Do we need race-specific resting metabolic rate prediction equations?

Abstract

Background Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR. Methods We measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin–St.Joer, Cunningham, and Harris–Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR. Results Mean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m 2 , and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race ( p < 0.0001) and FFM ( p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day ( p < 0.0001). AA race was associated with 144 kcal/day ( p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR ( p < 0.005). Mifflin–St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 ( p < 0.0001), respectively. Conclusion We found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.

Development of a New Equation for the Prediction of Resting Metabolic Rate in Sri Lankan Adults

“…Asians are prone to have more adipose tissue compared to the Western population for a given body weight, thus having less muscle mass for a given weight. [ 27 , 28 ]. RMR is related to muscle mass which is the metabolically active tissue.…”

“…RMR is affected by several factors other than body weight. These factors are sex, height, BMI, age, and body composition, particularly metabolically active tissue [ 27 ]. Sex, height, and BMI were positively correlated with RMR measured by indirect calorimetry in our study.…”

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago

“…This is further supported by Bland–Altman plots which showed negative proportion bias suggesting underestimation of requirements at higher levels of RMR M . (Frankenfield et al, 2005; Reneau et al, 2019; Weijs, 2008).…”

Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups

“…This is not completely surprising, since individual variances in metabolic activity of FM and FFM in adults have been reported in the literature [54]. Metabolic activity of FFM is not consistent across all organs and skeletal muscle [13,55,56], and in children the effect of growth on the size of FFM and its composition may further contribute to variations in REE [13,57,58].…”

“…These results are in line with previous studies [8,11,18,19] performed on mainly American children. However, it is often argued that the lower REE of African American population groups may be explained by differences in the type and distribution of FFM rather than an absolute difference in REE [18,58,60]. Since FFM is considered the most metabolically active tissue, variations in metabolic activity between types of muscle mass may lead to considerable differences in REE [55].…”