Machine learning-based estimation of trunk fat percentage and its association with cardiometabolic risk leveraging two large national cohorts

Purpose This study aimed to develop and validate a machine learning model for accurate estimation of trunk fat percentage using readily available anthropometric measures, and to evaluate its discriminative performance for cardiometabolic diseases compared with conventional whole-body fat percentage. Methods We utilized data from the National Health and Nutrition Examination Survey (NHANES; 1999–2006 and 2011–2018) as the development cohort ( n = 30,443). Trunk fat percentage, measured by dual-energy X-ray absorptiometry (DXA), served as the gold standard. Six regression algorithms were evaluated, with model performance assessed by the coefficient of determination (R 2 ). External validation was performed using the China Health and Retirement Longitudinal Study (CHARLS) cohort ( n = 13,524), where the discriminative power for hypertension, dyslipidemia, diabetes, heart disease, and stroke was evaluated using the area under the receiver operating characteristic curve (AUC). Results The XGBoost model demonstrated superior performance in the development cohort, achieving an R 2 of 0.8509 on the test set. A simplified model utilizing only five variables (sex, waist circumference, height, weight, and age) retained 99.3% of the full model’s accuracy ( R 2 = 0.8450). In external validation, the machine learning-estimated trunk fat percentage consistently outperformed whole-body fat percentage across all cardiometabolic conditions, with the highest AUC improvement observed for diabetes (trunk fat AUC = 0.6607 vs. whole-body fat AUC = 0.6401; relative improvement of 3.22%). The average relative improvement in AUC across all endpoints was 2.77%. Conclusion This study presents a highly accurate and clinically practical machine learning model for trunk fat percentage estimation using five basic anthropometric measurements. External validation confirms that trunk fat percentage is a superior biomarker for identifying cardiometabolic risks compared to whole-body fat percentage. The model provides a reliable tool for non-invasive central adiposity assessment in large-scale epidemiological studies and clinical practice.