Resting Energy Expenditure and Body Composition in Overweight Men and Women Living in a Temperate Climate

Marcos Martin-Rincon, Mario Perez-Valera, David Morales-Alamo, Ismael Perez-Suarez, Cecilia Dorado, Juan J Gonzalez-Henriquez, Julian W Juan-Habib, Cristian Quintana-Garcia, Victor Galvan-Alvarez, Pablo B Pedrianes-Martin, Carmen Acosta, David Curtelin, Jose A L Calbet, Pedro de Pablos-Velasco, Marcos Martin-Rincon, Mario Perez-Valera, David Morales-Alamo, Ismael Perez-Suarez, Cecilia Dorado, Juan J Gonzalez-Henriquez, Julian W Juan-Habib, Cristian Quintana-Garcia, Victor Galvan-Alvarez, Pablo B Pedrianes-Martin, Carmen Acosta, David Curtelin, Jose A L Calbet, Pedro de Pablos-Velasco

Abstract

This study aimed to determine whether the measured resting energy expenditure (REE) in overweight and obese patients living in a temperate climate is lower than the predicted REE; and to ascertain which equation should be used in patients living in a temperate climate. REE (indirect calorimetry) and body composition (DXA) were measured in 174 patients (88 men and 86 women; 20-68 years old) with overweight or obesity (BMI 27-45 kg m-2). All volunteers were residents in Gran Canaria (monthly temperatures: 18-24 °C). REE was lower than predicted by most equations in our population. Age and BMI were similar in both sexes. In the whole population, the equations of Mifflin, Henry and Rees, Livingston and Owen, had similar levels of accuracy (non-significant bias of 0.7%, 1.1%, 0.6%, and -2.2%, respectively). The best equation to predict resting energy expenditure in overweight and moderately obese men and women living in a temperate climate all year round is the Mifflin equation. In men, the equations by Henry and Rees, Livingston, and by Owen had predictive accuracies comparable to that of Mifflin. The body composition-based equation of Johnston was slightly more accurate than Mifflin's in men. In women, none of the body composition-based equations outperformed Mifflin's.

Keywords: exercise; obesity; overweight; resting energy expenditure.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Bland–Altman plots displaying the agreement between measured and predicted REE by the equations of (A) Mifflin, (B) Livingston, (C) Huang, (D) Owen, (E) Schofield WTHT, (F) Muller, (G) WHO WTHT, and (H) FAO WTHT. All these equations included obese subjects in their populations. The thick continuous line indicates the mean value of the differences between predicted and measured REE (bias). The thin lines delimit the 95% confidence interval. All the regression lines were statistically significant at p < 0.001, indicating a systematic bias. Note that “Y” and “X” axes have different scales.
Figure 2
Figure 2
Bland–Altman plots displaying the agreement between measured and predicted REE by the equations of (A) Lazzer, (B) Kleiber, (C) Korth, (D) FAO WT, (E) De Lorenzo, (F) Weijs, (G) Bernstein, and (H) De Luis. All these equations included obese subjects in their populations. The thick continuous line indicates the mean value of the differences between predicted and measured REE (bias). The thin lines delimit the 95% confidence interval. All the regression lines were statistically significant at p < 0.001, indicating a systematic bias. Note that “Y” and “X” axes have different scales. De Luis equation was obtained using a portable, hand-held device (MedGem) less accurate than metabolic carts.
Figure 3
Figure 3
Bland–Altman plots displaying the agreement between measured and predicted REE by the equations of (A) Henry and Rees (tropical populations), (B) Henry WTHT, (C) Harris–Benedict 1984, and (D) Harris–Benedict 1919. All these equations did not include obese subjects in their populations. The thick continuous line indicates the mean value of the differences between predicted and measured REE (bias). The thin lines delimit the 95% confidence interval. All the regression lines were statistically significant at p < 0.001, indicating a systematic bias. Note that “Y” and “X” axes have different scales.
Figure 4
Figure 4
Bland–Altman plots displaying the agreement between measured and predicted REE by the body composition-based equations of (A) Muller, (B) Johnstone, (C) Korth, (D) Lazzer 2010, (E) Owen, and (F) Bernstein. All these equations included obese subjects in their populations. The thick continuous line indicates the mean value of the differences between predicted and measured REE (bias). The thin lines delimit the 95% confidence interval. All the regression lines were statistically significant at p < 0.001, indicating a systematic bias. Note that “Y” and “X” axes have different scales.

References

    1. Redman L.M., Kraus W.E., Bhapkar M., Das S.K., Racette S.B., Martin C.K., Fontana L., Wong W.W., Roberts S.B., Ravussin E. Energy requirements in nonobese men and women: Results from calerie. Am. J. Clin. Nutr. 2014;99:71–78.
    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. Schofield W.N. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. Clin. Nutr. 1985;39(Suppl. 1):5–41.
    1. Frankenfield D.C., Muth E.R., Rowe W.A. The harris-benedict studies of human basal metabolism: History and limitations. J. Am. Diet. Assoc. 1998;98:439–445. doi: 10.1016/S0002-8223(98)00100-X.
    1. Henry C.J., Rees D.G. New predictive equations for the estimation of basal metabolic rate in tropical peoples. Eur. J. Clin. Nutr. 1991;45:177–185.
    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. FAO. WHO. UNU . FAO Food and Nutrition Technical Report Series. FAO; Rome, Italy: 2001. Human Energy Requirements: Report of a Joint FAO/WHO/UNU Expert Consultation; pp. 1–96.
    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. 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. Frankenfield D.C. 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. Cancello R., Soranna D., Brunani A., Scacchi M., Tagliaferri A., Mai S., Marzullo P., Zambon A., Invitti C. Analysis of predictive equations for estimating resting energy expenditure in a large cohort of morbidly obese patients. Front. Endocrinol. 2018;9:367. doi: 10.3389/fendo.2018.00367.
    1. Rolfe D.F., Brown G.C. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol. Rev. 1997;77:731–758. doi: 10.1152/physrev.1997.77.3.731.
    1. Arciero P.J., Goran M.I., Poehlman E.T. Resting metabolic rate is lower in women than in men. J. Appl. Physiol. 1993;75:2514–2520. doi: 10.1152/jappl.1993.75.6.2514.
    1. Spaeth A.M., Dinges D.F., Goel N. Resting metabolic rate varies by race and by sleep duration. Obesity. 2015;23:2349–2356. doi: 10.1002/oby.21198.
    1. Klausen B., Toubro S., Astrup A. Age and sex effects on energy expenditure. Am. J. Clin. Nutr. 1997;65:895–907. doi: 10.1093/ajcn/65.4.895.
    1. Johannsen D.L., Knuth N.D., Huizenga R., Rood J.C., Ravussin E., Hall K.D. Metabolic slowing with massive weight loss despite preservation of fat-free mass. J. Clin. Endocrinol. Metab. 2012;97:2489–2496. doi: 10.1210/jc.2012-1444.
    1. Lonac M.C., Richards J.C., Schweder M.M., Johnson T.K., Bell C. Influence of short-term consumption of the caffeine-free, epigallocatechin-3-gallate supplement, teavigo, on resting metabolism and the thermic effect of feeding. Obesity. 2011;19:298–304. doi: 10.1038/oby.2010.181.
    1. Speakman J.R., Selman C. Physical activity and resting metabolic rate. Proc. Nutr. Soc. 2003;62:621–634. doi: 10.1079/PNS2003282.
    1. Haugen H.A., Melanson E.L., Tran Z.V., Kearney J.T., Hill J.O. Variability of measured resting metabolic rate. Am. J. Clin. Nutr. 2003;78:1141–1145. doi: 10.1093/ajcn/78.6.1141.
    1. Creber C., Cooper R.S., Plange-Rhule J., Bovet P., Lambert E.V., Forrester T.E., Schoeller D., Riesen W., Korte W., Cao G., et al. Independent association of resting energy expenditure with blood pressure: Confirmation in populations of the african diaspora. BMC Cardiovasc. Disord. 2018;18:4. doi: 10.1186/s12872-017-0737-5.
    1. Luke A., Adeyemo A., Kramer H., Forrester T., Cooper R.S. Association between blood pressure and resting energy expenditure independent of body size. Hypertension. 2004;43:555–560. doi: 10.1161/01.HYP.0000118020.44335.20.
    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. Lazzer S., Bedogni G., Lafortuna C.L., Marazzi N., Busti C., Galli R., De Col A., Agosti F., Sartorio A. Relationship between basal metabolic rate, gender, age, and body composition in 8780 white obese subjects. Obesity. 2010;18:71–78. doi: 10.1038/oby.2009.162.
    1. Muller M.J., Bosy-Westphal A., Kutzner D., Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: Recent lessons from imaging technologies. Obes. Rev. 2002;3:113–122. doi: 10.1046/j.1467-789X.2002.00057.x.
    1. Wang Z., Heshka S., Wang J., Gallagher D., Deurenberg P., Chen Z., Heymsfield S.B. Metabolically active portion of fat-free mass: A cellular body composition level modeling analysis. Am. J. Physiol. Endocrinol. Metab. 2007;292:E49–E53. doi: 10.1152/ajpendo.00485.2005.
    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. Lazzer S., Agosti F., Silvestri P., Derumeaux-Burel H., Sartorio A. Prediction of resting energy expenditure in severely obese italian women. J. Endocrinol. Investig. 2007;30:20–27. doi: 10.1007/BF03347391.
    1. Karhunen L., Franssila-Kallunki A., Rissanen A., Kervinen K., Kesaniemi Y.A., Uusitupa M. Determinants of resting energy expenditure in obese non-diabetic caucasian women. Int. J. Obes. Relat. Metab. Disord. 1997;21:197–202. doi: 10.1038/sj.ijo.0800387.
    1. Muller M.J., Bosy-Westphal A., Klaus S., Kreymann G., Luhrmann P.M., Neuhauser-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 expenditure. Am. J. Clin. Nutr. 2004;80:1379–1390.
    1. Marra M., Cioffi I., Sammarco R., Montagnese C., Naccarato M., Amato V., Contaldo F., Pasanisi F. Prediction and evaluation of resting energy expenditure in a large group of obese outpatients. Int. J. Obes. 2017;41:697–705. doi: 10.1038/ijo.2017.34.
    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., et al. A reappraisal of the caloric requirements of men. Am. J. Clin. Nutr. 1987;46:875–885. doi: 10.1093/ajcn/46.6.875.
    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. Bernstein R.S., Thornton J.C., Yang M.U., Wang J., Redmond A.M., Pierson R.N., Jr., 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. Johnson F., Mavrogianni A., Ucci M., Vidal-Puig A., Wardle J. Could increased time spent in a thermal comfort zone contribute to population increases in obesity? Obes. Rev. 2011;12:543–551. doi: 10.1111/j.1467-789X.2010.00851.x.
    1. Valdes S., Maldonado-Araque C., Garcia-Torres F., Goday A., Bosch-Comas A., Bordiu E., Calle-Pascual A., Carmena R., Casamitjana R., Castano L., et al. Ambient temperature and prevalence of obesity in the spanish population: The study. Obesity. 2014;22:2328–2332. doi: 10.1002/oby.20866.
    1. Aranceta-Bartrina J., Perez-Rodrigo C., Alberdi-Aresti G., Ramos-Carrera N., Lazaro-Masedo S. Prevalence of general obesity and abdominal obesity in the spanish adult population (aged 25–64 years) 2014–2015: The enpe study. Rev. Esp. Cardiol. 2016;69:579–587. doi: 10.1016/j.recesp.2016.02.010.
    1. De Pablos-Velasco P.L., Martinez-Martin F.J., Rodriguez-Perez F. Prevalence of obesity in a canarian community. Association with type 2 diabetes mellitus: The guia study. Eur. J. Clin. Nutr. 2002;56:557–560. doi: 10.1038/sj.ejcn.1601401.
    1. Gallus S., Lugo A., Murisic B., Bosetti C., Boffetta P., La Vecchia C. Overweight and obesity in 16 european countries. Eur. J. Nutr. 2015;54:679–689. doi: 10.1007/s00394-014-0746-4.
    1. Turner J.B., Kumar A., Koch C.A. The effects of indoor and outdoor temperature on metabolic rate and adipose tissue—The Mississippi perspective on the obesity epidemic. Rev. Endocr. Metab. Disord. 2016;17:61–71. doi: 10.1007/s11154-016-9358-z.
    1. Calbet J.A.L., Ponce-Gonzalez J.G., Calle-Herrero J., Perez-Suarez I., Martin-Rincon M., Santana A., Morales-Alamo D., Holmberg H.C. Exercise preserves lean mass and performance during severe energy deficit: The role of exercise volume and dietary protein content. Front. Physiol. 2017;8:483. doi: 10.3389/fphys.2017.00483.
    1. Pickering T.G., Hall J.E., Appel L.J., Falkner B.E., Graves J., Hill M.N., Jones D.W., Kurtz T., Sheps S.G., Roccella E.J. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: Blood pressure measurement in humans: A statement for professionals from the subcommittee of professional and public education of the american heart association council on high blood pressure research. Hypertension. 2005;45:142–161.
    1. Isbell T.R., Klesges R.C., Meyers A.W., Klesges L.M. Measurement reliability and reactivity using repeated measurements of resting energy expenditure with a face mask, mouthpiece, and ventilated canopy. JPEN J. Parenter. Enter. Nutr. 1991;15:165–168. doi: 10.1177/0148607191015002165.
    1. Perez-Suarez I., Martin-Rincon M., Gonzalez-Henriquez J.J., Fezzardi C., Perez-Regalado S., Galvan-Alvarez V., Juan-Habib J.W., Morales-Alamo D., Calbet J.A.L. Accuracy and precision of the cosmed k5 portable analyser. Front. Physiol. 2018;9:1764. doi: 10.3389/fphys.2018.01764.
    1. Peronnet F., Massicotte D. Table of nonprotein respiratory quotient: An update. Can. J. Sport Sci. 1991;16:23–29.
    1. Lin L.I. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255–268. doi: 10.2307/2532051.
    1. Harris J.A., Benedict F.G. A biometric study of human basal metabolism. Proc. Natl. Acad. Sci. USA. 1918;4:370–373. doi: 10.1073/pnas.4.12.370.
    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. FAO. WHO. UNU . Energy and Protein Requirements: Report of a Joint FAO/WHO/UNU Expert Consultation. Volume 724 WHO; Geneva, Switzerland: 1985. (World Health Organization technical report series).
    1. Betz M.J., Enerback S. Human brown adipose tissue: What we have learned so far. Diabetes. 2015;64:2352–2360. doi: 10.2337/db15-0146.
    1. Blondin D.P., Tingelstad H.C., Mantha O.L., Gosselin C., Haman F. Maintaining thermogenesis in cold exposed humans: Relying on multiple metabolic pathways. Compr. Physiol. 2014;4:1383–1402.
    1. Van der Lans A.A., Hoeks J., Brans B., Vijgen G.H., Visser M.G., Vosselman M.J., Hansen J., Jorgensen J.A., Wu J., Mottaghy F.M., et al. Cold acclimation recruits human brown fat and increases nonshivering thermogenesis. J. Clin. Investig. 2013;123:3395–3403. doi: 10.1172/JCI68993.
    1. Periasamy M., Maurya S.K., Sahoo S.K., Singh S., Sahoo S.K., Reis F.C.G., Bal N.C. Role of serca pump in muscle thermogenesis and metabolism. Compr. Physiol. 2017;7:879–890.
    1. Ye L., Wu J., Cohen P., Kazak L., Khandekar M.J., Jedrychowski M.P., Zeng X., Gygi S.P., Spiegelman B.M. Fat cells directly sense temperature to activate thermogenesis. Proc. Natl. Acad. Sci. USA. 2013;110:12480–12485. doi: 10.1073/pnas.1310261110.
    1. Davis T.R., Johnston D.R. Seasonal acclimatization to cold in man. J. Appl. Physiol. 1961;16:231–234. doi: 10.1152/jappl.1961.16.2.231.
    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. Johnstone A.M., Murison S.D., Duncan J.S., Rance K.A., Speakman J.R. Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am. J. Clin. Nutr. 2005;82:941–948. doi: 10.1093/ajcn/82.5.941.
    1. Korth O., Bosy-Westphal A., Zschoche P., Gluer C.C., Heller M., Muller 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. Robertson J.D., Reid D.D. Standards for the basal metabolism of normal people in britain. Lancet. 1952;1:940–943. doi: 10.1016/S0140-6736(52)90543-6.
    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. Daly J.M., Heymsfield S.B., Head C.A., Harvey L.P., Nixon D.W., Katzeff H., Grossman G.D. Human energy requirements: Overestimation by widely used prediction equation. Am. J. Clin. Nutr. 1985;42:1170–1174. doi: 10.1093/ajcn/42.6.1170.
    1. Garrel D.R., Jobin N., de Jonge L.H. Should we still use the harris and benedict equations? Nutr. Clin. Pract. 1996;11:99–103. doi: 10.1177/011542659601100399.
    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. Dobratz J.R., Sibley S.D., Beckman T.R., Valentine B.J., Kellogg T.A., Ikramuddin S., Earthman C.P. Predicting energy expenditure in extremely obese women. JPEN J. Parenter. Enter. Nutr. 2007;31:217–227. doi: 10.1177/0148607107031003217.
    1. Carrasco F., Rojas P., Ruz M., Rebolledo A., Mizon C., Codoceo J., Inostroza J., Papapietro K., Csendes A. Agreement between measured and calculated by predictive formulas resting energy expenditure in severe and morbid obese women. Nutr. Hosp. 2007;22:410–416.
    1. Weijs P.J., Vansant G.A. 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. Martin-Rincon M., Gonzalez-Henriquez J.J., Losa-Reyna J., Perez-Suarez I., Ponce-Gonzalez J.G., de La Calle-Herrero J., Perez-Valera M., Perez-Lopez A., Curtelin D., Cherouveim E.D., et al. Impact of data averaging strategies on vo2max assessment: Mathematical modeling and reliability. Scand. J. Med. Sci. Sports. 2019;29:1473–1488. doi: 10.1111/sms.13495.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner