Histological aging signatures for monitoring tissue-specific aging and disease.

Deep learning tissue clocks trained on 25,712 whole-slide histology images across 983 Genotype-Tissue Expression individuals quantify tissue-specific biological age linked to aging markers and disease-relevant organ aging.

• Why it matters: Aging drives chronic disease risk through structural remodeling, but scalable ways to monitor tissue-specific aging and relate it to molecular changes and disease are limited.
• What they did: The study used deep learning on histopathology from 40 tissue types to predict biological age, then tested correlations with telomere attrition, subclinical pathologies, and comorbidities. It also integrated paired histology and transcriptomic data to predict tissue-specific age gaps from blood, validating across independent cohorts for eight diseases.
• The result: Tissue age acceleration is associated with demographic, lifestyle, and medical factors, and blood-based predictions identify disease-relevant organ aging in independent cohorts for Alzheimer's disease, stroke, and Crohn's disease, among eight prevalent diseases.

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