Validity of predictive equations to estimate RMR in females with varying BMI

George Thom, Konstantinos Gerasimidis, Eleni Rizou, Hani Alfheeaid, Nick Barwell, Eirini Manthou, Sadia Fatima, Jason M R Gill, Michael E J Lean, Dalia Malkova, George Thom, Konstantinos Gerasimidis, Eleni Rizou, Hani Alfheeaid, Nick Barwell, Eirini Manthou, Sadia Fatima, Jason M R Gill, Michael E J Lean, Dalia Malkova

Abstract

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/m2). 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 (P < 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.

Keywords: Harris–Benedict equations; Henry equations; Mifflin–St Jeor equations; Owen equations; Prediction equations; RMR; Schofield equations.

© The Author(s), 2020. Published by Cambridge University Press on behalf of The Nutrition Society. 2020.

Figures

Fig. 1.
Fig. 1.
Bland–Altman plots of differences in RMR measured by indirect calorimetry and predicted using five different equations in 125 adult women. The solid line represents the mean difference (predicted – measured RMR). Upper and lower dashed lines represent the 95 % limits of agreement (±2 sd). The regression line indicates the difference between predicted and measured RMR, plotted against the mean. REE, resting energy expenditure. * To convert kcal to kJ, multiply by 4·184.
Fig. 2.
Fig. 2.
Percentage of adult women for whom RMR predicted by Schofield (■), Owen (□), Mifflin–St Jeor (), Henry () and Harris–Benedict () equations was within ± 10 % of RMR measured by indirect calorimetry, according to BMI category (underweight, BMI <18 kg/m2; healthy weight, BMI ≥18⋅5–24⋅9 kg/m2; overweight, BMI ≥25–29⋅9 kg/m2, and obesity BMI ≥30 kg/m2).

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