Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, New Lister Building, Glasgow Royal Infirmary, Glasgow G31 2ER, UK.
Qassim University, Buraydah City, P. C. 51452, Saudi Arabia.
J Nutr Sci. 2020 May 26;9:e17. doi: 10.1017/jns.2020.11.
Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris-Benedict, Schofield, Henry, Mifflin-St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17-44 kg/m). Agreement between methods was assessed by Bland-Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin-St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin-St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly ( < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin-St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.
使用预测方程估算 RMR 是计算能量需求的基础。在本研究中，我们使用 Harris-Benedict、Schofield、Henry、Mifflin-St Jeor 和 Owen 方程预测了 125 名不同 BMI（17-44kg/m）的健康成年女性的 RMR，并通过间接测热法进行了测量。通过 Bland-Altman 分析评估了方法之间的一致性，并通过计算预测的 RMR 与实测 RMR 相差 10%的个体比例来评估每个方程的准确性。通过回归分析计算了平均 RMR（预测和实测 RMR 的平均值）作为斜率和截距偏差的函数。使用单变量和多变量线性回归分析了方程偏差的预测因素。在组水平上，只有 Mifflin-St Jeor（0（sd 153）kcal/d（0（sd 640）kJ/d））和 Henry（8（sd 163）kcal/d（33（sd 682）kJ/d））方程的偏差（预测 RMR 与实测 RMR 的差值）与零没有差异。Mifflin-St Jeor 和 Henry 方程在个体水平上最准确，分别有 71%和 66%的参与者预测的 RMR 与实测 RMR 的差值在 10%以内。对于所有方程，一致性界限都很宽，偏差斜率为负，偏差截距为正，且显著（<0.05）与零不同。年龄、身高和 BMI 的增加与 RMR 的低估有关，但这些变量总共仅解释了估计偏差方差的 15%。在个体水平上，预测 RMR 的方程的整体准确性较低，特别是在 RMR 较低和较高的女性中。对于这个数据集，Mifflin-St Jeor 方程是最准确的，但仍有大约三分之一的参与者存在预测误差。
