Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control.

AIMS/HYPOTHESIS: Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS: We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study. RESULTS: The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10-21 and p = 9.6 × 10-31, respectively) and a 4.4-fold (p = 6.8 × 10-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. CONCLUSIONS/INTERPRETATION: This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.

Impact of genetic factors (CYP2C9, VKORC1 and CYP4F2) on warfarin dose requirement in the Turkish population.

Warfarin has a narrow therapeutic index and displays marked person-to-person variation in dose requirement. Functional polymorphisms at candidate genes can therefore offer utility as biomarkers to individualize warfarin treatment. The main objective of this study was to determine whether and to what extent variability in warfarin dose requirements was determined by polymorphisms in CYP2C9, VKORC1, CYP4F2 (rs2108622) and EPHX1 (rs2292566) in the Turkish population. Patients (n = 107) who had stable doses and international normalized ratio (INRs) at their last three consecutive visits were registered. Their demographic factors, concurrent medications, warfarin-related bleedings or thromboembolisms, smoking, alcohol intake and weekly green vegetable consumption were recorded. From a blood sample, DNA was isolated and genotyped by real-time PCR for polymorphisms of CYP2C9, VKORC1, CYP4F2 and EPHX1. A regression analysis was used to determine the independent effects of genetic and non-genetic factors on warfarin dose optimization. In our study, in addition to age, genetic variants of CYP2C9, VKORC1 and CYP4F2 were found to be significant predictor variables for the maintenance dose for warfarin, explaining 39.3% of dose variability. VKORC1 and CYP2C9 genotypes remain predictor variables of the warfarin dose, and we first found that CYP4F2 (rs2108622) contributes to dose variability in the Turkish population as well. These observations may be of benefit to future translation research with a view to global personalized medicine in regions hitherto understudied such as the Turkish population so as to rationalize initial warfarin dose and reduce the burden of frequent INR measurements.