Deep Learning Chest X-Ray Age, Epigenetic Aging Clocks and Associations with Age-Related Subclinical Disease in the Project Baseline Health Study

Abstract

Chronological age is a cornerstone of medical decision-making but is limited because individuals age at different rates. We recently released an open-source deep learning model to assess biological age from chest radiograph images (CXR-Age), which predicts incident all-cause and cardiovascular mortality better than chronological age. Here, we compare CXR-Age to two established epigenetic aging clocks (First generation- Horvath Age; Second generation- DNAm PhenoAge), to test which is more strongly associated with measures of cardiopulmonary disease. Our cohort consisted of 2,097 participants from the Project Baseline Health Study (PBHS), a prospective cohort study of individuals from four US sites enriched for cardiovascular and cardiometabolic disease risk factors. We found that CXR-Age was most strongly associated with the presence of coronary calcium, cardiovascular risk factors, worsening pulmonary function, increased frailty, and abundance in plasma of two proteins implicated in neuroinflammation and aging. Associations with second generation epigenetic clocks were weaker for pulmonary function and for all outcomes in younger adults. No associations were found with first generation clocks. These results suggest that opportunistic screening using CXR-Age may help identify high risk individuals who could benefit from directed screening and prevention.