Prediction of total and regional body composition from 3D body shape.
NPJ digital medicine 2024 ; 7: 298.
Qiao C, Rolfe EL, Mak E, Sengupta A, Powell R, Watson LPE, Heymsfield SB, Shepherd JA, Wareham N, Brage S, Cipolla R
DOI : 10.1038/s41746-024-01289-0
PubMed ID : 39443585
PMCID : PMC11500346
URL : https://www.nature.com/articles/s41746-024-01289-0
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.