Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention.

ABSTRACT: Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our

SUBMITTER: Fong S 

PROVIDER: S-EPMC11333290 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature