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We formed the GEnetics of Nephropathy–an International Effort (GENIE) consortium to examine previously reported genetic associations with diabetic nephropathy (DN) in type 1 diabetes. GENIE consists of 6,366 similarly ascertained participants of European ancestry with type 1 diabetes, with and without DN, from the All Ireland-Warren 3-Genetics of Kidneys in Diabetes U.K. and Republic of Ireland (U.K.-R.O.I.) collection and the Finnish Diabetic Nephropathy Study (FinnDiane), combined with reanalyzed data from the Genetics of Kidneys in Diabetes U.S. Study (U.S. GoKinD). We found little evidence for the association of the EPO promoter polymorphism, rs161740, with the combined phenotype of proliferative retinopathy and end-stage renal disease in U.K.-R.O.I. (odds ratio [OR] 1.14, P = 0.19) or FinnDiane (OR 1.06, P = 0.60). However, a fixed-effects meta-analysis that included the previously reported cohorts retained a genome-wide significant association with that phenotype (OR 1.31, P = 2 × 10−9). An expanded investigation of the ELMO1 locus and genetic regions reported to be associated with DN in the U.S. GoKinD yielded only nominal statistical significance for these loci. Finally, top candidates identified in a recent meta-analysis failed to reach genome-wide significance. In conclusion, we were unable to replicate most of the previously reported genetic associations for DN, and significance for the EPO promoter association was attenuated.

Common genetic variants have been recently associated with fasting glucose and insulin levels in white populations. Whether these associations replicate in pre-diabetes is not known. We extended these findings to the Diabetes Prevention Program, a clinical trial in which participants at high risk for diabetes were randomized to placebo, lifestyle modification or metformin for diabetes prevention. We genotyped previously reported polymorphisms (or their proxies) in/near G6PC2, MTNR1B, GCK, DGKB, GCKR, ADCY5, MADD, CRY2, ADRA2A, FADS1, PROX1, SLC2A2, GLIS3, C2CD4B, IGF1, and IRS1 in 3,548 Diabetes Prevention Program participants. We analyzed variants for association with baseline glycemic traits, incident diabetes and their interaction with response to metformin or lifestyle intervention. We replicated associations with fasting glucose at MTNR1B (P<0.001), G6PC2 (P = 0.002) and GCKR (P = 0.001). We noted impaired β-cell function in carriers of glucose-raising alleles at MTNR1B (P<0.001), and an increase in the insulinogenic index for the glucose-raising allele at G6PC2 (P<0.001). The association of MTNR1B with fasting glucose and impaired β-cell function persisted at 1 year despite adjustment for the baseline trait, indicating a sustained deleterious effect at this locus. We also replicated the association of MADD with fasting proinsulin levels (P<0.001). We detected no significant impact of these variants on diabetes incidence or interaction with preventive interventions. The association of several polymorphisms with quantitative glycemic traits is replicated in a cohort of high-risk persons. These variants do not have a detectable impact on diabetes incidence or response to metformin or lifestyle modification in the Diabetes Prevention Program.

Objective: Over 30 loci have been associated with risk of type 2 diabetes at genome-wide statistical significance. Genetic risk scores (GRSs) developed from these loci predict diabetes in the general population. We tested if a GRS based on an updated list of 34 type 2 diabetes–associated loci predicted progression to diabetes or regression toward normal glucose regulation (NGR) in the Diabetes Prevention Program (DPP). Research Design and Methods: We genotyped 34 type 2 diabetes–associated variants in 2,843 DPP participants at high risk of type 2 diabetes from five ethnic groups representative of the U.S. population, who had been randomized to placebo, metformin, or lifestyle intervention. We built a GRS by weighting each risk allele by its reported effect size on type 2 diabetes risk and summing these values. We tested its ability to predict diabetes incidence or regression to NGR in models adjusted for age, sex, ethnicity, waist circumference, and treatment assignment. Results: In multivariate-adjusted models, the GRS was significantly associated with increased risk of progression to diabetes (hazard ratio [HR] = 1.02 per risk allele [95% CI 1.00–1.05]; P = 0.03) and a lower probability of regression to NGR (HR = 0.95 per risk allele [95% CI 0.93–0.98]; P < 0.0001). At baseline, a higher GRS was associated with a lower insulinogenic index (P < 0.001), confirming an impairment in \(\beta\)-cell function. We detected no significant interaction between GRS and treatment, but the lifestyle intervention was effective in the highest quartile of GRS (P < 0.0001). Conclusions: A high GRS is associated with increased risk of developing diabetes and lower probability of returning to NGR in high-risk individuals, but a lifestyle intervention attenuates this risk.

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene \(SLC47A1\) with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene \(STK11\), the AMPK subunit genes \(PRKAA1\) and \(PRKAA2\), and a missense SNP in \(SLC22A1\), which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene \(PRKAG2\) (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10\(^{−4}\)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest \(P\) values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.

OBJECTIVE— The RETN gene encodes the adipokine resistin. Associations of RETN with plasma resistin levels, type 2 diabetes, and related metabolic traits have been inconsistent. Using comprehensive linkage disequilibrium mapping, we genotyped tag single nucleotide polymorphisms (SNPs) in RETN and tested associations with plasma resistin levels, risk of diabetes, and glycemic traits. RESEARCH DESIGN AND METHODS— We examined 2,531 Framingham Offspring Study participants for resistin levels, glycemic phenotypes, and incident diabetes over 28 years of follow-up. We genotyped 21 tag SNPs that capture common (minor allele frequency >0.05) or previously reported SNPs at r2 > 0.8 across RETN and its flanking regions. We used sex- and age-adjusted linear mixed-effects models (with/without BMI adjustment) to test additive associations of SNPs with traits, adjusted Cox proportional hazards models accounting for relatedness for incident diabetes, and generated empirical P values (Pe) to control for type 1 error. RESULTS— Four tag SNPs (rs1477341, rs4804765, rs1423096, and rs10401670) on the 3′ side of RETN were strongly associated with resistin levels (all minor alleles associated with higher levels, Pe<0.05 after multiple testing correction). rs10401670 was also associated with fasting plasma glucose (Pe = 0.02, BMI adjusted) and mean glucose over follow-up (Pe = 0.01; BMI adjusted). No significant association was observed for adiposity traits. On meta-analysis, the previously reported association of SNP −420C/G (rs1862513) with resistin levels remained significant (P = 0.0009) but with high heterogeneity across studies (P < 0.0001). CONCLUSIONS— SNPs in the 3′ region of RETN are associated with resistin levels, and one of them is also associated with glucose levels, although replication is needed.

Objective: Genome-wide association scans (GWASs) have identified novel diabetes-associated genes. We evaluated how these variants impact diabetes incidence, quantitative glycemic traits, and response to preventive interventions in 3,548 subjects at high risk of type 2 diabetes enrolled in the Diabetes Prevention Program (DPP), which examined the effects of lifestyle intervention, metformin, and troglitazone versus placebo. Research Design and Methods: We genotyped selected single nucleotide polymorphisms (SNPs) in or near diabetes-associated loci, including EXT2, CDKAL1, CDKN2A/B, IGF2BP2, HHEX, LOC387761, and SLC30A8 in DPP participants and performed Cox regression analyses using genotype, intervention, and their interactions as predictors of diabetes incidence. We evaluated their effect on insulin resistance and secretion at 1 year. Results: None of the selected SNPs were associated with increased diabetes incidence in this population. After adjustments for ethnicity, baseline insulin secretion was lower in subjects with the risk genotype at HHEX rs1111875 (P = 0.01); there were no significant differences in baseline insulin sensitivity. Both at baseline and at 1 year, subjects with the risk genotype at LOC387761 had paradoxically increased insulin secretion; adjustment for self-reported ethnicity abolished these differences. In ethnicity-adjusted analyses, we noted a nominal differential improvement in \(\beta\)-cell function for carriers of the protective genotype at CDKN2A/B after 1 year of troglitazone treatment (P = 0.01) and possibly lifestyle modification (P = 0.05). Conclusions: We were unable to replicate the GWAS findings regarding diabetes risk in the DPP. We did observe genotype associations with differences in baseline insulin secretion at the HHEX locus and a possible pharmacogenetic interaction at CDKNA2/B.

OBJECTIVE—A recent meta-analysis demonstrated a nominal association of the ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) K→Q missense single nucleotide polymorphism (SNP) at position 121 with type 2 diabetes. We set out to confirm the association of ENPP1 K121Q with hyperglycemia, expand this association to insulin resistance traits, and determine whether the association stems from K121Q or another variant in linkage disequilibrium with it. RESEARCH DESIGN AND METHODS—We characterized the haplotype structure of ENPP1 and selected 39 tag SNPs that captured 96% of common variation in the region (minor allele frequency ≥5%) with an r2 value ≥0.80. We genotyped the SNPs in 2,511 Framingham Heart Study participants and used age- and sex-adjusted linear mixed effects (LME) models to test for association with quantitative metabolic traits. We also examined whether interaction between K121Q and BMI affected glycemic trait levels. RESULTS—The Q allele of K121Q (rs1044498) was associated with increased fasting plasma glucose (FPG), A1C, fasting insulin, and insulin resistance by homeostasis model assessment (HOMA-IR; all P = 0.01–0.006). Two noncoding SNPs (rs7775386 and rs7773477) demonstrated similar associations, but LME models indicated that their effects were not independent from K121Q. We found no association of K121Q with obesity, but interaction models suggested that the effect of the Q allele on FPG and HOMA-IR was stronger in those with a higher BMI (P = 0.008 and 0.01 for interaction, respectively). CONCLUSIONS—The Q allele of ENPP1 K121Q is associated with hyperglycemia and insulin resistance in whites. We found an adiposity-SNP interaction, with a stronger association of K121Q with diabetes-related quantitative traits in people with a higher BMI.

OBJECTIVE— Variants in ADIPOQ have been inconsistently associated with adiponectin levels or diabetes. Using comprehensive linkage disequilibrium mapping, we genotyped single nucleotide polymorphisms (SNPs) in ADIPOQ to evaluate the association of common variants with adiponectin levels and risk of diabetes. RESEARCH DESIGN AND METHODS— Participants in the Framingham Offspring Study (n = 2,543, 53% women) were measured for glycemic phenotypes and incident diabetes over 28 years of follow-up; adiponectin levels were quantified at exam 7. We genotyped 22 tag SNPs that captured common (minor allele frequency >0.05) variation at r[sup]2 > 0.8 across ADIPOQ plus 20 kb 5′ and 10 kb 3′ of the gene. We used linear mixed effects models to test additive associations of each SNP with adiponectin levels and glycemic phenotypes. Hazard ratios (HRs) for incident diabetes were estimated using an adjusted Cox proportional hazards model. RESULTS— Two promoter SNPs in strong linkage disequilibrium with each other (r[sup]2 = 0.80) were associated with adiponectin levels (rs17300539; Pnominal [Pn] = 2.6 × 10−8; Pempiric [Pe] = 0.0005 and rs822387; Pn = 3.8 × 10−5; Pe = 0.001). A 3′-untranslated region (3′UTR) SNP (rs6773957) was associated with adiponectin levels (Pn = 4.4 × 10−4; Pe = 0.005). A nonsynonymous coding SNP (rs17366743, Y111H) was confirmed to be associated with diabetes incidence (HR 1.94 [95% CI 1.16–3.25] for the minor C allele; Pn = 0.01) and with higher mean fasting glucose over 28 years of follow-up (Pn = 0.0004; Pe = 0.004). No other significant associations were found with other adiposity and metabolic phenotypes. CONCLUSIONS— Adiponectin levels are associated with SNPs in two different regulatory regions (5′ promoter and 3′UTR), whereas diabetes incidence and time-averaged fasting glucose are associated with a missense SNP of ADIPOQ.