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.
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Objectives To determine the level of accuracy of recommended resting metabolic rate (RMR) prediction equations among healthy and acutely ill males: aged 18 to 65-years, in Trinidad and Tobago. Methods Following informed consent and enrolment, sixty-six male (acutely ill 33; healthy 33) volunteers had anthropometry and RMR (MedGem® indirect calorimeter, Micro life, USA) measured using recommended procedures. RMR from prediction equations were compared to RMR measured by indirect calorimetry with values between ±10% of measured RMR being considered accurate. The university's ethics committee approved the study. Results The top-four ranked recommended RMR prediction equations for acutely ill males, in decreasing order of accuracy were Müller (39.4%), Bernstein (39.4%), Korth (36.4%) and Mifflin St. Joer (36.4%). Among the healthy males, the top-five ranked recommended RMR prediction equations were Müller (54.5%), Huang (54.5%), Lürhmann (51.5%), Korth (48.5%) and Valencia (48.5). Population-derived RMR prediction equations had 54.5% and 63.6% accuracies among the acutely ill and healthy males respectively. These were significantly higher than the top-ranked recommended prediction equations for both groups. Notably, limiting the risk of malnutrition by at least 5%: through diet quality by way of accurate energy predictions could improve health-related quality of life. Increasing the predictability of energy needs within any population can also ensure the accuracy of RMR per weight (kilogram) needed daily and energy balance. On average, this male population utilized 17.5 kcal of energy per kilogram body weight. Conclusions All other recommended RMR prediction equations except Huang, Lürhmann, and Müller resulted in biases >50%. Substituting the commonly used prediction equations with our population-specific equation can increase the level of accuracy by at least 10%, thus limiting the risk of malnutrition by at least 5%, and improving health-related quality of life for this male population. Funding Sources nil.
Objectives To determine the level of accuracy of the recommended resting metabolic rate (RMR) prediction equations among healthy and acutely ill females: aged 18 to 65 years, in Trinidad and Tobago. Methods Following informed consent and enrollment, sixty female (acutely ill 30; healthy 30) volunteers had anthropometry and RMR (MedGem® indirect calorimeter, Micro life, USA) measured using recommended procedures. RMR from prediction equations were compared to RMR measured by indirect calorimetry with values between ±10% of measured RMR being considered accurate. The university's ethics committee approved the study. Results The top-two ranked recommended RMR prediction equations for acutely ill females, in decreasing order of accuracy were Owen (46.7%) and Bernstein (40%). Among the healthy females, the top-two ranked recommended RMR prediction equations with a similar level of accuracy (46.7%) were Livingston and Kohlstadt, and DeLorenzo. The population-derived RMR prediction equations had 56.7% and 70% accuracies among the acutely ill and healthy females respectively. These were significantly higher than the top-ranked recommended prediction equations for both groups. Notably, limiting the risk of malnutrition by at least 5%: through diet quality by way of accurate energy predictions could improve health-related quality of life. Increasing the predictability of energy needs within any population can also ensure accuracy of RMR per weight in kilogram (kcal/kg) needed daily and energy balance. On average, this female population utilized 18.4 kcal of energy per kilogram body weight (kcal/kg). Conclusions All the recommended RMR prediction equations resulted in biases > 50%, thus our population-derived RMR equations can be used as a superior alternative among participants to determining the energy-needs of acutely ill individuals as well as healthy females. Substituting the commonly used prediction equations with population-specific equations can increase the level of accuracy by at least 10%, thus limiting the risk of malnutrition by at least 5% and improving health-related quality of life. Funding Sources NIL.
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