College of Nursing and Health Sciences, Texas A&M International University, Laredo, TX, USA.
School of Health and Rehabilitation Science, College of Medicine, The Ohio State University, Columbus, OH, USA.
Eur J Clin Nutr. 2022 Apr;76(4):581-587. doi: 10.1038/s41430-021-00982-7. Epub 2021 Jul 19.
BACKGROUND/OBJECTIVES: Previous research has compared 2- and 3-compartment (2C and 3C, respectively) models against criterion 4-compartment (4C) models while utilizing the same body density (D) method for all measures. This design induces an inherent bias and obscures the added benefit of a 3C model over the simpler 2-compartment (2C) models.
The purpose of this study was to determine the effect of total body water estimates via single-frequency (SF-BIA) and multi-frequency (MF-BIA) bioimpedance analysis on body fat estimates derived from air displacement plethysmography (ADP)-derived 3C models.
SUBJECTS/METHODS: A sample of 95 females and 82 males (n = 177) participated in this study. Underwater weighing, dual energy X-ray absorptiometry, and bioimpedance spectroscopy were used to calculate percent fat (%Fat) via a criterion 4C model (4C). %Fat was predicted via 3C (ADP and MF-BIA), 3C (ADP and SF-BIA), and a stand-alone 2-compartment (2C) model, based upon ADP, when using Siri and Brozek body density conversion formulas (2C and 2C. respectively).
The standard error of estimate (SEE) was lowest for 3C when evaluated in females and males (2.72% and 2.31%, respectively) and highest for 2C (3.98% and 3.84%, respectively). Similarly, the total error (TE) for females and males was lowest for 3C (3.21% and 2.67%, respectively) and highest for 2C (4.58% and 4.48%, respectively) and 2C (4.65% and 4.33%, respectively).
Results suggest that SF-BIA and MF-BIA can improve the estimation of %Fat, beyond simpler 2C models, when integrated with ADP in a more advanced 3C model. Furthermore, the present study revealed that 3C was the best overall prediction model based upon TE values. The current study results support the integration of ADP and bioimpedance technology as part of a 3C model for the improvement of %Fat estimates over simpler 2C models.
背景/目的：先前的研究在利用相同的体密度（D）方法对所有测量值进行比较时，将 2 室（2C）和 3 室（3C）模型分别与标准 4 室（4C）模型进行了比较。这种设计引入了内在的偏差，掩盖了 3C 模型相对于更简单的 2 室（2C）模型的额外优势。
本研究旨在确定通过单频（SF-BIA）和多频（MF-BIA）生物阻抗分析估算总体水对通过空气置换体积描记法（ADP）得出的 3C 模型得出的体脂估算值的影响。
受试者/方法：本研究共有 95 名女性和 82 名男性（n=177）参与。水下称重、双能 X 射线吸收法和生物阻抗光谱法用于通过标准 4C 模型（4C）计算体脂百分比（%Fat）。通过 ADP、Siri 和 Brozek 体密度转换公式（分别为 2C 和 2C），基于 ADP，使用 3C（ADP 和 MF-BIA）、3C（ADP 和 SF-BIA）和独立的 2 室（2C）模型预测 %Fat。
在女性和男性中，3C 的估计标准误差（SEE）最低（分别为 2.72%和 2.31%），2C 最高（分别为 3.98%和 3.84%）。同样，女性和男性的总误差（TE）以 3C 最低（分别为 2.67%和 2.31%），2C 最高（分别为 4.58%和 4.48%）和 2C（分别为 4.65%和 4.33%）。
结果表明，当将 SF-BIA 和 MF-BIA 与 ADP 集成到更先进的 3C 模型中时，它们可以改善 2C 模型的体脂估计值。此外，本研究表明，3C 是基于 TE 值的最佳整体预测模型。本研究结果支持将 ADP 和生物阻抗技术集成到 3C 模型中，以改善简单 2C 模型的体脂估计值。
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