Background: Genetic risk scores (GRS) have shown to be useful for examining the cumulative predictive ability of genetic variation on cardiovascular disease, and they are independent to conventional demographic risk factors. However, it is still unknown if they contribute to risk prediction independently from traditional ECG markers. Our aim was to study the predictive value for all-cause mortality (ACM) of a model combining ECG and a GRS.
Methods: ECG and genetic data was analysed in 53,079 individuals without known cardiovascular disease from the UK Biobank. Included ECG markers were: heart rate, PR interval, QRS duration, corrected QT interval (QTc), and resting Tpe interval. A GRS for coronary artery disease was derived using 192 genome-wide significant variants downloaded from the Polygenic Score Catalog. The primary endpoint was ACM.
Results: A total of 986 ACM events were recorded during a median follow-up period of 7 years. Variables significantly associated with ACM in a univariate Cox regression were sex (hazard ratio, HR = 1.86, P = 2.38 x 1021), age (HR = 2.08 per standard deviation [SD], P = 1.88 x 1073), heart rate (HR = 1.12 per SD, P = 4.34 x 107), QTc (HR = 1.12 per SD, P = 1.31 x 106) and the GRS (HR = 1.07 per SD, P = 3.46 x 102). When combining them together in a multivariate Cox regression model, sex (HR = 1.80, P = 3.58 x 1019), age (HR = 2.05 per SD, P = 2.72 x 1071), heart rate (HR = 1.14 per SD, P = 1.24 x 109) and the GRS (HR = 1.08 per SD, P = 1.54 x 102) remained significantly associated with ACM.
Conclusions: Our findings suggest that ECG parameters and GRSs independently contribute to ACM risk prediction, indicating they can be combined together into standard predictive scores.