P4796Risk prediction of atrial fibrillation in the community combining biomarkers and genetics

Abstract

Abstract Background Classical cardiovascular risk factors (CVRF), biomarkers and genetic variation have been suggested for risk assessment of atrial fibrillation (AF). Purpose To evaluate their clinical potential, we analysed their individual and combined effectiveness in AF prediction. Methods 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) for incident AF. Results 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) per standard deviation (SD) 1.90, 95% confidence interval (CI): 1.71–2.11, P<0.001) and the polygenic risk score (PRS) (HR per SD 1.66, 95% CI: 1.51–1.84, P<0.001) were significantly related to incident AF. The discriminatory ability improved asymptotically with increasing numbers of SNPs. Compared to a clinical model, AF risk prediction was significantly improved by addition of NT-proBNP and the PRS. The C-statistic for the combination of all CVRF, NT-proBNP and the PRS reached 0.82 compared to 0.77 for CVRF only (P<0.001). Comparing the highest versus lowest quartile, age remained the strongest risk factor with a 15-fold increased risk of AF. The highest quartiles of NT-proBNP and the PRS both showed an approximately 3-fold increased AF risk compared to the lowest quartiles. C-Index for AF prediction Conclusions The PRS and the established biomarker NT-proBNP predicted incident AF comparably. Both provided incremental predictive value over standard clinical variables. Further improvements for the PRS are likely with the discovery of additional SNPs. Acknowledgement/Funding European Research Council, German Ministry of Research and Education, DZHK, European Union Seventh Framework Programme, CHANCES, THL