Influence of methods used in body composition analysis on the prediction of resting energy expenditure
Influence of methods used in body composition analysis on the prediction of resting energy expenditure
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DOI：
10.1038/sj.ejcn.1602556
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发表时间：
2007-05-01
影响因子：
4.7
通讯作者：
Mueller, M. J.
中科院分区：
文献类型：
--
作者：
Korth, O.;Bosy-Westphal, A.;Mueller, M. J.
目的：已发表的基于去脂体重 (FFM) 的静息能量消耗 (REE) 预测算法存在相当大的差异。本研究的目的是调查身体成分分析方法对 FFM 预测 REE 的影响。设计：在横截面设计中，对 REE 和身体成分进行了测量。受试者：研究人群包括 50 名男性（年龄 37.1 +/- 15.1 岁，体重指数 (BMI) 25.9 +/- 4.1 kg/m(2)）和 54 名女性（年龄 35.3 +/- 15.4 岁，BMI 25.5 +/- 4.4 kg/m(2)）。干预措施：通过间接量热法测量 REE，并通过 FFM 或体重进行预测。 FFM 的测量采用基于 2 室 (2C) 模型的方法进行：皮褶 (SF) 测量、生物电阻抗分析 (BIA)、双 X 射线吸收测定法 (DXA)、空气置换体积描记法 (ADP) 和氧化氘稀释 (D2O)。使用 4 室 (4C) 模型作为参考。结果：与 4C 模型相比，通过 2C 方法获得的 FFM 的 REE 预测没有显着差异。 FFM 预测 REE 的回归方程的截距从 1231 (FFMADP) 到 1645 kJ/24 h (FFMSF) 不等，斜率范围在 100.3 kJ (FFMSF) 和 108.1 kJ/FFM (kg) (FFMADP) 之间。在FFM的正常范围内，不同方法从FFM预测的REE仅表现出很小的差异。 FFM 解释的 REE 差异从 69% (FFMBIA) 到 75% (FFMDXA) 不等，而体重的差异仅为 46%。结论：REE 和 FFM 之间回归线的斜率和截距的差异取决于身体成分分析所用的方法。然而，REE 预测的差异很小，并不能解释从已发表的基于 FFM 的 REE 预测方程获得的结果的巨大差异，因此意味着 REE 预测算法的群体和/或研究者特异性。
Objective: There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. Design: In a cross- sectional design measurements of REE and body composition were performed. Subjects: The study population consisted of 50 men (age 37.1 +/- 15.1 years, body mass index (BMI) 25.9 +/- 4.1 kg/m(2)) and 54 women (age 35.3 +/- 15.4 years, BMI 25.5 +/- 4.4 kg/m(2)). Interventions: REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D2O). A 4-compartment (4C)-model was used as a reference. Results: When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFMADP) to 1645 kJ/24 h (FFMSF) and the slopes ranged between 100.3 kJ (FFMSF) and 108.1 kJ/FFM (kg) (FFMADP). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFMBIA) to 75% (FFMDXA) and was only 46% for body weight. Conclusion: Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population-and/or investigator specificity of algorithms for REE prediction.