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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" +} |