Prediction of Total and Regional Body Composition from 3D Body Shape

Abstract

Accurate assessment of body composition is essential for evaluating the risk of chronic disease. Access to medical imaging methods is limited due to practical and ethical constraints. 3D body shape, which can be obtained using smartphones, correlates strongly with body composition. However, large-scale datasets containing 3D body shapes with paired anthropometric and metabolic traits are scarce. Here, we present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette which is paired with anthropometric traits (height, waist and hip circumferences), using a large dataset from the UK population-based Fenland study (12,435 adults, age 30–65 years at baseline phase 1). We predict total and regional body composition metrics using these meshes, and monitor changes in body composition between the baseline (phase 1) and a follow-up assessment (phase 2). We also evaluate a 3D body shape smartphone app which reconstructs a 3D body mesh from phone images to predict body composition metrics, and compare the results against the reference methods DXA and air plethysmography. In the Fenland validation dataset (follow up), total and regional body composition metrics were predicted accurately, achieving r>0.86 for all metrics. Absolute mean bias expressed as percentage of the mean was less than 2% for all metrics except for visceral fat mass and subcutaneous abdominal fat mass (4%, 7% respectively). Predictions for changes achieved r>0.60 for all metrics. The predicted metrics from the smartphone generated avatars also showed strong correlations r>0.84 for all metrics. The 3D body shape approach is a valid alternative tool to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programs.