AI-based multiomics profiling for personalized prediction of cardiovascular disease: A prospective UK Biobank study

AI-based multiomics profiling for personalized prediction of cardiovascular disease: A prospective UK Biobank study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article AI-based multiomics profiling for personalized prediction of cardiovascular disease: A prospective UK Biobank study Qingpeng Zhang, Yan Luo, Nan Zhang, Jiannan Yang, Mengyao Cui, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6139124/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Genomics, metabolomics and proteomics offer complementary insights into the risk of cardiovascular diseases (CVDs), yet current risk prediction models lack the capability to comprehensively integrate such multiomics data and clinical information. Leveraging the in-depth data from 24,308 individuals in the UK Biobank, we developed a novel multitask deep learning model to simultaneously learn disease-specific, personalized proteomic (ProScore) and metabolomic (MetScore) risk scores for the nine most common CVD events by profiling 168 metabolites and 2,920 proteins of participants. Experiments demonstrated that, ProScore and MetScore could be used not only as the sole predictor of CVDs within 16.6 years (mean C-index range: 0.67–0.83 for ProScore and 0.62–0.73 for MetScore), but also showed complementary predictive power to clinical predictors. When combined with clinical data, these omics signatures significantly enhanced cardiovascular risk prediction, improving the mean delta C-index by 0.016–0.094 across various CVDs. Important CVD-related proteins, such as NT-proBNP, NPPB, and HAVCR1, and metabolites, including creatinine, albumin, and glycoprotein acetyls, were also identified by our model. Our findings suggest that incorporating multiomics profiling into clinical practice has the potential to enhance personalized risk assessments and enable earlier, more targeted interventions for various CVDs. The mechanistic insights provided by our models could guide the development of novel biomarkers and therapeutic targets and inform the repurposing of existing drugs, paving the way for precision medicine for primary prevention of CVDs. Health sciences/Diseases/Cardiovascular diseases Health sciences/Health care/Public health/Epidemiology Physical sciences/Engineering/Biomedical engineering Health sciences/Risk factors Genetic metabolomics proteomics cardiovascular diseases prediction models Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplementary material Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6139124","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424120703,"identity":"d1ad68a6-1185-4680-a400-2c84baaeb9a4","order_by":0,"name":"Qingpeng 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