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Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

ABSTRACT: Patients with established type 2 diabetes display both ?-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

SUBMITTER: Dimas AS

PROVIDER: S-EPMC4030103 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

Dimas Antigone S AS Lagou Vasiliki V Barker Adam A Knowles Joshua W JW Mägi Reedik R Hivert Marie-France MF Benazzo Andrea A Rybin Denis D Jackson Anne U AU Stringham Heather M HM Song Ci C Fischer-Rosinsky Antje A Boesgaard Trine Welløv TW Grarup Niels N Abbasi Fahim A FA Assimes Themistocles L TL Hao Ke K Yang Xia X Lecoeur Cécile C Barroso Inês I Bonnycastle Lori L LL Böttcher Yvonne Y Bumpstead Suzannah S Chines Peter S PS Erdos Michael R MR Graessler Jurgen J Kovacs Peter P Morken Mario A MA Narisu Narisu N Payne Felicity F Stancakova Alena A Swift Amy J AJ Tönjes Anke A Bornstein Stefan R SR Cauchi Stéphane S Froguel Philippe P Meyre David D Schwarz Peter E H PE Häring Hans-Ulrich HU Smith Ulf U Boehnke Michael M Bergman Richard N RN Collins Francis S FS Mohlke Karen L KL Tuomilehto Jaakko J Quertemous Thomas T Lind Lars L Hansen Torben T Pedersen Oluf O Walker Mark M Pfeiffer Andreas F H AF Spranger Joachim J Stumvoll Michael M Meigs James B JB Wareham Nicholas J NJ Kuusisto Johanna J Laakso Markku M Langenberg Claudia C Dupuis Josée J Watanabe Richard M RM Florez Jose C JC Ingelsson Erik E McCarthy Mark I MI Prokopenko Inga I

Diabetes 20131202 6

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adju ...[more]

PMID: 24296717

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Project description:OBJECTIVE:Shared genetic background may explain phenotypic associations between depression and Type 2 diabetes (T2D). We aimed to study, on a genome-wide level, if genetic correlation and pleiotropic loci exist between depressive symptoms and T2D or glycemic traits. METHODS:We estimated single-nucleotide polymorphism (SNP)-based heritability and analyzed genetic correlation between depressive symptoms and T2D and glycemic traits with the linkage disequilibrium score regression by combining summary statistics of previously conducted meta-analyses for depressive symptoms by CHARGE consortium (N = 51,258), T2D by DIAGRAM consortium (N = 34,840 patients and 114,981 controls), fasting glucose, fasting insulin, and homeostatic model assessment of ?-cell function and insulin resistance by MAGIC consortium (N = 58,074). Finally, we investigated pleiotropic loci using a bivariate genome-wide association study approach with summary statistics from genome-wide association study meta-analyses and reported loci with genome-wide significant bivariate association p value (p < 5 × 10). Biological annotation and function of significant pleiotropic SNPs were assessed in several databases. RESULTS:The SNP-based heritability ranged from 0.04 to 0.10 in each individual trait. In the linkage disequilibrium score regression analyses, depressive symptoms showed no significant genetic correlation with T2D or glycemic traits (p > 0.37). However, we identified pleiotropic genetic variations for depressive symptoms and T2D (in the IGF2BP2, CDKAL1, CDKN2B-AS, and PLEKHA1 genes), and fasting glucose (in the MADD, CDKN2B-AS, PEX16, and MTNR1B genes). CONCLUSIONS:We found no significant overall genetic correlations between depressive symptoms, T2D, or glycemic traits suggesting major differences in underlying biology of these traits. However, several potential pleiotropic loci were identified between depressive symptoms, T2D, and fasting glucose, suggesting that previously established phenotypic associations may be partly explained by genetic variation in these specific loci.

Project description:ObjectiveWe employed Mendelian randomization to explore the effects of genetic predisposition to type 2 diabetes (T2D), hyperglycemia, insulin resistance, and pancreatic β-cell dysfunction on risk of stroke subtypes and related cerebrovascular phenotypes.MethodsWe selected instruments for genetic predisposition to T2D (74,124 cases, 824,006 controls), HbA1c levels (n = 421,923), fasting glucose levels (n = 133,010), insulin resistance (n = 108,557), and β-cell dysfunction (n = 16,378) based on published genome-wide association studies. Applying 2-sample Mendelian randomization, we examined associations with ischemic stroke (60,341 cases, 454,450 controls), intracerebral hemorrhage (1,545 cases, 1,481 controls), and ischemic stroke subtypes (large artery, cardioembolic, small vessel stroke), as well as with related phenotypes (carotid atherosclerosis, imaging markers of cerebral white matter integrity, and brain atrophy).ResultsGenetic predisposition to T2D and higher HbA1c levels were associated with higher risk of any ischemic stroke, large artery stroke, and small vessel stroke. Similar associations were also noted for carotid atherosclerotic plaque, fractional anisotropy, a white matter disease marker, and markers of brain atrophy. We further found associations of genetic predisposition to insulin resistance with large artery and small vessel stroke, whereas predisposition to β-cell dysfunction was associated with small vessel stroke, intracerebral hemorrhage, lower gray matter volume, and total brain volume.ConclusionsThis study supports causal effects of T2D and hyperglycemia on large artery and small vessel stroke. We show associations of genetically predicted insulin resistance and β-cell dysfunction with large artery and small vessel stroke that might have implications for antidiabetic treatments targeting these mechanisms.Classification of evidenceThis study provides Class II evidence that genetic predisposition to T2D and higher HbA1c levels are associated with a higher risk of large artery and small vessel ischemic stroke.

Project description:ObjectivesObservational studies suggest glycemic traits and type 2 diabetes are positively associated, and metformin inversely associated with breast and prostate cancer risk. However, observational studies are susceptible to unmeasured confounding while studies of metformin use are also vulnerable to immortal time bias. The use of Mendelian randomization may reduce confounding due to random allocation of relevant genetic markers at birth, and may reduce immortal time bias (for metformin-related variants analysis) since the start of exposure is at birth.Research design and methodsWe identified strong genetic predictors of fasting glucose, glycated hemoglobin, and type 2 diabetes from the Meta-Analyses of Glucose and Insulin-related traits Consortium and Diabetes Genetics Replication And Meta-analysis Consortium (n=140 595 for glucose; n=123 665 for HbA1c; n=898 130 for type 2 diabetes) and of AMPK-instrumented HbA1c reduction as a proxy of metformin and applied them to large genome-wide association studies of breast cancer (Breast Cancer Association Consortium; BCAC) and prostate cancer (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome; PRACTICAL). We used inverse variance weighting to obtain estimates. Sensitivity analyses included use of MR-Egger, weighted median, exclusion of pleiotropic instruments, and validation using UK Biobank (breast cancer only).ResultsThere was no association of fasting glucose (OR 1.03 per mmol/L, 95% CI 0.85 to 1.25), HbA1c (OR 1.02 per %, 95% CI 0.73 to 1.45), or type 2 diabetes (OR 0.98 per log odds, 95% CI 0.95 to 1.01) with breast cancer in BCAC, with similar findings from UK Biobank. There was no association of fasting glucose (OR 0.93 per mmol/L, 95% CI 0.73 to 1.17), HbA1c (OR 0.90 per %, 95% CI 0.58 to 1.40) or type 2 diabetes (OR 1.02 per log odds, 95% CI 0.97 to 1.07) with prostate cancer in PRACTICAL. No strong evidence was observed for AMPK-instrumented HbA1c reduction on cancer risk.ConclusionGlycemic traits and type 2 diabetes unlikely cause breast and prostate cancer. Whether metformin can be repurposed for cancer prevention remains unclear.

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Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

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Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

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