Objective:

To develop tissue clocks that estimate biological age from histology images and identify organ-specific aging patterns linked to disease.

Approach:

• Study Design: Analyzed 25,712 whole-slide images from 40 tissue types collected from 983 postmortem donors.
• Model Development: Computer models examined tissue features to predict biological age and calculate the tissue age gap.
• Validation: Tested models in independent cohorts involving 295 donors and combined histology findings with gene expression data from 1,205 blood samples.

Key Findings:

• Models estimated biological age with an average error of approximately five years.
• Larger age gaps were associated with shorter telomeres, more comorbidities, and subclinical pathological changes.
• In the cerebellum, greater age gaps were linked to myelin loss and ischemic changes; in the aorta, they were associated with wall thickening and structural damage related to vascular disease.
• Blood-based models corresponded with age gaps in specific organs related to chronic diseases.

Interpretation:

Histology images and blood gene expression may contain measurable signs of tissue-specific aging.

Limitations:

• Study did not determine if age gaps could predict disease before symptoms or diagnosis.
• Used postmortem samples with unequal gender representation.
• Lacked matching tissue samples in external blood cohorts.

Conclusion:

Prospective studies using prediagnostic samples are needed to assess clinical utility.

Sources:

• Nature Medicine