Search

OBJECTIVE--We investigated whether metabolic biomarkers and single nucleotide polymorphisms (SNPs) improve diabetes prediction beyond age, anthropometry, and lifestyle risk factors. RESEARCH DESIGN AND METHODS--A case-cohort study within a prospective study [...]

Recent advances in high-throughput genotyping methods have facilitated the discovery and confirmation (1-7) of multiple common genetic risk factors for type 2 diabetes. The notion of using genetic information for [...], OBJECTIVE--Recent advances in type 2 diabetes genetics have culminated in the discovery and confirmation of multiple risk variants. Two important and largely unanswered questions are whether this information can be used to identify individuals most susceptible to the adverse consequences of sedentary behavior and to predict their response to lifestyle intervention; such evidence would be mechanistically informative and provide a rationale for targeting genetically susceptible subgroups of the population. RESEARCH DESIGN AND METHODS--Gene x physical activity interactions were assessed for 17 polymorphisms in a prospective population-based cohort of initially nondiabetic middle-aged adults. Outcomes were 1) impaired glucose regulation (IGR) versus normal glucose regulation determined with either fasting or 2-h plasma glucose concentrations (n = 16,003), 2) glucose intolerance (in mmol/1, n = 8,860), or 3) incident type 2 diabetes (n = 2,063 events). RESULTS--Tests of gene x physical activity interactions on IGR risk for 3 of the 17 polymorphisms were nominally statistically significant: CDKN2A/B rs10811661 ([P.sub.interaction] = 0.015), HNFIB rs4430796 ([P.sub.interaction] = 0.026), and PPARG rs1801282 ([P.sub.interaction] = 0.04). Consistent interactions were observed for the CDKN2A/B ([P.sub.interaction] = 0.013) and HNFIB ([P.sub.interaction] = 0.0009) variants on 2-h glucose concentrations. Where type 2 diabetes was the outcome, only one statistically significant interaction effect was observed, and this was for the HNFIB rs4430796 variant ([P.sub.interaction] = 0.0004). The interaction effects for HNFIB on IGR risk and incident diabetes remained significant after correction for multiple testing ([P.sub.interaction] = 0.015 and 0.0068, respectively). CONCLUSIONS--Our observations suggest that the genetic predisposition to hyperglycemia is partially dependent on a person's lifestyle.

Type 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 [...], 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 18 polymorphisms 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 = 6,544; prevalent cases, n = 686; incident cases during follow-up, n = 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, SCL30A8, 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.