Organ aging signatures in the plasma proteome track health and disease.
Level 3 - non-randomized controlled study
Multi-cohort observational study using machine learning to model proteomic aging markers and clinical outcomes.
PubMed 38057571 · doi:10.1038/s41586-023-06802-1
What was done
Machine learning models were trained on human blood plasma proteins originating from specific organs to measure organ-specific biological aging across 11 major organs. Organ age models were evaluated across five independent cohorts encompassing 5,676 adults across the lifespan.
What was found
Nearly 20% of individuals exhibited strongly accelerated aging in one organ, and 1.7% demonstrated multi-organ accelerated aging. Accelerated organ aging was associated with a 20% to 50% increase in mortality risk. Accelerated heart aging was linked to a 250% increased risk of heart failure. In addition, accelerated brain and vascular aging predicted Alzheimer's disease progression independently and with comparable strength to plasma pTau-181.
Why it matters
This framework allows non-invasive tracking of organ-specific aging from blood samples, linking organ-level biological decay directly to organ-specific pathology and mortality.
Limits
The study is observational, precluding causal determination between specific protein signatures and organ decline. The abstract does not report effect size confidence intervals, population demographics, or prospective intervention data to determine if organ aging rates can be modified.