CT-based biological age as a biomarker of aging and age-related disease assessment

Computed tomography (CT) enables in situ quantification of organ-level body composition and may provide a clinically interpretable marker of systemic aging. We developed and validated a CT-based biological age model using thoracoabdominal CT scans from 7837 adults (median age, 54 years [IQR 47–63]; 63.8% male) enrolled in a prospective health-screening cohort (2006–2017) with follow-up through 2021. The model was trained in a healthy population to define normal aging and evaluated in two independent datasets comprising participants with preexisting age-related diseases and disease-free participants with longitudinal follow-up. Chronological age was estimated from multi-organ body composition features, and the age-adjusted residual was defined as the age gap. In the development cohort, predicted age correlated moderately with chronological age ( R ² = 0.66; mean absolute error, 4.7 years). Each 1-SD increase in age gap was associated with higher odds of preexisting disease (odds ratio, 1.35; 95% CI, 1.26–1.45) and greater risk of incident age-related disease (hazard ratio, 1.24; 95% CI, 1.17–1.32) in the test datasets. Interpretability analyses revealed disease-specific aging patterns, while unhealthy lifestyle factors were associated with accelerated aging. CT-derived biological age may support opportunistic assessment of systemic and organ-specific aging and future disease risk.