Aging is the primary risk factor for chronic disease and is characterized by profound structural and architectural remodeling of human tissues. Here, we present a comprehensive assessment of these changes using 25,712 whole-slide histopathological images from 40 tissue types across 983 individuals in the Genotype-Tissue Expression cohort. By leveraging deep learning, we quantified nuanced morphological alterations to develop 'tissue clocks', predictors of biological age that reflect tissue structural integrity and physiological fitness. These clocks correlate with established aging markers, such as telomere attrition, subclinical pathologies and comorbidities. Through a systematic evaluation of biological aging rates across organs, we identified associations of tissue-specific age acceleration with demographic, lifestyle and medical factors, highlighting potentially modifiable risk factors that affect tissue aging. Furthermore, by integrating paired histology and transcriptomic data, we developed a strategy to predict tissue-specific age gaps directly from blood samples. We validated this approach by identifying disease-relevant organ aging across independent cohorts for eight prevalent diseases, including Alzheimer's disease, stroke and Crohn's disease. This work positions tissue architecture as a critical integrator of molecular and cellular changes over the course of aging, demonstrates that histopathological imaging provides a robust framework for monitoring tissue-specific aging and offers a scalable foundation for understanding organ-level physiological decline in health and disease.
