biostudies-literatureUnknownOlinger BNIA NIH HHSCellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset medRxiv : the preprint server for health sciences2024.08.01.24311368https://www.ebi.ac.uk/biostudies/studies/S-EPMC11451660biostudies-literatureA plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans.PMC11451660U54 AG079779Nyunt TSimonsick EMCandia JCui LGorospe MBasisty NRam APeng ZWalker KAOlinger BBanarjee RDey ADaya GFerrucci LTanaka TTsitsipatis DfalseA plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans.Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset 2024-01-01T00:00:00Z2024 Aug2025-08-27T03:10:56.676Z2025-04-04T20:54:47.289ZS-EPMC114516603937112610.1101/2024.08.01.24311368