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

Abstract

Background: Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity. Aim: We evaluated the accuracies of established and locally developed RMR prediction equations (RMRP) among adults. Methods: Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMRM) with a MedGem® indirect calorimeter according to recommended procedures. RMRP were compared to RMRM with values ± 10% of RMRM deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMRP equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study. Results: Males had higher mean absolute RMR ( p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMRP among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris–Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMRP. Locally developed RMRP had accuracies comparable to their top-ranked established RMRP counterparts. Conclusions: Accuracies of established RMRP depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMRP provide useful alternatives to established RMRP.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

“…One hundred different prediction equations from forty-six separate original articles were investigated across all included studies. Studies ranged from comparing the accuracy of 30 different equations [ 51 ] to examining the accuracy of a single equation [ 4 , 10 , 22 , 52 – 56 ]. The top five most included equations were the Cunningham (1980) (lean body mass [LBM]) ( n = 21 studies), the Harris-Benedict (1918) (age, weight, height) ( n = 21 studies), the Mifflin St. Jeor (1990) (age, weight, height) ( n = 11 studies), the De Lorenzo (1999) (age, weight, height) ( n = 8 studies), and the FAO/WHO/UNU (1985) (age, weight) equations and the FAO/WHO/UNU (1985) (age, weight, height) equations (both n = 7 studies).…”

“…Five studies classified participants as recreational athletes (resistance, aerobic, and concurrent) [ 29 , 30 , 51 , 57 , 58 ]. In the original study in which the Koehler DXA (2016) (sum of organelle masses) equation was developed, it was the only equation examined and found to over-predict RMR by 4% in female recreational athletes [ 58 ].…”

“…Elsewhere in female athletes, Mackay et al [ 29 ] found the Mifflin St. Jeor (1990) (age, weight, height) to be the most accurate out of three equations examined; however, RMR was overestimated by ~ 15%. In recreationally active males and females of varying BMI from Trinidad and Tobago, 30 equations were studied with several equations found to be the most accurate (range: within 1–2%) [ 51 ]. The most accurate equation for females with a BMI < 25 kg/m 2 was Johnstone (2006) (fat-free mass [FFM], fat mass [FM], age) and for BMI 25 kg/m 2 was Müller (MJ/d) (2004) (FFM, FM); and the most accurate equation for males with a BMI < 25 kg/m 2 was Owen (1988) (weight) and for BMI 25 kg/m 2 was Livingston and Kohlstadt (2005) (age, weight) [ 51 ].…”

“…Six articles could not be synthesised by meta-analysis for the following reasons: means and/or SDs for predicted or measured RMR were not provided ( n = 3) [ 19 , 51 , 56 ], RMR prediction equation used by less than three separate studies ( n = 2) [ 22 , 52 ] and study sample size of n = 1 [ 54 ].…”

Dietary intake patterns, nutrient adequacy and associated factors in a multi-ethnic Caribbean population

“…To compare the relative degree of under and over-reporting of energy intakes, an energy intake (EI) to resting metabolic rate (RMR) ratio (EI: RMR) was calculated for participants by dividing calories consumed by measured RMR An EI:RMR ratio of <1.35, between 1.35 and 2.39, and ≥2.4 represent under, plausible, and over-reporting respectively (Goldberg et al, 1991; Johansson et al, 1998). RMR estimates in this study were based on population sex-specific energy predictive equations derived from measured RMR measurements among 400 adults 18 to 65 years locally using the Med Gem® indirect calorimeter (Mac Doniel, 2007; Nichols et al, 2021; St-Onge et al, 2004).…”