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diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_01.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_01.json new file mode 100644 index 0000000..4b91711 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_01.json @@ -0,0 +1,394 @@ +{ + "created_at": "2024-04-18T18:19:31.857649Z", + "data": { + "amplify": false, + "answer": "The onset of diabetes in mice can occur as early as 3 to 4 weeks of age, with blood sugar concentrations often elevating between 5 and 8 weeks. However, in some cases, hyperglycemia can develop at approximately 20 weeks of age.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "52990c69-609c-448e-9f2c-36e1655ca6db": [ + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control. A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + } + ], + "c802cb60-1a15-4962-8e6d-f06608c00a54": [ + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control.\n A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "section_type": "main", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33].\n Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "section_type": "main", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + }, + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "section_type": "main", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nTo investigate the effects of genetic background variation on the measured traits, we also conducted a genetic cross using CAST as the diabetes-resistant strain (CAST cross).In the male BC1 progeny of this CAST cross, the onset of the hyperglycemia was slightly delayed compared to the B6 cross; 27% vs 45% of the male BC1 mice showed Ͼ300 mg/dl plasma glucose at 20 weeks.In the CAST cross the hyperglycemia was also maintained throughout the 30-week period studied.Therefore, the mean of three glucose measurements between 22 and 28 weeks of age for each BC1 progeny was used for genetic analysis.Body weights were measured at 24 weeks.At the end of the study (28 weeks), plasma insulin levels and nasal-anal lengths were measured, and five fat pads were dissected and weighed.In total, 95 male BC1 mice were collected and genotyped individually with 69 SSLP markers spaced through out the genome." + }, + { + "document_id": "a551b815-1d9d-4dae-a194-8f77e317b506", + "section_type": "main", + "text": "Diabetes monitoring\n\nCohorts of female mice were housed in an SPF facility and tested once a week for elevated urinary glucose (>110 mmol/L) using Diastix reagent strips (Bayer Australia, Ltd.) over a 300-d time course.Three consecutive elevated readings indicated the onset of diabetes.Pairwise comparisons of the diabetes incidence between mouse strains were done using the log-rank test." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Two of the mice had\nblood sugar concentrations only slightly above normal\nat the end of the 3 month period, while two others\nstabilized at the starting blood sugar concentrations.\n Weight gains of diabetic mice on this ration, were,\non the whole, variable but somewhat smaller than\nthose seen on the chow ration. However, those diabetic\nmice that showed the greatest decrease in rate of\nweight gain did not necessarily have the lowest blood\nsugar concentrations at the end of the treatment\n\nperiod." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "The diabetic mouse on the\nright weighs 50 per cent more t h a n the control mouse on the left and shows\ntypical f a t deposition\n\nwith age and concomitant elevations of blood sugar\nconcentration have been described previously [11]\nand will not be dealt with in detail here. Although\nthere are individual variations in the age of onset of\ndiabetes and the rate of increase in weight and blood\nsugar concentration, there is a general pattern, which\nis depicted in Fig. 2." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease.\n Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths.\n After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "\n\nM16 mouse: M16 mouse is a new model for obesity and type 2 diabetes which results from long-term selection for 3 to 6 wk weight gain from an Institute of Cancer Research, London, UK (ICR) base population.M16 mice exhibit early onset of obesity and are larger at all ages characterized by increased body fat percentage, fat cell size, fat cell numbers, and organ weights.These mice also exhibit hyperphagia, accompanied by moderate obesity, and are hyperinsulinaemic, hyperleptinaemic and hypercholesterolaemic relative to ICR.Both M16 males and females are hyperglycaemic relative to ICR, with 56 and 22 per cent higher fasted blood glucose levels at 8 wk of age.M16 mice represent an outbred animal model to facilitate gene discovery and pathway regulation controlling early onset polygenic obesity and type 2 diabetic phenotypes.Phenotypes prevalent in the M16 model, with obesity and diabesity exhibited at a young age, closely mirror current trends in human populations 36 ." + }, + { + "document_id": "38be907c-70ea-45f2-a8c1-7aed203a5256", + "section_type": "main", + "text": "Mice and Intervention Protocol\n\nProtocols were approved by the Rutgers University Institutional Care and Use Committee and followed federal and state laws.Five-week-old male C57BL/6J mice (10-20 g) were purchased from The Jackson Laboratory (Bar Harbor, ME) and fed a standard chow diet ad libitum (cat.no.5015; Purina) during their 1-week acclimatization period.Animals were housed, five per cage, with free access to water in a room with a temperature of 24 6 1°C and a 12:12-h light:dark cycle (7:00 A.M.-7:00 P.M.).At 6 weeks of age, oral glucose tolerance tests (OGTTs) were performed on 45 mice.The area under the curve (AUC) corresponding to the OGTT data from each mouse was calculated, and a mean AUC for each cage of five mice was determined.The nine cages were separated into three groups based on the average AUCs calculated for each cage so that each group of 15 mice would be similar at baseline with respect to oral glucose tolerance.This method of assignment was used as a way to normalize oral glucose tolerance at baseline and also keep mice in their original cage placements, as switching the animals around can sometimes lead to aggressive behavior in the new group.Mice were fed GP-SPI diet, SPI diet, or HFD (n = 15 mice/diet group) for a total of 13 weeks.The HFD group was used mainly as a control to monitor body weight gain and food intake between groups.Various end points were measured during the intervention period as described below.A second group of 5-week-old male C57BL/6J mice (10-20 g) (n = 10) was purchased at a later time to have an LFD cohort with which to compare body weights, food intake, and microbiome samples.These LFD-fed mice were similarly housed (five per cage) in the same experimental room and space.Mice were initially fed a regular chow diet ad libitum for 1 week and then switched to the LFD for 12 weeks with OGTT performed at the same intervals." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "main", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + }, + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "section_type": "main", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nLeptin-receptor-deficient db/db mice on the C57BLKS/J background largely recapitulate the obesity phenotype of the ob/ob mouse.The nomenclature of db (that is, diabetic) stems from the original observation of marked hyperglycaemia in these mice.db/db mice are hyperphagic and have reduced energy expenditure, leading to early-onset obesity 195 .They are also hypothermic, have decreased linear growth owing to GH deficiency and are infertile 195 , and leptin levels in db/db mice are markedly elevated 205 .Hyperinsulinaemia can be detected as early as 10 days of age, and insulin levels continue to increase until 3 months of age.The hyperinsulinaemia is accompanied by hyperplasia and hypertrophy of the pancreatic β-cells.After 3 months, levels of insulin in db/db mice drop profoundly, which is concomitant with the atrophy of β-cells.Consequently, marked and sustained hyper glycaemia with blood glucose values >400 mg/dl promotes premature death around 5-8 months of age.However, the db/db model does not capture all the diabetic complications observed in the human disease.Vascular and retinal complications, for example, are rarely documented in db/db mice, likely because of the dramatically shortened lifespan.Notably, db/db mice on a C57BL/6J background exhibit only mild diabetic symptoms and a normal lifespan, despite marked obesity 78,79,195 ." + }, + { + "document_id": "7d5b12ef-7b17-4b49-8da2-1a4179601520", + "section_type": "main", + "text": "LEW.1AR1/Ztm-Iddm Rats\n\nIn this strain, type 1 diabetes develops at age 2 months as result of immune damage caused by heavy infiltration of the islets of Langerhans by B and T lymphocytes, macrophages and NK cells and beta cell destruction by apoptosis [85][86][87].The mutation in this strain resides in the Dock8 gene, which encodes a member of the DOCK180 protein superfamily of guanine nucleotide exchange factors that act as activators of Rac/Rho family GTPases [88]." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted.\n\n Fig. 8." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "\n\nALS/Lt mouse: Alloxan susceptible (ALS) new mouse model is produced by inbreeding outbred CD-1 mice (a commercial stock of ICR mice from which inbred NSY and NON mouse are developed), with selection for susceptibility to alloxan (ALX), a generator of highly reactive oxygen free radicals and a potent betacell toxin.Initially, the type 2 diabetes predisposition of ALS mouse was recognized by congenic analysis of the yellow mutation (Ay) at the agouti locus on chromosome 2. Indeed, in ALS/Lt (a substrain maintained at Jackson Laboratory, Bar Habor) mice, hyperinsulinaemia and impaired glucose tolerance develop spontaneously between 6 and 8 wk of age in alloxan-untreated males.This mouse model with reduced ability to diffuse free radical stress is of obvious interest because free radical-mediated damage is implicated in the pathogenesis and complications of both type 1 and type 2 diabetes 62 ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "I n the latter three,\nbody weights were stabilized at that seen when treatment was initiated. However, no actual weight losses\nwere seen and the relative obesity of these mice was\nstill apparent.\n Discussion\nThe marked tendency to obesity,\nactivities of several insulin-dependent\nthe degranulation of fl-cells of the islets\nobserved in the younger diabetic mice\n\nthe increased\nenzymes, and\nof Langerhans\nare quite con-\nVol. 3, 2Vo. 2, 1967\n\nD.L. COLEMAXand K.P. I-IuMM]~L:Studies with the Mutation, Diabetes\n\nsistent with the increased levels of circulating insulin\nfound in these mice." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "\n\nRodent models of diabetic retinopathy iii)" + } + ], + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db/db&mice", + "diabetes", + "onset", + "age", + "obesity", + "hyperglycemia", + "C57BL/KsJ", + "C57BL/6J", + "insulin&resistance", + "albuminuria" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data suggest that circulating IGF-1 levels are higher, insulin resistance is worse, and lean mass is higher in mice with obesity induced at earlier age modeling peripubertal-onset obesity as compared to older mice modeling adult-onset obesity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab205540" + }, + { + "object": "We used young, leptin receptor deficient Db/Db mice to mimic the effect of diet and diabetes on adolescents. Db/Db and Control mice were fed either Western or Control diets, and were sacrificed at 3 months of age. Db/Db mice were obese, while only female mice developed diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014541" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "The mean age of Parkinsonism onset among LRRK2 G2385R carriers was 42.7 years old for early-onset compared to 74.3 for late-onset patients. LRRK2 G2385R mutation appears to be as prevalent among early-onset as late-onset patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab833283" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Galectin-3 and S100A9 are overexpressed in Pancreatic cancer-associated diabetes tumors and mediate insulin resistance. Galectin-3 and S100A9 distinguish Pancreatic cancer-associated diabetes from type 2 diabetes in subjects with new-onset diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab310949" + }, + { + "object": "This study investigates the involvement of a 14-bp deletion polymorphism rs371194629 at the 3' untranslated region of HLA-G in the context of T1DM and age of onset.the deletion/deletion DEL/DEL genotype was found to be associated with an early age of onset P = 0.001, while the presence of the insertion allele INS was associated to a later age of onset of type I diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab944007" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + } + ], + "question": "What is the age range in mouse for the onset of diabetes?", + "subquestions": null, + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "usage": { + "chatgpt": 6475, + "gpt-4": 4255, + "gpt-4-turbo-preview": 3283 + }, + "user_id": 2 + }, + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9" +}
\ No newline at end of file diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_02.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_02.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_03.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_03.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_03.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_04.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_04.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_04.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_05.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_05.json new file mode 100644 index 0000000..98933ac --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_05.json @@ -0,0 +1,390 @@ +{ + "created_at": "2024-04-18T18:24:11.260410Z", + "data": { + "amplify": false, + "answer": "Yes, there are phenotypes and genotypes associated with early onset diabetes in mice. The db/db mouse strain, which carries the diabetes (db) mutation, is one such genotype. These mice develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, which is an associated phenotype. The C57BL/Ks strain also exhibits early onset diabetes. The severity of the diabetes condition in these mice depends on the interaction of the mutant gene with the inbred background.", + "context": { + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain. Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + } + ] + }, + "data_source": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33].\n Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "section_type": "main", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain.\n Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "abstract", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "main", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease.\n Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths.\n After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "main", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Better Mouse Models. A key point to bear in mind in assessing the usefulness of mouse models is the relative plasticity displayed by rodents faced with gene deletions.Thus, differences between the penetrance of mutations in human genes linked to monogenic forms of diabetes, including maturity onset diabetes of the young (MODY), between humans and mice, are usually observed [114] with the mouse equivalents showing far less marked disturbances in glycemia or changes which are seen only after deletion of both alleles.This clearly reflects the limitations of the use of mice (weight ∼25 g, life expectancy ∼3 years) for comparisons with human subjects.Nonetheless, and although the phenotypes of the above murine models are thus often more subtle than the human counterparts, they remain useful models for the study of diabetes, allowing single-targeted gene deletions which are impossible in man.For example, human populations with different genetic backgrounds have different susceptibility to the R235W ZnT8 polymorphism.We should not, therefore, find surprising the results that different genetic backgrounds and different diet reveal different phenotypes in ZnT8 knockout models." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "f54c42a7-cba6-4d2c-b5a1-484d3ab107db", + "section_type": "abstract", + "text": "\nTo elucidate the genetic factors underlying non-insulindependent diabetes mellitus (NIDDM), we performed genomewide quantitative trait locus (QTL) analysis, using the Otsuka Long-Evans Tokushima Fatty (OLETF) rat.The OLETF rat is an excellent animal model of NIDDM because the features of the disease closely resemble human NIDDM.Genetic dissection with two kinds of F2 intercross progeny, from matings between the OLETF rat and non-diabetic control rats F344 or BN, allowed us to identify on Chromosome (Chr) 1 a major QTL associated with features of NIDDM that was common to both crosses.We also mapped two additional significant loci, on Chrs 7 and 14, in the (OLETF × F344)F 2 cross alone, and designated these three loci as Diabetes mellitus, OLETF type Dmo 1, Dmo2 and Dmo3 respectively.With regard to suggestive QTLs, we found loci on Chrs 10, 11, and 16 that were common to both crosses, as well as loci on Chrs 5 and 12 in the (OLETF × F344)F 2 cross and on Chrs 4 and 13 in the (OLETF × BN)F 2 cross.Our results showed that NIDDM in the OLETF rat is polygenic and demonstrated that different genetic backgrounds could affect ''fitness'' for QTLs and produce different phenotypic effects from the same locus. Microsatellite markers. Most markers were purchased from ResearchGenetics Inc.; some were synthesized here on the basis of information in public data bases and other reports (Du et al. 1996), and some were isolated directly in the manner described elsewhere (Bihoreau et al. 1997).Phenotyping.Measurements of body weight and oral glucose tolerance test (OGTT) were performed at 30 weeks of age.Each rat was not fed for 16 h before OGTT, and blood was taken (fasting glucose).Glucose solution (2g/kg body weight) was administered orally, and successively blood was collected at 30, 60, 90, 120 min (postprandial glucose).Plasma glucose was measured by a glucose oxidase method with Glucose-B Test Kit" + }, + { + "document_id": "e14d92cf-d1ff-4a75-beee-b3312defeffd", + "section_type": "main", + "text": "\n\nExperimental studies support epidemiological observations and have provided strong evidence for transmission of the obese and diabetic phenotype from parent to offspring through non-genetic mechanisms.Numerous studies in rodents have investigated the effects of maternal obesity obtained in response to high-fat (HF) only, or high-fat/high-sugar diet, before and/or throughout pregnancy and during lactation [32].Overnutrition and obesity in the F0 dam can also yield phenotypes in F2 and F3 generations [33,34].Despite the differences in diet composition, and length of maternal overnutrition, most of the studies showed increased offspring adiposity, insulin resistance, and finally development of poor glucose tolerance and T2D, which has been attributed to a combination of beta cell dysfunction [35] and insulin resistance [36][37][38].One must not forget that abnormalities in beta cell function are critical in defining the T2D risk, because T2D installs only when beta-cell function deteriorates and fails to compensate for insulin resistance in peripheral tissues [8].Prenatal and/or early postnatal exposure to undernutrition also causes increased adiposity and glucose intolerance/diabetes in the offspring (F1) [39,40] and reduction of the number and function of pancreatic islets [41].It also increased adiposity and glucose intolerance in the next (F2) generation [42,43].Moreover, if an undernutrition insult is sustained, there can be further propagation of metabolic phenotypes across many generations.When Wistar rats were subjected to 50% caloric restriction over 50 generations, offspring had fasting hyperinsulinemia, glucose intolerance, and increased adiposity.The impaired metabolic phenotype was not reversed by restoration of nutrition for two generations [44].In rat models of spontaneous diabetes, early beta cell alterations with decreased beta cell mass have been reported in fetuses from both spontaneously diabetic BB rats (T1D model) [45] and spontaneously diabetic GK rats (T2D model) [46].On evaluating the long-term consequences for the progeny in these models, IGT was observed in the offspring of mildly streptozotocin (STZ)-induced diabetic females due to lower insulin secretion in response to glucose, while insulin resistance was reported in the offspring of severely STZ-diabetic mothers [47][48][49].Glucose tolerance was also impaired in the offspring of normal mothers receiving glucose infusions during late gestation, and was associated with decreased glucose-induced insulin secretion [50].Since most of these models of diabetes in pregnancy have drawbacks (see discussion in [51]), we have proposed that embryo transfer experiments might represent a more relevant paradigm [52].When fertilized Wistar rat oocytes were transferred into diabetic GK female rats and the neonates were suckled by non-diabetic Wistar foster mothers, beta cell mass in the F1 offspring was decreased at fetal and adult ages, and impaired glucose tolerance was present at adult age (review in [51]).Control rats originating from Wistar oocyte transfer to normal Wistar females retained normal glucose tolerance.Therefore, maternal spontaneous diabetes shapes offspring beta cell mass and insulin secretion.Such a scenario is relevant to the GK rat model of spontaneous T2D [53] since the GK mothers are mildly hyperglycemic through their gestation and during the suckling period.This could represent one mechanism for initiation of pancreas programming in the F1 offspring of the first founders (F0), since the GK line is issued from intercrosses between females and males Wistar with borderline IGT but otherwise normal basal blood glucose level [53,54].This could also contribute to the lack of attenuation of the diabetic GK phenotype over time [53,54]." + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "abstract", + "text": "\nThe diabetes syndromes produced by the two single gene mutations, obese (ob), and diabetes (db) are identical when both genes are expressed on the same inbred background, whereas on different backgrounds the syndrome changes from a severeobesity, moderate-diabetes to a severe life-shortening diabetes.The same initial sequence of events occurs in both conditions.Increased secretion of insulin and hyperphagia is followed by moderate hyperglycaemia with a further compensatory increase in insulin secretion followed by an expansion of the beta-cell mass.On the BL/6 inbred background, hypertrophy and hyperplasia of the beta cells continues until hyperglycaemia is controlled, whereas on the BL/Ks background, beta cell expansion fails and islet atrophy occurs causing insulinopenia, marked hyperglycaemia, and severe diabetes.The data presented here suggest that hyperphagia, hyperinsulinaemia, or both, early in development trigger the abnormal sequence of metabolic events leading to the obesity-diabetes state.These primary events interact with unknown genetic modifiers to produce either a juvenile or maturity-onset type of diabetes.An understanding of the mode of action of these background modifiers influencing the severity of diabetes in mice should lead to a better understanding of the ways in which unknown genetic and environmental factors contribute to human diabetes." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nBecause hyperglycemia was detected in only a few animals in the colony of origin, and segregation in the early inbreeding experiments was consistent with a single recessive locus, it is conceivable that the hyperglycemia in TH mice is caused by a spontaneously arisen single gene mutation.However, in genetic crosses, a complex inheritance pattern emerges with multiple interacting genes determining the trait and susceptibility loci being contributed from both parental strains.This phenomenon has been observed in both the analysis of single gene obesity mutations (Suto et al., 1998;Leiter et al., 1999) and the analysis of polygenic obesity and diabetes (West et al., 1994;Leiter et al., 1998).This suggests that single gene mutations and QTLs affecting diabetes can manifest similarly and are equally challenging to study." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe diabetes syndromes produced by the two single gene mutations, obese (ob), and diabetes (db) are identical when both genes are expressed on the same inbred background, whereas on different backgrounds the syndrome changes from a severeobesity, moderate-diabetes to a severe life-shortening diabetes.The same initial sequence of events occurs in both conditions.Increased secretion of insulin and hyperphagia is followed by moderate hyperglycaemia with a further compensatory increase in insulin secretion followed by an expansion of the beta-cell mass.On the BL/6 inbred background, hypertrophy and hyperplasia of the beta cells continues until hyperglycaemia is controlled, whereas on the BL/Ks background, beta cell expansion fails and islet atrophy occurs causing insulinopenia, marked hyperglycaemia, and severe diabetes.The data presented here suggest that hyperphagia, hyperinsulinaemia, or both, early in development trigger the abnormal sequence of metabolic events leading to the obesity-diabetes state.These primary events interact with unknown genetic modifiers to produce either a juvenile or maturity-onset type of diabetes.An understanding of the mode of action of these background modifiers influencing the severity of diabetes in mice should lead to a better understanding of the ways in which unknown genetic and environmental factors contribute to human diabetes." + }, + { + "document_id": "39e48ed7-91ac-4062-b394-22606abe7e58", + "section_type": "main", + "text": "\n\nOur laboratory has modeled the genetics of obesityinduced type 2 diabetes in two mouse strains, diabetesresistant C57BL/6 (B6) mice and diabetes-susceptible BTBR T ?tf/J (BTBR) mice.When made morbidly obese by the leptin mutation (Lep ob/ob ), B6-ob/ob mice experience moderate and only transient hyperglycemia due to a large expansion of b-cell mass, resulting in a 20-50-fold increase in plasma insulin levels (Clee et al. 2005;Keller et al. 2008).In contrast, BTBR-ob/ob mice experience severe hyperglycemia due to a failure to increase their circulating insulin levels.An in vivo measure of cellular replication showed that B6-ob/ob mice experience an approximately threefold increase in islet cell proliferation, whereas BTBR-ob/ob mice do not increase islet cellular replication in response to obesity (Keller et al. 2008)." + }, + { + "document_id": "b3c2189b-270c-4b4a-9d40-cdc0dceebd9e", + "section_type": "main", + "text": "[PubMed: 1290452]\nPlum L, Kluge R, Giesen K, Altmuller J, Ortlepp JR, Joost HG. Type-2 diabetes-like hyperglycemia in\na backcross model of NZO and SJL mice: characterization of susceptibility locus on chromosome\n4 and its relationship with obesity. Diabetes. 2000; 49:1590–1596. [PubMed: 10969845]\n\nBrain Res. Author manuscript; available in PMC 2013 July 10.\n Boone et al.\n\n Page 9\n\nNIH-PA Author Manuscript\nNIH-PA Author Manuscript\nNIH-PA Author Manuscript\n\nRocha JL, Eisen EJ, Van Vleck LD, Pomp D. A large-sample QTL study in mice: II Body\ncomposition. Mamm Genome. 2004; 15:100–113. [PubMed: 15058381]\nSalinas A, Wilde JD, Maldve RE." + }, + { + "document_id": "c4c5c626-51f7-4b87-84a3-8323a9233ca1", + "section_type": "main", + "text": "\n\nMice homozygous for targeted disruption of the BLK gene have been generated and studied for 8 weeks with a focus on investigating the role of BLK in B-lymphocyte physiology (23).However, no phenotypes relevant to diabetes have been described for these mutants, and no phenotypic data are available with regard to responses to exposure to a diabetogenic environment such as a high-fat diet, or cross breeding with an insulinresistant strain.In light of our findings, further detailed studies are warranted to explore the phenotypes of global KO mice and/or  cell-specific knockouts, in the context of glucose homeostasis." + }, + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "section_type": "main", + "text": ", 2008) and specific genetic factors for predisposition to DN were\nrecently identified in several diabetic sibling studies (Bleyer et al. , 2008; Schelling et\nal.,2008; Tanaka et al. , 2005).\n Similar to humans, inbred strains of mice exhibit differences in their susceptibility to\ndiabetes, renal and cardiovascular diseases (Krolewski et al. , 1996). More recently,\ndifferential susceptibilities to DN have also been observed in well-defined strains of\n\n23" + }, + { + "document_id": "e14d92cf-d1ff-4a75-beee-b3312defeffd", + "section_type": "main", + "text": "\n\nThe heritability of the obese/diabetic paternal phenotype was confirmed by experimental approaches.Multiple animal studies have now demonstrated that offspring's metabolic phenotype is affected by paternal unbalanced diet.Female rats born to fathers on a HF diet had impaired pancreatic islet biology, insulin secretion and glucose tolerance in adulthood [105].The F1 offspring of male mice fed a HF diet exhibited the same obese phenotype as their fathers [99,106].The offspring metabolic phenotype can also be affected by paternal undernutrition.Male and female born to fathers fed a low protein and high sugar diet had increased hepatic expression of lipid biosynthetic genes [98].Offspring metabolic phenotype can also be affected by paternal diabetes.Paternal low-dose STZ-induced diabetes in mice was accompanied by insulitis and insulin secretion deficiency in their F1 offspring [107].Paternal T2D alone (i.e., without associated obesity) impairs early development of endocrine pancreas and adult tolerance du glucose in rat F1 offspring.This was previously suggested by our group using a spontaneous model of paternal T2D [46,108] (Figure 3).To our knowledge, the most comprehensive study to evaluate the transgenerational effects of paternal diabetes on offspring and the mechanisms that mediate these effects, has been provided by Wei et al. [109].Using a non-genetic diabetes mouse model (low dose of STZ combined to HF diet), this group showed that paternal diabetes did not alter body weight, fat mass, or energy intake in F1 offspring, but it induced fasting hyperglycemia, glucose intolerance and insulin insensitivity in the male offspring to an extent similar to that seen in their fathers.To determine the mechanisms of the glucose intolerance and insulin insensitivity observed in the F1 male offspring, Wei et al. performed genome-wide microarray analyses of their pancreatic islets.The expression of 402 genes was modified (97 up-regulated and 305 downregulated).A large proportion of these genes were related to insulin and glucose metabolism, including GTPase activity, GTP and ATP binding, sugar binding, and calcium binding.Wei et al. also found several differentially methylated loci in the F1 islets.The same group also asked whether the metabolic and epigenetic changes in the F1 generation can be passed to the next generation (F2 generation).For that purpose, they mated F1 diabetic males (F1-D) whose fathers were diabetic, with normal females, and then examined metabolic and epigenetic changes in their offspring (F2).The F2 generation also exhibited impaired glucose tolerance and decreased insulin sensitivity (but not fasting hyperglycemia).Examination of the methylation status for 10 regions distributed on different chromosomes that were most affected by paternal diabetes, showed that all of these regions were still significantly affected in the F2 generation.As the F1 animals received normal diet without any STZ treatment and their F2 offspring exhibited similar phenotypic and epigenetic changes, the observed effects of epigenetic inheritance are most likely attributable to the diabetes-associated physiological and metabolic conditions in F0 male founders." + }, + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "section_type": "main", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "Genetic Crosses\n\nHyperglycemic male TH (ՆF7) mice were mated to normal female C57BL/6J (B6) or CAST/Ei (CAST) mice.The resulting F1 hybrid female mice were backcrossed to hyperglycemic male TH mice, and the offspring were referred to as backcross 1 (BC1) animals.Only male BC1 mice were used for the genetic study, since female mice do not develop hyperglycemia.Plasma glucose and insulin levels (nonfasted), body weights, nasal-anal lengths, and five fat pad weights (inguinal, epidydimal, mesenteric, retroperitoneal, and subscapular fat pads) were measured as phenotypic traits." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "\n\nThese limitations support the increasing need of experimental systems to characterize the fundamental biological mechanisms responsible for diabetes inheritance and the function of risk genes.In the context of diabetes pathogenesis, in vitro systems are useful but often limited, in particular to assess glucose tolerance, insulin sensitivity, islet architecture and function and diabetes complications.The laboratory mouse provides a wide range of experimental models for diabetes gene discovery and for in vivo post-GWAS studies of diabetes that develops either spontaneously or following gene editing [5].The laboratory rat is also a powerful system to implement phenotyping methods required to record biological variables relevant to common chronic diseases.The rat is the preferred model to perform phenotyping procedures that are often technically challenging in mice or require the collection of large volumes of blood or organs.For these reasons, rat models of type 2 diabetes or hypertension have been successfully used to localise in the genome genes controlling endophenotypes relevant to these complex diseases.This review addresses strategies used to map the genetic determinants of physiological and molecular phenotypes relevant to type 2 diabetes pathogenesis and to characterize their biological function in vivo through examples derived from genetic and genomic research in the Goto-Kakizaki (GK) rat strain." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted.\n\n Fig. 8." + } + ], + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db", + "diabetes", + "C57BL/Ks", + "obesity", + "insulin", + "hyperglycaemia", + "beta&cells", + "mutation", + "C57BLKS/J", + "NZO" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "We identified 32 compound heterozygous mutations and 9 homozygous mutations in IL10 receptor subunit alpha and 1 homozygous mutation in IL10 receptor subunit beta. Among these mutations, 10 novel mutations were identified, and 6 pathogenic mutations had been previously described. In patients with IL10 receptor subunit alpha mutations, c.301C>T p.R101RW and c.537 G>A p.T179T were the most common mutations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007199" + }, + { + "object": "MicroRNA-26a miR-26a in pancreatic beta cells not only modulates insulin secretion and beta cell replication in an autocrine manner but also regulates peripheral insulin sensitivity in a paracrine manner through circulating exosomes. miR-26a is down-regulated in serum exosomes and islets of obese mice. miR-26a in beta cells alleviates obesity-induced insulin resistance and hyperinsulinemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab483374" + }, + { + "object": "Ten mutations were identified in five unrelated Chinese families and two sporadic patients with childhood, and adult hypophosphatasia including eight missense mutations and two frameshift mutations. Of which, four were novel: one frameshift mutation p.R138Pfsx45; three missense mutations p.C201R, p.V459A, p.C497S. No identical mutations and any other new ALPL mutations were found in unrelated 50 healthy controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab768168" + }, + { + "object": "Two patients harbored KRAS with codon 12 mutations; one harbored the gly12val mutation with a variation of leu597val in the BRAF exon 15 codon, the other harbored mutation in the BRAF exon 15 codon. One patient harbored a codon 117 mutation with a BRAF V600E mutation. The last patient harbored a NRAS exon 2 mutation with the GGT/GAT, V600G mutation in the BRAF exon 15 codon", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab978995" + }, + { + "object": "Our aim was to identify VHL gene mutations in Argentinian patients who fulfilled the clinical criteria for type 1 VHL disease and in patients with VHL-associated manifestations. VHL mutations were detected in 16/19 84.2% patients in Group 1 and included: gross deletions 4/16; nonsense mutations 6/16; frameshift mutations 4/16; missense mutations 1/16; and splicing mutations 1/16. Three mutations were novel.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab550929" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "WFS1 and GJB2 mutations were identified in eight of 74 cases of Low-Frequency Sensorineural Hearing Loss. Four cases had heterozygous WFS1 mutations; one had a heterozygous WFS1 mutation and a heterozygous GJB2 mutation; and three cases had biallelic GJB2 mutations. Three cases with WFS1 mutations were sporadic; two of them were confirmed to be caused by a de novo mutation based on the genetic analysis of their parents.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014986" + }, + { + "object": "Study revealed that the conserved HIF1alpha/PFKFB3 signaling pathway is activated by IAPP misfolded protein-driven stress in pancreatic beta-cells to trigger an adaptive protective metabolic response that slows beta-cell death at the expense of beta-cell function. This signaling pathway is activated in beta-cells in humans with type 2 diabetes providing a basis for slow beta-cell loss.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab748654" + }, + { + "object": "Study generated MafA and MafB double-knockout A0B0 mice in which MafB was specifically deleted from beta cells. As a result, the A0B0 mice became more vulnerable to diabetes under a high-fat diet treatment, with impaired islet formation and a decreased number of insulin+ beta cells because of increased beta-cell apoptosis, indicating MafB can take part in the maintenance of adult beta cells under certain pathologic...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab742544" + } + ], + "question": "Are there phenotypes or genotypes associated with early onset diabetes in mouse?", + "subquestions": null, + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "usage": { + "chatgpt": 8500, + "gpt-4": 4937, + "gpt-4-turbo-preview": 3964 + }, + "user_id": 2 + }, + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906" +}
\ No newline at end of file diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_06.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_06.json new file mode 100644 index 0000000..372a368 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_06.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:30:30.982190Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "The Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "section_type": "main", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "GENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nSummary of key literature on gene-environment interactions in obesity and type 2 diabetes" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "abstract", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nGene-nutrient or -dietary pattern interactions in the development of T2DM." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002).\n Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "Genes and enviromental factors in the development of type 2 diabetes\n\nThe susceptibility to the development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two groups of T2DM susceptibility genes.The substantial contribution of genetic factors to the development of diabetes has been known for many years.The important pieces of evidence for the role of genes are the results of twin studies showing higher concordance rate for T2DM among monozygotic twins (between 41% and 55%) in comparison to dizygotic twins (between 10% and 15%) [43,84].What is interesting, there are populations with extremely high prevalence of T2DM, for example Pima Indians, that can not be explained solely by environmental factors [117].Supporting evidence for the role of genes in development of T2DM include also familial clustering of diabetesrelated traits.It was shown that the level of insulin sensitivity in Caucasians is inherited and a low level is a poor prognostic factor that precedes the development of T2DM [68,69,115].Similar observations were published for other ethnic groups [9,36,60].Those facts underline the importance of genetic factors.However, it is well known that the incidence of T2DM is also associated with environmental factors.Increasing incidence of T2DM during the last few years with obvious links to lifestyle and diet points to the role of enviromental factors in the development of disease [80].The differences in the prevalence of T2DM in relative populations living in different geographical and cultural regions (for example Asians in Japan and USA) also support the role of non-genetic factors [27,125].The relations between genetic and eviromental factors in the development of T2DM may be complex.For instance, enviromental factors may be responsible for the initiation of b-cell damage or other metabolic abnormalities, while genes may regulate the rate of progression to overt diabetes.On the other hand, in some cases genetic factors may be nec-essary for environmental factors even to start processes leading to the development of the disease." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + }, + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "section_type": "main", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + }, + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "section_type": "main", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nWhy do we think GEIs cause type 2 diabetes?dTheevidence supporting the existence of gene-lifestyle interactions in type 2 diabetes comes primarily from 1) the pattern and distribution of diabetes across environmental settings and ethnic groups, 2) familybased intervention studies, in which response to interventions varies less between biologically related individuals than between unrelated individuals; and 3) animal studies in which genetic and environmental factors are experimentally manipulated to cause changes in the expression of metabolic phenotypes.A brief overview of pertinent literature from human studies is given below." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nEpidemiological studies have been the predominant source of literature on gene-lifestyle interactions in cardiovascular and metabolic disease.Dozens of casecontrol and cohort studies have been published since the late 1990s purporting to have identified gene-lifestyle interactions in type 2 diabetes or related quantitative metabolic traits.Until recently, however, most of these studies were small and often relied on imprecise estimates of environmental exposures and outcomes.These are prone to error and bias, and exposures may not be assessed at the time when they conveyed their effects; for example, the causative exposures may have occurred very early in life, perhaps even in utero.Moreover, the complexities of modeling interaction effects have forced geneticists to focus primarily on very simple models of interaction, whereas clinically relevant interaction effects likely involve multiple genetic and nongenetic biomarkers.In addition, barely a handful of studies have examined incident type 2 diabetes as an outcome, with most focusing on cross-sectional measures of glucose and others relying on analyses that include prevalent cases of diabetes; this may introduce labeling bias, where the recall of well-known diabetesassociated behaviors is less likely to be accurate in individuals recently diagnosed with disease than in those who have not been diagnosed with disease." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) has developed into a major public health concern.While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009).Lifestyle and behavioural factors play an important role in determining T2D risk.For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring (Ng et al, 2010).In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004).The pancreatic islets of Langerhans are of central importance in the development of T2D.Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet b-cells to regulate glucose homeostasis.b-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b;Tabak et al, 2009) and results in T2D (UKPDSG, 1995).The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007;Scott et al, 2007;Sladek et al, 2007;Zeggini et al, 2007Zeggini et al, , 2008) ) has placed the insulinproducing b-cells at centre stage.These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development.Most T2D risk genes identified in these genome-wide association studies (GWAS) affect b-cell mass and/or function (Florez, 2008).While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010).Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence.DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications.These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008;Park et al, 2008)." + } + ], + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [ + { + "object": "Data suggest that expression of Pparg can be regulated by dietary factors; expression of Pparg is down-regulated in preadipocytes by tannic acid, a form of tannins found in plant-based foods; Pparg appears to be a major factor in adipogenesis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab206776" + }, + { + "object": "Circulating adiponectin increased in obese physically active participants >/=180 min/week compared to non-physically active counterparts, indicating that physical activity may mediate baseline adiponectin levels irrespective of the fat mass regulatory effect.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141573" + }, + { + "object": "Upon stratifying the participants into tertiles by the Matsuda index, we observed an inhibitory relationship between the genetic risk score GRS and insulin secretion in low insulin sensitive but not in high insulin sensitive controls and treatment-naive Type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab985500" + }, + { + "object": "The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of physical activity in particular in those genetically predisposed to obesity.[Meta-analysis]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782259" + }, + { + "object": "Serum IGFBP-2 levels increase with age after the age of 50 years and evolve in parallel with insulin sensitivity. IGFBP-2 may therefore be a potential marker for insulin sensitivity. We further show that IGFBP-2 levels can predict mortality in this aging population. However, its predictive value for mortality can only be interpreted in relation to insulin sensitivity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699014" + }, + { + "object": "Our study validated the association between an FTO variant and BMI in Taiwanese individuals. In addition, individuals with TG and TT genotypes who were physically active had a decreased BMI. These results indicate that physical activity might be necessary to mitigate the deleterious effect of BMI among genetically susceptible Taiwanese individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab566865" + }, + { + "object": "Irrespective of the genetic defect, adenoviral delivery of C5 improved insulin sensitivity in both C5cont and C5def mice, indicating an insulin-sensitizing function of C5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab428686" + }, + { + "object": "there was a high heritability for IGF-I and IGFBP-3, but a low heritability for insulin secretion and insulin sensitivity in a group of elderly twins; in addition, study found a negative relationship between IGF-I and insulin sensitivity, which did not seem to be strongly genetically determined", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab887620" + }, + { + "object": "The authors showed that the expression of cysK is regulated by several genetic and environmental factors in addition to CysB: two genetic factors, OmpR and CysE, and lithium.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785160" + }, + { + "object": "Production of nitric oxide NO within eNOS-positive NGC neurons increases after environmental perturbations, indicating a role for eNOS/NO in modulating environmentally appropriate levels of GA. Inhibition of NO production causes dysregulated behavioral arousal after exposure to environmental perturbation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab615356" + } + ], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB" +}
\ No newline at end of file diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_07.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_07.json new file mode 100644 index 0000000..19e2777 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_07.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:34:34.295904Z", + "data": { + "amplify": false, + "answer": "MicroRNAs and long noncoding RNAs (lncRNAs) are involved in diabetes. MicroRNAs modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs. They can regulate pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism. They also hold potential as diagnostic biomarkers and possible drug-targets for regulation of dysfunctional cell responses. LncRNAs are implicated in complications associated with diabetes, such as diabetic retinopathy and diabetic nephropathy. They can regulate cell proliferation, viability, migration, and the expression of pathological genes via post-transcriptional mechanisms.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + } + ], + "18a35699-873a-4542-b35a-3a4a14edd628": [ + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + } + ], + "2dc80127-89ba-47be-9e94-d90c2105be8d": [ + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + } + ], + "34184c8d-b167-4ae8-bfce-01e18d78fe41": [ + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + } + ], + "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef": [ + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes. Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + } + ], + "6011e960-6a6e-47fe-94f2-2c21c224fd25": [ + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + } + ], + "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + } + ], + "80e1b2af-be79-4d9b-852f-46bf3e23c963": [ + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + } + ], + "88dde947-5255-40e1-92d5-afde089b517b": [ + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + } + ], + "d23e9456-8ee8-46e0-9870-18ff69965c28": [ + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + } + ], + "e66846a6-1546-481b-baae-a55fc524c8af": [ + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + } + ], + "ec62a4d9-2fe2-49b0-84d8-13b1597e2067": [ + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + } + ] + }, + "data_source": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "section_type": "main", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "section_type": "main", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + }, + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "section_type": "main", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + }, + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "section_type": "main", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "section_type": "main", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "main", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "abstract", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "section_type": "main", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + }, + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "section_type": "main", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes.\n Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "main", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + }, + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "section_type": "main", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "abstract", + "text": "\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + }, + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "section_type": "main", + "text": "\n\nDescription of some problems associated with diabetes and possible nanomedicine solutions." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "main", + "text": "Newly Identified Genes Relevant in the Progression of Diabetic Nephropathy\n\nThe cellular events such as increased flux of polyols and hexosamines; generation of AGEs; increased activity of PKC, transforming growth factor-β-Smad-MAPK (mitogen-activated protein kinase) pathway and GTP-binding proteins; G1 cell cycle arrest associated with altered expression of cyclin kinases and their inhibitors; and generation of ROS are responsible for a final outcome of increased synthesis and deposition of ECM.The ROS, whether mitochondrial or cell membrane-derived, are also responsible for the activation of the renin-angiotensin system that eventually contributes to glomerular hyperfiltration and subsequent renal fibrosis (fig. 1) [71].In addition to these macromolecules, newly identified genes, such as RSOR/MIOX, Tim44 and Rap1b, may also be an integral part of the hyperglycemia-induced cytosolic and mitochondrial processes that culminate in the development of diabetic nephropathy [48][49][50][51][52][53][54][55]." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + }, + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "section_type": "main", + "text": "Discussion\n\nAs is known, several mechanisms, mainly related to the dysfunction of the endothelium and smooth muscles, have been proposed in the aetiology of T2DMED.In this study, the four differentially expressed miRNAs may also be involved in the regulation of the endothelium and smooth muscle function based on a literature review.Numerous studies have reported their function in pathophysiological processes, such as cellular development, differentiation, and apoptosis, which are all essential mechanisms of T2DMED (Beaumont et al. 2014;Girard et al. 2008;Komatsu et al. 2014;Lee et al. 2012;Liu et al. 2008;Shan et al. 2010;Sweetman et al. 2006).Importantly, miR-206 may be involved in diabetes-associated complications by contributing to high glucose-mediated apoptosis (Shan et al. 2010), and miR-133a has anti-apoptosis effects (Xu et al. 2007).In addition, miR-133a and miR-206 are muscle-specific miRNAs (Chen et al. 2012;Liu et al. 2008) and thus could regulate muscular cell functions, such as the augmentation of smooth muscle contraction by miR-133a (Chiba et al. 2009).Additionally, miR-18a could also increase vascular smooth muscle cell differentiation (Kee et al. 2014)." + }, + { + "document_id": "230022b2-931e-42ab-b100-5e9776483d1a", + "section_type": "main", + "text": "| DISCUSSION\n\nThis study examined retinas from WT and diabetic SD male rats to investigate the changes in a variety of retinal transcripts as a result of diabetes using RNA-seq.We identified a total of 118 DEGs, of which 72 were up-regulated and 46 were down-regulated.We also found 66 GO terms and 41 KEGG pathways which were significantly enriched by GO and KEGG analysis.Top 10 most down-regulated and up-regulated genes are listed in Tables 3 and 4, and were confirmed by qRT-PCR showed in Figure 4. Asb15 gene is the most up-regulated one we identified and confirmed.Asb15 is a member of Asb gene family; the family has been reported to be involved in cell proliferation and differentiation (Hancock et al., 1991;Kohroki et al., 2001;Liu et al., 2003).The presence of both Ankyrin repeat and suppressors of cytokine signaling (SOCS) box motifs are characters of members of Asb gene family (McDaneld, Hancock, & Moody, 2004).Member of SOCS family plays important roles in the negative regulation of signaling pathways (Kile & Alexander, 2001;Zhang et al., 2001).SOCS3 acts as a regulator of inflammation through inhibiting JAK/STAT pathway (Tamiya, Kashiwagi, & Takahashi, 2011).Down-regulating SOCS3-STAT3 can alleviate DR (Chen, Lv, & Gan, 2017;Jiang, Thaksan, & Bheemreddy, 2014;Ye & Steinle, 2015).Ladinin-1(Lad1), a largely uncharacterized protein to date, was found to be related to the proliferation and migration of breast cancer cells (Roth, Srivastava, & Lindzen, 2018).Cell proliferation and migration are processes of neovascularization.Neovascularization is the sign of PDR, which can lead to serious vision loss of patients.Fibroblast growth factor 2 (Fgf2) is a member of fibroblast growth factors (FGFs) family.FGFs and their receptors have important roles in cell proliferation, migration, differentiation, and survival (Saichaemchan, Ariyawutyakorn, & Varella-Garcia, 2016).FGF2 was found overexpression in the early stage of DR, and it can destroy the blood-retinal barrier (Yang et al., 2018).Hemoglobin alpha adult chain 1 (Hba-a1) is one of the hemoglobin genes.Hemoglobin plays an important role in neuronal respiration, oxidative stress, and response to injury (He et al., 2010;Poh, Yeo, Stohler, & Ong, 2012;Richter, Meurers, Zhu, Medvedeva, & Chesselet, 2009).Neuronal respiration is an important life activity of neuronal cells.Neurological injury is one of the performances of DR.Inositol monophosphatase domain containing 1 (Impad1) encodes gPAPP, which is a Golgi-resident nucleotide phosphatase that hydrolyzes phosphoadenosine phosphate (PAP), the by-product of sulfotransferase reactions, to AMP.AMP-activated protein kinase (AMPK) signaling pathway plays vital roles in the diabetes-induced retinal inflammation (Kubota, Ozawa, & Kurihara, 2011).RT1-Bb, RT1-Ba, belongs to RT1 complex, which is the major histocompatibility complex (MHC) of rat (Eberhard & Lutz, 2001).It is believed that the MHC region is vital because it plays an important role in diseases, such as autoimmune and infectious diseases, vascular diseases like DR, hematological and neurological diseases (John, 2005).Collagen type III alpha 1 chain (Col3a1) is a kind of type III collagen, mainly existing in the extracellular matrix.Lacking of type III collagen can destroy the structure of connective tissues (Cortini et al., 2017).According to previous researches, it is associated with the aneurysm.Retinal microaneurysm is the early performance of DR.Col3a1 was also found significantly changed in RNA-seq of human PDR fibrovascular membranes (Lam et al., 2017).αA-crystallin (Cryga) and αF-crystallin (Crygf) are members of crystallins, which were involved in different functions in various tissues (Clayton, Jeanny, Bower, & Errington, 1986;Head, Peter, & Clayton, 1991;Smolich, Tarkington, Saha, & Grainger, 1994).Knockout of αA-crystallin can inhibit ocular neovascularization (Xu, Bai, & Huang, 2015).More and more evidence indicated that inflammation (Adamis, 2002;Gologorsky, Thanos, & Vavvas, 2012) and neovascularization (Gardner & Davila, 2017;Nguyen et al., 2018) are important in the pathogenesis of DR.The results of the KEGG pathway significant enrichment analysis revealed two most enrichment items-cell adhesion molecules (CAMs) and PI3K-Akt signaling pathway.CAMs are proteins located on cell surface; the binding of CAMs to their receptors is important in the mediation of inflammatory and immune reactions (Golias et al., 2007).Previous studies have suggested that CAMs are important in the development of DR (Khalfaoui et al., 2009;Ugurlu et al., 2013) of insulin and is associated with DR neovascularization (Qin, Zhang, & Xu, 2015;Sasore, Reynolds, & Kennedy, 2014)." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "abstract", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "72aa5d47-336b-4e4f-8593-ee215b8891d2", + "section_type": "main", + "text": "\n\nThe current study takes an important first step towards this goal by identifying specific sets of genes whose expression accurately classifies patient samples with regard to diabetic neuropathy progression and by analysing their interactions within known cellular pathways.Identifying common elements in these complex networks will yield novel insights into disease pathogenesis, provide new therapeutic targets and identify potential diabetic neuropathy biomarkers.The genes identified in the current study confirm data gathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in a single human tissue sample." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "\n\nFurthermore, the alpha kinase 1 gene (ALPK1) identified as a susceptibility gene for chronic kidney disease by GWAS [202] , was demonstrated in type 2 diabetes patients [203] .Three additional genes have been strongly correlated with this risk of diabetic retinopathy (DR) including the vascular endothelial growth receptor, aldose reductase and the receptor for advanced glycation products genes [204] where specific polymorphisms in these genes seem to increase the risk of DR development in diabetes patients [204] .A significant differential proteome (involving 56 out of 252 proteins) is evident that characterizes vitreous samples obtained from diabetes patients with the complication in comparison to diabetes patients without the complication and control individuals [205] .Interestingly, a large portion of these proteins (30 proteins) belong to the kallikrein-kinin, coagulation and complement systems including complement C3, complement factor 1, prothrombin, alpha-1antitrypsin and antithrombin III that are elevated in diabetic patients with retinopathy [205] .In addition, 2 single nucleotides polymorphisms in the human related B7-I gene seem to mediate podocyte injury in diabetic nephropathy [206] .Furthermore, increased concentration of the ligand of B7-1 correlates with the progression of end-stage renal disease (ESRD) in diabetes patients [206] .These results indicate that B7-I inhibition may serve as a potential target for diabetes nephropathy prevention and/or treatment.Recently, it was shown that direct correlation is evident between circulating levels of tumor necrosis factors 1 and 2 and increased risk of ESRD in American Indian patients [207] .The link between diabetes and proper bone development and health is evident.Studies using animal models with major significant reduction in insulin receptor (IR) in osteoprogenitor cells resulted in thin and rod-like weak bones with high risk of fractures [208] .Similar findings were observed in animal models with bone-specific IR knockdown animals which points to the central role of IR in the proper development of bones [208] .Type 2 diabetes is also associated with mitochondrial dysfunction in adipose tissues.Using knockout animal models of specific mitochondrial genes led to significant reduction in key electron transport complexes expression and eventually adipocytes death [209] .These animals exhibited Insulin resistance in addition to other complications that can potentially lead to cardiovascular disease [209] ." + }, + { + "document_id": "41fc22ce-f0dc-4d81-a2b5-14c563c7c767", + "section_type": "main", + "text": "Metabolism:\nA novel shared link between diabetes mellitus and Alzheimer’s disease. J. Diabetes\nRes. 2020:4981814. doi: 10.1155/2020/4981814\n\nLiu, C., Hu, J., Zhao, N., Wang, J., Wang, N., Cirrito, J. R., et al. (2017).\n Astrocytic LRP1 mediates brain abeta clearance and impacts amyloid deposition.\n J. Neurosci. 37, 4023–4031. doi: 10.1523/JNEUROSCI.3442-16.2017\n\nWainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A., Knowles,\nD., Golan, D., et al. (2019). Opportunities and challenges for transcriptome-wide\nassociation studies. Nat. Genet. 51, 592–599. doi: 10.1038/s41588-019-0385-z\n\nLiu, Q., Trotter, J., Zhang, J., Peters, M. M., Cheng, H., Bao, J., et al. (2010)." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "\n\nI should underscore the fact that this discussion has been a simplified review of the relationships among glycemia, the RAS, histopathologic change, and the genetics of diabetic nephropathy, but its simplification allows us to underscore certain principles.In the redundant path of this biology, angiotensin II stimulates and interacts with a large number of other molecules.These are just a few of the major ones: glut-1, tumor necrosis factora (TNF-a), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), basic fibroblast growth factor (bFGF), insulin-like growth factor-1 (IGF-1), advanced glycosylation end products (AGEs) (pentosidine), reactive oxygen species (ROS), oxidized low-density lipoprotein (LDL), vascular cell adhesion molecule (VCAM-1), osteopontin, NF-jB, RANTES (particularly in glomerular endothelial cells), and monocyte chemotactic protein (MCP).In closing, I'd like to leave you with the top 10 principles detailed by this discussion: (1) signaling systems, with their complexity and redundancy, are systems of great beauty, reflective of evolutionary order; (2) differentiated biologic tissues often use the same tools to achieve tissue-specific functions and express tissue-specific pathology; (3) diabetic nephropathy reflects cellular injury due to common biologic pathways manifested in different cell types/regions of the kidney; (4) the kidney's susceptibility to glomerulosclerosis and tubulointerstital fibrosis reflects the impact of the renal RAS and its interactions with other profibrotic molecular pathways; (5) defining these interactions and the downstream signaling mechanisms mediating them lays the foundation for discovering needed therapies beyond glycemic control and angiotensin II inhibition for the treatment of diabetic nephropathy; (6) signaling pathways downstream of angiotensin II represent prime targets for additional therapeutic interventions; (7) hypothesis-driven basic research on individual pathways has (and likely will continue to) shed light on the complexities of the pathologic interactions and the redundancies in the systems; (8) candidate gene studies are the genetic analogues of this type of hypothesis-driven basic research; (9) microarray and genomic scanning coupled with informatics technology offer the possibility of modeling these complex system interactions and hopefully will allow us to identify optimal targets for inhibition and/or up-regulation that can prevent progression and restore structure and function; and (10) given the redundancy and convergence of these pathways, the challenge will be in graded inhibition that will preserve salutary pathways, but inhibit deleterious ones." + }, + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "section_type": "main", + "text": "\n\nIn this article, we identify genes whose expression responds differently to glucose in cells derived from T1D individuals with and without diabetic retinopathy.We show that one of these genes, folliculin (FLCN), is causally implicated in diabetic retinopathy based on results from genetic association testing and Mendelian randomization." + }, + { + "document_id": "e8dd8ca2-6fab-4acd-9b29-4e8583365d6d", + "section_type": "main", + "text": "Discussion\n\nRecent studies suggest inflammation to be an essential component of type 2 DM and its complications.We measured hs-CRP as a marker of inflammation in our diabetic cohort and found its levels to be significantly higher in diabetic patients as compared to controls and in nephropathy group as compared to diabetic subjects without nephropathy indicating inflammation to be a relevant factor in the pathogenesis of DN.Our results are consistent with an earlier study which has also reported increased hs-CRP levels in diabetics with proteinuria [18].Different inflammatory molecules, including pro-inflammatory cytokines have been proposed as critical factors in the development of microvascular diabetic complications, including nephropathy [19].It has been suggested that genetic variations in the genes encoding the inflammatory cytokines might confer susceptibility to DN by altering the function and/or expression of these cytokines.We investigated the association of genetic polymorphism(s) in inflammatory genes with the risk of diabetic nephropathy and whether co-occurrence of risk conferring variants of inflammatory genes were associated with increased risk of diabetic nephropathy in Asian Indian type 2 diabetic subjects.The key finding of our study was that polymorphisms in IL8, CCL2, CCR5, and MMP9 genes were associated with increased risk of nephropathy in Asian Indian type 2 diabetics and co-occurrence of specific risk genotypes of these genes conferred several fold greater risk of diabetic nephropathy." + }, + { + "document_id": "0951ba9d-bb8f-424b-b63f-16d94cb7166c", + "section_type": "main", + "text": "Page 43\n\nAuthor Manuscript\nAuthor Manuscript\nFig. 2 |. Main signalling pathways that regulate cardiac remodelling in the diabetic heart.\n\n Author Manuscript\nAuthor Manuscript\n\nThe systemic glucotoxicity (as a result of increased production of advanced glycation end\nproducts (AGEs)), lipotoxicity and angiotensin II (Ang II) production associated with type 2\ndiabetes mellitus induce the generation of reactive oxygen species (ROS) and reactive\nnitrogen species (RNS) by endothelial cells, resulting in decreased nitric oxide (NO)\nbioavailability." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "abstract", + "text": "\nInsight into the molecular mechanisms that underlie the origin and progression of diabetic nephropathy remains limited in part because conventional research tools have restricted investigators to focus on single genes or isolated pathways.Microarray technologies provide opportunities for evaluating genetic factors and environmental effects at a genomic scale during the pathogenesis of diabetic nephropathy.Despite" + }, + { + "document_id": "230022b2-931e-42ab-b100-5e9776483d1a", + "section_type": "main", + "text": "Background:\n\nThe aim of this research was to investigate the retinal transcriptome changes in long-term streptozotocin (STZ)-induced rats' retinas using RNA sequencing (RNA-seq), to explore the molecular mechanisms of diabetic retinopathy (DR), and to identify novel targets for the treatment of DR by comparing the gene expression profile we obtained.Methods: In this study, 6 healthy male SD rats were randomly divided into wildtype (WT) group and streptozotocin (STZ)-induced group, 3 rats each group.After 6 months, 3 normal retina samples and 3 DM retina samples (2 retinas from the same rat were considered as 1 sample) were tested and differentially expressed genes (DEGs) were measured by RNA-seq technology.Then, we did Gene Ontology (GO) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and validated the results of RNA-seq through qRT-PCR.Results: A total of 118 DEGs were identified, of which 72 were up-regulated and 46 were down-regulated.The enriched GO terms showed that 3 most significant enrichment terms were binding (molecular function), cell part (cellular component), and biological regulation (biological process).The results of the KEGG pathway analysis revealed a significant enrichment in cell adhesion molecules, PI3K-Akt signaling pathway, and allograft rejection, etc. Conclusion: Our research has identified specific DEGs and also speculated their potential functions, which will provide novel targets to explore the molecular mechanisms of DR." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "Types of biomarkers include clinical, biochemical factors and molecular markers. Examples relevant to diabetic retinopathy include clinical factors (e.g.diabetes duration, obesity, smoking, ETDRS score, electroretinogram (ERGs) assessment; biochemical factors (e.g.HbA1c, lipoprotein related factors); and molecular factors (such as the results of GWAS analyses and miRNA profiles (discussed below).Cytokines, growth factors and/or hormones have been widely used, such as the case with adiponectin as an adipocyte-derived hormone that regulates glucose and lipid metabolism.Adiponectin has been shown to be significantly higher in T1D patients with severe diabetic retinopathy than in those without, even after adjustment for occurrence of microalbuminuria (Hadjadj et al., 2005).As retinopathy has multiple risk factors it is likely, as is increasingly used for cardiovascular disease and suggested for diabetic nephropathy (Elley et al., 2010;van Dieren et al., 2011;Vergouwe et al., 2010), and more recently for retinopathy (Harris Nwanyanwu et al., 2013) from genetic data (Sandholm et al., 2012;Williams et al., 2012).In terms of genetic association the diabetic retinopathy field is less advanced than that for nephropathy, although there have been a number of worthwhile studies (reviewed by (Kuo et al., 2014)).A genome-wide association study for diabetic retinopathy identified an association with a long intergenic non-coding RNA (LincRNA) sequence.LincRNAs are non-protein coding transcripts (>200 nucleotides in length) and the sequence called RP1-90L14 (adjacent to the CEP162 gene) has shown susceptibility to diabetic retinopathy (Awata et al., 2014).Interestingly, other LincRNAs are also being studied for their association with diabetic retinopathy such as MALAT1 (Yan et al., 2014) and MIAT (Yan et al., 2015).While some interesting leads are emerging, as yet there is no robust indication that diabetic retinopathy has a significant genetic component.Candidate gene and genome-wide studies may yet find genetic linkage to particular retinopathy phenotypes in T1D and T2D although both diabetes-types will need to be assessed separately in view of their distinct genetic architecture." + }, + { + "document_id": "72aa5d47-336b-4e4f-8593-ee215b8891d2", + "section_type": "main", + "text": "\n\nWe hypothesize that the genes identified in our classification models (Table 5) represent products or 'genetic biomarkers' of the biological networks involved in diabetic neuropathy onset and progression.This idea is reinforced by the fact that several of the genes have known associations with diabetes or diabetic complications.We are particularly interested in CST1, whose expression was increased by 10-fold in progressors.CST1, encoding a cysteine protease inhibitor, was initially implicated in gastric and colorectal tumourigenesis (Choi et al., 2009;Yoneda et al., 2009).Another member of this protein family, cystatin C (CST3), has been identified as a prime predictor of diabetic nephropathy progression (Shimizu et al., 2003;Taglieri et al., 2009).Although the CST1 gene product has not been investigated in the context of diabetic complications, it is detectable in saliva, tears and urine (Choi et al., 2009).To date, there are no definitive biomarkers of diabetic neuropathy progression easily accessed from body fluids, and we speculate that CST1 could prove to be an easily measureable biomarker for diabetic neuropathy." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "In vivo relevance\n\nWhat is the evidence that these pathways are relevant in vivo?In rats with streptozotocin-induced diabetes, glomerular 12/15-LO mRNA and protein were upregulated 1, 2, 3, and 4 months after diabetes induction as demonstrated by reverse transcription-polymerase chain reaction (RT-PCR) and by Western analysis and immunohistochemistry, respectively [14].Upstream of p38 MAPK is the signaling molecule MKK3/6, which is activated during the first 2 months in diabetic rats compared to controls [14].A similar pattern was observed for phospho-p38 MAPK and phospho-CREB.At 4 months, mesangial (and, parenthetically, podocyte) fibronectin accretion was increased; this phenomenon presumably contributes to mesangial expansion [14].I will loosely refer to this change as glomerulosclerosis.Thus, in diabetic rats, just as in mesangial cells and VSMCs in vitro, angiotensin II and high ambient glucose concentration activate a novel lipid-mediating signal transduction pathway, and in conjunction with MAPKs and transcription factors, lead to fibronectin synthesis; this process then accelerates renal disease." + }, + { + "document_id": "8f6c3be4-4598-4ae2-a7a8-8ea5a7a52794", + "section_type": "main", + "text": "Wnt signaling in diabetic nephropathy\n\nThe potential relevance of Wnt signaling in advanced DN was investigated in more detail.Mapping the respective genes found by each approach onto the canonical Wnt pathway was performed (KEGG [13] and Biocarta databases (BioCarta Pathways; http:// www.biocarta.com/genes/index.asp)).As shown in Fig. 4, and in line with previous findings, the CI-analysis identified a much larger fraction of the pathway as regulated than did the RMA analysis (23 versus 15 out of 27 genes, see Table S3 and Table S4).The potential downstream effects of this pathway on known Wnt target genes were then examined.Of the known Wnt target genes regulated on the microarray 15 of 15 were identified by CI while RMA identified 10 (Fig. 4 and Table S4).Matrix metalloproteinase 7 (MMP7) [14] showed the highest fold-change in Wnt-associated genes and was confirmed by RT-PCR on the cDNA used for the array analysis (DN 40.09623.88,LD: 1.061.73(p,0.05)) as well as on an independent cohort of patients with DN (DN: 6.4566.62;LD: 1.0060.79(p,0.05)) (Fig. 5a).The induction of MMP7 protein was verified by immunohistochemistry: MMP7 protein expression was strongly increased in the tubulo-interstitial compartment of patients with DN (Fig. 2 and Fig. 5b,c)" + }, + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "section_type": "main", + "text": "\n\nIn the past, many scientific studies were focused on ED in type 1 DM (Chitaley et al. 2009).However, there are more complicated but less comprehensive mechanisms in T2DMED (Chitaley 2009).The potential underlying mechanisms include hypogonadism, vascular dysfunction, veno-occlusive disorders, and others (Hidalgo-Tamola and Chitaley 2009).Some mechanisms, such as non-adrenergic and non-cholinergic dysfunction, are still debated in the pathogenesis of T2DMED (Chitaley et al. 2009).To our knowledge, only a few studies regarding of miRNA expression or function in DMED have been reported.Recently, miRNA expression was investigated in a murine model with vasculogenic ED induced by a long-term high fat diet (Barbery et al. 2015).Though accompanied with impaired glucose tolerance, this animal model could not fully represent the pathogenic processes of DMED.Instead, a classical genetic modified murine model with T2DMED was used in the present study, to investigate differentially expressed microRNAs.The bioinformatic analyses of differentially expressed miRNAs were further performed to detect whether these miRNAs played potential roles in the mechanisms of T2DMED." + }, + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "section_type": "abstract", + "text": "\nGenetic variations in key inflammatory cytokines exacerbates the risk of diabetic nephropathy by influencing the gene expression.The address for the corresponding author was captured as affiliation for all authors.Please check if appropriate.Gene(2017)," + } + ], + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "microRNAs", + "lncRNAs", + "diabetic&nephropathy", + "diabetic&retinopathy", + "TGF-β1", + "angiogenesis", + "fibrosis", + "inflammation", + "hyperglycemia" + ], + "metadata": [ + { + "object": "in this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab403726" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Reporter assays reveal regulation by microRNA-339, microRNA-556, and, to a lesser extent, microRNA-10 and microRNA-199. MicroRNA-339 and microRNA-556 were further found to directly decrease Klotho protein expression in aging tissue.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab642566" + }, + { + "object": "after orthotopic lung transplantation, in the IL-17A KO group, less inflammation in the bronchovascular axis was observed and a non-significant trend towards less bronchovascular fibrosis, pleural/septal inflammation and fibrosis, and parenchymal inflammation and fibrosis when compared to WT mice", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab49527" + }, + { + "object": "*TFEB overexpression inhibits vascular inflammation in diabetic db/db mice. TFEB overexpression inhibits vascular inflammation in diabetic db/db mice .TFEB suppresses IKK activity to protect IkappaBalpha from degradation, thereby, inhibiting NF-kappaB p65 nuclear localization and attenuating vascular inflammation in endothelial cells of these mice. laminar shear stress induces TFEB through KLF2 which activates its pro...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab7633" + }, + { + "object": "Data suggest that urine AQP5/creatinine ratio is significantly higher in patients with diabetic nephropathy than in control subjects, subjects diabetes, or subjects with nephropathy of unknown etiology; urine AQP5/creatinine ratio increases with stage of diabetic nephropathy; this biomarker may improve clinical models in distinguishing diabetic nephropathy from normal controls and subjects with type 2 diabetic alone.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab213643" + }, + { + "object": "Angiogenesis and hepatic fibrosis are mutually stimulatory, such that fibrosis requires angiogenesis and angiogenesis requires angiopoietin 1 from activated HSCs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab72845" + }, + { + "object": "MicroRNA-625-3p was highly expressed in oral squamous cell carcinoma OSCC tissues. OSCC patients with T3+T4 stage had higher expression of microRNA-625-3p than those with T1+T2 stage. SCAI was identified as a target gene of microRNA-625-3p. ROC curve showed that microRNA-625-3p and SCAI exert certain values in diagnosing OSCC. MicroRNA-625-3p promoted migration of OSCC cells, which was reversed by SCAI knockdown.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab483708" + }, + { + "object": "Angiogenesis and hepatic fibrosis are mutually stimulatory, such that fibrosis requires angiogenesis and angiogenesis requires angiopoietin 1 from activated HSCs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab72844" + }, + { + "object": "Hyperglycemia could induce pathological angiogenesis in subcutaneous Matrigel of diabetic rats, and Ang-1 could upregulate the expression of intercellular junction protein in subcutaneous Matrigel of diabetic rats and promote the integrity of neovascularization in the subcutaneous Matrigel of diabetic rats", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367213" + } + ], + "question": "What non-coding RNAs are involved in diabetes, and what roles do they play?", + "subquestions": null, + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "usage": { + "chatgpt": 13184, + "gpt-4": 7924, + "gpt-4-turbo-preview": 6969 + }, + "user_id": 2 + }, + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333" +}
\ No newline at end of file diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_08.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_08.json new file mode 100644 index 0000000..560c7aa --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_08.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:36:24.689995Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "The Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "section_type": "main", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "GENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nSummary of key literature on gene-environment interactions in obesity and type 2 diabetes" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "abstract", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nGene-nutrient or -dietary pattern interactions in the development of T2DM." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002).\n Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "Genes and enviromental factors in the development of type 2 diabetes\n\nThe susceptibility to the development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two groups of T2DM susceptibility genes.The substantial contribution of genetic factors to the development of diabetes has been known for many years.The important pieces of evidence for the role of genes are the results of twin studies showing higher concordance rate for T2DM among monozygotic twins (between 41% and 55%) in comparison to dizygotic twins (between 10% and 15%) [43,84].What is interesting, there are populations with extremely high prevalence of T2DM, for example Pima Indians, that can not be explained solely by environmental factors [117].Supporting evidence for the role of genes in development of T2DM include also familial clustering of diabetesrelated traits.It was shown that the level of insulin sensitivity in Caucasians is inherited and a low level is a poor prognostic factor that precedes the development of T2DM [68,69,115].Similar observations were published for other ethnic groups [9,36,60].Those facts underline the importance of genetic factors.However, it is well known that the incidence of T2DM is also associated with environmental factors.Increasing incidence of T2DM during the last few years with obvious links to lifestyle and diet points to the role of enviromental factors in the development of disease [80].The differences in the prevalence of T2DM in relative populations living in different geographical and cultural regions (for example Asians in Japan and USA) also support the role of non-genetic factors [27,125].The relations between genetic and eviromental factors in the development of T2DM may be complex.For instance, enviromental factors may be responsible for the initiation of b-cell damage or other metabolic abnormalities, while genes may regulate the rate of progression to overt diabetes.On the other hand, in some cases genetic factors may be nec-essary for environmental factors even to start processes leading to the development of the disease." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + }, + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "section_type": "main", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + }, + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "section_type": "main", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nWhy do we think GEIs cause type 2 diabetes?dTheevidence supporting the existence of gene-lifestyle interactions in type 2 diabetes comes primarily from 1) the pattern and distribution of diabetes across environmental settings and ethnic groups, 2) familybased intervention studies, in which response to interventions varies less between biologically related individuals than between unrelated individuals; and 3) animal studies in which genetic and environmental factors are experimentally manipulated to cause changes in the expression of metabolic phenotypes.A brief overview of pertinent literature from human studies is given below." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nEpidemiological studies have been the predominant source of literature on gene-lifestyle interactions in cardiovascular and metabolic disease.Dozens of casecontrol and cohort studies have been published since the late 1990s purporting to have identified gene-lifestyle interactions in type 2 diabetes or related quantitative metabolic traits.Until recently, however, most of these studies were small and often relied on imprecise estimates of environmental exposures and outcomes.These are prone to error and bias, and exposures may not be assessed at the time when they conveyed their effects; for example, the causative exposures may have occurred very early in life, perhaps even in utero.Moreover, the complexities of modeling interaction effects have forced geneticists to focus primarily on very simple models of interaction, whereas clinically relevant interaction effects likely involve multiple genetic and nongenetic biomarkers.In addition, barely a handful of studies have examined incident type 2 diabetes as an outcome, with most focusing on cross-sectional measures of glucose and others relying on analyses that include prevalent cases of diabetes; this may introduce labeling bias, where the recall of well-known diabetesassociated behaviors is less likely to be accurate in individuals recently diagnosed with disease than in those who have not been diagnosed with disease." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) has developed into a major public health concern.While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009).Lifestyle and behavioural factors play an important role in determining T2D risk.For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring (Ng et al, 2010).In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004).The pancreatic islets of Langerhans are of central importance in the development of T2D.Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet b-cells to regulate glucose homeostasis.b-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b;Tabak et al, 2009) and results in T2D (UKPDSG, 1995).The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007;Scott et al, 2007;Sladek et al, 2007;Zeggini et al, 2007Zeggini et al, , 2008) ) has placed the insulinproducing b-cells at centre stage.These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development.Most T2D risk genes identified in these genome-wide association studies (GWAS) affect b-cell mass and/or function (Florez, 2008).While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010).Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence.DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications.These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008;Park et al, 2008)." + } + ], + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [ + { + "object": "Data suggest that expression of Pparg can be regulated by dietary factors; expression of Pparg is down-regulated in preadipocytes by tannic acid, a form of tannins found in plant-based foods; Pparg appears to be a major factor in adipogenesis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab206776" + }, + { + "object": "Circulating adiponectin increased in obese physically active participants >/=180 min/week compared to non-physically active counterparts, indicating that physical activity may mediate baseline adiponectin levels irrespective of the fat mass regulatory effect.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141573" + }, + { + "object": "Upon stratifying the participants into tertiles by the Matsuda index, we observed an inhibitory relationship between the genetic risk score GRS and insulin secretion in low insulin sensitive but not in high insulin sensitive controls and treatment-naive Type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab985500" + }, + { + "object": "The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of physical activity in particular in those genetically predisposed to obesity.[Meta-analysis]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782259" + }, + { + "object": "Serum IGFBP-2 levels increase with age after the age of 50 years and evolve in parallel with insulin sensitivity. IGFBP-2 may therefore be a potential marker for insulin sensitivity. We further show that IGFBP-2 levels can predict mortality in this aging population. However, its predictive value for mortality can only be interpreted in relation to insulin sensitivity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699014" + }, + { + "object": "Our study validated the association between an FTO variant and BMI in Taiwanese individuals. In addition, individuals with TG and TT genotypes who were physically active had a decreased BMI. These results indicate that physical activity might be necessary to mitigate the deleterious effect of BMI among genetically susceptible Taiwanese individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab566865" + }, + { + "object": "Irrespective of the genetic defect, adenoviral delivery of C5 improved insulin sensitivity in both C5cont and C5def mice, indicating an insulin-sensitizing function of C5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab428686" + }, + { + "object": "there was a high heritability for IGF-I and IGFBP-3, but a low heritability for insulin secretion and insulin sensitivity in a group of elderly twins; in addition, study found a negative relationship between IGF-I and insulin sensitivity, which did not seem to be strongly genetically determined", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab887620" + }, + { + "object": "The authors showed that the expression of cysK is regulated by several genetic and environmental factors in addition to CysB: two genetic factors, OmpR and CysE, and lithium.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785160" + }, + { + "object": "Production of nitric oxide NO within eNOS-positive NGC neurons increases after environmental perturbations, indicating a role for eNOS/NO in modulating environmentally appropriate levels of GA. Inhibition of NO production causes dysregulated behavioral arousal after exposure to environmental perturbation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab615356" + } + ], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "ADE056BF251274D15922CA6E7B5C3133", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "task_id": "ADE056BF251274D15922CA6E7B5C3133" +}
\ No newline at end of file diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_09.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_09.json new file mode 100644 index 0000000..0d3f71d --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_09.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-04-18T18:40:22.873037Z", + "data": { + "amplify": false, + "answer": "Yes, genetic predictors of diabetes complications can be identified. Studies have revealed several susceptibility loci for diabetic complications such as retinopathy and nephropathy. However, these genetic factors only explain a small proportion of the phenotypic variation observed in type 2 diabetes patients, indicating a need for the identification of more novel genetic risk factors.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "091ab13a-1b8a-4849-b698-48db7b1a948f": [ + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "3548bb7f-727c-4ccb-acc7-a97553b89992": [ + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + } + ], + "45cdaf79-d881-43e6-8555-ff47f04ae3d4": [ + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "a7bad429-5f6a-464f-a666-f9cb1be60338": [ + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + } + ], + "cf022812-00a2-42ba-88fb-5c2014c86c43": [ + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "section_type": "main", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "section_type": "abstract", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "section_type": "main", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "section_type": "main", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + }, + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "section_type": "main", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Results\n\nStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nTo date, however, the improvement in predictive value of known genetic variants over that of classic clinical risk factors (BMI, family history, glucose) has proven minimal in type 2 diabetes." + }, + { + "document_id": "553ae95d-0a2b-4f2a-8123-da9a9e9e7a77", + "section_type": "main", + "text": "\n\nTwo more recent population -based studies using a longitudinal design with prospectively investigated cohorts have examined the predictive value of a genotype score in addition to common risk factors for prediction of T2DM [194,195] .Meigs et al. [194] reported that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common clinical risk factors alone [195] .A similar conclusion was drawn in the paper by Lyssenko et al. [196] , along with an improved value of genetic factors with an increasing duration of follow -up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.They also showed that β -cell function adjusted for insulin resistance (using the disposition index) was the strongest predictor of future diabetes, although subjects in the prediabetic stage presented with many features of insulin resistance.It is also noteworthy that many of the variants that were genotyped appear to infl uence β -cell function.The addition of DNA data to the clinical model improved not only the discriminatory power, but also the reclassifi cation of the subjects into different risk strategies.Identifying subgroups of the population at substantially different risk of disease is important to target these subgroups of individuals with more effective preventative measures.As more genetic variants are now identifi ed, tests with better predictive performance should become available with a valuable addition to clinical practice." + }, + { + "document_id": "5782c1a9-6ab1-4c66-b1e6-116ac6a0e50b", + "section_type": "main", + "text": "\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "abstract", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "section_type": "main", + "text": "\n\nGenomic information associated with Type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "\n\nMajor consortia addressing the genetic basis of diabetes complications and associated traits" + }, + { + "document_id": "a5a0cd4f-8acf-4e89-9033-04f448dc0b15", + "section_type": "main", + "text": "CONCLUSIONS\n\nDuring the past several years, the identification of genetic risk factors for diabetic microvascular complications has improved.However, most of the studies were not fully powered for GWASs, with the exception of the GENIE study.Therefore, most of the results associated with the genetic risk factors were below the genome-wide significance threshold and inconsistent among studies.In addition, the definition of cases and controls differed, thereby introducing significant heterogeneity.Based on the findings reported, these genetic association results should be validated in other populations.In addition, a collaborative effort to harmonize phenotype definitions and to increase sample size is necessary." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nUntil recently, genome-wide linkage and candidate studies have been the main genetic epidemiological approaches to identifying the precise genetic variants underlying T2D heritability.These efforts confirmed only a few susceptibility variants, including those in PPARG, KCNJ11, WFS1, HNF1A, HNF1B, HNF4A, TCF7L2, and ADIPOQ (1,6,27,56,81,102).Recent genome-wide association studies (GWAS) have unveiled over 50 novel loci associated with T2D and more than 40 associated with T2D-related traits including fasting insulin, glucose, and proinsulin (16,48,57,82,87,97,105) (Table 1).Clinical investigations of some of the T2D loci, thus far, suggest that the genetic components of T2D risk act preferentially through β-cell function (20).This pattern may only be a function of case diagnostic criteria, which weigh heavily on parameters reflecting advanced stages of the disease.This notion is supported by the incomplete overlap of single-nucleotide polymorphisms (SNPs) contributing to variation in quantitative traits with those associated with overt T2D (20).With the exception of TCF7L2, most variants contribute modestly to T2D risk and together explain only a small proportion of the familial clustering of T2D, suggesting that many more loci await discovery (10,12,97)." + }, + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "section_type": "main", + "text": "\n\nGenome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "The future of type 1 diabetes genetics\n\nAfter more than two decades of work, type 1 diabetes is probably the best characterized of all common multigenic diseases.Thus far, the identified genetic risk factors have been plausible candidate genes with common variants that affect susceptibility.Of these, variation at HLA alone explains much of the risk to siblings (HLA provides a l s of 3.4 out of a total of 15, leaving a l s of 15/3.4 ¼ 4.4 to be explained), and INS and CTLA4 have also been identified as disease loci.What, then, is left to be done?First, many risk alleles remain undiscovered.Although their effect will be much weaker than is seen for HLA (and almost certainly weaker than for INS), they may identify genes or pathways that provide insight into etiology, pathogenesis, and perhaps even prevention or treatment.Each additional variant that is clearly proven to increase risk will also help to identify high-risk non-diabetic individuals who might participate in studies of prevention and, in turn, benefit from preventive interventions.These alleles might also be relevant to the genetics of diabetic complications (not discussed in this review), perhaps identifying patients who would benefit most from intensive treatment and monitoring." + }, + { + "document_id": "1ecd1047-39d1-44ea-b3a2-3d8472be3435", + "section_type": "main", + "text": "Genomic Analyses for Diabetes Risk\n\nGenes signifying increased risk for both type 1 and type 2 diabetes have been identified.Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes.Several T1D candidate genes for increased risk of developing type 1 diabetes have been suggested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12Also, several candidate genes for increased risk of developing type 2 diabetes have been identified, including peroxisome proliferatoractivated receptor gamma (PPARγ2), angiotensin converting enzyme (ACE), methylene tetrahydrofolate reductase (MTHR), fatty acid binding protein-2 (FABP2), and fat mass and obesity associated gene (FTO). 13he conclusions of a \"Workshop on Metformin Pharmacogenomics,\" sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, were published in 2014. 14The meeting was intended to review metformin pharmacogenomics and identify both novel targets and more effective agents for diabetes.The idea behind the meeting was that understanding the genes and pathways that determine the response to metformin has the potential to reveal new drug targets for the treatment of diabetes.The group noted that there have been few genes associated with glycemic control by metformin, and the most reproducible associations have been in metformin transporter genes.They acknowledged that nongenetic factors also contribute to response to metformin and that broader system biology approaches will be required to model the combined effects of multiple gene variants and their interaction with nongenetic factors.They concluded that the overall challenge to the field of precision medicine as it relates to antidiabetes treatment is to identify the individualized factors that can lead to improved glycemic control." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "Future prospects\n\nWhilst the examples above provide interesting insights, it is clear that we are only at the beginning of mining the information generated by genome-wide association studies for Type 2 diabetes and other complex traits.work in human genetics, involving ever larger cohorts, meta-analyses and the search for rarer and more penetrant variants will in future be important to identify all of the heritable elements that control Type 2 diabetes risk; however, the useful deployment of this information for either disease prediction or the development of new therapies will require considerable further efforts at the cellular and molecular level to understand the function of the identified genes.Moreover, and although not the subject of this particular review, actions of single nucleotide polymorphisms through non-coding genes, e.g.mi-croRNAs and long non-coding RNAs, will require deeper investigation." + }, + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "section_type": "abstract", + "text": "\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "genetic&predictors", + "diabetes&complications", + "GWAS", + "genome-wide&association&study", + "polygenic&score", + "susceptibility&loci", + "T2DM", + "genetic&variants", + "diabetic&neuropathy" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Serum levels of APN and AdipoR1 are significantly lower in type 2 diabetes mellitus T2DM group and T2DM + macrovascular complications MVC group, showing lowest value in T2DM + MVC group. APN and AdipoR1 levels may influence glucose and lipid metabolism in T2DM patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699512" + }, + { + "object": "this case control study showed that NET gene polymorphism G1287A, rs5569 was significantly associated with type 2 diabetes mellitus T2DM in North Indian male population where AG genotype and A allele was found to be protective against the risk of T2DM while the GG genotype and G allele were found to increase the risk of T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab928949" + }, + { + "object": "The results suggest that LEPR rs1327118 may be associated with elevated blood pressure and HDL-C levels in women with type 2 diabetes mellitus T2DM, and rs3806318 may be associated with T2DM and elevated blood pressure in men with T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab864916" + }, + { + "object": "of the five variants, SNP rs2236935T/C was significantly associated with type 2 diabetes mellitus T2DM in this study population; conclude that MAP4K4 gene is associated with T2DM in a Chinese Han population, and MAP4K4 gene variants may contribute to the risk toward the development of T2DM", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab545662" + }, + { + "object": "Study evaluated the associations between 6 SNPs in CDH13 and type 2 diabetes mellitus T2DM in a Han Chinese population. Results showed that the rs12596316 AG genotype was a risk genotype for the development of T2DM in the overdominant inheritance model; rs11646213, rs3865188, rs12444338, rs12051272, and rs7195409 had no observed associations with T2DM in terms of alleles, genotypes, and the various inheritance models.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab740648" + }, + { + "object": "data suggest a possible association of C332C-genotype of the glyoxalase 1 gene with diabetic neuropathy in type 2 diabetes, supporting the hypothesis that methylglyoxal might be an important mediator of diabetic neuropathy in type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab202777" + }, + { + "object": "Compared with normal glucose tolerance NGT groups, the PTEN mRNA expression was significantly higher in Uyghur patients with mild type 2 diabetes mellitus T2DM groups; PTEN protein expression was upregulated in Uyghur patients with mild T2DM groups. PTEN methylation in T2DM patients was significantly lower than that in NGT groups. 2 CpG units demonstrated a significant difference between NGT and Uyghur patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab151151" + }, + { + "object": "Haplotype-based interaction between the PPARGC1A and UCP1 genes is associated with impaired fasting glucose IFG or type 2 diabetes mellitus T2DM among the residents of Henan province, China. Individuals with the haplotype AAG PPARGC1A gene and CTCG UCP1 gene have increased susceptibility to IFG or T2DM, while those with haplotype AAG PPARGC1A gene and CTCA UCP1 gene have a lower risk of IFG or T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab332396" + } + ], + "question": "Can we identify genetic predictors of diabetes complications?", + "subquestions": null, + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "usage": { + "chatgpt": 6305, + "gpt-4": 3589, + "gpt-4-turbo-preview": 2668 + }, + "user_id": 2 + }, + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7" +}
\ No newline at end of file diff --git a/gnqa/data/study1/responses/diabetes/full_response/suga_resp_10.json b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_10.json new file mode 100644 index 0000000..c9762c0 --- /dev/null +++ b/gnqa/data/study1/responses/diabetes/full_response/suga_resp_10.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-04-18T18:45:53.040778Z", + "data": { + "amplify": false, + "answer": "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "context": { + "063a0254-1d1b-4caa-b782-6a1fe4ebca0d": [ + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + } + ], + "08858a32-d736-4d8d-a135-f86568152a81": [ + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + } + ], + "183f165e-4d5c-4580-9aff-4e6b2e5a6463": [ + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + } + ], + "4feda561-1914-404d-9092-3c629d5251bd": [ + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + } + ], + "b00b9753-c198-4f8a-a8b9-dd5e94dc5896": [ + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + } + ], + "c8c58fdf-06e3-4da4-a920-d5bcbcd18289": [ + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + }, + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "section_type": "main", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "section_type": "main", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "abstract", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "e2c1cfb0-9cfc-4a59-9df6-8599708b25ed", + "section_type": "main", + "text": "\n\nc With increasing efforts to map patients with T2D in etiological space using clinical and molecular phenotype, physiology, and genetics, it is likely that this increasingly granular view of T2D will lead to increasing precision therapeutic paradigms requiring evaluation and potential implementation.Genetic variation not only can capture etiological variation (i.e., genetic variants associated with diabetes risk) but also variation in drug pharmacokinetics (absorption, distribution, metabolism, and excretion [ADME]) and in drug action (pharmacodynamics)." + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "abstract", + "text": "\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 5×10 −8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D." + }, + { + "document_id": "a49c4251-7a66-44f1-9f95-0d6e8191a2ad", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "b29b3621-cdb5-4723-b771-8b48546241a5", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "f3b925cc-2556-4f30-809b-6bfe63a805b8", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "section_type": "main", + "text": "Conclusions\n\nPharmacogenetics research provides a means to better understand and improve on pharmacotherapy.However, pharmacogenetic studies of T2D therapies lag behind those for other complex diseases, despite the fact that pharmacologic interventions for T2D have been studied extensively at both the clinical and epidemiologic levels.Among the studies that have been conducted, several have identified variants that are potentially associated with differential response to anti-diabetes medications; these preliminary results are promising and warrant investigations in larger, well-designed cohorts to assess their potential roles in optimal drug selection and individualized pharmacotherapy in patients with T2D.At this time, larger, well-powered studies with clearly defined outcomes and utilizing a global approach are needed, as they will not only be more informative than extant candidate gene investigations, but will also be necessary to define the array of genetic variants that may underlie drug response.Such results will likely enable achievement of optimal glucose control, improvement of therapeutic efficacy, and reduction in risk of adverse drug events in at-risk patients, which together will lead to personalized treatment strategies for all individuals with T2D." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "Pharmacogenetics in disease progression\n\nOver the recent years, more than 90 susceptibility genes have been identified by genome-wide association studies (GWAS) [55][56][57][58].However, the knowledge of the potential interactions between T2D predisposing genetic variants and the efficacy of treatment of T2D is sparse.Identification of gene-treatment interactions is challenging and requires large sample sizes and sophisticated analytical methods.Furthermore, detailed information on lifestyle and compliance to treatment as well as a long follow-up period are necessary for analysis of pharmacogenomics in T2D." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "main", + "text": "\n\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "abstract", + "text": "\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow." + }, + { + "document_id": "dcd88798-0248-45e0-8d45-8614c7697266", + "section_type": "main", + "text": "\n\ndiabetes (DoD) and poor glycemic control (2).Genetic factors are also implicated, with heritability of 52% for proliferative DR (PDR) (3,4).Several candidate gene and genome-wide association studies (GWAS) have been conducted (5)(6)(7)(8)(9)(10)(11).Although several polymorphisms have been suggested to be associated with DR, few have been convincingly replicated (10,(12)(13)(14)(15).There are several reasons why studies have not yielded consistent findings.The genetic effects are likely modest, and identification requires large sample sizes.Previous studies have not consistently accounted for the strongest two covariates, DoD and glycemic control.Liability threshold (LT) modeling is one way to incorporate these covariates while also increasing statistical power (16).Finally, previous genetic studies have largely examined individual variants.Techniques that examine top GWAS findings collectively for variants that cluster in biological networks based on known protein-protein interactions have the potential to identify variants where there is insufficient power to detect their individual effects." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "Genetic Predisposition\n\nThe fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk.The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this diseaseif one twin developed type 2 diabetes, then the other one invariably developed it (9).However, this kind of study provides no insight into how genetics act in the disease.Is there a defective gene that directly impairs the glucose homeostasis system?Alternatively, does it cause insulin resistance or some other defect that acts indirectly by exceeding the capacity of an otherwise normal glucose homeostasis system to compensate?Also, are there one or many genetic defects that predispose to this disease?" + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe purpose of this review is to summarize current knowledge of pharmacogenetics in T2D and provide a perspective on the relationships between human genetic variants, antidiabetic treatment, and disease progression.This topic is of utmost importance as an improved understanding of gene-treatment interactions may provide a basis for development of future individualized therapies and treatment guidelines." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "\n\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 5×10 −8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D." + }, + { + "document_id": "3e53b34f-5bdf-43d5-9594-736cf83071db", + "section_type": "main", + "text": "\n\nTo extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent.We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association.Genomewide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D.Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "DIABETES AND GENETICS\n\nDiabetes is a complex disease that involves a wide range of genetic and environmental factors.Over the past several years, many studies have focused on the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development [142][143][144] .However, despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive [145,146] .Evidently, a major complication is the fact that a single gene mutation or polymorphism will not impose the same effect among different individuals within a population or different populations.This variation is directly or indirectly affected by the overall genetic background at the individual, family or population levels that are potentially further complicated by interaction with highly variable environmental modifier factors [147,148] ." + } + ], + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "pharmacogenetics", + "pharmacogenomics", + "GWAS", + "genetic&variants", + "OCT&genes", + "KCNJ11", + "ABCC8", + "CYP2C9", + "TCF7L2" + ], + "metadata": [ + { + "object": "The intrinsic clearance Vmax/Km values of all variants, with the exception of CYP2C9*2, CYP2C9*11, CYP2C9*23, CYP2C9*29, CYP2C9*34, CYP2C9*38, CYP2C9*44, CYP2C9*46 and CYP2C9*48, were significantly different from CYP2C9*1. CYP2C9*27, *40, *41, *47, *49, *51, *53, *54, *56 and N418T variant exhibited markedly larger values than CYP2C9*1.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab827642" + }, + { + "object": "genetic association studies in pediatric population in Japan: Data confirm that mutations in KCNJ11 or ABCC8 are associated with neonatal diabetes mellitus. Novel mutations were identified; 2 in KCNJ11 V64M, R201G and 6 in ABCC8 R216C, G832C, F1176L, A1263V, I196N, T229N. KCNJ11 = ATP-sensitive inward rectifier potassium channel-11; ABCC8 = ATP-binding cassette subfamily C member-8", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316321" + }, + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "genetic association studies in population in Scotland: data suggest, in type 2 diabetes treated with sulfonylureas, 2 SNPs in CYP2C9 CYP2C9*2, R144C, rs1799853; CYP2C9*3, I359L, rs1057910 are associated with drug-induced hypoglycemia; an SNP in POR POR*28, A503V, rs1057868 is associated with better response to sulfonylureas. CYP2C9 = cytochrome P450 family 2 subfamily C member 9; POR = cytochrome p450 oxidoreductase", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316392" + }, + { + "object": "Novel mutations were detected in ABCC8 and KCNJ11 gene in Chinese patients with congenital hyperinsulinism CHI. Hotspot mutations such as T1042Qfs*75, I1511K, E501K, G111R in ABCC8 gene, and R34H in KCNJ11 gene are predominantly responsible for Chinese CHI patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab535847" + }, + { + "object": "he aim of this study was to ascertain the polymorphic markers profile of ADIPOQ, KCNJ11 and TCF7L2 genes in Kyrgyz population and to analyze the association of polymorphic markers and combinations of ADIPOQ gene's G276T locus, KCNJ11 gene's Glu23Lys locus and TCF7L2 gene's VS3C>T locus with type two diabetes T2D in Kyrgyz population", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab334669" + }, + { + "object": "genetic variants in TCF7L2 confer a strong risk of future type 2 diabetes possibly mediated by altering expression of TCF7L2 in pancreatic islets [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab318653" + }, + { + "object": "Considering that CYP2C9*2 and CYP2C9*3 alleles have altered catalytic activities relative to CYP2C9*1, the present data suggest the need for pharmacogenetic studies to optimize drug dosages in different populations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab155248" + }, + { + "object": "The association of variants in IRS1 with type 2 diabetes and type 2 diabetes-related phenotypes and the differential expression of IRS1 in adipocytes and skeletal muscle suggest a role of this gene in the pathogenesis of type 2 diabetes in Pima Indians.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782328" + }, + { + "object": "trend for augmented exercise-induced IL6 release in type 2 diabetics; results also suggest that neither type 2 diabetic nor healthy skeletal muscle releases IL6 at rest,indicating that other organs contribute to elevated basal IL6 in type 2 diabetics", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab971647" + } + ], + "question": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "subquestions": null, + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "usage": { + "chatgpt": 7037, + "gpt-4": 4436, + "gpt-4-turbo-preview": 3522 + }, + "user_id": 2 + }, + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B" +}
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