ArticleScientific reports2022
Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 31 citations in OpenAlex.
- BubR1 Insufficiency Drives Transcriptomic Alterations and Pathology Associated With Cardiac Aging and Heart Failure.Aging cell · 2025Article
- Determinants of artificial intelligence electrocardiogram-derived age and its association with cardiovascular events and mortality: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Molecular convergence of risk variants for congenital heart defects leveraging a regulatory map of the human fetal heart.medRxiv : the preprint server for health sciences · 2024Article
- Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity.Blood advances · 2024Article
- Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.Cell reports. Medicine · 2024Article
- Genetics of Cardiac Aging Implicate Organ-Specific Variation.medRxiv : the preprint server for health sciences · 2024Article
- Advanced cardiac imaging, machine learning, and heart age for cardiovascular risk stratification.The international journal of cardiovascular imaging · 2024Article
- Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.European heart journal. Digital health · 2024Article
- Atrial fibrillation: pathophysiology, genetic and epigenetic mechanisms.The Lancet regional health. Europe · 2024Review
- The Potential for Artificial Intelligence Applied to Epigenetics.Mayo Clinic proceedings. Digital health · 2023Article
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- DNN-derived electrocardiographic age is associated with the atrial fibrillation risk in a Chinese population.Digital healthArticle
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Authors and funding
9 authors at 3 institutions in 4 countries.
Funding
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.
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