VAM: A Multimodal Dynamical Foundation Model for Characterizing Human Aging Dynamics and Enabling Virtual Aging Perturbation

VAM: A Multimodal Dynamical Foundation Model for Characterizing Human Aging Dynamics and Enabling Virtual Aging Perturbation | 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 Technical Report VAM: A Multimodal Dynamical Foundation Model for Characterizing Human Aging Dynamics and Enabling Virtual Aging Perturbation Saijuan Chen, Zhenyi Wang, Chaofei Gao, Yongge Li, Rui Xia, Yuqi Yang, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9402213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Delaying aging and preventing age-related diseases require a precise, dynamic understanding of human aging. While biological age is widely used to quantify aging status, most existing methods rely on static estimates and fail to capture aging’s nonlinear dynamics or support in silico perturbation. Here, we propose the Virtual Aging Model (VAM), a multimodal framework that unites foundation models with dynamical network biomarker theory. Validated across the UK Biobank, NSPT, and VPPG cohorts, VAM captures nonlinear aging dynamics while its derived representations enable the identification of aging-associated molecular patterns and support in silico perturbation to prioritize aging-modulating candidates. Furthermore, we show that model-derived indices are associated with higher risk of multiple chronic diseases, indicating potential utility as early indicators of systemic instability. Collectively, our study presents VAM as an integrative framework that unifies aging quantification, dynamic modeling, and hypothesis generation for intervention strategies, thereby providing a systems-level view of aging and laying the foundation for future integrative and translational research. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Biological techniques/Bioinformatics Biological sciences/Systems biology/Nonlinear dynamics Biological sciences/Systems biology/Dynamic networks Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable3.Functionalenrichmentanalysisresults.xlsx Supplementary Table 3 SupplementaryTable2.Benchmarkresults.xlsx Supplementary Table 2 SupplementaryTable6.Mediationanalysisresults.xlsx Supplementary Table 6 SupplementaryTable5.TAandTDinvariouscancertypes.xlsx Supplementary Table 5 SupplementaryTable1.DatafieldsintheUKBiobank.xlsx Supplementary Table 1 SupplementaryNotesubmit.docx Supplementary Note SupplementaryTable4.Organspecificagingbiomarkers.xlsx Supplementary Table 4 Cite Share Download PDF Status: Posted 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. 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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-9402213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Technical Report","associatedPublications":[],"authors":[{"id":623913753,"identity":"5b1872f6-d628-4fe7-83ad-a2aee7901ac2","order_by":0,"name":"Saijuan 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