Risk prediction of atrial fibrillation in the community combining biomarkers and genetics

Abstract

Abstract Aims Classical cardiovascular risk factors (CVRFs), biomarkers, and common genetic variation have been suggested for risk assessment of atrial fibrillation (AF). To evaluate their clinical potential, we analysed their individual and combined ability of AF prediction. Methods and results In N = 6945 individuals of the FINRISK 1997 cohort, we assessed the predictive value of CVRF, N-terminal pro B-type natriuretic peptide (NT-proBNP), and 145 recently identified single-nucleotide polymorphisms (SNPs) combined in a developed polygenic risk score (PRS) for incident AF. Over a median follow-up of 17.8 years, n = 551 participants (7.9%) developed AF. In multivariable-adjusted Cox proportional hazard models, NT-proBNP [hazard ratio (HR) of log transformed values 4.77; 95% confidence interval (CI) 3.66–6.22; P < 0.001] and the PRS (HR 2.18; 95% CI 1.88–2.53; P < 0.001) were significantly related to incident AF. The discriminatory ability improved asymptotically with increasing numbers of SNPs. Compared with a clinical model, AF risk prediction was significantly improved by addition of NT-proBNP and the PRS. The C-statistic for the combination of CVRF, NT-proBNP, and the PRS reached 0.83 compared with 0.79 for CVRF only (P < 0.001). A replication in the Dutch Prevention of REnal and Vascular ENd-stage Disease (PREVEND) cohort revealed similar results. Comparing the highest vs. lowest quartile, NT-proBNP and the PRS both showed a more than three-fold increased AF risk. Age remained the strongest risk factor with a 16.7-fold increased risk of AF in the highest quartile. Conclusion The PRS and the established biomarker NT-proBNP showed comparable predictive ability. Both provided incremental predictive value over standard clinical variables. Further improvements for the PRS are likely with the discovery of additional SNPs.