Novel biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity

We derived and tested a CT-based biological age (CTBA) model for predicting longevity, using an automated pipeline of explainable deep learning AI algorithms that quantify skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. These AI tool were applied to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median clinical follow-up, 5.3 years). Final weighted CT biomarker selection was based on index of prediction accuracy (IPA). The CTBA model significantly outperformed standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p < 0.001), despite any knowledge of the latter. Age- and sex-corrected survival hazard ratio (HR) for the highest-vs-lowest risk CTBA quartile was 8.73 (95% CI,8.14–9.36). Muscle density, aortic plaque burden, visceral fat density, and bone density contributed most. Unlike (epi)genetic and metabolomic approaches, this personalized phenotypic CTBA model can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment.