A blood pressure genetic risk score is a significant predictor of incident cardiovascular events in 32,669 individuals

Abstract

Recent genome-wide association studies have identified genetic variants associated with blood pressure (BP). We investigated whether genetic risk scores (GRSs) constructed of these variants would predict incident cardiovascular disease (CVD) events. We genotyped 32 common single nucleotide polymorphisms in several Finnish cohorts, with up to 32,669 individuals after exclusion of prevalent CVD cases. The median follow-up was 9.8 years, during which 2295 incident CVD events occurred. We created GRSs separately for systolic BP and diastolic BP by multiplying the risk allele count of each single nucleotide polymorphism by the effect size estimated in published genome-wide association studies. We performed Cox regression analyses with and without adjustment for clinical factors, including BP at baseline in each cohort. The results were combined by inverse variance-weighted fixed-effects meta-analysis. The GRSs were strongly associated with systolic BP and diastolic BP, and baseline hypertension (all P<10(-62)). Hazard ratios comparing the highest quintiles of systolic BP and diastolic BP GRSs with the lowest quintiles after adjustment for age, age squared, and sex were 1.25 (1.07-1.46; P=0.006) and 1.23 (1.05-1.43; P=0.01), respectively, for incident coronary heart disease; 1.24 (1.01-1.53; P=0.04) and 1.35 (1.09-1.66; P=0.005), respectively, for incident stroke; and 1.23 (1.08-1.40; P=2 × 10(-6)) and 1.26 (1.11-1.44; P=5 × 10(-4)), respectively, for composite CVD. In conclusion, BP findings from genome-wide association studies are strongly replicated. GRSs comprising bona fide BP-single nucleotide polymorphisms predicted CVD risk, consistent with a lifelong effect on BP of these variants collectively.

Figure 1

Association of the SBP and DBP GRS quintiles with incident CVD. Cox regression models were adjusted for sex and study region. Age was used as the time scale and results were combined using inverse variance-weighted fixed-effects meta-analysis.