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- W4313397186 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 ([Formula: see text]), 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." @default.
- W4313397186 created "2023-01-06" @default.
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- W4313397186 date "2022-12-31" @default.
- W4313397186 modified "2023-10-18" @default.
- W4313397186 title "Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes" @default.
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- W4313397186 doi "https://doi.org/10.1038/s41598-022-27254-z" @default.
- W4313397186 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36587059" @default.
- W4313397186 hasPublicationYear "2022" @default.
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