Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry
Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic
Document type Journal Article
Grant support
CZ-DRO-VFN64165
Metabolic Diseases - 207037-1
LM2023064
METROFOOD-CZ
PubMed
39408053
PubMed Central
PMC11478319
DOI
10.3390/jcm13195993
PII: jcm13195993
Knihovny.cz E-resources
- Keywords
- energy expenditure, indirect calorimetry, obesity, weight loss,
- Publication type
- Journal Article MeSH
Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.
See more in PubMed
Lobstein T., Cooper K. Obesity: A Ghost at the Feast of the Sustainable Development Goals. Curr. Obes. Rep. 2020;9:470–478. doi: 10.1007/s13679-020-00405-x. PubMed DOI
Haththotuwa R.N., Wijeyaratne C.N., Senarath U. Obesity and Obstetrics. Elsevier; Amsterdam, The Netherlands: 2020. Worldwide epidemic of obesity; pp. 3–8. DOI
Bischoff S.C., Schweinlin A. Obesity therapy. Clin. Nutr. ESPEN. 2020;38:9–18. doi: 10.1016/j.clnesp.2020.04.013. PubMed DOI
Kushner R.F. Weight loss strategies for treatment of obesity. Prog. Cardiovasc. Dis. 2014;56:465–472. doi: 10.1016/j.pcad.2013.09.005. PubMed DOI
Westerterp K.R. Control of energy expenditure in humans. Eur. J. Clin. Nutr. 2017;71:340–344. doi: 10.1038/ejcn.2016.237. PubMed DOI
Soares M.J., Müller M.J. Resting energy expenditure and body composition: Critical aspects for clinical nutrition. Eur. J. Clin. Nutr. 2018;72:1208–1214. doi: 10.1038/s41430-018-0220-0. PubMed DOI
Lam Y.Y., Ravussin E. Indirect calorimetry: An indispensable tool to understand and predict obesity. Eur. J. Clin. Nutr. 2017;71:318–322. doi: 10.1038/ejcn.2016.220. PubMed DOI
Ndahimana D., Kim E.-K. Measurement Methods for Physical Activity and Energy Expenditure: A Review. Clin. Nutr. Res. 2017;6:68–80. doi: 10.7762/cnr.2017.6.2.68. PubMed DOI PMC
Pavlidou E., Petridis D., Tolia M., Tsoukalas N., Poultsidi A., Fasoulas A., Kyrgias G., Giaginis C. Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: An effort to develop a new equation for predicting resting metabolic rate. Nutr. Metab. 2018;15:41. doi: 10.1186/s12986-018-0278-7. PubMed DOI PMC
Macena M.L., Pureza I.R.O.M., Melo I.S.V., Clemente A.G., Ferreira H.S., Florêncio T.M.M.T., Pfrimer K., Ferrioli E., Sawaya A.L., Bueno N.B. Agreement between the total energy expenditure calculated with accelerometry data and the BMR yielded by predictive equations v. the total energy expenditure obtained with doubly labelled water in low-income women with excess weight. Br. J. Nutr. 2019;122:1398–1408. doi: 10.1017/S0007114519002460. PubMed DOI
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. PubMed DOI PMC
Carneiro I.P., A Elliott S., Siervo M., Padwal R., Bertoli S., Battezzati A., Prado C.M. Is obesity associated with altered energy expenditure? Adv. Nutr. Int. Rev. J. 2016;7:476–487. doi: 10.3945/an.115.008755. PubMed DOI PMC
Löffler M.C., Betz M.J., Blondin D.P., Augustin R., Sharma A.K., Tseng Y.-H., Scheele C., Zimdahl H., Mark M., Hennige A.M., et al. Challenges in tackling energy expenditure as obesity therapy: From preclinical models to clinical application. Mol. Metab. 2021;51:101237. doi: 10.1016/j.molmet.2021.101237. PubMed DOI PMC
Most J., Tosti V., Redman L.M., Fontana L. Calorie restriction in humans: An update. Ageing Res. Rev. 2017;39:36–45. doi: 10.1016/j.arr.2016.08.005. PubMed DOI PMC
Redman L.M., Smith S.R., Burton J.H., Martin C.K., Il’yasova D., Ravussin E. Metabolic Slowing and Reduced Oxidative Damage with Sustained Caloric Restriction Support the Rate of Living and Oxidative Damage Theories of Aging. Cell Metab. 2018;27:805–815.e4. doi: 10.1016/j.cmet.2018.02.019. PubMed DOI PMC
Flack K.D., Siders W.A., Johnson L.A., Roemmich J.N. Cross-Validation of Resting Metabolic Rate Prediction Equations. J. Acad. Nutr. Diet. 2016;116:1413–1422. doi: 10.1016/j.jand.2016.03.018. PubMed DOI
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. PubMed DOI
Achamrah N., Delsoglio M., De Waele E., Berger M.M., Pichard C. Indirect calorimetry: The 6 main issues. Clin. Nutr. 2021;40:4–14. doi: 10.1016/j.clnu.2020.06.024. PubMed DOI
Tatucu-Babet O.A., Ridley E.J., Tierney A.C. Prevalence of Underprescription or Overprescription of Energy Needs in Critically Ill Mechanically Ventilated Adults as Determined by Indirect Calorimetry. J. Parenter. Enter. Nutr. 2016;40:212–225. doi: 10.1177/0148607114567898. PubMed DOI
Oshima T., Berger M.M., De Waele E., Guttormsen A.B., Heidegger C.-P., Hiesmayr M., Singer P., Wernerman J., Pichard C. Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group. Clin. Nutr. 2017;36:651–662. doi: 10.1016/j.clnu.2016.06.010. PubMed DOI
Most J., Redman L.M. Impact of calorie restriction on energy metabolism in humans. Exp. Gerontol. 2020;133:110875. doi: 10.1016/j.exger.2020.110875. PubMed DOI PMC
Das Gupta R., Ramachandran R., Venkatesan P., Anoop S., Joseph M., Thomas N. Indirect calorimetry: From bench to bedside. Indian J. Endocrinol. Metab. 2017;21:594–599. doi: 10.4103/ijem.IJEM_484_16. PubMed DOI PMC
Rattanachaiwong S., Singer P. Indirect calorimetry as point of care testing. Clin. Nutr. 2019;38:2531–2544. doi: 10.1016/j.clnu.2018.12.035. PubMed DOI
Harris B.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. PubMed DOI PMC
Delsoglio M., Achamrah N., Berger M.M., Pichard C. Indirect calorimetry in Clinical Practice. J. Clin. Med. 2019;8:1387. doi: 10.3390/jcm8091387. PubMed DOI PMC
Jagim A.R., Camic C.L., Kisiolek J., Luedke J., Erickson J., Jones M.T., Oliver J.M. Accuracy of resting metabolic rate prediction equations in athletes. J. Strength Cond. Res. 2018;32:1875–1881. doi: 10.1519/JSC.0000000000002111. PubMed DOI
Zusman O., Kagan I., Bendavid I., Theilla M., Cohen J., Singer P. Predictive equations versus measured energy expenditure by indirect calorimetry: A retrospective validation. Clin. Nutr. 2019;38:1206–1210. doi: 10.1016/j.clnu.2018.04.020. PubMed DOI
Barcellos P.S., Borges N., Torres D.P.M. Resting energy expenditure in cancer patients: Agreement between predictive equations and indirect calorimetry. Clin. Nutr. ESPEN. 2021;42:286–291. doi: 10.1016/j.clnesp.2021.01.019. PubMed DOI
Anderson E.J., Sylvia L.G., Lynch M., Sonnenberg L., Lee H., Nathan D.M. Comparison of energy assessment methods in overweight individuals. J. Acad. Nutr. Diet. 2014;114:273–278. doi: 10.1016/j.jand.2013.07.008. PubMed DOI PMC
Al-Domi H., Al-Shorman A. Validation of resting metabolic rate equations in obese and non-obese young healthy adults. Clin. Nutr. ESPEN. 2018;26:91–96. doi: 10.1016/j.clnesp.2018.04.008. PubMed DOI
Kruizenga H.M., Hofsteenge G.H., Weijs P.J.M. Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients. Nutr. Metab. 2016;13:85. doi: 10.1186/s12986-016-0145-3. PubMed DOI PMC
Macena M.d.L., Paula D.T.d.C., Júnior A.E.d.S., Praxedes D.R.S., Pureza I.R.d.O.M., de Melo I.S.V., Bueno N.B. Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: A systematic review with meta-analysis. Nutr. Rev. 2022;80:2113–2135. doi: 10.1093/nutrit/nuac031. PubMed DOI
Poli V.F.S., Sanches R.B., Moraes A.d.S., Fidalgo J.P.N., Nascimento M.A., Andrade-Silva S.G., Clemente J.C., Yi L.C., Caranti D.A. Resting energy expenditure in obese women: Comparison between measured and estimated values. Br. J. Nutr. 2016;116:1306–1313. doi: 10.1017/S0007114516003172. PubMed DOI
Trexler E.T., Smith-Ryan A.E., Norton L.E. Metabolic adaptation to weight loss: Implications for the athlete. J. Int. Soc. Sports Nutr. 2014;11:7. doi: 10.1186/1550-2783-11-7. PubMed DOI PMC
Hirsch K.R., Smith-Ryan A.E., Blue M.N.M., Mock M.G., Trexler E.T. Influence of segmental body composition and adiposity hormones on resting metabolic rate and substrate utilization in overweight and obese adults. J. Endocrinol. Investig. 2017;40:635–643. doi: 10.1007/s40618-017-0616-z. PubMed DOI PMC
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. PubMed DOI PMC
Rodrigues A.M.d.S., Costa A.B.P., Campos D.L., Silva M.P.S., Cândido A.L., dos Santos L.C., Ferreira A.V.M. Low validity of predictive equations for calculating resting energy expenditure in overweight and obese women with polycystic ovary syndrome. J. Hum. Nutr. Diet. 2018;31:266–275. doi: 10.1111/jhn.12498. PubMed DOI
Swift D.L., Johannsen N.M., Lavie C.J., Earnest C.P., Church T.S. The role of exercise and physical activity in weight loss and maintenance. Prog. Cardiovasc. Dis. 2014;56:441–447. doi: 10.1016/j.pcad.2013.09.012. PubMed DOI PMC
Popp C.J., Butler M., Curran M., Illiano P., Sevick M.A., St-Jules D.E. Evaluating steady-state resting energy expenditure using indirect calorimetry in adults with overweight and obesity. Clin. Nutr. 2020;39:2220–2226. doi: 10.1016/j.clnu.2019.10.002. PubMed DOI
Ocagli H., Lanera C., Azzolina D., Piras G., Soltanmohammadi R., Gallipoli S., Gafare C.E., Cavion M., Roccon D., Vedovelli L., et al. Resting energy expenditure in the elderly: Systematic review and comparison of equations in an experimental population. Nutrients. 2021;13:458. doi: 10.3390/nu13020458. PubMed DOI PMC
Anthanont P., Jensen M.D. Does basal metabolic rate predict weight gain? Am. J. Clin. Nutr. 2016;104:959–963. doi: 10.3945/ajcn.116.134965. PubMed DOI PMC