Plasma proteomic signatures of cellular aging predict human disease - PubMed
4 hours ago
- #Proteomics
- #Aging
- #Disease Prediction
- Plasma proteomic analysis enables estimation of biological age for over 40 cell types using machine learning models.
- Accelerated aging in one cell type was observed in 20-25% of individuals, while 1-3% showed accelerated aging in 10+ cell types.
- Cellular aging signatures predicted disease risk and mortality over 15 years, with specific genotypes like APOE4 linked to astrocyte and macrophage aging differences.
- Extreme astrocyte aging in APOE4 carriers tripled Alzheimer's disease risk, while youthful astrocytes reduced it.
- Extreme skeletal myocyte aging increased amyotrophic lateral sclerosis risk by 12.7-fold, and smoking with respiratory epithelial cell aging raised lung cancer risk by 58%.
- A polycellular aging risk score stratified mortality risk, highlighting protective effects from youthful immune and neuronal cells.