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Abstract
Analysis of over 7,000 plasma proteins from 60,542 individuals revealed that 20-25% exhibited accelerated aging in a single cell type.
- Machine learning models were developed to estimate biological age across more than 40 different cell types.
- Cellular aging signatures were found to be associated with disease status and could predict disease incidence and mortality over a 15-year follow-up period.
- Individuals with the APOE4 genotype exhibited older astrocytes but younger macrophages compared to those with the APOE3 genotype, while the APOE2 genotype showed opposite effects.
- Extreme aging of astrocytes was linked to a tripled risk of developing Alzheimer's Disease in individuals with two APOE4 alleles.
- Aged skeletal myocytes were associated with a 12.7-fold increased risk of amyotrophic lateral sclerosis compared to youthful myocytes.
- In smokers, extreme aging of respiratory epithelial cells was correlated with a 58% higher risk of lung cancer compared to smoking alone.
Simplified
Longevity & Aging