Organ Function-Derived Protein Clocks Demonstrate Enhanced Prediction of Organ-Specific Disease and Mortality Risk

Organ Function-Derived Protein Clocks Demonstrate Enhanced Prediction of Organ-Specific Disease and Mortality Risk | 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 Organ Function-Derived Protein Clocks Demonstrate Enhanced Prediction of Organ-Specific Disease and Mortality Risk Sijia Wang, Qinglin Song, Yi Li, He Huang, Yuqi Yang, Chenyihang Xiong, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6273836/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 Traditional aging clocks, typically taking chronological age or mortality as targets, face a trade-off between predictive accuracy and biological interpretability. To address this limitation, we developed protein organ clocks (POA), a novel approach that integrates aging indices derived from organ function indicators and blood proteomic data. Systematic comparison with four traditional blood protein clocks and three organ-specific clocks revealed that POA outperformed these models in associating with aging markers, predicting the risk of major organ-related diseases and mortality, and identifying clinically relevant organ aging subtypes, capturing inter-individual heterogeneity in organ aging. Critically, we constructed reduced POA models using 387 proteins that closely replicate the performance of full models, significantly lowering analytical burden and enabling feasible large-scale clinical application. The superior performance of POA stems from its integrated analysis of organ function and plasma proteomics, which enhances aging signal detection, improves biological interpretability, and strengthens clinical translational potential. To facilitate broad adoption, we developed a web server ( https://www.biosino.org/poa ) that allows users to upload proteomics data and calculate POAs for the body and eight organs. This work establishes a new paradigm for aging quantification and informs strategies to extend healthy lifespan and optimize disease prevention. Health sciences/Biomarkers/Predictive markers Biological sciences/Physiology/Ageing Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable.xlsx supplementary table SupplementaryInformation.docx supplementary information 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-6273836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449071877,"identity":"bbe3cf1a-ddb4-44ae-ac1d-9b4237a46c74","order_by":0,"name":"Sijia 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