Accuracy and Validity of Resting Energy Expenditure Predictive Equations in Middle-Aged Adults

Francisco J Amaro-Gahete, Lucas Jurado-Fasoli, Alejandro De-la-O, Ángel Gutierrez, Manuel J Castillo, Jonatan R Ruiz, Francisco J Amaro-Gahete, Lucas Jurado-Fasoli, Alejandro De-la-O, Ángel Gutierrez, Manuel J Castillo, Jonatan R Ruiz

Abstract

Indirect calorimetry (IC) is considered the reference method to determine the resting energy expenditure (REE), but its use in a clinical context is limited. Alternatively, there is a number of REE predictive equations to estimate the REE. However, it has been shown that the available REE predictive equations could either overestimate or underestimate the REE as measured by IC. Moreover, the role of the weight status in the accuracy and validity of the REE predictive equations requires further attention. Therefore, this study aimed to determine the accuracy and validity of REE predictive equations in normal-weight, overweight, and obese sedentary middle-aged adults. A total of 73 sedentary middle-aged adults (53% women, 40⁻65 years old) participated in the study. We measured REE by indirect calorimetry, strictly following the standard procedures, and we compared it with the values obtained from 33 predictive equations. The most accurate predictive equations in middle-aged sedentary adults were: (i) the equation of FAO/WHO/UNU in normal-weight individuals (50.0% of prediction accuracy), (ii) the equation of Livingston in overweight individuals (46.9% of prediction accuracy), and (iii) the equation of Owen in individuals with obesity (52.9% of prediction accuracy). Our study shows that the weight status plays an important role in the accuracy and validity of different REE predictive equations in middle-aged adults.

Keywords: basal metabolism; energy balance; indirect calorimetry; metabolic rate; obesity.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Percentage of accurate prediction of resting energy predictive equations and mean differences between predicted and measured resting energy expenditure in absolute values in normal-weight individuals. (A) Percentage of prediction accuracy at 5% and 10% of resting energy expenditure. (B) Mean (SD) differences between predicted and measured resting energy expenditure in absolute values; p value of repeated measures analysis of variance (with Bonferroni post-hoc analysis) among the predictive equations; * = p < 0.05; ** = p < 0.01; *** = p < 0.001 when compared with the predictive equation that presented the least absolute differences with respect to the measured resting energy expenditure (FAO_ht); ¥ = p < 0.05; ¥¥ = p < 0.01; ¥¥¥ = p < 0.001 when compared with the predictive equation that presented the best resting energy expenditure prediction accuracy (10%) with respect to the measured resting energy expenditure (FAO_ht); # = p < 0.05; ## = p < 0.01; ### = p < 0.001 when compared with the predictive equation that presented the best resting energy expenditure prediction accuracy (5%) with respect to the measured resting energy expenditure (FAO_ht). AP: Accurate prediction; “_a” refers to predictive equations which require only anthropometric parameters to calculate REE, “_b” refers to predictive equations which require body composition parameters to calculate REE, and “_ht” refers to predictive equations which are proposed by the same author and include height.
Figure 2
Figure 2
Percentage of accurate prediction of resting energy predictive equations and mean differences between predicted and measured resting energy expenditure in absolute values in overweight individuals. (A) Percentage of prediction accuracy at 5% and 10% of resting energy expenditure. (B) Mean (SD) differences between predicted and measured resting energy expenditure in absolute values; p value of repeated measures analysis of variance (with Bonferroni post-hoc analysis) among the predictive equations; * = p < 0.05; ** = p < 0.01; *** = p < 0.001 when compared with the predictive equation that presented the least absolute differences with respect to the measured resting energy expenditure (Livingston); ¥ = p < 0.05; ¥¥ = p < 0.01; ¥¥¥ = p < 0.001 when compared with the predictive equation that presented the best resting energy expenditure prediction accuracy (10%) with respect to the measured resting energy expenditure (Roza); # = p < 0.05; ## = p < 0.01; ### = p < 0.001 when compared with the predictive equation that presented the best resting energy expenditure prediction accuracy (5%) with respect to the measured resting energy expenditure (Livingston). AP: Accurate prediction; “_a” refers to predictive equations which require only anthropometric parameters to calculate REE, “_b” refers to predictive equations which require body composition parameters to calculate REE, and “_ht” refers to predictive equations which are proposed by the same author and include height.
Figure 3
Figure 3
Percentage of accurate prediction of resting energy predictive equations and mean differences between predicted and measured resting energy expenditure in absolute values in individuals with obesity. (A) Percentage of prediction accuracy at 5% and 10% of resting energy expenditure. (B) Mean (SD) differences between predicted and measured resting energy expenditure in absolute values; p value of repeated measures analysis of variance (with Bonferroni post-hoc analysis) among the predictive equations; * = p < 0.05; ** = p < 0.01; *** = p < 0.001 when compared with the predictive equation that presented the least absolute differences with respect to the measured resting energy expenditure (Owen_a); ¥ = p < 0.05; ¥¥ = p < 0.01; ¥¥¥ = p < 0.001 when compared with the predictive equation that presented the best resting energy expenditure prediction accuracy (10%) with respect to the measured resting energy expenditure (Roza); # = p < 0.05; ## = p < 0.01; ### = p < 0.001 when compared with the predictive equation that presented the best resting energy expenditure prediction accuracy (5%) with respect to the measured resting energy expenditure (Owen_a). AP: Accurate prediction; “_a” refers to predictive equations which require only anthropometric parameters to calculate REE, “_b” refers to predictive equations which require body composition parameters to calculate REE, and “_ht” refers to predictive equations which are proposed by the same author and include height.
Figure 4
Figure 4
Bland–Altman plots for selected resting energy expenditure (REE) predictive equations. The solid lines represent the mean difference (BIAS) between predicted and measured REE. The upper and lower dashed lines represent the 95% limits of agreement; “_a” refers to predictive equations which required only anthropometric parameters to calculate REE, and “_ht” refers to predictive equations which are proposed by the same author and include height.
Figure 5
Figure 5
Percentage of accurate prediction of the most accurate predictive equations and mean differences between predicted and measured resting energy expenditure in absolute values by weight status. (A) Percentage of prediction accuracy at 5% and 10% of resting energy expenditure. (B) Mean (SD) differences between predicted and measured resting energy expenditure in absolute values; p value of an analysis of variance (with Bonferroni post-hoc analysis) across weight status. Similar letters (i.e., a-a, b-b) indicate significant differences (p < 0.05) considering Bonferroni post-hoc analysis. AP: Accurate prediction; “_a” refers to predictive equations which require only anthropometric parameters to calculate REE, and “_ht” refers to predictive equations which are proposed by the same author and include height.
Figure 6
Figure 6
Decision tree to select a REE predictive equation by weight status. “_a” refers to predictive equations which require only anthropometric parameters to calculate REE, and “_ht” refers to predictive equations which are proposed by the same author and include height. Abbreviations: M: Men; W: Women; W: Weight; H: Height; A: Age; y: years.

References

    1. Smith K.B., Smith M.S. Obesity statistics. Prim. Care Clin. Off. Pract. 2016;43:121–135. doi: 10.1016/j.pop.2015.10.001.
    1. Fleming T., Robinson M., Thomson B., Graetz N., Margono C., Mullany E.C., Biryukov S., Abbafati C., Abera S.F., Abraham J.P. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the Global Burden of Disease Study. Lancet. 2013;384:766–781.
    1. Noreik M., Maurmann M., Meier V., Becker I., Röhrig G., Polidori M.C., Schulz R.-J. Resting energy expenditure (REE) in an old-old population: Implications for metabolic stress. Exp. Gerontol. 2014;59:47–50. doi: 10.1016/j.exger.2014.06.009.
    1. Matarese L.E. Indirect Calorimetry: Technical Aspects. J. Am. Diet. Assoc. 1997;97:S154–S160. doi: 10.1016/S0002-8223(97)00754-2.
    1. Blasco Redondo R. Resting energy expenditure; assessment methods and applications. Nutr. Hosp. 2015;31:245–254.
    1. Harris J., Benedict F. A biometric study of basal metabolism in man. Proc. Natl. Acad. Sci. USA. 1918;4:370–373. doi: 10.1073/pnas.4.12.370.
    1. Roza A.M., Shizgal H.M. The Harris Benedict equation reevaluated: Resting energy requirements and the body cell mass. Am. J. Clin. Nutr. 1984;40:168–182. doi: 10.1093/ajcn/40.1.168.
    1. Owen O.E., Holup J.L., D’Alessio D.A., Craig E.S., Polansky M., Smalley K.J., Kavle E.C., Bushman M.C., Owen L.R., Mozzoli M.A. A reappraisal of the caloric requirements of men. Am. J. Clin. Nutr. 1987;46:875–885. doi: 10.1093/ajcn/46.6.875.
    1. Owen O.E., Kavle E., Owen R.S., Polansky M., Caprio S., Mozzoli M.A., Kendrick Z.V., Bushman M.C., Boden G. A reappraisal of caloric requirements in healthy women. Am. J. Clin. Nutr. 1986;44:1–19. doi: 10.1093/ajcn/44.1.1.
    1. Mifflin M.D., St Jeor S.T., Hill L.A., Scott B.J., Daugherty S.A., Koh Y.O. A new predictive equation for resting energy expenditure in healthy individuals. Am. J. Clin. Nutr. 1990;51:241–247. doi: 10.1093/ajcn/51.2.241.
    1. Livingston E.H., Kohlstadt I. Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals. Obes. Res. 2005;13:1255–1262. doi: 10.1038/oby.2005.149.
    1. Schofield W.N. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. Clin. Nutr. 1985;39:5–41.
    1. FAO/WHO/UNU . Energy and Protein Requirements. FAO/WHO; Geneva, Switzerland: 1985.
    1. Henry C.J.K. Basal metabolic rate studies in humans: Measurement and development of new equations. Public Health Nutr. 2005;8:1133–1152. doi: 10.1079/PHN2005801.
    1. Müller M.J., Bosy-Westphal A., Klaus S., Kreymann G., Lührmann P.M., Neuhäuser-Berthold M., Noack R., Pirke K.M., Platte P., Selberg O., et al. World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: Generation of a new reference standard from a retrospective analysis of a German database of resting energy expe. Am. J. Clin. Nutr. 2004;80:1379–1390. doi: 10.1093/ajcn/80.5.1379.
    1. Korth O., Bosy-Westphal A., Zschoche P., Glüer C.C., Heller M., Müller M.J. Influence of methods used in body composition analysis on the prediction of resting energy expenditure. Eur. J. Clin. Nutr. 2007;61:582–589. doi: 10.1038/sj.ejcn.1602556.
    1. Lorenzo A., De Andreoli A., Bertoli S., Testolin G., Oriani G., Deurenberg P. Resting metabolic rate in Italians: Relation with body composition and anthropometric parameters. Acta Diabetol. 2000;37:77–81. doi: 10.1007/s005920070023.
    1. Johnstone A.M., Rance K.A., Murison S.D., Duncan J.S., Speakman J.R. Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects. Eur. J. Clin. Nutr. 2006;60:1437–1444. doi: 10.1038/sj.ejcn.1602477.
    1. Weijs P.J.M. Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18–65 y. Am. J. Clin. Nutr. 2008;88:959–970. doi: 10.1093/ajcn/88.4.959.
    1. Frankenfield D.C., Rowe W.A., Smith J.S., Cooney R.N. Validation of several established equations for resting metabolic rate in obese and nonobese people. J. Am. Diet. Assoc. 2003;103:1152–1159. doi: 10.1016/S0002-8223(03)00982-9.
    1. de la Cruz Marcos S., de Mateo Silleras B., Camina Martín M.A., Carreño Enciso L., Miján de la Torre A., Galgani Fuentes J.E., Redondo del Rio M.P. Proposal for a new formula for estimating resting energy expenditure for healthy Spanish population. Nutr. Hosp. 2015;32:2346–2352.
    1. Cunningham J.J. A reanalysis of the factors influencing basal metabolic rate in normal adults. Am. J. Clin. Nutr. 1980;33:2372–2374. doi: 10.1093/ajcn/33.11.2372.
    1. Huang K.-C., Kormas N., Steinbeck K., Loughnan G., Caterson I.D. Resting Metabolic Rate in Severely Obese Diabetic and Nondiabetic Subjects. Obes. Res. 2004;12:840–845. doi: 10.1038/oby.2004.101.
    1. De Luis D.A., Aller R., Izaola O., Romero E. Prediction equation of resting energy expenditure in an adult spanish population of obese adult population. Ann. Nutr. Metab. 2006;50:193–196. doi: 10.1159/000090740.
    1. Bernstein R.S., Thornton J.C., Yang M.U., Wang J., Redmond A.M., Pierson R.M., Pi-Sunyer F.X., Van Itallie T.B. Prediction of the resting metabolic rate in obese patients. Am. J. Clin. Nutr. 1983;37:595–602. doi: 10.1093/ajcn/37.4.595.
    1. Siervo M., Bertoli S., Battezzati A., Wells J.C., Lara J., Ferraris C., Tagliabue A. Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin. Nutr. 2014;33:613–619. doi: 10.1016/j.clnu.2013.09.009.
    1. Acar-Tek N., Ağagündüz D., Çelik B., Bozbulut R. Estimation of resting energy expenditure: Validation of previous and new predictive equations in obese children and adolescents. J. Am. Coll. Nutr. 2017;36:470–480. doi: 10.1080/07315724.2017.1320952.
    1. Wahrlich V., Teixeira T.M., Anjos L.A. Validity of a population-specific BMR predictive equation for adults from an urban tropical setting. Clin. Nutr. 2016;37:208–213. doi: 10.1016/j.clnu.2016.12.005.
    1. Johannsen D., Welk G., Sharp R., Flakoll P. Differences in daily energy expenditure in lean and obese women: The role of posture allocation. Obesity. 2008;16:34–39. doi: 10.1038/oby.2007.15.
    1. Amaro-Gahete F.J., De-la-O A., Jurado-Fasoli L., Espuch-Oliver A., Robles-González L., Navarro-Lomas G., de Haro T., Femia P., Castillo M.J., Gutierrez A. Exercise training as S-Klotho protein stimulator in sedentary healthy adults: Rationale, design, and methodology. Contemp. Clin. Trials Commun. 2018;11:10–19. doi: 10.1016/j.conctc.2018.05.013.
    1. World Health Organization Obesity: Preventing and managing the Global Epidemic. Exec. Summ. WHO Tech. Rep. Ser. 2010;1997:5–8.
    1. Compher C., Frankenfield D., Keim N., Roth-Yousey L. Evidence Analysis Working Group. Best practice methods to apply to measurement of resting metabolic rate in adults: A systematic review. J. Am. Diet. Assoc. 2006;106:881–903. doi: 10.1016/j.jada.2006.02.009.
    1. Fullmer S., Benson-Davies S., Earthman C.P., Frankenfield D.C., Gradwell E., Lee P.S.P., Piemonte T., Trabulsi J. Evidence analysis library review of best practices for performing indirect calorimetry in healthy and non-critically ill individuals. J. Acad. Nutr. Diet. 2015;115:1417–1446. doi: 10.1016/j.jand.2015.04.003.
    1. Sanchez-Delgado G., Alcantara J.M.A., Ortiz-Alvarez L., Xu H., Martinez-Tellez B., Labayen I., Ruiz J.R. Reliability of resting metabolic rate measurements in young adults: Impact of methods for data analysis. Clin. Nutr. 2017;37:1618–1624. doi: 10.1016/j.clnu.2017.07.026.
    1. Weir J. New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 1949;109:1–9. doi: 10.1113/jphysiol.1949.sp004363.
    1. Willis E.A., Herrmann S.D., Ptomey L.T., Honas J.J., Bessmer C.T., Donnelly J.E., Washburn R.A. Predicting resting energy expenditure in young adults. Obes. Res. Clin. Pract. 2014;8:201–208. doi: 10.1016/j.orcp.2015.07.002.
    1. Frankenfield D. Bias and accuracy of resting metabolic rate equations in non-obese and obese adults. Clin. Nutr. 2013;32:976–982. doi: 10.1016/j.clnu.2013.03.022.
    1. Ruiz J.R., Ortega F.B., Rodríguez G., Alkorta P., Labayen I. Validity of resting energy expenditure predictive equations before and after an energy-restricted diet intervention in obese women. PLoS ONE. 2011 doi: 10.1371/journal.pone.0023759.
    1. Frankenfield D., Roth-Yousey L., Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: A systematic review. J. Am. Diet. Assoc. 2005;105:775–789. doi: 10.1016/j.jada.2005.02.005.
    1. Bland J.M., Altman D.G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–310. doi: 10.1016/S0140-6736(86)90837-8.
    1. De Lorenzo A., Tagliabue A., Andreoli A., Testolin G., Comelli M., Deurenberg P. Measured and predicted resting metabolic rate in Italian males and females, aged 18–59 y. Eur. J. Clin. Nutr. 2001;55:208–214. doi: 10.1038/sj.ejcn.1601149.
    1. Lazzer S., Agosti F., Resnik M., Marazzi N., Mornati D., Sartorio A. Prediction of resting energy expenditure in severely obese Italian males. J. Endocrinol. Investig. 2007;30:754–761. doi: 10.1007/BF03350813.
    1. Weijs P.J.M., Vansant G.A.A.M. Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women. Clin. Nutr. 2010;29:347–351. doi: 10.1016/j.clnu.2009.09.009.
    1. Wright T.G., Dawson B., Jalleh G., Guelfi K.J. Accuracy of resting metabolic rate prediction in overweight and obese Australian adults. Obes. Res. Clin. Pract. 2016;10:S74–S83. doi: 10.1016/j.orcp.2015.07.008.
    1. Madden A.M., Mulrooney H.M., Shah S. Estimation of energy expenditure using prediction equations in overweight and obese adults: A systematic review. J. Hum. Nutr. Diet. 2016;29:458–476. doi: 10.1111/jhn.12355.
    1. Tseng C.-K., Hsu H.-S., Ho C.-T., Huang H.-Y., Liu C.-S., Lin C.-C., Lin W.-Y. Predictive equation of resting energy expenditure in obese adult Taiwanese. Obes. Res. Clin. Pract. 2011;5:e313–e319. doi: 10.1016/j.orcp.2011.03.009.

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다