Researchers developed tissue clocks to estimate biological age across 40 tissue types, identifying organ-specific aging patterns linked to chronic disease.
Scientists at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences and the Ludwig Boltzmann Institute for Network Medicine at the University of Vienna have developed artificial intelligence (AI)-based “tissue clocks” that estimate the biological age of human organs.
The study, published in Nature Medicine, analyzed more than 25,000 tissue samples across 40 tissue types. The findings show that organs age at different rates and that these changes can be detected via blood samples, potentially offering new methods for disease monitoring and early diagnosis.
Researchers utilized images from the Genotype-Tissue Expression Project, which included 25,712 high-resolution photographs of tissue slices from 983 individuals. These images represented approximately 480 million individual image tiles analyzed with vision models to determine how tissue architecture changes over time.
The predictive models achieved a mean prediction error of 4.9 years and outperformed existing DNA-based aging estimates in capturing tissue-specific pathology, according to the study. The predicted biological age was linked to hallmarks of aging such as telomere shortening, tissue pathology, and chronic disease count.
“Our tissues carry a remarkably detailed record of the aging process,” says André Rendeiro, principal investigator at the CeMM Research Center for Molecular Medicine and corresponding author of the study, in a release. “By combining histology images with artificial intelligence, we can detect patterns of biological aging that are invisible to the human eye and begin to understand how aging unfolds differently across the body.”
Variable Aging Rates Across Organs
The analysis revealed that aging does not occur uniformly across the body. Tissues such as the lung, kidney, pancreas, and adrenal gland showed signs of accelerated aging between the ages of 20 and 40. Other organs followed different trajectories, with the uterus showing a significant shift around the age of menopause.
“What stands out is how differently each organ ages, and how that shows up in tissue architecture,” says Ernesto Abila, co-first author of the study, in a release. “Deep learning lets us read these spatial patterns, capturing aging as architectural remodeling, not just molecular drift.”
While the tissue clocks captured the normal pace of aging, they also identified outliers whose tissues showed structural shifts ahead of their chronological age, the researchers report.
Predicting Organ Health from Blood Samples
By linking blood-based gene expression profiles with histologically derived tissue age gaps, the researchers developed predictors of tissue-specific biological age from blood samples. These predictors identified aging patterns associated with Alzheimer’s disease, Crohn’s disease, cystic fibrosis, vasculitis, diabetes, and stroke.
In Alzheimer’s disease, the strongest aging signal was detected specifically in the brain, while Crohn’s disease showed accelerated aging across the gastrointestinal tract, according to the study.
“This is a conceptual leap: using the language of tissue aging, learned from images, and translating it into something readable from a routine blood draw,” says Iva Buljan, co-first author of the study, in a release.
The researchers noted that different organs age in ways shaped by both systemic and tissue-specific factors. Future applications of this technology could contribute to minimally invasive diagnostics that monitor organ health through routine blood tests.
“This study highlights that aging is not simply a matter of chronological time,” says Yimin Zheng, co-first author of the study, in a release. “Different organs age in different ways, and these processes appear to be shaped by both systemic and tissue-specific factors.”
By connecting histology imaging, gene expression, and clinical data, the work provides a detailed view of how aging manifests throughout the human body, according to the research team.
