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Article

Plasma proteomic signatures of cellular aging predict human disease.

Nature medicine - 1 Jun 2026

Ding Daisy Yi, Bot Veronica Augustina, Chen Kenneth L, Groves James W, Pálovics Róbert, Masuda Daisuke, Farinas Amelia, Oh Hamilton Se-Hwee, Wagner Viktoria, Lu Nannan, Cruchaga Carlos, Isakova Alina, Schott Jonathan M, Wyss-Coray Tony

Abstract excerpt

Aging is asynchronous across cells and organs. Here we tested whether plasma proteomics can be used to analyze cell type-specific aging. From analyses of over 7,000 plasma proteins measured in 60,542 individuals, we developed machine learning models to estimate the biological age of over 40 cell types spanning neuronal, immune, glial, endocrine, epithelial and musculoskeletal origins. We observed that 20-25% of...

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Identifiers

PMID: 42297981
DOI: 10.1038/s41591-026-04446-y
PMCID: PMC13279268

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