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Best-Fitting Prediction Equations for Basal Metabolic Rate International Journal of Obesity (2013) 37, 1364–1370 & 2013 Macmillan Publishers Limited All rights reserved 0307-0565/13 www.nature.com/ijo REVIEW Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations NS Sabounchi1, H Rahmandad2 and A Ammerman3 Basal metabolic rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat-free mass, fat mass, height, waist-to-hip ratio, body mass index and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to 20 specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender and weight. International Journal of Obesity (2013) 37, 1364–1370; doi:10.1038/ijo.2012.218; published online 15 January 2013 Keywords: basal metabolic rate; resting metabolic rate; prediction; meta-analysis; review; meta-regression INTRODUCTION mass (FFM)),13 gender and race15 on BMR. Most of the developed Obesity is the result of a positive imbalance between energy equations have focused on cross-sectional data from targeted intake and energy expenditure.1 The basal metabolic rate (BMR), sub-populations (for example, the young adults) and have used a 16 defined as the energy required for performing vital body functions specific equation structure. A review by Frankenfield et al. at rest, is the largest contributor to energy expenditure. Therefore, compares commonly applied BMR prediction equations including 17 18,19 8 20 estimating the total contribution of individual BMR to total daily Mifflin et al., Owen et al., H&B and WHO report to energy expenditure is an important calculation for understanding, determine the most reliable prediction equation. However, current developing and executing weight-related interventions.1,2 For reviews often fail to include different equation structures and example, BMR estimation is applied to determine target energy populations with different age ranges and race. In fact, a single intake in weight loss programs, develop dynamic prediction equation cannot adequately capture the variations across age, models of weight gain and loss, identify patients with potential race, gender and body composition. metabolic abnormalities, inform the design of public health The purpose of the current paper is to conduct a comprehen- programs promoting obesity prevention in diverse populations sive review of studies that estimate the relationship between BMR and assess potential energy deficits in metabolically stressed and known covariates. The results of the analysis were combined patients, such as burn victims. Direct measurement of BMR to determine the best-fitting BMR prediction equations targeted through indirect calorimetry3 is not feasible for frequent and to different population characteristics. A simple online tool (we timely individual use. As a result, the majority of BMR estimates thank one of the reviewers for recommending the development of obtained for weight loss interventions rely on BMR prediction this tool, which is available at http://www.sdl.ise.vt.edu/tutor- equations. Over the past few decades, a large body of obesity- ials.html and also as part of the online supplement on Journal’s related literature has been devoted to development of BMR website) that determines the best BMR estimate after user input of equations targeted to specific population demographics. age, race, gender, weight and height was developed to enhance BMR and the terms resting metabolic rate and resting energy clinical application of study results. expenditure are often used interchangeably,4 however, some investigators differentiate between these terms based on the 5 conditions under which they are measured. We limited our MATERIALS AND METHODS review of BMR studies to experiments that have measured the Overview dependent variable, consistent with the definition of BMR,6,7 in the Models that predict BMR, include different determinants and metabolically post-absorptive state, after at least 10 hours of fasting, determined 6,7 active components at four different levels of molecular (FM and FFM), through direct or indirect calorimetry. The extensive literature cellular (extracellular fluid and extracellular solids), tissue/organ and whole on BMR equations is based on the pioneering work of Harris and body (body mass).21 In this systematic review, our purpose is to find all 8 Benedict (H&B) and includes the impact of covariates such as regression equations reported in the literature for predicting BMR based age,2,8–14 body composition (that is, fat mass (FM) and fat-free on molecular or whole-body level factors. More detailed levels of cellular or 1Social System Design Lab, George Warren Brown School of Social Work, Washington University, St Louis, MO, USA; 2Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, VA, USA and 3Department of Nutrition, Gillings School of Global Public Health, and the Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill, NC, USA. Correspondence: Professor H Rahmandad, Department of Industrial and Systems Engineering, Virginia Tech, Northern Virginia Center, 7054 Haycock Road, # 430, Falls Church, VA 22043, USA. E-mail: [email protected] Received 27 April 2012; revised 28 November 2012; accepted 2 December 2012; published online 15 January 2013 Best-fitting prediction equations for basal metabolic rate NS Sabounchi et al 1365 tissue/organ components, while very important for advancement of our Supplementary Information S1). Variance or s.d. of reported regression understanding of drivers of BMR, are not considered because of the parameters is needed for conducting meta-regression analysis. Therefore, complexity involved in quantifying those factors, which reduces their value we follow a set of procedures to consistently extract/estimate this for widespread clinical application. In our review, the selected papers factor, which is described in online Supplementary Information S2. The contain prediction models for healthy obese or non-obese individuals, full set of references without estimates of variance is reported in online differentiated based on their race, gender and age. Owing to limited Supplementary Information S4. number of primary studies that account for dynamic changes in metabolic rate of a single individual over time (for notable exceptions, see Johannsen et al.22 and Hall23), our analysis focuses on equations using cross-sectional Meta-regression analysis data. Then, we apply a meta-regression analysis to generate a combined Resulting equations are grouped based on sub-populations they were equation that is more robust in predicting BMR for each sub-population developed for (based on gender, age group and race) and the structure of group. In our study, we refer to meta-regression analysis as a method to the regression equation (that is, independent variables included and the develop a single regression equation that summarizes the findings of transformations used). Within each group with homogenous population multiple regressions found in a number of studies. and equation structure, we combined all the extracted equations to form a single new equation that aggregates different previous findings. If a study estimates a single equation for two different groups (for example, 18–50 Literature search and filtering and 450 years old groups), we used that equation separately in estimating The comprehensive search of the literature was performed in four stages the aggregate equations for each of the groups. to identify all studies that predict a BMR equation based on an empirical There are different approaches in the literature for synthesizing data set. The specific keywords, used to search PubMed and the Web of coefficients (slopes) of a set of similar regressions found in different Science databases for studies published in any language between the studies into a single slope,24 which in this paper we refer to as meta- earliest available date 31 October 1923 and 3 March 2011, are shown in regression. (Some refer to meta-regression as running a regression on a Figure 1. Search terms are selected so that any publication, which finds a common summary statistic drawn from the primary studies as the prediction model for BMR or its alternative terms (resting metabolic rate, dependent variable.25 Our use of the term differs from this definition). resting energy expenditure or basal energy expenditure), is included. The more statistically precise methods, such as the multivariate Retrieved articles were reviewed in two steps. First abstracts were reviewed generalized linear square approach,24 require the covariance between and items obviously not fitting were excluded. Then, the full text for the different slopes. However, the covariance matrices are rarely
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