EPIGENETIC clocks built from a small set of tissue-specific DNA methylation sites accurately predicted chronological age in human colon tissue, while also capturing anatomical differences between proximal and distal regions.
Researchers developed the machine learning model using healthy colon tissue and focused on tissue-unique CpG sites, where the DNA bases cytosine and guanine appear next to each other. These sites had previously been shown to undergo predictable methylation changes during ageing and disease. The approach aimed to address limitations of conventional epigenetic clocks, which generally rely on hundreds of thousands of CpG sites and large training cohorts.
The resulting epigenetic clock achieved a correlation of r=0.978 with chronological age, with a mean absolute error of 3.9 years. It also required an order of magnitude fewer sites and samples than traditional approaches, indicating that accurate age prediction could be achieved using a substantially more compact feature set.
The researchers next applied the colon epigenetic clock to tissue from individuals with HIV infection, inflammatory bowel disease, and colonic polyps. The data revealed consistent patterns of accelerated ageing across these conditions.
These findings linked disease-associated changes in the colon with biological age estimates derived from DNA methylation. In particular, the consistent acceleration observed across HIV infection, inflammatory bowel disease, and colonic polyps suggested that chronic disease and neoplasia were associated with measurable shifts in the ageing landscape of colon tissue.
Aspirin treatment was associated with partial deceleration of the ageing signal. However, the abstract did not provide further details on treatment duration, patient characteristics, or the magnitude of this association.
The study established tissue-unique CpGs as a potential basis for efficient and interpretable epigenetic clocks. By using a compact feature set and limited training data, the approach could support more focused assessment of biological ageing within specific tissues.
For healthcare professionals, the findings highlighted a possible relationship between disease processes and tissue ageing that may warrant further investigation. The observed associations with inflammation, polyps, and aspirin treatment also suggested that colon-specific ageing measures could provide a way to investigate how disease and treatment relate to biological ageing.
The study did not establish whether the clock could be used as a clinical diagnostic or prognostic tool. Further research would therefore be needed to determine how these tissue-specific epigenetic clocks could be applied in clinical practice and whether changes in predicted age translate into meaningful patient outcomes.
Reference
Sagy N et al. Colon-specific epigenetic clocks from minimal features reveal disease-driven aging. Sci Rep. 2026;DOI:10.1038/s41598-026-64349-3.
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