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

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.