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Cellular Aging Signatures in the Plasma Proteome Record Human Health and Disease

2026-02-12

Abstract excerpt

<h4>ABSTRACT</h4> Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality remains unknown. We developed machine learning models to estimate the biological age of more than 40 distinct cell types—spanning neuronal, immune, glial, endocrine, epithelial, and musculoskeletal origins—using over 7,000 plasma proteins measured in 6...

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Literature Corpus work
1ca56275-c21e-5c5b-a655-041bb6bf3000
DOI
10.64898/2026.02.10.704909
Open publication

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Cellular Aging Signatures in the Plasma Proteome Record Human Health and DiseaseDOI 10.64898/2026.02.10.704909
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