Plasma proteomic models of cell-type aging predict disease in 60,542 people
In an abstract, machine-learning models built from over 7,000 plasma proteins in 60,542 people estimated biological age for over 40 cell types; these cell-type aging signatures were associated with disease status and predicted incident disease and mortality over 15 years. [1]
Automatic summary · not editorially reviewed
The key findings
Machine-learning models estimated biological age for over 40 cell types from over 7,000 plasma proteins in 60,542 individuals. [1]
Cellular aging signatures were associated with disease status and predicted incident disease and mortality over 15 years of follow-up. [1]
A polycellular aging risk score stratified mortality risk across cohorts and proteomics platforms. [1]
The source at a glance
- Study type / format
- Abstract of a proteomic cohort analysis with machine-learning models
- Population / context
- 60,542 individuals with plasma protein measurements
- Summary based on
- Study abstract
- Original publication
- June 15, 2026
Proteomic age models for over 40 cell types
The authors describe aging as asynchronous across cells and organs. They tested whether plasma proteomics can capture cell type-specific aging. Using measurements of over 7,000 plasma proteins in 60,542 individuals, they developed machine-learning models to estimate the biological age of over 40 cell types from neuronal, immune, glial, endocrine, epithelial and musculoskeletal origins.
Supporting source [1]
Accelerated aging patterns and disease associations
About 20–25% of individuals showed accelerated aging in a single cell type, and 1–3% in 10 or more cell types. The cellular aging signatures were associated with disease status and predicted incident disease and mortality over 15 years of follow-up. APOE4 carriers had older astrocytes but younger macrophages than APOE3 carriers; APOE2 showed inverse associations. Extreme astrocyte aging tripled incident Alzheimer's Disease risk in people with two APOE4 alleles, while youthful astrocytes reduced risk. Extremely aged versus youthful skeletal myocytes carried a 12.7-fold higher risk of amyotrophic lateral sclerosis. In smokers, extreme respiratory epithelial cell aging was associated with 58% higher lung cancer risk than smoking alone.
Supporting source [1]
Survival, risk score and abstract-only scope
Specific cellular vulnerabilities and cumulative cellular aging burden influenced survival, with youthful immune and neuronal cell types conferring protective effects. The authors also developed a polycellular aging risk score that stratified mortality risk across cohorts and proteomics platforms. They frame these findings as a framework for quantifying human physiology at cellular resolution. Because only the abstract was reviewed, details of design, adjustment and validation are not available here, and these associations do not establish causation.
Supporting source [1]
How to interpret this
Only the abstract was reviewed; the full text was not reviewed. Design details, adjustment and validation are not reported here. Associations are not causal, and the risk score is a research framework, not a validated clinical tool.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Ding DY, Bot VA, Chen KL, Groves JW, Pálovics R, Masuda D, Farinas A, Oh HS, Wagner V, Lu N, Global Neurodegeneration Proteomics Consortium (GNPC), Cruchaga C, Isakova A, Schott JM, Wyss-Coray T. · europepmc.org · June 15, 2026