestation and during the suckling period.This could represent one mechanism for initiation of pancreas programming in the F1 offspring of the first founders (F0), since the GK line is issued from intercrosses between females and males Wistar with borderline IGT but otherwise normal basal blood glucose level [53,54].This could also contribute to the lack of attenuation of the diabetic GK phenotype over time [53,54]." + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "abstract", + "text": "\nThe diabetes syndromes produced by the two single gene mutations, obese (ob), and diabetes (db) are identical when both genes are expressed on the same inbred background, whereas on different backgrounds the syndrome changes from a severeobesity, moderate-diabetes to a severe life-shortening diabetes.The same initial sequence of events occurs in both conditions.Increased secretion of insulin and hyperphagia is followed by moderate hyperglycaemia with a further compensatory increase in insulin secretion followed by an expansion of the beta-cell mass.On the BL/6 inbred background, hypertrophy and hyperplasia of the beta cells continues until hyperglycaemia is controlled, whereas on the BL/Ks background, beta cell expansion fails and islet atrophy occurs causing insulinopenia, marked hyperglycaemia, and severe diabetes.The data presented here suggest that hyperphagia, hyperinsulinaemia, or both, early in development trigger the abnormal sequence of metabolic events leading to the obesity-diabetes state.These primary events interact with unknown genetic modifiers to produce either a juvenile or maturity-onset type of diabetes.An understanding of the mode of action of these background modifiers influencing the severity of diabetes in mice should lead to a better understanding of the ways in which unknown genetic and environmental factors contribute to human diabetes." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nBecause hyperglycemia was detected in only a few animals in the colony of origin, and segregation in the early inbreeding experiments was consistent with a single recessive locus, it is conceivable that the hyperglycemia in TH mice is caused by a spontaneously arisen single gene mutation.However, in genetic crosses, a complex inheritance pattern emerges with multiple interacting genes determining the trait and susceptibility loci being contributed from both parental strains.This phenomenon has been observed in both the analysis of single gene obesity mutations (Suto et al., 1998;Leiter et al., 1999) and the analysis of polygenic obesity and diabetes (West et al., 1994;Leiter et al., 1998).This suggests that single gene mutations and QTLs affecting diabetes can manifest similarly and are equally challenging to study." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe diabetes syndromes produced by the two single gene mutations, obese (ob), and diabetes (db) are identical when both genes are expressed on the same inbred background, whereas on different backgrounds the syndrome changes from a severeobesity, moderate-diabetes to a severe life-shortening diabetes.The same initial sequence of events occurs in both conditions.Increased secretion of insulin and hyperphagia is followed by moderate hyperglycaemia with a further compensatory increase in insulin secretion followed by an expansion of the beta-cell mass.On the BL/6 inbred background, hypertrophy and hyperplasia of the beta cells continues until hyperglycaemia is controlled, whereas on the BL/Ks background, beta cell expansion fails and islet atrophy occurs causing insulinopenia, marked hyperglycaemia, and severe diabetes.The data presented here suggest that hyperphagia, hyperinsulinaemia, or both, early in development trigger the abnormal sequence of metabolic events leading to the obesity-diabetes state.These primary events interact with unknown genetic modifiers to produce either a juvenile or maturity-onset type of diabetes.An understanding of the mode of action of these background modifiers influencing the severity of diabetes in mice should lead to a better understanding of the ways in which unknown genetic and environmental factors contribute to human diabetes." + }, + { + "document_id": "39e48ed7-91ac-4062-b394-22606abe7e58", + "section_type": "main", + "text": "\n\nOur laboratory has modeled the genetics of obesityinduced type 2 diabetes in two mouse strains, diabetesresistant C57BL/6 (B6) mice and diabetes-susceptible BTBR T ?tf/J (BTBR) mice.When made morbidly obese by the leptin mutation (Lep ob/ob ), B6-ob/ob mice experience moderate and only transient hyperglycemia due to a large expansion of b-cell mass, resulting in a 20-50-fold increase in plasma insulin levels (Clee et al. 2005;Keller et al. 2008).In contrast, BTBR-ob/ob mice experience severe hyperglycemia due to a failure to increase their circulating insulin levels.An in vivo measure of cellular replication showed that B6-ob/ob mice experience an approximately threefold increase in islet cell proliferation, whereas BTBR-ob/ob mice do not increase islet cellular replication in response to obesity (Keller et al. 2008)." + }, + { + "document_id": "b3c2189b-270c-4b4a-9d40-cdc0dceebd9e", + "section_type": "main", + "text": "[PubMed: 1290452]\nPlum L, Kluge R, Giesen K, Altmuller J, Ortlepp JR, Joost HG. Type-2 diabetes-like hyperglycemia in\na backcross model of NZO and SJL mice: characterization of susceptibility locus on chromosome\n4 and its relationship with obesity. Diabetes. 2000; 49:1590–1596. [PubMed: 10969845]\n\nBrain Res. Author manuscript; available in PMC 2013 July 10.\n Boone et al.\n\n Page 9\n\nNIH-PA Author Manuscript\nNIH-PA Author Manuscript\nNIH-PA Author Manuscript\n\nRocha JL, Eisen EJ, Van Vleck LD, Pomp D. A large-sample QTL study in mice: II Body\ncomposition. Mamm Genome. 2004; 15:100–113. [PubMed: 15058381]\nSalinas A, Wilde JD, Maldve RE." + }, + { + "document_id": "c4c5c626-51f7-4b87-84a3-8323a9233ca1", + "section_type": "main", + "text": "\n\nMice homozygous for targeted disruption of the BLK gene have been generated and studied for 8 weeks with a focus on investigating the role of BLK in B-lymphocyte physiology (23).However, no phenotypes relevant to diabetes have been described for these mutants, and no phenotypic data are available with regard to responses to exposure to a diabetogenic environment such as a high-fat diet, or cross breeding with an insulinresistant strain.In light of our findings, further detailed studies are warranted to explore the phenotypes of global KO mice and/or  cell-specific knockouts, in the context of glucose homeostasis." + }, + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "section_type": "main", + "text": ", 2008) and specific genetic factors for predisposition to DN were\nrecently identified in several diabetic sibling studies (Bleyer et al. , 2008; Schelling et\nal.,2008; Tanaka et al. , 2005).\n Similar to humans, inbred strains of mice exhibit differences in their susceptibility to\ndiabetes, renal and cardiovascular diseases (Krolewski et al. , 1996). More recently,\ndifferential susceptibilities to DN have also been observed in well-defined strains of\n\n23" + }, + { + "document_id": "e14d92cf-d1ff-4a75-beee-b3312defeffd", + "section_type": "main", + "text": "\n\nThe heritability of the obese/diabetic paternal phenotype was confirmed by experimental approaches.Multiple animal studies have now demonstrated that offspring's metabolic phenotype is affected by paternal unbalanced diet.Female rats born to fathers on a HF diet had impaired pancreatic islet biology, insulin secretion and glucose tolerance in adulthood [105].The F1 offspring of male mice fed a HF diet exhibited the same obese phenotype as their fathers [99,106].The offspring metabolic phenotype can also be affected by paternal undernutrition.Male and female born to fathers fed a low protein and high sugar diet had increased hepatic expression of lipid biosynthetic genes [98].Offspring metabolic phenotype can also be affected by paternal diabetes.Paternal low-dose STZ-induced diabetes in mice was accompanied by insulitis and insulin secretion deficiency in their F1 offspring [107].Paternal T2D alone (i.e., without associated obesity) impairs early development of endocrine pancreas and adult tolerance du glucose in rat F1 offspring.This was previously suggested by our group using a spontaneous model of paternal T2D [46,108] (Figure 3).To our knowledge, the most comprehensive study to evaluate the transgenerational effects of paternal diabetes on offspring and the mechanisms that mediate these effects, has been provided by Wei et al. [109].Using a non-genetic diabetes mouse model (low dose of STZ combined to HF diet), this group showed that paternal diabetes did not alter body weight, fat mass, or energy intake in F1 offspring, but it induced fasting hyperglycemia, glucose intolerance and insulin insensitivity in the male offspring to an extent similar to that seen in their fathers.To determine the mechanisms of the glucose intolerance and insulin insensitivity observed in the F1 male offspring, Wei et al. performed genome-wide microarray analyses of their pancreatic islets.The expression of 402 genes was modified (97 up-regulated and 305 downregulated).A large proportion of these genes were related to insulin and glucose metabolism, including GTPase activity, GTP and ATP binding, sugar binding, and calcium binding.Wei et al. also found several differentially methylated loci in the F1 islets.The same group also asked whether the metabolic and epigenetic changes in the F1 generation can be passed to the next generation (F2 generation).For that purpose, they mated F1 diabetic males (F1-D) whose fathers were diabetic, with normal females, and then examined metabolic and epigenetic changes in their offspring (F2).The F2 generation also exhibited impaired glucose tolerance and decreased insulin sensitivity (but not fasting hyperglycemia).Examination of the methylation status for 10 regions distributed on different chromosomes that were most affected by paternal diabetes, showed that all of these regions were still significantly affected in the F2 generation.As the F1 animals received normal diet without any STZ treatment and their F2 offspring exhibited similar phenotypic and epigenetic changes, the observed effects of epigenetic inheritance are most likely attributable to the diabetes-associated physiological and metabolic conditions in F0 male founders." + }, + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "section_type": "main", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "Genetic Crosses\n\nHyperglycemic male TH (ՆF7) mice were mated to normal female C57BL/6J (B6) or CAST/Ei (CAST) mice.The resulting F1 hybrid female mice were backcrossed to hyperglycemic male TH mice, and the offspring were referred to as backcross 1 (BC1) animals.Only male BC1 mice were used for the genetic study, since female mice do not develop hyperglycemia.Plasma glucose and insulin levels (nonfasted), body weights, nasal-anal lengths, and five fat pad weights (inguinal, epidydimal, mesenteric, retroperitoneal, and subscapular fat pads) were measured as phenotypic traits." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "\n\nThese limitations support the increasing need of experimental systems to characterize the fundamental biological mechanisms responsible for diabetes inheritance and the function of risk genes.In the context of diabetes pathogenesis, in vitro systems are useful but often limited, in particular to assess glucose tolerance, insulin sensitivity, islet architecture and function and diabetes complications.The laboratory mouse provides a wide range of experimental models for diabetes gene discovery and for in vivo post-GWAS studies of diabetes that develops either spontaneously or following gene editing [5].The laboratory rat is also a powerful system to implement phenotyping methods required to record biological variables relevant to common chronic diseases.The rat is the preferred model to perform phenotyping procedures that are often technically challenging in mice or require the collection of large volumes of blood or organs.For these reasons, rat models of type 2 diabetes or hypertension have been successfully used to localise in the genome genes controlling endophenotypes relevant to these complex diseases.This review addresses strategies used to map the genetic determinants of physiological and molecular phenotypes relevant to type 2 diabetes pathogenesis and to characterize their biological function in vivo through examples derived from genetic and genomic research in the Goto-Kakizaki (GK) rat strain." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted.\n\n Fig. 8." + } + ], + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db", + "diabetes", + "C57BL/Ks", + "obesity", + "insulin", + "hyperglycaemia", + "beta&cells", + "mutation", + "C57BLKS/J", + "NZO" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "We identified 32 compound heterozygous mutations and 9 homozygous mutations in IL10 receptor subunit alpha and 1 homozygous mutation in IL10 receptor subunit beta. Among these mutations, 10 novel mutations were identified, and 6 pathogenic mutations had been previously described. In patients with IL10 receptor subunit alpha mutations, c.301C>T p.R101RW and c.537 G>A p.T179T were the most common mutations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007199" + }, + { + "object": "MicroRNA-26a miR-26a in pancreatic beta cells not only modulates insulin secretion and beta cell replication in an autocrine manner but also regulates peripheral insulin sensitivity in a paracrine manner through circulating exosomes. miR-26a is down-regulated in serum exosomes and islets of obese mice. miR-26a in beta cells alleviates obesity-induced insulin resistance and hyperinsulinemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab483374" + }, + { + "object": "Ten mutations were identified in five unrelated Chinese families and two sporadic patients with childhood, and adult hypophosphatasia including eight missense mutations and two frameshift mutations. Of which, four were novel: one frameshift mutation p.R138Pfsx45; three missense mutations p.C201R, p.V459A, p.C497S. No identical mutations and any other new ALPL mutations were found in unrelated 50 healthy controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab768168" + }, + { + "object": "Two patients harbored KRAS with codon 12 mutations; one harbored the gly12val mutation with a variation of leu597val in the BRAF exon 15 codon, the other harbored mutation in the BRAF exon 15 codon. One patient harbored a codon 117 mutation with a BRAF V600E mutation. The last patient harbored a NRAS exon 2 mutation with the GGT/GAT, V600G mutation in the BRAF exon 15 codon", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab978995" + }, + { + "object": "Our aim was to identify VHL gene mutations in Argentinian patients who fulfilled the clinical criteria for type 1 VHL disease and in patients with VHL-associated manifestations. VHL mutations were detected in 16/19 84.2% patients in Group 1 and included: gross deletions 4/16; nonsense mutations 6/16; frameshift mutations 4/16; missense mutations 1/16; and splicing mutations 1/16. Three mutations were novel.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab550929" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "WFS1 and GJB2 mutations were identified in eight of 74 cases of Low-Frequency Sensorineural Hearing Loss. Four cases had heterozygous WFS1 mutations; one had a heterozygous WFS1 mutation and a heterozygous GJB2 mutation; and three cases had biallelic GJB2 mutations. Three cases with WFS1 mutations were sporadic; two of them were confirmed to be caused by a de novo mutation based on the genetic analysis of their parents.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014986" + }, + { + "object": "Study revealed that the conserved HIF1alpha/PFKFB3 signaling pathway is activated by IAPP misfolded protein-driven stress in pancreatic beta-cells to trigger an adaptive protective metabolic response that slows beta-cell death at the expense of beta-cell function. This signaling pathway is activated in beta-cells in humans with type 2 diabetes providing a basis for slow beta-cell loss.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab748654" + }, + { + "object": "Study generated MafA and MafB double-knockout A0B0 mice in which MafB was specifically deleted from beta cells. As a result, the A0B0 mice became more vulnerable to diabetes under a high-fat diet treatment, with impaired islet formation and a decreased number of insulin+ beta cells because of increased beta-cell apoptosis, indicating MafB can take part in the maintenance of adult beta cells under certain pathologic...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab742544" + } + ], + "question": "Are there phenotypes or genotypes associated with early onset diabetes in mouse?", + "subquestions": null, + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "usage": { + "chatgpt": 8500, + "gpt-4": 4937, + "gpt-4-turbo-preview": 3964 + }, + "user_id": 2 + }, + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_06.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_06.json new file mode 100644 index 0000000..372a368 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_06.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:30:30.982190Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "The Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "section_type": "main", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "GENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nSummary of key literature on gene-environment interactions in obesity and type 2 diabetes" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "abstract", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nGene-nutrient or -dietary pattern interactions in the development of T2DM." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002).\n Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "Genes and enviromental factors in the development of type 2 diabetes\n\nThe susceptibility to the development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two groups of T2DM susceptibility genes.The substantial contribution of genetic factors to the development of diabetes has been known for many years.The important pieces of evidence for the role of genes are the results of twin studies showing higher concordance rate for T2DM among monozygotic twins (between 41% and 55%) in comparison to dizygotic twins (between 10% and 15%) [43,84].What is interesting, there are populations with extremely high prevalence of T2DM, for example Pima Indians, that can not be explained solely by environmental factors [117].Supporting evidence for the role of genes in development of T2DM include also familial clustering of diabetesrelated traits.It was shown that the level of insulin sensitivity in Caucasians is inherited and a low level is a poor prognostic factor that precedes the development of T2DM [68,69,115].Similar observations were published for other ethnic groups [9,36,60].Those facts underline the importance of genetic factors.However, it is well known that the incidence of T2DM is also associated with environmental factors.Increasing incidence of T2DM during the last few years with obvious links to lifestyle and diet points to the role of enviromental factors in the development of disease [80].The differences in the prevalence of T2DM in relative populations living in different geographical and cultural regions (for example Asians in Japan and USA) also support the role of non-genetic factors [27,125].The relations between genetic and eviromental factors in the development of T2DM may be complex.For instance, enviromental factors may be responsible for the initiation of b-cell damage or other metabolic abnormalities, while genes may regulate the rate of progression to overt diabetes.On the other hand, in some cases genetic factors may be nec-essary for environmental factors even to start processes leading to the development of the disease." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + }, + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "section_type": "main", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + }, + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "section_type": "main", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nWhy do we think GEIs cause type 2 diabetes?dTheevidence supporting the existence of gene-lifestyle interactions in type 2 diabetes comes primarily from 1) the pattern and distribution of diabetes across environmental settings and ethnic groups, 2) familybased intervention studies, in which response to interventions varies less between biologically related individuals than between unrelated individuals; and 3) animal studies in which genetic and environmental factors are experimentally manipulated to cause changes in the expression of metabolic phenotypes.A brief overview of pertinent literature from human studies is given below." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nEpidemiological studies have been the predominant source of literature on gene-lifestyle interactions in cardiovascular and metabolic disease.Dozens of casecontrol and cohort studies have been published since the late 1990s purporting to have identified gene-lifestyle interactions in type 2 diabetes or related quantitative metabolic traits.Until recently, however, most of these studies were small and often relied on imprecise estimates of environmental exposures and outcomes.These are prone to error and bias, and exposures may not be assessed at the time when they conveyed their effects; for example, the causative exposures may have occurred very early in life, perhaps even in utero.Moreover, the complexities of modeling interaction effects have forced geneticists to focus primarily on very simple models of interaction, whereas clinically relevant interaction effects likely involve multiple genetic and nongenetic biomarkers.In addition, barely a handful of studies have examined incident type 2 diabetes as an outcome, with most focusing on cross-sectional measures of glucose and others relying on analyses that include prevalent cases of diabetes; this may introduce labeling bias, where the recall of well-known diabetesassociated behaviors is less likely to be accurate in individuals recently diagnosed with disease than in those who have not been diagnosed with disease." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) has developed into a major public health concern.While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009).Lifestyle and behavioural factors play an important role in determining T2D risk.For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring (Ng et al, 2010).In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004).The pancreatic islets of Langerhans are of central importance in the development of T2D.Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet b-cells to regulate glucose homeostasis.b-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b;Tabak et al, 2009) and results in T2D (UKPDSG, 1995).The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007;Scott et al, 2007;Sladek et al, 2007;Zeggini et al, 2007Zeggini et al, , 2008) ) has placed the insulinproducing b-cells at centre stage.These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development.Most T2D risk genes identified in these genome-wide association studies (GWAS) affect b-cell mass and/or function (Florez, 2008).While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010).Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence.DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications.These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008;Park et al, 2008)." + } + ], + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [ + { + "object": "Data suggest that expression of Pparg can be regulated by dietary factors; expression of Pparg is down-regulated in preadipocytes by tannic acid, a form of tannins found in plant-based foods; Pparg appears to be a major factor in adipogenesis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab206776" + }, + { + "object": "Circulating adiponectin increased in obese physically active participants >/=180 min/week compared to non-physically active counterparts, indicating that physical activity may mediate baseline adiponectin levels irrespective of the fat mass regulatory effect.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141573" + }, + { + "object": "Upon stratifying the participants into tertiles by the Matsuda index, we observed an inhibitory relationship between the genetic risk score GRS and insulin secretion in low insulin sensitive but not in high insulin sensitive controls and treatment-naive Type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab985500" + }, + { + "object": "The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of physical activity in particular in those genetically predisposed to obesity.[Meta-analysis]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782259" + }, + { + "object": "Serum IGFBP-2 levels increase with age after the age of 50 years and evolve in parallel with insulin sensitivity. IGFBP-2 may therefore be a potential marker for insulin sensitivity. We further show that IGFBP-2 levels can predict mortality in this aging population. However, its predictive value for mortality can only be interpreted in relation to insulin sensitivity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699014" + }, + { + "object": "Our study validated the association between an FTO variant and BMI in Taiwanese individuals. In addition, individuals with TG and TT genotypes who were physically active had a decreased BMI. These results indicate that physical activity might be necessary to mitigate the deleterious effect of BMI among genetically susceptible Taiwanese individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab566865" + }, + { + "object": "Irrespective of the genetic defect, adenoviral delivery of C5 improved insulin sensitivity in both C5cont and C5def mice, indicating an insulin-sensitizing function of C5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab428686" + }, + { + "object": "there was a high heritability for IGF-I and IGFBP-3, but a low heritability for insulin secretion and insulin sensitivity in a group of elderly twins; in addition, study found a negative relationship between IGF-I and insulin sensitivity, which did not seem to be strongly genetically determined", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab887620" + }, + { + "object": "The authors showed that the expression of cysK is regulated by several genetic and environmental factors in addition to CysB: two genetic factors, OmpR and CysE, and lithium.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785160" + }, + { + "object": "Production of nitric oxide NO within eNOS-positive NGC neurons increases after environmental perturbations, indicating a role for eNOS/NO in modulating environmentally appropriate levels of GA. Inhibition of NO production causes dysregulated behavioral arousal after exposure to environmental perturbation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab615356" + } + ], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_07.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_07.json new file mode 100644 index 0000000..19e2777 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_07.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:34:34.295904Z", + "data": { + "amplify": false, + "answer": "MicroRNAs and long noncoding RNAs (lncRNAs) are involved in diabetes. MicroRNAs modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs. They can regulate pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism. They also hold potential as diagnostic biomarkers and possible drug-targets for regulation of dysfunctional cell responses. LncRNAs are implicated in complications associated with diabetes, such as diabetic retinopathy and diabetic nephropathy. They can regulate cell proliferation, viability, migration, and the expression of pathological genes via post-transcriptional mechanisms.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + } + ], + "18a35699-873a-4542-b35a-3a4a14edd628": [ + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + } + ], + "2dc80127-89ba-47be-9e94-d90c2105be8d": [ + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + } + ], + "34184c8d-b167-4ae8-bfce-01e18d78fe41": [ + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + } + ], + "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef": [ + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes. Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + } + ], + "6011e960-6a6e-47fe-94f2-2c21c224fd25": [ + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + } + ], + "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + } + ], + "80e1b2af-be79-4d9b-852f-46bf3e23c963": [ + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + } + ], + "88dde947-5255-40e1-92d5-afde089b517b": [ + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + } + ], + "d23e9456-8ee8-46e0-9870-18ff69965c28": [ + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + } + ], + "e66846a6-1546-481b-baae-a55fc524c8af": [ + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + } + ], + "ec62a4d9-2fe2-49b0-84d8-13b1597e2067": [ + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + } + ] + }, + "data_source": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "section_type": "main", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "section_type": "main", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + }, + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "section_type": "main", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + }, + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "section_type": "main", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "section_type": "main", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "main", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "abstract", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "section_type": "main", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + }, + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "section_type": "main", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes.\n Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "main", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + }, + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "section_type": "main", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "abstract", + "text": "\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + }, + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "section_type": "main", + "text": "\n\nDescription of some problems associated with diabetes and possible nanomedicine solutions." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "main", + "text": "Newly Identified Genes Relevant in the Progression of Diabetic Nephropathy\n\nThe cellular events such as increased flux of polyols and hexosamines; generation of AGEs; increased activity of PKC, transforming growth factor-β-Smad-MAPK (mitogen-activated protein kinase) pathway and GTP-binding proteins; G1 cell cycle arrest associated with altered expression of cyclin kinases and their inhibitors; and generation of ROS are responsible for a final outcome of increased synthesis and deposition of ECM.The ROS, whether mitochondrial or cell membrane-derived, are also responsible for the activation of the renin-angiotensin system that eventually contributes to glomerular hyperfiltration and subsequent renal fibrosis (fig. 1) [71].In addition to these macromolecules, newly identified genes, such as RSOR/MIOX, Tim44 and Rap1b, may also be an integral part of the hyperglycemia-induced cytosolic and mitochondrial processes that culminate in the development of diabetic nephropathy [48][49][50][51][52][53][54][55]." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + }, + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "section_type": "main", + "text": "Discussion\n\nAs is known, several mechanisms, mainly related to the dysfunction of the endothelium and smooth muscles, have been proposed in the aetiology of T2DMED.In this study, the four differentially expressed miRNAs may also be involved in the regulation of the endothelium and smooth muscle function based on a literature review.Numerous studies have reported their function in pathophysiological processes, such as cellular development, differentiation, and apoptosis, which are all essential mechanisms of T2DMED (Beaumont et al. 2014;Girard et al. 2008;Komatsu et al. 2014;Lee et al. 2012;Liu et al. 2008;Shan et al. 2010;Sweetman et al. 2006).Importantly, miR-206 may be involved in diabetes-associated complications by contributing to high glucose-mediated apoptosis (Shan et al. 2010), and miR-133a has anti-apoptosis effects (Xu et al. 2007).In addition, miR-133a and miR-206 are muscle-specific miRNAs (Chen et al. 2012;Liu et al. 2008) and thus could regulate muscular cell functions, such as the augmentation of smooth muscle contraction by miR-133a (Chiba et al. 2009).Additionally, miR-18a could also increase vascular smooth muscle cell differentiation (Kee et al. 2014)." + }, + { + "document_id": "230022b2-931e-42ab-b100-5e9776483d1a", + "section_type": "main", + "text": "| DISCUSSION\n\nThis study examined retinas from WT and diabetic SD male rats to investigate the changes in a variety of retinal transcripts as a result of diabetes using RNA-seq.We identified a total of 118 DEGs, of which 72 were up-regulated and 46 were down-regulated.We also found 66 GO terms and 41 KEGG pathways which were significantly enriched by GO and KEGG analysis.Top 10 most down-regulated and up-regulated genes are listed in Tables 3 and 4, and were confirmed by qRT-PCR showed in Figure 4. Asb15 gene is the most up-regulated one we identified and confirmed.Asb15 is a member of Asb gene family; the family has been reported to be involved in cell proliferation and differentiation (Hancock et al., 1991;Kohroki et al., 2001;Liu et al., 2003).The presence of both Ankyrin repeat and suppressors of cytokine signaling (SOCS) box motifs are characters of members of Asb gene family (McDaneld, Hancock, & Moody, 2004).Member of SOCS family plays important roles in the negative regulation of signaling pathways (Kile & Alexander, 2001;Zhang et al., 2001).SOCS3 acts as a regulator of inflammation through inhibiting JAK/STAT pathway (Tamiya, Kashiwagi, & Takahashi, 2011).Down-regulating SOCS3-STAT3 can alleviate DR (Chen, Lv, & Gan, 2017;Jiang, Thaksan, & Bheemreddy, 2014;Ye & Steinle, 2015).Ladinin-1(Lad1), a largely uncharacterized protein to date, was found to be related to the proliferation and migration of breast cancer cells (Roth, Srivastava, & Lindzen, 2018).Cell proliferation and migration are processes of neovascularization.Neovascularization is the sign of PDR, which can lead to serious vision loss of patients.Fibroblast growth factor 2 (Fgf2) is a member of fibroblast growth factors (FGFs) family.FGFs and their receptors have important roles in cell proliferation, migration, differentiation, and survival (Saichaemchan, Ariyawutyakorn, & Varella-Garcia, 2016).FGF2 was found overexpression in the early stage of DR, and it can destroy the blood-retinal barrier (Yang et al., 2018).Hemoglobin alpha adult chain 1 (Hba-a1) is one of the hemoglobin genes.Hemoglobin plays an important role in neuronal respiration, oxidative stress, and response to injury (He et al., 2010;Poh, Yeo, Stohler, & Ong, 2012;Richter, Meurers, Zhu, Medvedeva, & Chesselet, 2009).Neuronal respiration is an important life activity of neuronal cells.Neurological injury is one of the performances of DR.Inositol monophosphatase domain containing 1 (Impad1) encodes gPAPP, which is a Golgi-resident nucleotide phosphatase that hydrolyzes phosphoadenosine phosphate (PAP), the by-product of sulfotransferase reactions, to AMP.AMP-activated protein kinase (AMPK) signaling pathway plays vital roles in the diabetes-induced retinal inflammation (Kubota, Ozawa, & Kurihara, 2011).RT1-Bb, RT1-Ba, belongs to RT1 complex, which is the major histocompatibility complex (MHC) of rat (Eberhard & Lutz, 2001).It is believed that the MHC region is vital because it plays an important role in diseases, such as autoimmune and infectious diseases, vascular diseases like DR, hematological and neurological diseases (John, 2005).Collagen type III alpha 1 chain (Col3a1) is a kind of type III collagen, mainly existing in the extracellular matrix.Lacking of type III collagen can destroy the structure of connective tissues (Cortini et al., 2017).According to previous researches, it is associated with the aneurysm.Retinal microaneurysm is the early performance of DR.Col3a1 was also found significantly changed in RNA-seq of human PDR fibrovascular membranes (Lam et al., 2017).αA-crystallin (Cryga) and αF-crystallin (Crygf) are members of crystallins, which were involved in different functions in various tissues (Clayton, Jeanny, Bower, & Errington, 1986;Head, Peter, & Clayton, 1991;Smolich, Tarkington, Saha, & Grainger, 1994).Knockout of αA-crystallin can inhibit ocular neovascularization (Xu, Bai, & Huang, 2015).More and more evidence indicated that inflammation (Adamis, 2002;Gologorsky, Thanos, & Vavvas, 2012) and neovascularization (Gardner & Davila, 2017;Nguyen et al., 2018) are important in the pathogenesis of DR.The results of the KEGG pathway significant enrichment analysis revealed two most enrichment items-cell adhesion molecules (CAMs) and PI3K-Akt signaling pathway.CAMs are proteins located on cell surface; the binding of CAMs to their receptors is important in the mediation of inflammatory and immune reactions (Golias et al., 2007).Previous studies have suggested that CAMs are important in the development of DR (Khalfaoui et al., 2009;Ugurlu et al., 2013) of insulin and is associated with DR neovascularization (Qin, Zhang, & Xu, 2015;Sasore, Reynolds, & Kennedy, 2014)." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "abstract", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "72aa5d47-336b-4e4f-8593-ee215b8891d2", + "section_type": "main", + "text": "\n\nThe current study takes an important first step towards this goal by identifying specific sets of genes whose expression accurately classifies patient samples with regard to diabetic neuropathy progression and by analysing their interactions within known cellular pathways.Identifying common elements in these complex networks will yield novel insights into disease pathogenesis, provide new therapeutic targets and identify potential diabetic neuropathy biomarkers.The genes identified in the current study confirm data gathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in a single human tissue sample." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "\n\nFurthermore, the alpha kinase 1 gene (ALPK1) identified as a susceptibility gene for chronic kidney disease by GWAS [202] , was demonstrated in type 2 diabetes patients [203] .Three additional genes have been strongly correlated with this risk of diabetic retinopathy (DR) including the vascular endothelial growth receptor, aldose reductase and the receptor for advanced glycation products genes [204] where specific polymorphisms in these genes seem to increase the risk of DR development in diabetes patients [204] .A significant differential proteome (involving 56 out of 252 proteins) is evident that characterizes vitreous samples obtained from diabetes patients with the complication in comparison to diabetes patients without the complication and control individuals [205] .Interestingly, a large portion of these proteins (30 proteins) belong to the kallikrein-kinin, coagulation and complement systems including complement C3, complement factor 1, prothrombin, alpha-1antitrypsin and antithrombin III that are elevated in diabetic patients with retinopathy [205] .In addition, 2 single nucleotides polymorphisms in the human related B7-I gene seem to mediate podocyte injury in diabetic nephropathy [206] .Furthermore, increased concentration of the ligand of B7-1 correlates with the progression of end-stage renal disease (ESRD) in diabetes patients [206] .These results indicate that B7-I inhibition may serve as a potential target for diabetes nephropathy prevention and/or treatment.Recently, it was shown that direct correlation is evident between circulating levels of tumor necrosis factors 1 and 2 and increased risk of ESRD in American Indian patients [207] .The link between diabetes and proper bone development and health is evident.Studies using animal models with major significant reduction in insulin receptor (IR) in osteoprogenitor cells resulted in thin and rod-like weak bones with high risk of fractures [208] .Similar findings were observed in animal models with bone-specific IR knockdown animals which points to the central role of IR in the proper development of bones [208] .Type 2 diabetes is also associated with mitochondrial dysfunction in adipose tissues.Using knockout animal models of specific mitochondrial genes led to significant reduction in key electron transport complexes expression and eventually adipocytes death [209] .These animals exhibited Insulin resistance in addition to other complications that can potentially lead to cardiovascular disease [209] ." + }, + { + "document_id": "41fc22ce-f0dc-4d81-a2b5-14c563c7c767", + "section_type": "main", + "text": "Metabolism:\nA novel shared link between diabetes mellitus and Alzheimer’s disease. J. Diabetes\nRes. 2020:4981814. doi: 10.1155/2020/4981814\n\nLiu, C., Hu, J., Zhao, N., Wang, J., Wang, N., Cirrito, J. R., et al. (2017).\n Astrocytic LRP1 mediates brain abeta clearance and impacts amyloid deposition.\n J. Neurosci. 37, 4023–4031. doi: 10.1523/JNEUROSCI.3442-16.2017\n\nWainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A., Knowles,\nD., Golan, D., et al. (2019). Opportunities and challenges for transcriptome-wide\nassociation studies. Nat. Genet. 51, 592–599. doi: 10.1038/s41588-019-0385-z\n\nLiu, Q., Trotter, J., Zhang, J., Peters, M. M., Cheng, H., Bao, J., et al. (2010)." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "\n\nI should underscore the fact that this discussion has been a simplified review of the relationships among glycemia, the RAS, histopathologic change, and the genetics of diabetic nephropathy, but its simplification allows us to underscore certain principles.In the redundant path of this biology, angiotensin II stimulates and interacts with a large number of other molecules.These are just a few of the major ones: glut-1, tumor necrosis factora (TNF-a), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), basic fibroblast growth factor (bFGF), insulin-like growth factor-1 (IGF-1), advanced glycosylation end products (AGEs) (pentosidine), reactive oxygen species (ROS), oxidized low-density lipoprotein (LDL), vascular cell adhesion molecule (VCAM-1), osteopontin, NF-jB, RANTES (particularly in glomerular endothelial cells), and monocyte chemotactic protein (MCP).In closing, I'd like to leave you with the top 10 principles detailed by this discussion: (1) signaling systems, with their complexity and redundancy, are systems of great beauty, reflective of evolutionary order; (2) differentiated biologic tissues often use the same tools to achieve tissue-specific functions and express tissue-specific pathology; (3) diabetic nephropathy reflects cellular injury due to common biologic pathways manifested in different cell types/regions of the kidney; (4) the kidney's susceptibility to glomerulosclerosis and tubulointerstital fibrosis reflects the impact of the renal RAS and its interactions with other profibrotic molecular pathways; (5) defining these interactions and the downstream signaling mechanisms mediating them lays the foundation for discovering needed therapies beyond glycemic control and angiotensin II inhibition for the treatment of diabetic nephropathy; (6) signaling pathways downstream of angiotensin II represent prime targets for additional therapeutic interventions; (7) hypothesis-driven basic research on individual pathways has (and likely will continue to) shed light on the complexities of the pathologic interactions and the redundancies in the systems; (8) candidate gene studies are the genetic analogues of this type of hypothesis-driven basic research; (9) microarray and genomic scanning coupled with informatics technology offer the possibility of modeling these complex system interactions and hopefully will allow us to identify optimal targets for inhibition and/or up-regulation that can prevent progression and restore structure and function; and (10) given the redundancy and convergence of these pathways, the challenge will be in graded inhibition that will preserve salutary pathways, but inhibit deleterious ones." + }, + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "section_type": "main", + "text": "\n\nIn this article, we identify genes whose expression responds differently to glucose in cells derived from T1D individuals with and without diabetic retinopathy.We show that one of these genes, folliculin (FLCN), is causally implicated in diabetic retinopathy based on results from genetic association testing and Mendelian randomization." + }, + { + "document_id": "e8dd8ca2-6fab-4acd-9b29-4e8583365d6d", + "section_type": "main", + "text": "Discussion\n\nRecent studies suggest inflammation to be an essential component of type 2 DM and its complications.We measured hs-CRP as a marker of inflammation in our diabetic cohort and found its levels to be significantly higher in diabetic patients as compared to controls and in nephropathy group as compared to diabetic subjects without nephropathy indicating inflammation to be a relevant factor in the pathogenesis of DN.Our results are consistent with an earlier study which has also reported increased hs-CRP levels in diabetics with proteinuria [18].Different inflammatory molecules, including pro-inflammatory cytokines have been proposed as critical factors in the development of microvascular diabetic complications, including nephropathy [19].It has been suggested that genetic variations in the genes encoding the inflammatory cytokines might confer susceptibility to DN by altering the function and/or expression of these cytokines.We investigated the association of genetic polymorphism(s) in inflammatory genes with the risk of diabetic nephropathy and whether co-occurrence of risk conferring variants of inflammatory genes were associated with increased risk of diabetic nephropathy in Asian Indian type 2 diabetic subjects.The key finding of our study was that polymorphisms in IL8, CCL2, CCR5, and MMP9 genes were associated with increased risk of nephropathy in Asian Indian type 2 diabetics and co-occurrence of specific risk genotypes of these genes conferred several fold greater risk of diabetic nephropathy." + }, + { + "document_id": "0951ba9d-bb8f-424b-b63f-16d94cb7166c", + "section_type": "main", + "text": "Page 43\n\nAuthor Manuscript\nAuthor Manuscript\nFig. 2 |. Main signalling pathways that regulate cardiac remodelling in the diabetic heart.\n\n Author Manuscript\nAuthor Manuscript\n\nThe systemic glucotoxicity (as a result of increased production of advanced glycation end\nproducts (AGEs)), lipotoxicity and angiotensin II (Ang II) production associated with type 2\ndiabetes mellitus induce the generation of reactive oxygen species (ROS) and reactive\nnitrogen species (RNS) by endothelial cells, resulting in decreased nitric oxide (NO)\nbioavailability." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "abstract", + "text": "\nInsight into the molecular mechanisms that underlie the origin and progression of diabetic nephropathy remains limited in part because conventional research tools have restricted investigators to focus on single genes or isolated pathways.Microarray technologies provide opportunities for evaluating genetic factors and environmental effects at a genomic scale during the pathogenesis of diabetic nephropathy.Despite" + }, + { + "document_id": "230022b2-931e-42ab-b100-5e9776483d1a", + "section_type": "main", + "text": "Background:\n\nThe aim of this research was to investigate the retinal transcriptome changes in long-term streptozotocin (STZ)-induced rats' retinas using RNA sequencing (RNA-seq), to explore the molecular mechanisms of diabetic retinopathy (DR), and to identify novel targets for the treatment of DR by comparing the gene expression profile we obtained.Methods: In this study, 6 healthy male SD rats were randomly divided into wildtype (WT) group and streptozotocin (STZ)-induced group, 3 rats each group.After 6 months, 3 normal retina samples and 3 DM retina samples (2 retinas from the same rat were considered as 1 sample) were tested and differentially expressed genes (DEGs) were measured by RNA-seq technology.Then, we did Gene Ontology (GO) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and validated the results of RNA-seq through qRT-PCR.Results: A total of 118 DEGs were identified, of which 72 were up-regulated and 46 were down-regulated.The enriched GO terms showed that 3 most significant enrichment terms were binding (molecular function), cell part (cellular component), and biological regulation (biological process).The results of the KEGG pathway analysis revealed a significant enrichment in cell adhesion molecules, PI3K-Akt signaling pathway, and allograft rejection, etc. Conclusion: Our research has identified specific DEGs and also speculated their potential functions, which will provide novel targets to explore the molecular mechanisms of DR." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "Types of biomarkers include clinical, biochemical factors and molecular markers. Examples relevant to diabetic retinopathy include clinical factors (e.g.diabetes duration, obesity, smoking, ETDRS score, electroretinogram (ERGs) assessment; biochemical factors (e.g.HbA1c, lipoprotein related factors); and molecular factors (such as the results of GWAS analyses and miRNA profiles (discussed below).Cytokines, growth factors and/or hormones have been widely used, such as the case with adiponectin as an adipocyte-derived hormone that regulates glucose and lipid metabolism.Adiponectin has been shown to be significantly higher in T1D patients with severe diabetic retinopathy than in those without, even after adjustment for occurrence of microalbuminuria (Hadjadj et al., 2005).As retinopathy has multiple risk factors it is likely, as is increasingly used for cardiovascular disease and suggested for diabetic nephropathy (Elley et al., 2010;van Dieren et al., 2011;Vergouwe et al., 2010), and more recently for retinopathy (Harris Nwanyanwu et al., 2013) from genetic data (Sandholm et al., 2012;Williams et al., 2012).In terms of genetic association the diabetic retinopathy field is less advanced than that for nephropathy, although there have been a number of worthwhile studies (reviewed by (Kuo et al., 2014)).A genome-wide association study for diabetic retinopathy identified an association with a long intergenic non-coding RNA (LincRNA) sequence.LincRNAs are non-protein coding transcripts (>200 nucleotides in length) and the sequence called RP1-90L14 (adjacent to the CEP162 gene) has shown susceptibility to diabetic retinopathy (Awata et al., 2014).Interestingly, other LincRNAs are also being studied for their association with diabetic retinopathy such as MALAT1 (Yan et al., 2014) and MIAT (Yan et al., 2015).While some interesting leads are emerging, as yet there is no robust indication that diabetic retinopathy has a significant genetic component.Candidate gene and genome-wide studies may yet find genetic linkage to particular retinopathy phenotypes in T1D and T2D although both diabetes-types will need to be assessed separately in view of their distinct genetic architecture." + }, + { + "document_id": "72aa5d47-336b-4e4f-8593-ee215b8891d2", + "section_type": "main", + "text": "\n\nWe hypothesize that the genes identified in our classification models (Table 5) represent products or 'genetic biomarkers' of the biological networks involved in diabetic neuropathy onset and progression.This idea is reinforced by the fact that several of the genes have known associations with diabetes or diabetic complications.We are particularly interested in CST1, whose expression was increased by 10-fold in progressors.CST1, encoding a cysteine protease inhibitor, was initially implicated in gastric and colorectal tumourigenesis (Choi et al., 2009;Yoneda et al., 2009).Another member of this protein family, cystatin C (CST3), has been identified as a prime predictor of diabetic nephropathy progression (Shimizu et al., 2003;Taglieri et al., 2009).Although the CST1 gene product has not been investigated in the context of diabetic complications, it is detectable in saliva, tears and urine (Choi et al., 2009).To date, there are no definitive biomarkers of diabetic neuropathy progression easily accessed from body fluids, and we speculate that CST1 could prove to be an easily measureable biomarker for diabetic neuropathy." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "In vivo relevance\n\nWhat is the evidence that these pathways are relevant in vivo?In rats with streptozotocin-induced diabetes, glomerular 12/15-LO mRNA and protein were upregulated 1, 2, 3, and 4 months after diabetes induction as demonstrated by reverse transcription-polymerase chain reaction (RT-PCR) and by Western analysis and immunohistochemistry, respectively [14].Upstream of p38 MAPK is the signaling molecule MKK3/6, which is activated during the first 2 months in diabetic rats compared to controls [14].A similar pattern was observed for phospho-p38 MAPK and phospho-CREB.At 4 months, mesangial (and, parenthetically, podocyte) fibronectin accretion was increased; this phenomenon presumably contributes to mesangial expansion [14].I will loosely refer to this change as glomerulosclerosis.Thus, in diabetic rats, just as in mesangial cells and VSMCs in vitro, angiotensin II and high ambient glucose concentration activate a novel lipid-mediating signal transduction pathway, and in conjunction with MAPKs and transcription factors, lead to fibronectin synthesis; this process then accelerates renal disease." + }, + { + "document_id": "8f6c3be4-4598-4ae2-a7a8-8ea5a7a52794", + "section_type": "main", + "text": "Wnt signaling in diabetic nephropathy\n\nThe potential relevance of Wnt signaling in advanced DN was investigated in more detail.Mapping the respective genes found by each approach onto the canonical Wnt pathway was performed (KEGG [13] and Biocarta databases (BioCarta Pathways; http:// www.biocarta.com/genes/index.asp)).As shown in Fig. 4, and in line with previous findings, the CI-analysis identified a much larger fraction of the pathway as regulated than did the RMA analysis (23 versus 15 out of 27 genes, see Table S3 and Table S4).The potential downstream effects of this pathway on known Wnt target genes were then examined.Of the known Wnt target genes regulated on the microarray 15 of 15 were identified by CI while RMA identified 10 (Fig. 4 and Table S4).Matrix metalloproteinase 7 (MMP7) [14] showed the highest fold-change in Wnt-associated genes and was confirmed by RT-PCR on the cDNA used for the array analysis (DN 40.09623.88,LD: 1.061.73(p,0.05)) as well as on an independent cohort of patients with DN (DN: 6.4566.62;LD: 1.0060.79(p,0.05)) (Fig. 5a).The induction of MMP7 protein was verified by immunohistochemistry: MMP7 protein expression was strongly increased in the tubulo-interstitial compartment of patients with DN (Fig. 2 and Fig. 5b,c)" + }, + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "section_type": "main", + "text": "\n\nIn the past, many scientific studies were focused on ED in type 1 DM (Chitaley et al. 2009).However, there are more complicated but less comprehensive mechanisms in T2DMED (Chitaley 2009).The potential underlying mechanisms include hypogonadism, vascular dysfunction, veno-occlusive disorders, and others (Hidalgo-Tamola and Chitaley 2009).Some mechanisms, such as non-adrenergic and non-cholinergic dysfunction, are still debated in the pathogenesis of T2DMED (Chitaley et al. 2009).To our knowledge, only a few studies regarding of miRNA expression or function in DMED have been reported.Recently, miRNA expression was investigated in a murine model with vasculogenic ED induced by a long-term high fat diet (Barbery et al. 2015).Though accompanied with impaired glucose tolerance, this animal model could not fully represent the pathogenic processes of DMED.Instead, a classical genetic modified murine model with T2DMED was used in the present study, to investigate differentially expressed microRNAs.The bioinformatic analyses of differentially expressed miRNAs were further performed to detect whether these miRNAs played potential roles in the mechanisms of T2DMED." + }, + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "section_type": "abstract", + "text": "\nGenetic variations in key inflammatory cytokines exacerbates the risk of diabetic nephropathy by influencing the gene expression.The address for the corresponding author was captured as affiliation for all authors.Please check if appropriate.Gene(2017)," + } + ], + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "microRNAs", + "lncRNAs", + "diabetic&nephropathy", + "diabetic&retinopathy", + "TGF-β1", + "angiogenesis", + "fibrosis", + "inflammation", + "hyperglycemia" + ], + "metadata": [ + { + "object": "in this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab403726" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Reporter assays reveal regulation by microRNA-339, microRNA-556, and, to a lesser extent, microRNA-10 and microRNA-199. MicroRNA-339 and microRNA-556 were further found to directly decrease Klotho protein expression in aging tissue.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab642566" + }, + { + "object": "after orthotopic lung transplantation, in the IL-17A KO group, less inflammation in the bronchovascular axis was observed and a non-significant trend towards less bronchovascular fibrosis, pleural/septal inflammation and fibrosis, and parenchymal inflammation and fibrosis when compared to WT mice", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab49527" + }, + { + "object": "*TFEB overexpression inhibits vascular inflammation in diabetic db/db mice. TFEB overexpression inhibits vascular inflammation in diabetic db/db mice .TFEB suppresses IKK activity to protect IkappaBalpha from degradation, thereby, inhibiting NF-kappaB p65 nuclear localization and attenuating vascular inflammation in endothelial cells of these mice. laminar shear stress induces TFEB through KLF2 which activates its pro...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab7633" + }, + { + "object": "Data suggest that urine AQP5/creatinine ratio is significantly higher in patients with diabetic nephropathy than in control subjects, subjects diabetes, or subjects with nephropathy of unknown etiology; urine AQP5/creatinine ratio increases with stage of diabetic nephropathy; this biomarker may improve clinical models in distinguishing diabetic nephropathy from normal controls and subjects with type 2 diabetic alone.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab213643" + }, + { + "object": "Angiogenesis and hepatic fibrosis are mutually stimulatory, such that fibrosis requires angiogenesis and angiogenesis requires angiopoietin 1 from activated HSCs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab72845" + }, + { + "object": "MicroRNA-625-3p was highly expressed in oral squamous cell carcinoma OSCC tissues. OSCC patients with T3+T4 stage had higher expression of microRNA-625-3p than those with T1+T2 stage. SCAI was identified as a target gene of microRNA-625-3p. ROC curve showed that microRNA-625-3p and SCAI exert certain values in diagnosing OSCC. MicroRNA-625-3p promoted migration of OSCC cells, which was reversed by SCAI knockdown.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab483708" + }, + { + "object": "Angiogenesis and hepatic fibrosis are mutually stimulatory, such that fibrosis requires angiogenesis and angiogenesis requires angiopoietin 1 from activated HSCs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab72844" + }, + { + "object": "Hyperglycemia could induce pathological angiogenesis in subcutaneous Matrigel of diabetic rats, and Ang-1 could upregulate the expression of intercellular junction protein in subcutaneous Matrigel of diabetic rats and promote the integrity of neovascularization in the subcutaneous Matrigel of diabetic rats", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367213" + } + ], + "question": "What non-coding RNAs are involved in diabetes, and what roles do they play?", + "subquestions": null, + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "usage": { + "chatgpt": 13184, + "gpt-4": 7924, + "gpt-4-turbo-preview": 6969 + }, + "user_id": 2 + }, + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_08.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_08.json new file mode 100644 index 0000000..560c7aa --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_08.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:36:24.689995Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "The Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "section_type": "main", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "GENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nSummary of key literature on gene-environment interactions in obesity and type 2 diabetes" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "abstract", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nGene-nutrient or -dietary pattern interactions in the development of T2DM." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002).\n Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "Genes and enviromental factors in the development of type 2 diabetes\n\nThe susceptibility to the development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two groups of T2DM susceptibility genes.The substantial contribution of genetic factors to the development of diabetes has been known for many years.The important pieces of evidence for the role of genes are the results of twin studies showing higher concordance rate for T2DM among monozygotic twins (between 41% and 55%) in comparison to dizygotic twins (between 10% and 15%) [43,84].What is interesting, there are populations with extremely high prevalence of T2DM, for example Pima Indians, that can not be explained solely by environmental factors [117].Supporting evidence for the role of genes in development of T2DM include also familial clustering of diabetesrelated traits.It was shown that the level of insulin sensitivity in Caucasians is inherited and a low level is a poor prognostic factor that precedes the development of T2DM [68,69,115].Similar observations were published for other ethnic groups [9,36,60].Those facts underline the importance of genetic factors.However, it is well known that the incidence of T2DM is also associated with environmental factors.Increasing incidence of T2DM during the last few years with obvious links to lifestyle and diet points to the role of enviromental factors in the development of disease [80].The differences in the prevalence of T2DM in relative populations living in different geographical and cultural regions (for example Asians in Japan and USA) also support the role of non-genetic factors [27,125].The relations between genetic and eviromental factors in the development of T2DM may be complex.For instance, enviromental factors may be responsible for the initiation of b-cell damage or other metabolic abnormalities, while genes may regulate the rate of progression to overt diabetes.On the other hand, in some cases genetic factors may be nec-essary for environmental factors even to start processes leading to the development of the disease." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + }, + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "section_type": "main", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + }, + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "section_type": "main", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nWhy do we think GEIs cause type 2 diabetes?dTheevidence supporting the existence of gene-lifestyle interactions in type 2 diabetes comes primarily from 1) the pattern and distribution of diabetes across environmental settings and ethnic groups, 2) familybased intervention studies, in which response to interventions varies less between biologically related individuals than between unrelated individuals; and 3) animal studies in which genetic and environmental factors are experimentally manipulated to cause changes in the expression of metabolic phenotypes.A brief overview of pertinent literature from human studies is given below." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nEpidemiological studies have been the predominant source of literature on gene-lifestyle interactions in cardiovascular and metabolic disease.Dozens of casecontrol and cohort studies have been published since the late 1990s purporting to have identified gene-lifestyle interactions in type 2 diabetes or related quantitative metabolic traits.Until recently, however, most of these studies were small and often relied on imprecise estimates of environmental exposures and outcomes.These are prone to error and bias, and exposures may not be assessed at the time when they conveyed their effects; for example, the causative exposures may have occurred very early in life, perhaps even in utero.Moreover, the complexities of modeling interaction effects have forced geneticists to focus primarily on very simple models of interaction, whereas clinically relevant interaction effects likely involve multiple genetic and nongenetic biomarkers.In addition, barely a handful of studies have examined incident type 2 diabetes as an outcome, with most focusing on cross-sectional measures of glucose and others relying on analyses that include prevalent cases of diabetes; this may introduce labeling bias, where the recall of well-known diabetesassociated behaviors is less likely to be accurate in individuals recently diagnosed with disease than in those who have not been diagnosed with disease." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) has developed into a major public health concern.While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009).Lifestyle and behavioural factors play an important role in determining T2D risk.For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring (Ng et al, 2010).In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004).The pancreatic islets of Langerhans are of central importance in the development of T2D.Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet b-cells to regulate glucose homeostasis.b-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b;Tabak et al, 2009) and results in T2D (UKPDSG, 1995).The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007;Scott et al, 2007;Sladek et al, 2007;Zeggini et al, 2007Zeggini et al, , 2008) ) has placed the insulinproducing b-cells at centre stage.These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development.Most T2D risk genes identified in these genome-wide association studies (GWAS) affect b-cell mass and/or function (Florez, 2008).While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010).Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence.DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications.These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008;Park et al, 2008)." + } + ], + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [ + { + "object": "Data suggest that expression of Pparg can be regulated by dietary factors; expression of Pparg is down-regulated in preadipocytes by tannic acid, a form of tannins found in plant-based foods; Pparg appears to be a major factor in adipogenesis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab206776" + }, + { + "object": "Circulating adiponectin increased in obese physically active participants >/=180 min/week compared to non-physically active counterparts, indicating that physical activity may mediate baseline adiponectin levels irrespective of the fat mass regulatory effect.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141573" + }, + { + "object": "Upon stratifying the participants into tertiles by the Matsuda index, we observed an inhibitory relationship between the genetic risk score GRS and insulin secretion in low insulin sensitive but not in high insulin sensitive controls and treatment-naive Type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab985500" + }, + { + "object": "The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of physical activity in particular in those genetically predisposed to obesity.[Meta-analysis]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782259" + }, + { + "object": "Serum IGFBP-2 levels increase with age after the age of 50 years and evolve in parallel with insulin sensitivity. IGFBP-2 may therefore be a potential marker for insulin sensitivity. We further show that IGFBP-2 levels can predict mortality in this aging population. However, its predictive value for mortality can only be interpreted in relation to insulin sensitivity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699014" + }, + { + "object": "Our study validated the association between an FTO variant and BMI in Taiwanese individuals. In addition, individuals with TG and TT genotypes who were physically active had a decreased BMI. These results indicate that physical activity might be necessary to mitigate the deleterious effect of BMI among genetically susceptible Taiwanese individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab566865" + }, + { + "object": "Irrespective of the genetic defect, adenoviral delivery of C5 improved insulin sensitivity in both C5cont and C5def mice, indicating an insulin-sensitizing function of C5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab428686" + }, + { + "object": "there was a high heritability for IGF-I and IGFBP-3, but a low heritability for insulin secretion and insulin sensitivity in a group of elderly twins; in addition, study found a negative relationship between IGF-I and insulin sensitivity, which did not seem to be strongly genetically determined", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab887620" + }, + { + "object": "The authors showed that the expression of cysK is regulated by several genetic and environmental factors in addition to CysB: two genetic factors, OmpR and CysE, and lithium.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785160" + }, + { + "object": "Production of nitric oxide NO within eNOS-positive NGC neurons increases after environmental perturbations, indicating a role for eNOS/NO in modulating environmentally appropriate levels of GA. Inhibition of NO production causes dysregulated behavioral arousal after exposure to environmental perturbation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab615356" + } + ], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "ADE056BF251274D15922CA6E7B5C3133", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "task_id": "ADE056BF251274D15922CA6E7B5C3133" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_09.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_09.json new file mode 100644 index 0000000..0d3f71d --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_09.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-04-18T18:40:22.873037Z", + "data": { + "amplify": false, + "answer": "Yes, genetic predictors of diabetes complications can be identified. Studies have revealed several susceptibility loci for diabetic complications such as retinopathy and nephropathy. However, these genetic factors only explain a small proportion of the phenotypic variation observed in type 2 diabetes patients, indicating a need for the identification of more novel genetic risk factors.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "091ab13a-1b8a-4849-b698-48db7b1a948f": [ + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "3548bb7f-727c-4ccb-acc7-a97553b89992": [ + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + } + ], + "45cdaf79-d881-43e6-8555-ff47f04ae3d4": [ + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "a7bad429-5f6a-464f-a666-f9cb1be60338": [ + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + } + ], + "cf022812-00a2-42ba-88fb-5c2014c86c43": [ + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "section_type": "main", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "section_type": "abstract", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "section_type": "main", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "section_type": "main", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + }, + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "section_type": "main", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Results\n\nStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nTo date, however, the improvement in predictive value of known genetic variants over that of classic clinical risk factors (BMI, family history, glucose) has proven minimal in type 2 diabetes." + }, + { + "document_id": "553ae95d-0a2b-4f2a-8123-da9a9e9e7a77", + "section_type": "main", + "text": "\n\nTwo more recent population -based studies using a longitudinal design with prospectively investigated cohorts have examined the predictive value of a genotype score in addition to common risk factors for prediction of T2DM [194,195] .Meigs et al. [194] reported that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common clinical risk factors alone [195] .A similar conclusion was drawn in the paper by Lyssenko et al. [196] , along with an improved value of genetic factors with an increasing duration of follow -up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.They also showed that β -cell function adjusted for insulin resistance (using the disposition index) was the strongest predictor of future diabetes, although subjects in the prediabetic stage presented with many features of insulin resistance.It is also noteworthy that many of the variants that were genotyped appear to infl uence β -cell function.The addition of DNA data to the clinical model improved not only the discriminatory power, but also the reclassifi cation of the subjects into different risk strategies.Identifying subgroups of the population at substantially different risk of disease is important to target these subgroups of individuals with more effective preventative measures.As more genetic variants are now identifi ed, tests with better predictive performance should become available with a valuable addition to clinical practice." + }, + { + "document_id": "5782c1a9-6ab1-4c66-b1e6-116ac6a0e50b", + "section_type": "main", + "text": "\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "abstract", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "section_type": "main", + "text": "\n\nGenomic information associated with Type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "\n\nMajor consortia addressing the genetic basis of diabetes complications and associated traits" + }, + { + "document_id": "a5a0cd4f-8acf-4e89-9033-04f448dc0b15", + "section_type": "main", + "text": "CONCLUSIONS\n\nDuring the past several years, the identification of genetic risk factors for diabetic microvascular complications has improved.However, most of the studies were not fully powered for GWASs, with the exception of the GENIE study.Therefore, most of the results associated with the genetic risk factors were below the genome-wide significance threshold and inconsistent among studies.In addition, the definition of cases and controls differed, thereby introducing significant heterogeneity.Based on the findings reported, these genetic association results should be validated in other populations.In addition, a collaborative effort to harmonize phenotype definitions and to increase sample size is necessary." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nUntil recently, genome-wide linkage and candidate studies have been the main genetic epidemiological approaches to identifying the precise genetic variants underlying T2D heritability.These efforts confirmed only a few susceptibility variants, including those in PPARG, KCNJ11, WFS1, HNF1A, HNF1B, HNF4A, TCF7L2, and ADIPOQ (1,6,27,56,81,102).Recent genome-wide association studies (GWAS) have unveiled over 50 novel loci associated with T2D and more than 40 associated with T2D-related traits including fasting insulin, glucose, and proinsulin (16,48,57,82,87,97,105) (Table 1).Clinical investigations of some of the T2D loci, thus far, suggest that the genetic components of T2D risk act preferentially through β-cell function (20).This pattern may only be a function of case diagnostic criteria, which weigh heavily on parameters reflecting advanced stages of the disease.This notion is supported by the incomplete overlap of single-nucleotide polymorphisms (SNPs) contributing to variation in quantitative traits with those associated with overt T2D (20).With the exception of TCF7L2, most variants contribute modestly to T2D risk and together explain only a small proportion of the familial clustering of T2D, suggesting that many more loci await discovery (10,12,97)." + }, + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "section_type": "main", + "text": "\n\nGenome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "The future of type 1 diabetes genetics\n\nAfter more than two decades of work, type 1 diabetes is probably the best characterized of all common multigenic diseases.Thus far, the identified genetic risk factors have been plausible candidate genes with common variants that affect susceptibility.Of these, variation at HLA alone explains much of the risk to siblings (HLA provides a l s of 3.4 out of a total of 15, leaving a l s of 15/3.4 ¼ 4.4 to be explained), and INS and CTLA4 have also been identified as disease loci.What, then, is left to be done?First, many risk alleles remain undiscovered.Although their effect will be much weaker than is seen for HLA (and almost certainly weaker than for INS), they may identify genes or pathways that provide insight into etiology, pathogenesis, and perhaps even prevention or treatment.Each additional variant that is clearly proven to increase risk will also help to identify high-risk non-diabetic individuals who might participate in studies of prevention and, in turn, benefit from preventive interventions.These alleles might also be relevant to the genetics of diabetic complications (not discussed in this review), perhaps identifying patients who would benefit most from intensive treatment and monitoring." + }, + { + "document_id": "1ecd1047-39d1-44ea-b3a2-3d8472be3435", + "section_type": "main", + "text": "Genomic Analyses for Diabetes Risk\n\nGenes signifying increased risk for both type 1 and type 2 diabetes have been identified.Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes.Several T1D candidate genes for increased risk of developing type 1 diabetes have been suggested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12Also, several candidate genes for increased risk of developing type 2 diabetes have been identified, including peroxisome proliferatoractivated receptor gamma (PPARγ2), angiotensin converting enzyme (ACE), methylene tetrahydrofolate reductase (MTHR), fatty acid binding protein-2 (FABP2), and fat mass and obesity associated gene (FTO). 13he conclusions of a \"Workshop on Metformin Pharmacogenomics,\" sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, were published in 2014. 14The meeting was intended to review metformin pharmacogenomics and identify both novel targets and more effective agents for diabetes.The idea behind the meeting was that understanding the genes and pathways that determine the response to metformin has the potential to reveal new drug targets for the treatment of diabetes.The group noted that there have been few genes associated with glycemic control by metformin, and the most reproducible associations have been in metformin transporter genes.They acknowledged that nongenetic factors also contribute to response to metformin and that broader system biology approaches will be required to model the combined effects of multiple gene variants and their interaction with nongenetic factors.They concluded that the overall challenge to the field of precision medicine as it relates to antidiabetes treatment is to identify the individualized factors that can lead to improved glycemic control." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "Future prospects\n\nWhilst the examples above provide interesting insights, it is clear that we are only at the beginning of mining the information generated by genome-wide association studies for Type 2 diabetes and other complex traits.work in human genetics, involving ever larger cohorts, meta-analyses and the search for rarer and more penetrant variants will in future be important to identify all of the heritable elements that control Type 2 diabetes risk; however, the useful deployment of this information for either disease prediction or the development of new therapies will require considerable further efforts at the cellular and molecular level to understand the function of the identified genes.Moreover, and although not the subject of this particular review, actions of single nucleotide polymorphisms through non-coding genes, e.g.mi-croRNAs and long non-coding RNAs, will require deeper investigation." + }, + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "section_type": "abstract", + "text": "\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "genetic&predictors", + "diabetes&complications", + "GWAS", + "genome-wide&association&study", + "polygenic&score", + "susceptibility&loci", + "T2DM", + "genetic&variants", + "diabetic&neuropathy" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Serum levels of APN and AdipoR1 are significantly lower in type 2 diabetes mellitus T2DM group and T2DM + macrovascular complications MVC group, showing lowest value in T2DM + MVC group. APN and AdipoR1 levels may influence glucose and lipid metabolism in T2DM patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699512" + }, + { + "object": "this case control study showed that NET gene polymorphism G1287A, rs5569 was significantly associated with type 2 diabetes mellitus T2DM in North Indian male population where AG genotype and A allele was found to be protective against the risk of T2DM while the GG genotype and G allele were found to increase the risk of T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab928949" + }, + { + "object": "The results suggest that LEPR rs1327118 may be associated with elevated blood pressure and HDL-C levels in women with type 2 diabetes mellitus T2DM, and rs3806318 may be associated with T2DM and elevated blood pressure in men with T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab864916" + }, + { + "object": "of the five variants, SNP rs2236935T/C was significantly associated with type 2 diabetes mellitus T2DM in this study population; conclude that MAP4K4 gene is associated with T2DM in a Chinese Han population, and MAP4K4 gene variants may contribute to the risk toward the development of T2DM", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab545662" + }, + { + "object": "Study evaluated the associations between 6 SNPs in CDH13 and type 2 diabetes mellitus T2DM in a Han Chinese population. Results showed that the rs12596316 AG genotype was a risk genotype for the development of T2DM in the overdominant inheritance model; rs11646213, rs3865188, rs12444338, rs12051272, and rs7195409 had no observed associations with T2DM in terms of alleles, genotypes, and the various inheritance models.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab740648" + }, + { + "object": "data suggest a possible association of C332C-genotype of the glyoxalase 1 gene with diabetic neuropathy in type 2 diabetes, supporting the hypothesis that methylglyoxal might be an important mediator of diabetic neuropathy in type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab202777" + }, + { + "object": "Compared with normal glucose tolerance NGT groups, the PTEN mRNA expression was significantly higher in Uyghur patients with mild type 2 diabetes mellitus T2DM groups; PTEN protein expression was upregulated in Uyghur patients with mild T2DM groups. PTEN methylation in T2DM patients was significantly lower than that in NGT groups. 2 CpG units demonstrated a significant difference between NGT and Uyghur patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab151151" + }, + { + "object": "Haplotype-based interaction between the PPARGC1A and UCP1 genes is associated with impaired fasting glucose IFG or type 2 diabetes mellitus T2DM among the residents of Henan province, China. Individuals with the haplotype AAG PPARGC1A gene and CTCG UCP1 gene have increased susceptibility to IFG or T2DM, while those with haplotype AAG PPARGC1A gene and CTCA UCP1 gene have a lower risk of IFG or T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab332396" + } + ], + "question": "Can we identify genetic predictors of diabetes complications?", + "subquestions": null, + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "usage": { + "chatgpt": 6305, + "gpt-4": 3589, + "gpt-4-turbo-preview": 2668 + }, + "user_id": 2 + }, + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_10.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_10.json new file mode 100644 index 0000000..c9762c0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_10.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-04-18T18:45:53.040778Z", + "data": { + "amplify": false, + "answer": "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "context": { + "063a0254-1d1b-4caa-b782-6a1fe4ebca0d": [ + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + } + ], + "08858a32-d736-4d8d-a135-f86568152a81": [ + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + } + ], + "183f165e-4d5c-4580-9aff-4e6b2e5a6463": [ + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + } + ], + "4feda561-1914-404d-9092-3c629d5251bd": [ + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + } + ], + "b00b9753-c198-4f8a-a8b9-dd5e94dc5896": [ + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + } + ], + "c8c58fdf-06e3-4da4-a920-d5bcbcd18289": [ + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + }, + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "section_type": "main", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "section_type": "main", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "abstract", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "e2c1cfb0-9cfc-4a59-9df6-8599708b25ed", + "section_type": "main", + "text": "\n\nc With increasing efforts to map patients with T2D in etiological space using clinical and molecular phenotype, physiology, and genetics, it is likely that this increasingly granular view of T2D will lead to increasing precision therapeutic paradigms requiring evaluation and potential implementation.Genetic variation not only can capture etiological variation (i.e., genetic variants associated with diabetes risk) but also variation in drug pharmacokinetics (absorption, distribution, metabolism, and excretion [ADME]) and in drug action (pharmacodynamics)." + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "abstract", + "text": "\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 5×10 −8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D." + }, + { + "document_id": "a49c4251-7a66-44f1-9f95-0d6e8191a2ad", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "b29b3621-cdb5-4723-b771-8b48546241a5", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "f3b925cc-2556-4f30-809b-6bfe63a805b8", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "section_type": "main", + "text": "Conclusions\n\nPharmacogenetics research provides a means to better understand and improve on pharmacotherapy.However, pharmacogenetic studies of T2D therapies lag behind those for other complex diseases, despite the fact that pharmacologic interventions for T2D have been studied extensively at both the clinical and epidemiologic levels.Among the studies that have been conducted, several have identified variants that are potentially associated with differential response to anti-diabetes medications; these preliminary results are promising and warrant investigations in larger, well-designed cohorts to assess their potential roles in optimal drug selection and individualized pharmacotherapy in patients with T2D.At this time, larger, well-powered studies with clearly defined outcomes and utilizing a global approach are needed, as they will not only be more informative than extant candidate gene investigations, but will also be necessary to define the array of genetic variants that may underlie drug response.Such results will likely enable achievement of optimal glucose control, improvement of therapeutic efficacy, and reduction in risk of adverse drug events in at-risk patients, which together will lead to personalized treatment strategies for all individuals with T2D." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "Pharmacogenetics in disease progression\n\nOver the recent years, more than 90 susceptibility genes have been identified by genome-wide association studies (GWAS) [55][56][57][58].However, the knowledge of the potential interactions between T2D predisposing genetic variants and the efficacy of treatment of T2D is sparse.Identification of gene-treatment interactions is challenging and requires large sample sizes and sophisticated analytical methods.Furthermore, detailed information on lifestyle and compliance to treatment as well as a long follow-up period are necessary for analysis of pharmacogenomics in T2D." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "main", + "text": "\n\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "abstract", + "text": "\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow." + }, + { + "document_id": "dcd88798-0248-45e0-8d45-8614c7697266", + "section_type": "main", + "text": "\n\ndiabetes (DoD) and poor glycemic control (2).Genetic factors are also implicated, with heritability of 52% for proliferative DR (PDR) (3,4).Several candidate gene and genome-wide association studies (GWAS) have been conducted (5)(6)(7)(8)(9)(10)(11).Although several polymorphisms have been suggested to be associated with DR, few have been convincingly replicated (10,(12)(13)(14)(15).There are several reasons why studies have not yielded consistent findings.The genetic effects are likely modest, and identification requires large sample sizes.Previous studies have not consistently accounted for the strongest two covariates, DoD and glycemic control.Liability threshold (LT) modeling is one way to incorporate these covariates while also increasing statistical power (16).Finally, previous genetic studies have largely examined individual variants.Techniques that examine top GWAS findings collectively for variants that cluster in biological networks based on known protein-protein interactions have the potential to identify variants where there is insufficient power to detect their individual effects." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "Genetic Predisposition\n\nThe fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk.The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this diseaseif one twin developed type 2 diabetes, then the other one invariably developed it (9).However, this kind of study provides no insight into how genetics act in the disease.Is there a defective gene that directly impairs the glucose homeostasis system?Alternatively, does it cause insulin resistance or some other defect that acts indirectly by exceeding the capacity of an otherwise normal glucose homeostasis system to compensate?Also, are there one or many genetic defects that predispose to this disease?" + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe purpose of this review is to summarize current knowledge of pharmacogenetics in T2D and provide a perspective on the relationships between human genetic variants, antidiabetic treatment, and disease progression.This topic is of utmost importance as an improved understanding of gene-treatment interactions may provide a basis for development of future individualized therapies and treatment guidelines." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "\n\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 5×10 −8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D." + }, + { + "document_id": "3e53b34f-5bdf-43d5-9594-736cf83071db", + "section_type": "main", + "text": "\n\nTo extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent.We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association.Genomewide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D.Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "DIABETES AND GENETICS\n\nDiabetes is a complex disease that involves a wide range of genetic and environmental factors.Over the past several years, many studies have focused on the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development [142][143][144] .However, despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive [145,146] .Evidently, a major complication is the fact that a single gene mutation or polymorphism will not impose the same effect among different individuals within a population or different populations.This variation is directly or indirectly affected by the overall genetic background at the individual, family or population levels that are potentially further complicated by interaction with highly variable environmental modifier factors [147,148] ." + } + ], + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "pharmacogenetics", + "pharmacogenomics", + "GWAS", + "genetic&variants", + "OCT&genes", + "KCNJ11", + "ABCC8", + "CYP2C9", + "TCF7L2" + ], + "metadata": [ + { + "object": "The intrinsic clearance Vmax/Km values of all variants, with the exception of CYP2C9*2, CYP2C9*11, CYP2C9*23, CYP2C9*29, CYP2C9*34, CYP2C9*38, CYP2C9*44, CYP2C9*46 and CYP2C9*48, were significantly different from CYP2C9*1. CYP2C9*27, *40, *41, *47, *49, *51, *53, *54, *56 and N418T variant exhibited markedly larger values than CYP2C9*1.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab827642" + }, + { + "object": "genetic association studies in pediatric population in Japan: Data confirm that mutations in KCNJ11 or ABCC8 are associated with neonatal diabetes mellitus. Novel mutations were identified; 2 in KCNJ11 V64M, R201G and 6 in ABCC8 R216C, G832C, F1176L, A1263V, I196N, T229N. KCNJ11 = ATP-sensitive inward rectifier potassium channel-11; ABCC8 = ATP-binding cassette subfamily C member-8", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316321" + }, + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "genetic association studies in population in Scotland: data suggest, in type 2 diabetes treated with sulfonylureas, 2 SNPs in CYP2C9 CYP2C9*2, R144C, rs1799853; CYP2C9*3, I359L, rs1057910 are associated with drug-induced hypoglycemia; an SNP in POR POR*28, A503V, rs1057868 is associated with better response to sulfonylureas. CYP2C9 = cytochrome P450 family 2 subfamily C member 9; POR = cytochrome p450 oxidoreductase", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316392" + }, + { + "object": "Novel mutations were detected in ABCC8 and KCNJ11 gene in Chinese patients with congenital hyperinsulinism CHI. Hotspot mutations such as T1042Qfs*75, I1511K, E501K, G111R in ABCC8 gene, and R34H in KCNJ11 gene are predominantly responsible for Chinese CHI patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab535847" + }, + { + "object": "he aim of this study was to ascertain the polymorphic markers profile of ADIPOQ, KCNJ11 and TCF7L2 genes in Kyrgyz population and to analyze the association of polymorphic markers and combinations of ADIPOQ gene's G276T locus, KCNJ11 gene's Glu23Lys locus and TCF7L2 gene's VS3C>T locus with type two diabetes T2D in Kyrgyz population", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab334669" + }, + { + "object": "genetic variants in TCF7L2 confer a strong risk of future type 2 diabetes possibly mediated by altering expression of TCF7L2 in pancreatic islets [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab318653" + }, + { + "object": "Considering that CYP2C9*2 and CYP2C9*3 alleles have altered catalytic activities relative to CYP2C9*1, the present data suggest the need for pharmacogenetic studies to optimize drug dosages in different populations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab155248" + }, + { + "object": "The association of variants in IRS1 with type 2 diabetes and type 2 diabetes-related phenotypes and the differential expression of IRS1 in adipocytes and skeletal muscle suggest a role of this gene in the pathogenesis of type 2 diabetes in Pima Indians.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782328" + }, + { + "object": "trend for augmented exercise-induced IL6 release in type 2 diabetics; results also suggest that neither type 2 diabetic nor healthy skeletal muscle releases IL6 at rest,indicating that other organs contribute to elevated basal IL6 in type 2 diabetics", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab971647" + } + ], + "question": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "subquestions": null, + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "usage": { + "chatgpt": 7037, + "gpt-4": 4436, + "gpt-4-turbo-preview": 3522 + }, + "user_id": 2 + }, + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_01.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_01.json new file mode 100644 index 0000000..8720f97 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_01.json @@ -0,0 +1,142 @@ +{ + "created_at": "2024-04-18T18:19:31.857649Z", + "data": { + "amplify": false, + "answer": "The onset of diabetes in mice can occur as early as 3 to 4 weeks of age, with blood sugar concentrations often elevating between 5 and 8 weeks. However, in some cases, hyperglycemia can develop at approximately 20 weeks of age.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "52990c69-609c-448e-9f2c-36e1655ca6db": [ + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control. A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + } + ], + "c802cb60-1a15-4962-8e6d-f06608c00a54": [ + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db/db&mice", + "diabetes", + "onset", + "age", + "obesity", + "hyperglycemia", + "C57BL/KsJ", + "C57BL/6J", + "insulin&resistance", + "albuminuria" + ], + "metadata": [], + "question": "What is the age range in mouse for the onset of diabetes?", + "subquestions": null, + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "usage": { + "chatgpt": 6475, + "gpt-4": 4255, + "gpt-4-turbo-preview": 3283 + }, + "user_id": 2 + }, + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_02.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_02.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_03.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_03.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_03.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_04.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_04.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_04.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_05.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_05.json new file mode 100644 index 0000000..fafa914 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_05.json @@ -0,0 +1,138 @@ +{ + "created_at": "2024-04-18T18:24:11.260410Z", + "data": { + "amplify": false, + "answer": "Yes, there are phenotypes and genotypes associated with early onset diabetes in mice. The db/db mouse strain, which carries the diabetes (db) mutation, is one such genotype. These mice develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, which is an associated phenotype. The C57BL/Ks strain also exhibits early onset diabetes. The severity of the diabetes condition in these mice depends on the interaction of the mutant gene with the inbred background.", + "context": { + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain. Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + } + ] + }, + "data_source": [], + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db", + "diabetes", + "C57BL/Ks", + "obesity", + "insulin", + "hyperglycaemia", + "beta&cells", + "mutation", + "C57BLKS/J", + "NZO" + ], + "metadata": [], + "question": "Are there phenotypes or genotypes associated with early onset diabetes in mouse?", + "subquestions": null, + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "usage": { + "chatgpt": 8500, + "gpt-4": 4937, + "gpt-4-turbo-preview": 3964 + }, + "user_id": 2 + }, + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_06.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_06.json new file mode 100644 index 0000000..50efdb4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_06.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-18T18:30:30.982190Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [], + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_07.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_07.json new file mode 100644 index 0000000..93414e3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_07.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-18T18:34:34.295904Z", + "data": { + "amplify": false, + "answer": "MicroRNAs and long noncoding RNAs (lncRNAs) are involved in diabetes. MicroRNAs modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs. They can regulate pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism. They also hold potential as diagnostic biomarkers and possible drug-targets for regulation of dysfunctional cell responses. LncRNAs are implicated in complications associated with diabetes, such as diabetic retinopathy and diabetic nephropathy. They can regulate cell proliferation, viability, migration, and the expression of pathological genes via post-transcriptional mechanisms.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + } + ], + "18a35699-873a-4542-b35a-3a4a14edd628": [ + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + } + ], + "2dc80127-89ba-47be-9e94-d90c2105be8d": [ + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + } + ], + "34184c8d-b167-4ae8-bfce-01e18d78fe41": [ + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + } + ], + "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef": [ + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes. Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + } + ], + "6011e960-6a6e-47fe-94f2-2c21c224fd25": [ + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + } + ], + "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + } + ], + "80e1b2af-be79-4d9b-852f-46bf3e23c963": [ + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + } + ], + "88dde947-5255-40e1-92d5-afde089b517b": [ + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + } + ], + "d23e9456-8ee8-46e0-9870-18ff69965c28": [ + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + } + ], + "e66846a6-1546-481b-baae-a55fc524c8af": [ + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + } + ], + "ec62a4d9-2fe2-49b0-84d8-13b1597e2067": [ + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + } + ] + }, + "data_source": [], + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "microRNAs", + "lncRNAs", + "diabetic&nephropathy", + "diabetic&retinopathy", + "TGF-β1", + "angiogenesis", + "fibrosis", + "inflammation", + "hyperglycemia" + ], + "metadata": [], + "question": "What non-coding RNAs are involved in diabetes, and what roles do they play?", + "subquestions": null, + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "usage": { + "chatgpt": 13184, + "gpt-4": 7924, + "gpt-4-turbo-preview": 6969 + }, + "user_id": 2 + }, + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_08.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_08.json new file mode 100644 index 0000000..878a503 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_08.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-18T18:36:24.689995Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [], + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "ADE056BF251274D15922CA6E7B5C3133", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "task_id": "ADE056BF251274D15922CA6E7B5C3133" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_09.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_09.json new file mode 100644 index 0000000..1bece07 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_09.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-18T18:40:22.873037Z", + "data": { + "amplify": false, + "answer": "Yes, genetic predictors of diabetes complications can be identified. Studies have revealed several susceptibility loci for diabetic complications such as retinopathy and nephropathy. However, these genetic factors only explain a small proportion of the phenotypic variation observed in type 2 diabetes patients, indicating a need for the identification of more novel genetic risk factors.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "091ab13a-1b8a-4849-b698-48db7b1a948f": [ + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "3548bb7f-727c-4ccb-acc7-a97553b89992": [ + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + } + ], + "45cdaf79-d881-43e6-8555-ff47f04ae3d4": [ + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "a7bad429-5f6a-464f-a666-f9cb1be60338": [ + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + } + ], + "cf022812-00a2-42ba-88fb-5c2014c86c43": [ + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [], + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "genetic&predictors", + "diabetes&complications", + "GWAS", + "genome-wide&association&study", + "polygenic&score", + "susceptibility&loci", + "T2DM", + "genetic&variants", + "diabetic&neuropathy" + ], + "metadata": [], + "question": "Can we identify genetic predictors of diabetes complications?", + "subquestions": null, + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "usage": { + "chatgpt": 6305, + "gpt-4": 3589, + "gpt-4-turbo-preview": 2668 + }, + "user_id": 2 + }, + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_10.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_10.json new file mode 100644 index 0000000..fe3b2cf --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_10.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-18T18:45:53.040778Z", + "data": { + "amplify": false, + "answer": "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "context": { + "063a0254-1d1b-4caa-b782-6a1fe4ebca0d": [ + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + } + ], + "08858a32-d736-4d8d-a135-f86568152a81": [ + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + } + ], + "183f165e-4d5c-4580-9aff-4e6b2e5a6463": [ + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + } + ], + "4feda561-1914-404d-9092-3c629d5251bd": [ + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + } + ], + "b00b9753-c198-4f8a-a8b9-dd5e94dc5896": [ + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + } + ], + "c8c58fdf-06e3-4da4-a920-d5bcbcd18289": [ + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [], + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "pharmacogenetics", + "pharmacogenomics", + "GWAS", + "genetic&variants", + "OCT&genes", + "KCNJ11", + "ABCC8", + "CYP2C9", + "TCF7L2" + ], + "metadata": [], + "question": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "subquestions": null, + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "usage": { + "chatgpt": 7037, + "gpt-4": 4436, + "gpt-4-turbo-preview": 3522 + }, + "user_id": 2 + }, + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B" +}
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