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Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies

Kaustubh Chakradeo, Pernille Nielsen, Lise Mette Rahbek Gjerdrum, Gry Sahl Hansen, David A Duchêne, Laust H Mortensen, Majken K Jensen, Samir Bhatt

TL;DR

This 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.

Abstract

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.

Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies

TL;DR

This 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.

Abstract

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.

Paper Structure

This paper contains 26 sections, 2 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Results
  3. Discussion
  4. Methods
  5. Acknowledgements
  6. Author contributions statement
  7. Supplementary

Figures (5)

  • Figure 1: Overview of the Workflow
  • Figure 2: Predicting ages using extracted features from the biopsies
  • Figure 3: Attention maps for predicting age- patches from skin biopsies showing ageing biomarkers
  • Figure 4: Hazard ratios comparison. Left- Using observed or actual age, and actual prevalent diseases at the time of biopsy.Right- Using predicted age and predicted diseases from the CDL extracted features.
  • Figure 5: Contrastive Learning Method for extracting ageing information from skin biopsies