Candidate Gene Genotypes, Along with Conventional Risk Factor Assessment, Improve Estimation of Coronary Heart Disease Risk in Healthy UK Men

Abstract

Abstract Background: One of the aims of cardiovascular genetics is to test the efficacy of the use of genetic information to predict cardiovascular risk. We therefore investigated whether inclusion of a set of common variants in candidate genes along with conventional risk factor (CRF) assessment enhanced coronary heart disease (CHD)-risk algorithms. Methods: We followed middle-aged men in the prospective Northwick Park Heart Study II (NPHSII) for 10.8 years and analyzed complete trait and genotype information available on 2057 men (183 CHD events). Results: Of the 12 genes previously associated with CHD risk, in stepwise multivariate risk analysis, uncoupling protein 2 (UCP2; P = 0.0001), apolipoprotein E (APOE; P = 0.0003), lipoprotein lipase (LPL; P = 0.007), and apolipoprotein AIV (APOA4; P = 0.04) remained in the model. Their combined area under the ROC curve (AROC) was 0.62 (0.58–0.66) [12.6% detection rate for a 5% false positive rate (DR5)]. The AROC for the CRFs age, triglyceride, cholesterol, systolic blood pressure, and smoking was 0.66 (0.61–0.70) (DR5 = 14.2%). Combining CRFs and genotypes significantly improved discrimination (P = 0.001). Inclusion of previously demonstrated interactions of smoking with LPL, interleukin-6 (IL6), and platelet/endothelial cell adhesion molecule (PECAM1) genotypes increased the AROC to 0.72 (0.68–0.76) for a DR5 of 19.1% (P = 0.01 vs CRF combined with genotypes). Conclusions: For a modest panel of selected genotypes, CHD-risk estimates incorporating CRFs and genotype–risk factor interactions were more effective than risk estimates that used CRFs alone.