Cross-Validation of Resting Metabolic Rate Prediction Equations

Background Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence the accuracy of RMR predictions will help to revise existing, or develop new and improved, equations. Objective Our aim was to test the validity of RMR predicted in healthy adults by the Harris-Benedict, World Health Organization, Mifflin-St Jeor, Nelson, Wang equations, and three meta-equations of Sabounchi. Design Predicted RMR was tested for agreement with indirect calorimetry. Participants/setting Men and women (n=30) age 18 to 65 years from Grand Forks, ND, were recruited and included for analysis during spring/summer 2014. Participants were nonobese or obese (body mass index range=19 to 39) and primarly white. Main outcome measure Agreement between measured (indirect calorimetry) and predicted RMR was measured. Statistical analysis The methods of Bland and Altman were employed to determine mean bias (predicted minus measured RMR, kcal/day) and limits of agreement between predicted and measured RMR. Repeated-measures analysis of variance was used to test for bias in RMR predicted from each equation vs the measured RMR. Results Bias (mean +/- 2 standard deviations) was lowest for the Harris-Benedict (-14 +/- 378 kcal/24 h) and World Health Organization (-25 +/- 394 kcal/24 h) equations. These equations also predicted RMR that were not different from measured. Mean RMR predictions from all other equations significantly differed from indirect calorimetry. The 2 standard deviation limits of agreement were moderate or large for all equations tested, ranging from 314 to 445 kcal/24 h. Prediction bias was inversely associated with the magnitude of RMR and with fat-free mass. Conclusions At the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.