Polokhin Aleksandr, Pronina Anna, Boev Andrey, Gorbunov Stas
AURA Devices, Inc., Wilmington, DE, USA.
J Electr Bioimpedance. 2022 Jun 25;13(1):31-38. doi: 10.2478/joeb-2022-0006. eCollection 2022 Jan.
Fat-free mass (FFM) estimation has dramatic importance for body composition evaluation, often providing a basis for treatment of obesity and muscular dystrophy. However, current methods of FFM estimation have several drawbacks, usually related to either cost-effectiveness and equipment size (dual-energy X-ray absorptiometry (DEXA) scan) or model limitations. In this study, we present and validate a new FFM estimation model based on hand-to-hand bioimpedance analysis (BIA) and arm volume. Forty-two participants underwent a full-body DEXA scan, a series of anthropometric measurements, and upper-body BIA measurements with the custom-designed wearable wrist-worn impedance meter. A new two truncated cones (TTC) model was trained on DEXA data and achieved the best performance metrics of 0.886 ± 0.051 r, 0.052 ± 0.009 % mean average error, and 6.884 ± 1.283 kg maximal residual error in FFM estimation. The model further demonstrated its effectiveness in Bland-Altman comparisons with the skinfold thickness-based FFM estimation method, achieving the least mean bias (0.007 kg). The novel TTC model can provide an alternative to full-body BIA measurements, demonstrating an accurate FFM estimation independently of population variables.
去脂体重（FFM）估计对于身体成分评估具有极其重要的意义，常常为肥胖症和肌肉萎缩症的治疗提供依据。然而，目前的FFM估计方法存在若干缺点，通常与成本效益和设备尺寸（双能X线吸收法（DEXA）扫描）或模型局限性有关。在本研究中，我们提出并验证了一种基于双手生物电阻抗分析（BIA）和手臂体积的新型FFM估计模型。42名参与者接受了全身DEXA扫描、一系列人体测量以及使用定制的可穿戴腕部阻抗仪进行的上身BIA测量。在DEXA数据上训练了一种新的双截锥（TTC）模型，该模型在FFM估计中实现了最佳性能指标，相关系数为0.886±0.051，平均平均误差为0.052±0.009%，最大残余误差为6.884±1.283 kg。该模型在与基于皮褶厚度的FFM估计方法的Bland-Altman比较中进一步证明了其有效性，实现了最小平均偏差（0.007 kg）。这种新型TTC模型可以替代全身BIA测量，独立于人群变量准确地估计FFM。
