Prediction of total and regional body composition from 3D body shape - Chexuan Qiao; Emanuella De Lucia Rolfe; Ethan Mak; Akash Sengupta; Richard Powell; Laura P. E. Watson; Steven B. Heymsfield; John A. Shepherd; Nicholas Wareham; Soren Brage; Roberto Cipolla (npj Digital Medicine, 2024)
Abstract Accurate assessment of body composition is essential for evaluating the risk of chronic disease. 3D body shape, obtainable using smartphones, correlates strongly with body composition. We present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette (emulating a single photograph) paired with anthropometric traits, and apply it to the multi-phase Fenland study comprising 12,435 adults. Using baseline data, we derive models predicting total and regional body composition metrics from these meshes. In Fenland follow-up data, all metrics were predicted with high correlations (r > 0.86). We also evaluate a smartphone app which reconstructs a 3D mesh from phone images to predict body composition metrics; this analysis also showed strong correlations (r > 0.84) for all metrics. The 3D body shape approach is a valid alternative to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programmes.