Circulating glucose levels are tightly regulated. To identify novel glycemic loci, we performed meta-analyses of 21 genome-wide associations studies informative for fasting glucose (FG), fasting insulin (FI) and indices of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 non-diabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with FG/HOMA-B and two associated with FI/HOMA-IR. These include nine new FG loci (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and FAM148B) and one influencing FI/HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB/TMEM195 with type 2 diabetes (T2D). Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify T2D risk loci, as well as loci that elevate FG modestly, but do not cause overt diabetes.

By 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 combinedP < 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.

Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958–30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, β (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 × 10−15). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 × 10−17; ratio of insulin to glucose area under the curve, P = 1.3 × 10−16) and diminished incretin effect (n = 804; P = 4.3 × 10−4). We also identified variants at ADCY5 (rs2877716, P = 4.2 × 10−16), VPS13C (rs17271305, P = 4.1 × 10−8), GCKR (rs1260326, P = 7.1 × 10−11) and TCF7L2 (rs7903146, P = 4.2 × 10−10) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09–1.15, P = 4.8 × 10−18).

OBJECTIVE -To investigate the association between serum uric acid level and risk of type 2 diabetes. RESEARCH DESIGN AND METHODS -The population for analysis consisted of 4,536 subjects free from diabetes at baseline. During a mean of 10.1 years of follow-up, 462 subjects developed diabetes.RESULTS -The age-and sex-adjusted hazard ratios (HRs) (95% CIs) for diabetes were 1.30 (0.96 -1.76) for the second, 1.63 (1.21-2.19) for the third, and 2.83 (2.13-3.76) for the fourth quartile of serum uric acid, in comparison with the first quartile. After adjustment for BMI, waist circumference, systolic and diastolic blood pressure, and HDL cholesterol, the HRs decreased to 1.08 (0.78 -1.49), 1.12 (0.81-1.53), and 1.68 (1.22-2.30), respectively.CONCLUSIONS -The results of this population-based study suggest that serum uric acid is a strong and independent risk factor for diabetes. Diabetes Care 31:361-362, 2008S erum uric acid is positively associated with serum glucose in healthy subjects.(1,2) However, this association is not consistent between healthy and diabetic individuals (3-5), as a low serum level of uric acid is reported in the hyperglycemic state (6). Since most individuals experience a phase of impaired glucose tolerance before progression to diabetes, it is not clear whether raised serum uric acid predicts the risk of type 2 diabetes (4,5). We investigated the association between serum uric acid and risk of diabetes in the Rotterdam Study, a large population-based, prospective cohort study among subjects aged 55 years and older.RESEARCH DESIGN AND METHODS -The Rotterdam Study has been described in detail elsewhere (7). Written informed consent was obtained from all participants, and the Medical Ethics Committee of Erasmus Medical Center approved the study. At baseline, serum uric acid was measured (8,9) and prevalent diabetes cases were excluded (10). Incident cases of diabetes were diagnosed during follow-up based on the guidelines of the American Diabetes Association (11) and World Health Organizatioin (12) using the information from general practitioners, pharmacies' databases, and fasting blood samples that were taken during follow-up examinations (10). The association was assessed by Cox regression analysis. The population-attributable risk and 95% CIs were calculated with use of the Interactive Risk Assessment Program (IRAP) (13).RESULTS -Serum uric acid was ranged from 107 to 756 mol/l with a mean Ϯ SD of 323.7 Ϯ 82.2 mol/l. Age BMI, waist circumference, systolic and diastolic blood pressure, and HDL cholesterol were significantly correlated with serum uric acid. The correlation coefficient ranged from 0.03 for diastolic blood pressure to 0.35 for waist circumference. Except for HDL cholesterol, the correlations were positive.During a mean follow-up of 10.1 years, 462 of 4,536 participants developed diabetes (incidence rate 10.1 per 1,000 person-years). The age-and sexadjusted hazard ratios (HRs) (95% CIs) for diabetes were 1.30 (0.96 -1.76) for the second, 1.63 (1.21-2.19) for the third, and 2.83 (2.13...

OBJECTIVE-Prediction of type 2 diabetes based on genetic testing might improve identification of high-risk subjects. Genome-wide association (GWA) studies identified multiple new genetic variants that associate with type 2 diabetes. The predictive value of genetic testing for prediction of type 2 diabetes in the general population is unclear. RESEARCH DESIGN AND METHODS-We investigated 18polymorphisms from recent GWA studies on type 2 diabetes in the Rotterdam Study, a prospective, population-based study among homogeneous Caucasian individuals of 55 years and older (genotyped subjects, n ϭ 6,544; prevalent cases, n ϭ 686; incident cases during follow-up, n ϭ 601; mean follow-up 10.6 years). The predictive value of these polymorphisms was examined alone and in addition to clinical characteristics using logistic and Cox regression analyses. The discriminative accuracy of the prediction models was assessed by the area under the receiver operating characteristic curves (AUCs).RESULTS-Of the 18 polymorphisms, the ADAMTS9, CDKAL1, CDKN2A/B-rs1412829, FTO, IGF2BP2, JAZF1, SLC30A8, TCF7L2, and WFS1 variants were associated with type 2 diabetes risk in our population. The AUC was 0.60 (95% CI 0.57-0.63) for prediction based on the genetic polymorphisms; 0.66 (0.63-0.68) for age, sex, and BMI; and 0.68 (0.66 -0.71) for the genetic polymorphisms and clinical characteristics combined.CONCLUSIONS-We showed that 9 of 18 well-established genetic risk variants were associated with type 2 diabetes in a population-based study. Combining genetic variants has low predictive value for future type 2 diabetes at a population-based level. The genetic polymorphisms only marginally improved the prediction of type 2 diabetes beyond clinical characteristics. Diabetes 57:3122-3128, 2008 T ype 2 diabetes is a multifactorial disease caused by a complex interplay of multiple genetic variants and many environmental factors. With the recent genome-wide association (GWA) studies, the number of replicated common genetic variants associated with type 2 diabetes has rapidly increased (1-7). A total of 18 polymorphisms have been firmly replicated (1-7). It is unclear whether and how the currently known genetic variants can be used in practice, because the combined effect of these variants has not been investigated in a population-based study. Particularly, because most GWA studies were enriched for patients with a positive family history and early onset of the disease, association of these variants to type 2 diabetes risk in the general population, including elderly individuals, remains to be determined.Because complex diseases are caused by multiple genetic variants, predictive testing based on a single genetic marker will be of limited value (8,9). Simulation studies suggest that the predictive value could be improved by combining multiple common low-risk variants (10 -13). Several empirical studies on the predictive value of genetic polymorphisms have been conducted before the recent GWA data were available (14 -16). In a case-control study, Weedo...