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

Julian Libiseller-Egger, Jody E. Phelan, Zachi I. Attia, Ernest Diez Benavente, Susana Campino, Paul Andrew Friedman, Francisco Lopez-Jimenez, David A. Leon, Taane G. Clark

Research output: Contribution to journalArticlepeer-review

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, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and 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.

Original languageEnglish (US)
Article number22625
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

ASJC Scopus subject areas

  • General

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