Phenotypic Age Acceleration for Stratification of Mortality Risk and Survival Benefits in major chronic disease Populations: A Prospective Cohort Study

Phenotypic Age Acceleration for Stratification of Mortality Risk and Survival Benefits in major chronic disease Populations: A Prospective Cohort 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 Phenotypic Age Acceleration for Stratification of Mortality Risk and Survival Benefits in major chronic disease Populations: A Prospective Cohort Study Xi-jian Dai, Zheng Zhang, Xintong Ge, Han Zhou, Pengfei Yu, Yihui Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9024544/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 Background Biological aging reflects multisystem physiological decline and is a key driver of chronic diseases and mortality. However, integrative metrics that capture its complexity and mediate the effects of lifestyle and socioeconomic factors remain limited. We aimed to evaluate Phenotypic Age Acceleration (PhenoAgeAccel) as an indicator of multisystem aging and its utility in predicting mortality risk and residual life expectancy in individuals with chronic diseases (diabetes, dementia, cancer, and chronic respiratory diseases). Methods This prospective analysis utilized data from the UK Biobank (n = 353,619) and the Chinese cohort (n = 307,329). Multivariable-adjusted Cox regression and Royston-Parmar flexible parametric survival models were used to assess mortality risk and life expectancy. Mediation analyses quantified pathways through which lifestyle and socioeconomic factors influence outcomes via PhenoAgeAccel. Findings: In both the UK Biobank and Chinese cohorts, accelerated phenotypic aging was consistently associated with higher mortality risk and reduced life expectancy, with particularly pronounced effects among individuals with chronic diseases. Non-accelerated aging conferred substantial survival benefits, with risk reductions of 18–33% compared to severe acceleration. Life expectancy analyses revealed survival advantages of 3.87 years in Chinese cohort and 5.94 years in the the UK Biobank associated with non-accelerated versus severe aging. The association remained independent of sociodemographic, lifestyle, and both sexes, and was validated through extensive sensitivity analyses. Mediation analyses demonstrated PhenoAgeAccel partially mediated the pathways of adverse socioeconomic and poor lifestyle leading to mortality. Longitudinal cluster analysis further identified distinct aging patterns, where stable non-accelerated aging correlated with a survival advantage exceeding 12 years compared to rapidly accelerating trajectories. Finally, we developed PhenoDis-M, a digital tool to translate these insights into actionable preventive strategies, which was validated by an independent prospective Chinese cohort (n = 2789, AUC = 0.82). Interpretation: PhenoAgeAccel serves as a robust, integrative biomarker of biological aging that captures multisystem physiological decline and mediates key risk pathways. This work supports the development of targeted prevention strategies and advances the translation of geroscience into public health practice. Clinical Trial Registration Not applicable. This study is an observational, prospective cohort study and is not a clinical trial. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors phenotypic aging PhenoAgeAccel life expectancy mortality risk major chronic diseases biological aging Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx 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. 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-9024544","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604909982,"identity":"79799f25-40da-4019-95ed-dabab9782ce0","order_by":0,"name":"Xi-jian 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aging","lastPublishedDoi":"10.21203/rs.3.rs-9024544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9024544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBiological aging reflects multisystem physiological decline and is a key driver of chronic diseases and mortality. However, integrative metrics that capture its complexity and mediate the effects of lifestyle and socioeconomic factors remain limited. We aimed to evaluate Phenotypic Age Acceleration (PhenoAgeAccel) as an indicator of multisystem aging and its utility in predicting mortality risk and residual life expectancy in individuals with chronic diseases (diabetes, dementia, cancer, and chronic respiratory diseases).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective analysis utilized data from the UK Biobank (n\u0026thinsp;=\u0026thinsp;353,619) and the Chinese cohort (n\u0026thinsp;=\u0026thinsp;307,329). Multivariable-adjusted Cox regression and Royston-Parmar flexible parametric survival models were used to assess mortality risk and life expectancy. Mediation analyses quantified pathways through which lifestyle and socioeconomic factors influence outcomes via PhenoAgeAccel.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eIn both the UK Biobank and Chinese cohorts, accelerated phenotypic aging was consistently associated with higher mortality risk and reduced life expectancy, with particularly pronounced effects among individuals with chronic diseases. Non-accelerated aging conferred substantial survival benefits, with risk reductions of 18\u0026ndash;33% compared to severe acceleration. Life expectancy analyses revealed survival advantages of 3.87 years in Chinese cohort and 5.94 years in the the UK Biobank associated with non-accelerated versus severe aging. The association remained independent of sociodemographic, lifestyle, and both sexes, and was validated through extensive sensitivity analyses. Mediation analyses demonstrated PhenoAgeAccel partially mediated the pathways of adverse socioeconomic and poor lifestyle leading to mortality. Longitudinal cluster analysis further identified distinct aging patterns, where stable non-accelerated aging correlated with a survival advantage exceeding 12 years compared to rapidly accelerating trajectories. Finally, we developed PhenoDis-M, a digital tool to translate these insights into actionable preventive strategies, which was validated by an independent prospective Chinese cohort (n\u0026thinsp;=\u0026thinsp;2789, AUC\u0026thinsp;=\u0026thinsp;0.82).\u003c/p\u003e\u003ch2\u003eInterpretation:\u003c/h2\u003e \u003cp\u003ePhenoAgeAccel serves as a robust, integrative biomarker of biological aging that captures multisystem physiological decline and mediates key risk pathways. This work supports the development of targeted prevention strategies and advances the translation of geroscience into public health practice.\u003c/p\u003e\u003ch2\u003eClinical Trial Registration\u003c/h2\u003e \u003cp\u003eNot applicable. This study is an observational, prospective cohort study and is not a clinical trial.\u003c/p\u003e","manuscriptTitle":"Phenotypic Age Acceleration for Stratification of Mortality Risk and Survival Benefits in major chronic disease Populations: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 19:28:24","doi":"10.21203/rs.3.rs-9024544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce7b70d5-0767-481b-954f-5295e7d11e24","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64373647,"name":"Health sciences/Biomarkers"},{"id":64373648,"name":"Health sciences/Diseases"},{"id":64373649,"name":"Health sciences/Health care"},{"id":64373650,"name":"Health sciences/Medical research"},{"id":64373651,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-10T13:56:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 19:28:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9024544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9024544","identity":"rs-9024544","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}