{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:16:45Z","timestamp":1783700205018,"version":"3.55.0"},"posted":{"date-parts":[[2026,4,24]]},"group-title":"Cardiovascular Medicine","reference-count":30,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Age is the dominant risk factor for cardiovascular disease, yet individuals of the same chronological age can differ markedly in the organs and biological pathways through which cardiovascular vulnerability emerges. We used deep learning to estimate biological age from four cardiovascular data streams in more than 100,000 UK Biobank participants - 12-lead electrocardiograms, cardiac magnetic resonance imaging, carotid ultrasound, and retinal fundus photographs - representing electrical, structural, macrovascular, and microvascular domains. The resulting biological age gaps showed limited overlap across modalities in participants with complete phenotyping, and phenome-wide analyses linked each axis to distinct disease profiles. Genome-wide association analyses identified 38 independent loci, with largely modality-specific signals involving electrophysiologic, myocardial structural, vascular regulatory, and ocular\/microvascular pathways. Cross-trait LD score regression and polygenic risk scores further supported partial genetic separation of the four axes, with modality-specific associations for atrial fibrillation, heart failure, peripheral arterial disease, hypertension, diabetes, and diabetic retinopathy in UK Biobank and broadly concordant patterns in All of Us. Integration with myocardial single-cell transcriptomic data nominated distinct cellular contexts for these genetic signals. These findings suggest that AI-derived cardiovascular age is not a single biomarker of systemic senescence, but a family of related phenotypes that decompose cardiovascular aging into measurable electrical, myocardial, macrovascular, and microvascular modules.<\/jats:p>","DOI":"10.64898\/2026.04.22.26351478","type":"posted-content","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T22:35:13Z","timestamp":1777070113000},"source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Deep Learning Reveals the Modular Genetic Architecture of Cardiovascular Aging"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2980-3977","authenticated-orcid":false,"given":"Ryan B.","family":"Choi","sequence":"first","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4490-8857","authenticated-orcid":false,"given":"Philip M.","family":"Croon","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"},{"name":"Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9674-7130","authenticated-orcid":false,"given":"Sudheesha","family":"Perera","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lovedeep S.","family":"Dhingra","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Batinica","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4362-0720","authenticated-orcid":false,"given":"Evangelos K.","family":"Oikonomou","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9467-6199","authenticated-orcid":false,"given":"Rohan","family":"Khera","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA"},{"name":"Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA"},{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA"},{"name":"Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026071008500734000_2026.04.22.26351478v2.1","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1016\/S0140-6736(08)60313-X","article-title":"Age as a modifiable risk factor for cardiovascular disease","volume":"371","year":"2008","journal-title":"The Lancet"},{"key":"2026071008500734000_2026.04.22.26351478v2.2","doi-asserted-by":"publisher","DOI":"10.1093\/ehjci\/jet132"},{"key":"2026071008500734000_2026.04.22.26351478v2.3","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCEP.119.007284"},{"key":"2026071008500734000_2026.04.22.26351478v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2021.01.008"},{"key":"2026071008500734000_2026.04.22.26351478v2.5","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1038\/s41591-025-03856-8","article-title":"Large language model-based biological age prediction in large-scale populations","volume":"31","year":"2025","journal-title":"Nat. Med"},{"key":"2026071008500734000_2026.04.22.26351478v2.6","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1038\/s43587-022-00219-7","article-title":"Deep learning-based brain age prediction in normal aging and dementia","volume":"2","year":"2022","journal-title":"Nat. Aging"},{"key":"2026071008500734000_2026.04.22.26351478v2.7","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-026-04302-z"},{"key":"2026071008500734000_2026.04.22.26351478v2.8","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1038\/s41467-025-56741-w","article-title":"Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity","volume":"16","year":"2025","journal-title":"Nat. Commun"},{"key":"2026071008500734000_2026.04.22.26351478v2.9","doi-asserted-by":"crossref","unstructured":"Shah, M. et al. Environmental and genetic predictors of human cardiovascular ageing. Nat. Commun. 14, (2023).","DOI":"10.1038\/s41467-023-40566-6"},{"key":"2026071008500734000_2026.04.22.26351478v2.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25351-7"},{"key":"2026071008500734000_2026.04.22.26351478v2.11","doi-asserted-by":"crossref","unstructured":"Tay, J. H. , Barros, D. , Wang, W. , Wazny, V. K. & Maier, A. B . Biological age measured by DNA methylation clocks and frailty: a systematic review and meta-analysis. Lancet Healthy Longev. 6, (2025).","DOI":"10.1016\/j.lanhl.2025.100773"},{"key":"2026071008500734000_2026.04.22.26351478v2.12","doi-asserted-by":"crossref","first-page":"9044","DOI":"10.1038\/s41467-025-64835-8","article-title":"Gene expression composite scores of cellular senescence predict aging health outcomes in the Health and Retirement Study","volume":"16","year":"2025","journal-title":"Nat. Commun"},{"key":"2026071008500734000_2026.04.22.26351478v2.13","first-page":"158","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat","volume":"2","year":"2018","journal-title":"Biomed. Eng"},{"key":"2026071008500734000_2026.04.22.26351478v2.14","doi-asserted-by":"publisher","DOI":"10.1002\/clc.22934"},{"key":"2026071008500734000_2026.04.22.26351478v2.15","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03164-7"},{"key":"2026071008500734000_2026.04.22.26351478v2.16","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/s41467-025-67369-1","article-title":"Integrative epigenetics and transcriptomics identify aging genes in human blood","volume":"17","year":"2026","journal-title":"Nat. Commun"},{"key":"2026071008500734000_2026.04.22.26351478v2.17","first-page":"121","article-title":"Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain","volume":"3","year":"2023","journal-title":"Nat. Aging"},{"key":"2026071008500734000_2026.04.22.26351478v2.18","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/s41586-026-10542-3","article-title":"Universal transcriptomic hallmarks of mammalian ageing and mortality","volume":"654","year":"2026","journal-title":"Nature"},{"key":"2026071008500734000_2026.04.22.26351478v2.19","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0719-5"},{"key":"2026071008500734000_2026.04.22.26351478v2.20","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.jacep.2024.02.011","article-title":"Assessing Biological Age","volume":"10","year":"2024","journal-title":"JACC Clin. Electrophysiol."},{"key":"2026071008500734000_2026.04.22.26351478v2.21","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0579-z"},{"key":"2026071008500734000_2026.04.22.26351478v2.22","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06957-x"},{"key":"2026071008500734000_2026.04.22.26351478v2.23","doi-asserted-by":"publisher","DOI":"10.1038\/S41588-021-00870-7"},{"key":"2026071008500734000_2026.04.22.26351478v2.24","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3211"},{"key":"2026071008500734000_2026.04.22.26351478v2.25","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-01261-5"},{"key":"2026071008500734000_2026.04.22.26351478v2.26","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz082"},{"key":"2026071008500734000_2026.04.22.26351478v2.27","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/s44161-022-00028-6","article-title":"Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat","volume":"1","year":"2022","journal-title":"Cardiovasc. Res"},{"key":"2026071008500734000_2026.04.22.26351478v2.28","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.4096"},{"key":"2026071008500734000_2026.04.22.26351478v2.29","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-022-01167-z"},{"key":"2026071008500734000_2026.04.22.26351478v2.30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004219"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.04.22.26351478","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:50:18Z","timestamp":1783698618000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.04.22.26351478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":30,"URL":"https:\/\/doi.org\/10.64898\/2026.04.22.26351478","relation":{},"subject":[],"published":{"date-parts":[[2026,4,24]]},"subtype":"preprint"}}