A deep learning sex-specific body composition ageing biomarker using dual-energy X-ray absorptiometry scan.
Lian J., Cai P., Huang F., Huang J., Vardhanabhuti V.
BACKGROUND: Chronic diseases are closely linked to alterations in body composition, yet there is a need for reliable biomarkers to assess disease risk and progression. This study aimed to develop and validate a biological age indicator based on body composition derived from dual-energy X-ray absorptiometry (DXA) scans, offering a novel approach to evaluating health status and predicting disease outcomes. METHODS: A deep learning model was trained on a reference population from the UK Biobank to estimate body composition biological age (BCBA). The model's performance was assessed across various groups, including individuals with typical and atypical body composition, those with pre-existing diseases, and those who developed diseases after DXA imaging. Key metrics such as c-index were employed to examine BCBA's diagnostic and prognostic potential for type 2 diabetes, major adverse cardiovascular events (MACE), atherosclerotic cardiovascular disease (ASCVD), and hypertension. RESULTS: Here we show that BCBA strongly correlates with chronic disease diagnoses and risk prediction. BCBA demonstrated significant associations with type 2 diabetes (odds ratio 1.08 for females and 1.04 for males, p