{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:44:33Z","timestamp":1792611873367,"version":"4.5.0"},"reference-count":43,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30AG067988"],"award-info":[{"award-number":["P30AG067988"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG072694"],"award-info":[{"award-number":["R01AG072694"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Aging Cell"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The accumulation of senescent cells is a recognized hallmark of biological aging and is associated with the onset of multiple chronic medical conditions. Senescent cells exhibit a distinct secretory profile, known as the senescence\u2010associated secretory phenotype (\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    ), which can propagate cellular senescence to neighboring and distant tissues. Measuring\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    factors in blood serves as a practical proxy for cellular senescence burden and may help track disease states and intervention outcomes. We developed and validated a composite\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    Score by integrating large\u2010scale population proteomics data with a semi\u2010supervised deep learning framework. The analytical workflow included: (1) selection of biologically curated\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    proteins; (2) development of a Guided autoencoder with Transformer (\n                    <jats:styled-content style=\"fixed-case\">GAET<\/jats:styled-content>\n                    ) model using data from the\n                    <jats:styled-content style=\"fixed-case\">UK<\/jats:styled-content>\n                    Biobank Pharma Proteomics Project (\n                    <jats:styled-content style=\"fixed-case\">UKB<\/jats:styled-content>\n                    \u2010\n                    <jats:styled-content style=\"fixed-case\">PPP<\/jats:styled-content>\n                    ); (3) internal evaluation and association analyses within the\n                    <jats:styled-content style=\"fixed-case\">UK<\/jats:styled-content>\n                    Biobank; and (4) external validation and longitudinal assessment in an independent randomized clinical trial cohort. The deep learning\u2010based\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    Score was a strong, independent predictor of mortality risk and incident serious, chronic medical conditions (e.g., dementia,\n                    <jats:styled-content style=\"fixed-case\">COPD<\/jats:styled-content>\n                    , myocardial infarction, stroke). In an independent cohort, multimodal exercise significantly changed the\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    Score trajectory over 18\u2009months. Our findings support the potential of a deep learning\u2010derived\n                    <jats:styled-content style=\"fixed-case\">SASP<\/jats:styled-content>\n                    Score as a biomarker for systemic cellular senescence burden. Our statistical approach can offer enhanced interpretability and cross\u2010platform utility, providing a valuable tool for aging research and the evaluation of geroscience\u2010guided interventions.\n                  <\/jats:p>","DOI":"10.1111\/acel.70737","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:56:14Z","timestamp":1790394974000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep\u2010Learning Based Biomarker of Systemic Cellular Senescence Burden to Predict Mortality and Health Outcomes"],"prefix":"10.1111","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5284-4685","authenticated-orcid":false,"given":"Shangshu","family":"Zhao","sequence":"first","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4452-2380","authenticated-orcid":false,"given":"Chia\u2010Ling","family":"Kuo","sequence":"additional","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"},{"name":"Department of Public Health Sciences UConn Health  Farmington Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric J.","family":"Lenze","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Washington University School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie L.","family":"Wetherell","sequence":"additional","affiliation":[{"name":"Department of Psychiatry University of California San Diego  San Diego California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5006-3339","authenticated-orcid":false,"given":"Laura","family":"Haynes","sequence":"additional","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9278-5161","authenticated-orcid":false,"given":"Perla","family":"El\u2010Ahmad","sequence":"additional","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2013-719X","authenticated-orcid":false,"given":"Richard","family":"Fortinsky","sequence":"additional","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8387-7040","authenticated-orcid":false,"given":"George","family":"Kuchel","sequence":"additional","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trevor","family":"Harris","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Connecticut  Storrs Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Breno S.","family":"Diniz","sequence":"additional","affiliation":[{"name":"UConn Center on Aging UConn Health  Farmington Connecticut USA"},{"name":"Department of Psychiatry Washington University School of Medicine  St. Louis Missouri 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