Results from observational studies examining dyslipidemia as a risk factor for diabetic retinopathy (DR) have been inconsistent. We evaluated the causal relationship between plasma lipids and DR using a Mendelian randomization approach. We pooled genome-wide association studies summary statistics from 18 studies for two DR phenotypes: any DR (N = 2,969 case and 4,096 control subjects) and severe DR (N = 1,277 case and 3,980 control subjects). Previously identified lipid-associated single nucleotide polymorphisms served as instrumental variables. Meta-analysis to combine the Mendelian randomization estimates from different cohorts was conducted. There was no statistically significant change in odds ratios of having any DR or severe DR for any of the lipid fractions in the primary analysis that used single nucleotide polymorphisms that did not have a pleiotropic effect on another lipid fraction. Similarly, there was no significant association in the Caucasian and Chinese subgroup analyses. This study did not show evidence of a causal role of the four lipid fractions on DR. However, the study had limited power to detect odds ratios less than 1.23 per SD in genetically induced increase in plasma lipid levels, thus we cannot exclude that causal relationships with more modest effect sizes exist.

Elevated concentrations of albumin in the urine, albuminuria, are a hallmark of diabetic kidney disease and are associated with an increased risk for end-stage renal disease and cardiovascular events. To gain insight into the pathophysiological mechanisms underlying albuminuria, we conducted meta-analyses of genome-wide association studies and independent replication in up to 5,825 individuals of European ancestry with diabetes and up to 46,061 without diabetes, followed by functional studies. Known associations of variants in CUBN, encoding cubilin, with the urinary albumin-to-creatinine ratio (UACR) were confirmed in the overall sample (P = 2.4 × 10(-10)). Gene-by-diabetes interactions were detected and confirmed for variants in HS6ST1 and near RAB38/CTSC. Single nucleotide polymorphisms at these loci demonstrated a genetic effect on UACR in individuals with but not without diabetes. The change in the average UACR per minor allele was 21% for HS6ST1 (P = 6.3 × 10(-7)) and 13% for RAB38/CTSC (P = 5.8 × 10(-7)). Experiments using streptozotocin-induced diabetic Rab38 knockout and control rats showed higher urinary albumin concentrations and reduced amounts of megalin and cubilin at the proximal tubule cell surface in Rab38 knockout versus control rats. Relative expression of RAB38 was higher in tubuli of patients with diabetic kidney disease compared with control subjects. The loci identified here confirm known pathways and highlight novel pathways influencing albuminuria.

Glycated hemoglobin A1c (HbA1c) is used as a measure of glycemic control and also as a diagnostic criterion for diabetes. To discover novel loci harboring common variants associated with HbA1c in East Asians, we conducted a meta-analysis of 13 genome-wide association studies (GWAS; N = 21,026). We replicated our findings in three additional studies comprising 11,576 individuals of East Asian ancestry. Ten variants showed associations that reached genome-wide significance in the discovery data set, of which nine (four novel variants at TMEM79 [P value = 1.3 × 10(-23)], HBS1L/MYB [8.5 × 10(-15)], MYO9B [9.0 × 10(-12)], and CYBA [1.1 × 10(-8)] as well as five variants at loci that had been previously identified [CDKAL1, G6PC2/ABCB11, GCK, ANK1, and FN3KI]) showed consistent evidence of association in replication data sets. These variants explained 1.76% of the variance in HbA1c. Several of these variants (TMEM79, HBS1L/MYB, CYBA, MYO9B, ANK1, and FN3K) showed no association with either blood glucose or type 2 diabetes. Among individuals with nondiabetic levels of fasting glucose (<7.0 mmol/L) but elevated HbA1c (≥6.5%), 36.1% had HbA1c <6.5% after adjustment for these six variants. Our East Asian GWAS meta-analysis has identified novel variants associated with HbA1c as well as demonstrated that the effects of known variants are largely transferable across ethnic groups. Variants affecting erythrocyte parameters rather than glucose metabolism may be relevant to the use of HbA1c for diagnosing diabetes in these populations.

No abstract available

Epigenetic changes may contribute substantially to risks of diseases of aging. Previous studies reported seven methylation variable positions (MVPs) robustly associated with incident type 2 diabetes mellitus (T2DM). However, their causal roles in T2DM are unclear. In an incident T2DM case-cohort study nested within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort, we used whole blood DNA collected at baseline, up to 11 years before T2DM onset, to investigate the role of methylation in the etiology of T2DM. We identified 15 novel MVPs with robust associations with incident T2DM and robustly confirmed three MVPs identified previously (near to TXNIP, ABCG1, and SREBF1). All 18 MVPs showed directionally consistent associations with incident and prevalent T2DM in independent studies. Further conditional analyses suggested that the identified epigenetic signals appear related to T2DM via glucose and obesity-related pathways acting before the collection of baseline samples. We integrated genome-wide genetic data to identify methylation-associated quantitative trait loci robustly associated with 16 of the 18 MVPs and found one MVP, cg00574958 at CPT1A, with a possible direct causal role in T2DM. None of the implicated genes were previously highlighted by genetic association studies, suggesting that DNA methylation studies may reveal novel biological mechanisms involved in tissue responses to glycemia.

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.

Type 2 diabetes (T2D) disproportionally affects African Americans (AfA) but, to date, genetic variants identified from genome-wide association studies (GWAS) are primarily from European and Asian populations. We examined the single nucleotide polymorphism (SNP) and locus transferability of 40 reported T2D loci in six AfA GWAS consisting of 2,806 T2D case subjects with or without end-stage renal disease and 4,265 control subjects from the Candidate Gene Association Resource Plus Study. Our results revealed that seven index SNPs at the TCF7L2, KLF14, KCNQ1, ADCY5, CDKAL1, JAZF1, and GCKR loci were significantly associated with T2D (P < 0.05). The strongest association was observed at TCF7L2 rs7903146 (odds ratio [OR] 1.30; P = 6.86 × 10⁻⁸). Locus-wide analysis demonstrated significant associations (P(emp) < 0.05) at regional best SNPs in the TCF7L2, KLF14, and HMGA2 loci as well as suggestive signals in KCNQ1 after correction for the effective number of SNPs at each locus. Of these loci, the regional best SNPs were in differential linkage disequilibrium (LD) with the index and adjacent SNPs. Our findings suggest that some loci discovered in prior reports affect T2D susceptibility in AfA with similar effect sizes. The reduced and differential LD pattern in AfA compared with European and Asian populations may facilitate fine mapping of causal variants at loci shared across populations.

Genome-wide association studies (GWAS) have successfully identified genetic loci associated with glycemic traits. However, characterizing the functional significance of these loci has proven challenging. We sought to gain insights into the regulation of fasting insulin and fasting glucose through the use of gene expression microarray data from peripheral blood samples of participants without diabetes in the Framingham Heart Study (FHS) (n = 5,056), the Rotterdam Study (RS) (n = 723), and the InCHIANTI Study (Invecchiare in Chianti) (n = 595). Using a false discovery rate q <0.05, we identified three transcripts associated with fasting glucose and 433 transcripts associated with fasting insulin levels after adjusting for age, sex, technical covariates, and complete blood cell counts. Among the findings, circulating IGF2BP2 transcript levels were positively associated with fasting insulin in both the FHS and RS. Using 1000 Genomes-imputed genotype data, we identified 47,587 cis-expression quantitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 significant insulin-associated transcripts. Of note, we identified a trans-eQTL (rs592423), where the A allele was associated with higher IGF2BP2 levels and with fasting insulin in an independent genetic meta-analysis comprised of 50,823 individuals. We conclude that integration of genomic and transcriptomic data implicate circulating IGF2BP2 mRNA levels associated with glucose and insulin homeostasis.

Few genome-wide association studies (GWAS) of type 2 diabetes (T2D) have been conducted in U.S. Hispanics/Latinos of diverse backgrounds who are disproportionately affected by diabetes. We conducted a GWAS in 2,499 T2D case subjects and 5,247 control subjects from six Hispanic/Latino background groups in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Our GWAS identified two known loci (TCF7L2 and KCNQ1) reaching genome-wide significance levels. Conditional analysis on known index single nucleotide polymorphisms (SNPs) indicated an additional independent signal at KCNQ1, represented by an African ancestry-specific variant, rs1049549 (odds ratio 1.49 [95% CI 1.27-1.75]). This association was consistent across Hispanic/Latino background groups and replicated in the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium. Among 80 previously known index SNPs at T2D loci, 66 SNPs showed consistency with the reported direction of associations and 14 SNPs significantly generalized to the HCHS/SOL. A genetic risk score based on these 80 index SNPs was significantly associated with T2D (odds ratio 1.07 [1.06-1.09] per risk allele), with a stronger effect observed in nonobese than in obese individuals. Our study identified a novel independent signal suggesting an African ancestry-specific allele at KCNQ1 for T2D. Associations between previously identified loci and T2D were generally shown in a large cohort of U.S. Hispanics/Latinos.

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10(-11)), rs12454712 (BCL2; P = 2.7 × 10(-8)), and rs10506418 (FAM19A2; P = 1.9 × 10(-8)). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.

Obesity is linked to type 2 diabetes (T2D) and cardiovascular diseases; however, the underlying molecular mechanisms remain unclear. We aimed to identify obesity-associated molecular features that may contribute to obesity-related diseases. Using circulating monocytes from 1,264 Multi-Ethnic Study of Atherosclerosis (MESA) participants, we quantified the transcriptome and epigenome. We discovered that alterations in a network of coexpressed cholesterol metabolism genes are a signature feature of obesity and inflammatory stress. This network included 11 BMI-associated genes related to sterol uptake (↑LDLR, ↓MYLIP), synthesis (↑SCD, FADS1, HMGCS1, FDFT1, SQLE, CYP51A1, SC4MOL), and efflux (↓ABCA1, ABCG1), producing a molecular profile expected to increase intracellular cholesterol. Importantly, these alterations were associated with T2D and coronary artery calcium (CAC), independent from cardiometabolic factors, including serum lipid profiles. This network mediated the associations between obesity and T2D/CAC. Several genes in the network harbored C-phosphorus-G dinucleotides (e.g., ABCG1/cg06500161), which overlapped Encyclopedia of DNA Elements (ENCODE)-annotated regulatory regions and had methylation profiles that mediated the associations between BMI/inflammation and expression of their cognate genes. Taken together with several lines of previous experimental evidence, these data suggest that alterations of the cholesterol metabolism gene network represent a molecular link between obesity/inflammation and T2D/CAC.

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like (NVL) was associated with DR in European discovery cohorts (P = 2.1 × 10-9), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity (P = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in NVL, as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.

Insulin sensitivity, insulin secretion, insulin clearance, and glucose effectiveness exhibit strong genetic components, although few studies have examined their genetic architecture or influence on type 2 diabetes (T2D) risk. We hypothesized that loci affecting variation in these quantitative traits influence T2D. We completed a multicohort genome-wide association study to search for loci influencing T2D-related quantitative traits in 4,176 Mexican Americans. Quantitative traits were measured by the frequently sampled intravenous glucose tolerance test (four cohorts) or euglycemic clamp (three cohorts), and random-effects models were used to test the association between loci and quantitative traits, adjusting for age, sex, and admixture proportions (Discovery). Analysis revealed a significant (P < 5.00 × 10(-8)) association at 11q14.3 (MTNR1B) with acute insulin response. Loci with P < 0.0001 among the quantitative traits were examined for translation to T2D risk in 6,463 T2D case and 9,232 control subjects of Mexican ancestry (Translation). Nonparametric meta-analysis of the Discovery and Translation cohorts identified significant associations at 6p24 (SLC35B3/TFAP2A) with glucose effectiveness/T2D, 11p15 (KCNQ1) with disposition index/T2D, and 6p22 (CDKAL1) and 11q14 (MTNR1B) with acute insulin response/T2D. These results suggest that T2D and insulin secretion and sensitivity have both shared and distinct genetic factors, potentially delineating genomic components of these quantitative traits that drive the risk for T2D.

Type 2 diabetes (T2D) is a heterogeneous disorder with contributions from peripheral insulin resistance and β-cell dysfunction. For minimization of phenotypic heterogeneity, quantitative intermediate phenotypes characterizing basal glucose homeostasis (insulin resistance and HOMA of insulin resistance [HOMAIR] and of β-cell function [HOMAB]) have shown promise in relatively large samples. We investigated the utility of dynamic measures of glucose homeostasis (insulin sensitivity [SI] and acute insulin response [AIRg]) evaluating T2D-susceptibility variants (n = 57) in Hispanic Americans from the GUARDIAN Consortium (n = 2,560). Basal and dynamic measures were genetically correlated (HOMAB-AIRg: ρG = 0.28-0.73; HOMAIR-SI: ρG = -0.73 to -0.83) with increased heritability for the dynamic measure AIRg Significant association of variants with dynamic measures (P < 8.77 × 10(-4)) was observed. A pattern of superior performance of AIRg was observed for well-established loci including MTNR1B (P = 9.46 × 10(-12)), KCNQ1 (P = 1.35 × 10(-4)), and TCF7L2 (P = 5.10 × 10(-4)) with study-wise statistical significance. Notably, significant association of MTNR1B with AIRg (P < 1.38 × 10(-9)) was observed in a population one-fourteenth the size of the initial discovery cohort. These observations suggest that basal and dynamic measures provide different views and levels of sensitivity to discrete elements of glucose homeostasis. Although more costly to obtain, dynamic measures yield significant results that could be considered physiologically "closer" to causal pathways and provide insight into the discrete mechanisms of action.

To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (P < 5 × 10-8), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

The increasing prevalence of type 2 diabetes poses a major challenge to societies worldwide. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach, measuring serum levels of 4,137 proteins in 5,438 elderly Icelanders, and identified 536 proteins associated with prevalent and/or incident type 2 diabetes. We validated a subset of the observed associations in an independent case-control study of type 2 diabetes. These protein associations provide novel biological insights into the molecular mechanisms that are dysregulated prior to and following the onset of type 2 diabetes and can be detected in serum. A bidirectional two-sample Mendelian randomization analysis indicated that serum changes of at least 23 proteins are downstream of the disease or its genetic liability, while 15 proteins were supported as having a causal role in type 2 diabetes.