A Circulating Proteomic Signature of Allostatic Load Predicts Multisystem Disease and Mortality

A Circulating Proteomic Signature of Allostatic Load Predicts Multisystem Disease and Mortality | 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 A Circulating Proteomic Signature of Allostatic Load Predicts Multisystem Disease and Mortality Hua Zhao, Yufan Guan, Jie Shen, Li Li, Kai Zhang, Song liu, qianqian zhu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8881432/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Chronic stress contributes to the development of cardiometabolic, malignant, and other chronic diseases through cumulative multisystem physiological dysregulation, conceptualized as allostatic load (AL). However, traditional AL relies on heterogeneous clinical biomarkers that limit reproducibility and translational utility. Here, we develop and validate ProAL50, a proteomics-based measure of AL derived from 50 circulating proteins. Using high-dimensional plasma proteomic data from the UK Biobank, we constructed ProAL50 via penalized regression and stability selection and externally validated it in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. ProAL50 closely mirrored traditional AL in its associations with sociodemographic characteristics, lifestyle behaviors, physical and mental health, inflammation, and biological aging, supporting strong construct validity. Beyond replication, ProAL50 consistently demonstrated stronger associations with incident chronic diseases, including all cancers, type 2 diabetes, ischemic heart disease, chronic lung disease, and chronic kidney disease, and with all-cause and cause-specific mortality. Functional enrichment analyses revealed that ProAL50 proteins cluster within lipid metabolic and immune–inflammatory pathways. These findings establish ProAL50 as a scalable, biologically grounded measure of cumulative stress that not only replaces traditional AL but surpasses it in predicting disease risk and mortality, offering a novel tool for population health and translational research. Health sciences/Biomarkers/Predictive markers Health sciences/Risk factors Full Text Additional Declarations There is NO Competing Interest. Supplementary Files CopyofSupplements02022026.xlsx Supplement materials Cite Share Download PDF Status: Under Review 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-8881432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619070507,"identity":"3567d949-41f3-49f0-9db1-8bfed9554637","order_by":0,"name":"Hua 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