Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University, CA, USA; Stanford Cardiovascular Institute, CA, USA; Stanford Diabetes Research Center, Stanford, CA, 94305, USA.
Department of Genetics, Stanford University, CA, USA.
Clin Nutr ESPEN. 2023 Feb;53:43-52. doi: 10.1016/j.clnesp.2022.11.015. Epub 2022 Nov 24.
BACKGROUND & AIMS: Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention.
Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340 at 6 months and 323 at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention.
A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM × FM] was body size and sex independent (p = 0.91 and p = 0.73, respectively) in contrast to BW based indexing which showed a significant inverse relationship to BW (r = -0.47 for female and r = -0.44 for male, both p < 0.001). When indexed to BW, significant baseline differences in REE were observed between male and female (p < 0.001) and between individuals who are overweight and obese (p < 0.001) while no significant differences were observed when indexed to REE/[LBM × FM], p > 0.05). Percentage predicted REE adjusted for LBM, FM and DP remained stable following weight loss intervention (p = 0.614).
Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.
静息能量消耗(REE)是能量平衡的主要组成部分。虽然 REE 通常与总体重(BW)相关联,但在评估肥胖或减肥干预期间的 REE 时,这可能会引入偏差。本研究的主要目的是量化通过在基线和减肥干预后均使用 BW 对 REE 进行比率标度所引入的偏差。
参加 DIETFITS 研究(饮食干预检查治疗成功的相互作用因素)并完成间接热量测定法和双能 X 射线吸收法(DXA)的参与者被纳入研究。在基线时共有 438 名参与者、6 个月时 340 名参与者和 12 个月时 323 名参与者有可用数据。我们使用基于瘦体重(LBM)和脂肪量(FM)的乘法异速生长模型来推导出与身体大小无关的 REE 标度。然后,在减肥干预后评估 REE 的指数变化。
包括 LBM、FM、年龄、黑种人、收缩压和心率的乘积(DP)在内的乘法模型解释了 REE 变异的 79%。REE 与[LBM×FM]相关联,与 BW 无关(p=0.91 和 p=0.73,分别),而基于 BW 的索引显示出与 BW 呈显著负相关(女性 r=-0.47,男性 r=-0.44,均 p<0.001)。当与 BW 相关联时,在 REE 方面,男性和女性之间(p<0.001)以及超重和肥胖个体之间(p<0.001)存在显著的基线差异,而当与 REE/[LBM×FM]相关联时,差异不显著,p>0.05)。在调整了 LBM、FM 和 DP 后,预测的 REE 百分比在减肥干预后保持稳定(p=0.614)。
基于 LBM 和 FM 的 REE 异速生长标度消除了与身体成分相关的偏差,在肥胖和基于体重的干预研究中应予以考虑。