Cellular Aging Signatures in the Plasma Proteome Record Human Health and Disease - PubMed
4 hours ago
- #plasma proteomics
- #aging
- #disease prediction
- Machine learning models estimate biological age of over 40 cell types using plasma proteomics.
- Study involves 7,000 plasma proteins from 60,000 individuals across three cohorts.
- 20-25% of individuals show accelerated aging in a single cell type; 1-3% in ten or more.
- APOE genotype has antagonistic aging effects: APOE4 carriers show older astrocytes but younger macrophages.
- Cellular aging signatures predict disease and mortality over 15 years.
- ALS strongly associated with skeletal myocyte aging (hazard ratio = 12.7).
- Alzheimer's disease linked to accelerated aging in multiple neural and peripheral cell types.
- Extreme astrocyte aging increases Alzheimer's risk, especially in APOE4/4 carriers.
- Respiratory cell aging identifies smokers at higher lung cancer risk.
- Myeloid aging predicts diabetes risk in normoglycemic individuals.
- Polycellular aging risk score provides robust mortality risk stratification.