Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Abstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age as recent work has found differences from chronological age ("delta age") to be associated with mortality and co-morbidities. However, the genetic underpinning of delta age is unknown, but crucial for understanding underlying individual risk. By performing a genome-wide association study using UK Biobank data (n=34,432), we identified eight loci associated with delta age (p ≤ 5 × 10−8 ), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.