Deep-learning tissue clocks map aging across 40 human tissue types
- What happened
- Researchers analyzed 25,712 histopathology whole-slide images from 40 tissue types across 983 GTEx donors and trained models that estimated tissue-specific biological age. The resulting signatures were associated with established aging markers and disease-relevant organ aging in independent cohorts.
- What it means
- Tissue architecture may provide another scalable way to measure organ-specific aging, potentially strengthening biomarker validation and future trial endpoints.
- Caveat
- This is observational biomarker research, not an intervention trial. The models identify age-related signatures and disease associations; they do not show that changing a tissue-clock score improves health or lifespan.
