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.

准确评估身体成分对于评估慢性病风险至关重要。使用智能手机可以获得的 3D 体型与身体成分密切相关。我们提出了一种新颖的方法，该方法将 3D 体网拟合到双能 X 射线吸收测定法 （DXA） 轮廓（模拟单张照片）与人体测量特征配对，并将其应用于由 12,435 名成年人组成的多阶段 Fenland 研究。使用基线数据，我们从这些网格中得出预测总和区域身体成分指标的模型。在 Fenland 随访数据中，所有指标均以高度相关性预测 （r > 0.86）。我们还评估了一款智能手机应用程序，该应用程序从手机图像中重建 3D 网格以预测身体成分指标;该分析还显示所有指标都具有很强的相关性 （r > 0.84）。3D 体型方法是医学成像的有效替代方案，它可以提供可访问的健康参数来监测生活方式干预计划的有效性。