发表机构
Louisiana State University; Pennington Biomedical Research Center（路易斯安那州立大学; 彭宁顿生物医学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对非侵入式身体成分估计问题，提出$p$SADE-GNR框架，在临床数据验证中其相关加权模型的预测误差优于传统回归方法，为该领域提供了有效解决方案。
AI 中文摘要
身体成分指标（包括体脂率BFP、骨密度BMD、四肢瘦质量ALM）的准确估计对评估代谢、骨骼和肌肉健康至关重要。然而，使用双能X线吸收法DXA直接评估需要专用设备，且涉及电离辐射。本文提出一种目标感知、状态自适应$p$-狄利克雷能流图神经回归$p$SADE-GNR框架，用于从非侵入式人体测量数据中估计上述指标。神经编码器将受试者表征映射为隐藏状态，通过图$p$-狄利克雷能流的状态自适应前向欧拉离散化，在特定于指标的受试者相似度图上传播。图距离通过原始或潜在坐标与指标的归一化绝对训练折相关度进行加权。使用彭宁顿生物医学研究中心的临床数据及五折交叉验证，采用原始标准化测量值的相关加权模型在全部9个主要指标-队列组合中实现了最低均方根误差，且在9次比较中的8次优于此前报道的支持向量回归或最小二乘支持向量回归参考值。自编码器、变分自编码器及高斯混合变分自编码器表征通常未提升主要指标预测性能，也未降低计算成本。在包含ALM、BMD、BFP作为预测因子的探索性年龄预测分析中，相关加权GMVAE模型在全部3个队列中实现了最低平均误差。这些结果支持采用目标感知、状态自适应$p$-狄利克雷图神经回归进行非侵入式身体成分估计。
英文摘要
Accurate estimation of body-composition outcomes, including body fat percentage (BFP), bone mineral density (BMD), and appendicular lean mass (ALM), is important for evaluating metabolic, skeletal, and muscular health. Direct assessment using dual-energy X-ray absorptiometry (DXA), however, requires specialized equipment and involves ionizing radiation. We propose a target-aware, state-adaptive $p$-Dirichlet energy-flow graph neural regression ($p$SADE-GNR) framework for estimating these outcomes from non-invasive anthropometric measurements. A neural encoder maps participant representations to hidden states that are propagated over an outcome-specific participant-similarity graph by a state-adaptive forward-Euler discretization of the graph $p$-Dirichlet energy flow. Graph distances weight each original or latent coordinate by its normalized absolute training-fold correlation with the outcome. Using clinical data from the Pennington Biomedical Research Center and five-fold cross-validation, the correlation-weighted model using the original standardized measurements achieved the lowest root mean squared error in all nine primary outcome-cohort combinations and outperformed previously reported support vector regression or least-squares support vector regression reference values in eight of nine comparisons. Autoencoder, variational-autoencoder, and Gaussian-mixture variational-autoencoder representations generally did not improve primary-outcome prediction or reduce computational cost. In an exploratory age-prediction analysis including ALM, BMD, and BFP as predictors, the correlation-weighted GMVAE model achieved the lowest mean error in all three cohorts. These results support target-aware, state-adaptive $p$-Dirichlet graph neural regression for non-invasive body-composition estimation.
Comments21 pages, 7 figures, 3 tables. The first four authors contribute equally to this work and are listed alphabetically. Corresponding authors: N. Drenska, Y. Wang and S. B. Heymsfield
