Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies
This work addresses the need for biomarkers to distinguish fast from slow ageing, which is crucial for understanding ageing biology and improving disease prevention, though it is incremental in applying deep learning to existing health data.
The researchers tackled the problem of identifying biomarkers for ageing by using contrastive deep learning on skin biopsy images to predict an individual's age and link these visual features to mortality and chronic diseases, demonstrating predictive capabilities from routine health data.
As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time.