Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Resting energy expenditure of females mid- to long-term after bariatric surgery: agreement between indirect calorimetry and predictive methods

European Journal of Clinical Nutrition volume 79, pages 587–596 (2025)Cite this article

Subjects

Abstract

Background/Objectives

Predictive equations estimate post-bariatric surgery resting energy expenditure (REE), but lack accuracy assessment, especially for the remaining body mass. This study aimed to evaluate the agreement between indirect calorimetry and REE predictive methods.

Subjects/Methods

It enrolled 226 females [median age 43.0 (36.2; 50.4) years] who underwent mid- to long-term post-Roux-en-Y gastric bypass [median postoperative time 6.1 (4.0; 9.0) years]. The measured REE (mREE) was obtained using indirect calorimetry, while the estimated REE (eREE) was derived from 18 predictive equations and an artificial neural network model. Analyses were performed for the total sample and body mass index (BMI) subgroups (<30 kg/m² and ≥30 kg/m²). eREE within ±10% of mREE was considered accurate; Bland–Altman plots were performed to evaluate agreement.

Results

In the BMI < 30 kg/m² subgroup [n = 115; 1372 ± 153 kcal (5744.3 ± 640.6 kJ)], mREE did not differ from four predictive equations; Henry [1371 ± 95 kcal (5740.1 ± 397.8 kJ), p = 0.922, bias −1.0 kcal (−4.2 kJ)] and Dietary Reference Intakes-Institute of Medicine [1382 ± 102 kcal (5786.2 ± 427.1 kJ), p = 0.315, bias 10.2 kcal (42.7 kJ)] equations showed better agreement and accurate prediction performance among BMI categories (79.1 and 82.6%, respectively). The BMI ≥ 30 kg/m² subgroup mREE [n = 111; 1516 ± 186 kcal (6347.2 ± 778.7 kJ)] was significantly lower than all predictive methods and had higher bias and over-prediction, except for Mifflin-St Jeor equation [1523 ± 186 kcal (6376.5 ± 778.7 kJ), p = 0.469, bias 7.7 kcal (32.2 kJ)].

Conclusion

Equations for estimating REE show wide performance variation, with limited accurate options in this population, especially among those with BMI > 30 kg/m².

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Comparison between measured versus predicted resting energy expenditure of females mid-to long-term post-Roux-en-Y gastric bypass surgery.
Fig. 2: Bland–Altman plots of agreement between resting energy expenditure measured by indirect calorimetry and estimated equations in females mid-to long-term post-Roux-en-Y gastric bypass surgery.

Similar content being viewed by others

Data availability

The data presented in this study are available on request from the corresponding author.

References

  1. Welbourn R, Hollyman M, Kinsman R, Dixon J, Liem R, Ottosson J, et al. Bariatric Surgery Worldwide: Baseline Demographic Description and One-Year Outcomes from the Fourth IFSO Global Registry Report 2018. Obes Surg. 2019;29:782–95.

  2. Angrisani L, Santonicola A, Iovino P, Ramos A, Shikora S, Kow L. Bariatric Surgery Survey 2018: Similarities and Disparities Among the 5 IFSO Chapters. Obes Surg. 2021;31:1937–48

  3. Iovino P, Himpens J, Scopinaro N, Vitiello A, Santonicola A, Buchwald H, et al. IFSO Worldwide Survey 2016: Primary, Endoluminal, and Revisional Procedures. Obes Surg. 2018;28:3783–94.

    Article  PubMed  Google Scholar 

  4. Maciejewski ML, Arterburn DE, Van Scoyoc L, Smith VA, Yancy WS, Weidenbacher HJ, et al. Bariatric surgery and long-term durability of weight loss. JAMA Surg. 2016;151:1046–55.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ahmed B, King WC, Gourash W, Belle SH, Hinerman A, Pomp A, et al. Long-term weight change and health outcomes for sleeve gastrectomy (SG) and matched Roux-en-Y gastric bypass (RYGB) participants in the Longitudinal Assessment of Bariatric Surgery (LABS) study. Surgery. 2018;164:774–83.

    Article  PubMed  Google Scholar 

  6. Golzarand M, Toolabi K, Farid R. The bariatric surgery and weight losing: a meta-analysis in the long- and very long-term effects of laparoscopic adjustable gastric banding, laparoscopic Roux-en-Y gastric bypass and laparoscopic sleeve gastrectomy on weight loss in adults. Surg Endosc. 2017;31:4331–45.

    Article  PubMed  Google Scholar 

  7. Hayoz C, Hermann T, Raptis DA, Bronnimann A, Peterli R, Zuber M. Comparison of metabolic outcomes in patients undergoing laparoscopic roux-en-Y gastric bypass versus sleeve gastrectomy - a systematic review and meta-analysis of randomised controlled trials. Swiss Med Wkly. 2018;148:w14633.

    Article  PubMed  Google Scholar 

  8. Silva LB, Oliveira BMPM, Correia F. Evolution of body composition of obese patients undergoing bariatric surgery. Clin Nutr ESPEN. 2019;31:95–9.

  9. Magro DO, Ueno M, Coelho-Neto JS, Callejas-Neto F, Pareja JC, Cazzo E. Long-term weight loss outcomes after banded Roux-en-Y gastric bypass: a prospective 10-year follow-up study. Surg Obes Relat Dis. 2018;14:910–7.

    Article  PubMed  Google Scholar 

  10. King WC, Hinerman AS, Belle SH, Wahed AS, Courcoulas AP. Comparison of the Performance of Common Measures of Weight Regain after Bariatric Surgery for Association with Clinical Outcomes. JAMA J Am Med Assoc. 2018;320:1560–9.

    Article  Google Scholar 

  11. Westerterp KR. Control of energy expenditure in humans. Eur J Clin Nutr. 2017;71:340–4.

    Article  CAS  PubMed  Google Scholar 

  12. Schadewaldt P, Nowotny B, Straßburger K, Kotzka J, Roden M. Indirect calorimetry in humans: A postcalorimetric evaluation procedure for correction of metabolic monitor variability. Am J Clin Nutr. 2013;97:763–73.

    Article  CAS  PubMed  Google Scholar 

  13. Haugen AH, Chan LN, Li F. Indirect calorimetry: A practical guide for clinicians. Nutr Clin Pract. 2007;22:377–88.

    Article  PubMed  Google Scholar 

  14. Oshima T, Berger MM, De Waele E, Guttormsen AB, Heidegger C-P, Hiesmayr M, et al. Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group. Clin Nutr. 2017;36:651–62.

    Article  PubMed  Google Scholar 

  15. AARC. AARC Clinical Practice Guideline. Metabolic measurement using indirect calorimetry during mechanical ventilation – 2004 Revision & Update. Respir Care. 2004;49:1073e9.

    Google Scholar 

  16. Byham-Gray LD, Parrott JS, Peters EN, Fogerite SG, Hand RK, Ahrens S, et al. Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis. J Parenter Enter Nutr. 2018;42:587–96.

    Article  CAS  Google Scholar 

  17. Jésus P, Marin B, Fayemendy P, Nicol M, Lautrette G, Sourisseau H, et al. Resting energy expenditure equations in amyotrophic lateral sclerosis, creation of an ALS-specific equation. Clin Nutr. 2019;38:1657–65.

    Article  PubMed  Google Scholar 

  18. de Oliveira Fernandes T, Avesani CM, Aoike DT, Cuppari L. New predictive equations to estimate resting energy expenditure of non-dialysis dependent chronic kidney disease patients. J Nephrol. 2021;34:1235–42.

    Article  PubMed  Google Scholar 

  19. Osuna-Padilla I, Aguilar-Vargas A, Rodríguez-Moguel NC, Villazón-De la Rosa A, Osuna-Ramírez I, Ormsby CE, et al. Resting energy expenditure in HIV/AIDS patients: Development and validation of a predictive equation. Clin Nutr ESPEN. 2020;40:288–92.

    Article  PubMed  Google Scholar 

  20. Disse E, Ledoux S, Bétry C, Caussy C, Maitrepierre C, Coupaye M, et al. An artificial neural network to predict resting energy expenditure in obesity. Clin Nutr. 2018;37:1661–9.

    Article  PubMed  Google Scholar 

  21. Harris JA, Benedict FG. A Biometric Study of Human Basal Metabolism. Proc Natl Acad Sci. 1918;4:370–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Roza AM, Shizgal HM. The Harris Benedict equation reevaluated: Resting energy requirements and the body cell mass. Am J Clin Nutr. 1984;40:168–82.

    Article  CAS  PubMed  Google Scholar 

  23. World Health Organization. Energy and protein requirements. Report of a joint FAO/WHO/UNU Expert Consultation. Switzerland: World Health Organization; 1985.

  24. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39:5–41.

    PubMed  Google Scholar 

  25. Henry C. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005;8:1133–52.

    Article  CAS  PubMed  Google Scholar 

  26. Institute of Medicine (U.S.). Panel on Macronutrients., Institute of Medicine (U.S.). Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. National Academies Press; 2005.

  27. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241–7.

    Article  CAS  PubMed  Google Scholar 

  28. Müller MJ, Bosy-Westphal A, Klaus S, Kreymann G, Lührmann PM, Neuhäuser-Berthold M, 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;80:1379–90.

    Article  PubMed  Google Scholar 

  29. Weijs PJM, Vansant GAAM. Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women. Clin Nutr. 2010;29:347–51.

    Article  PubMed  Google Scholar 

  30. Frankenfield DC. Bias and accuracy of resting metabolic rate equations in non-obese and obese adults. Clin Nutr. 2013;32:976–82.

    Article  PubMed  Google Scholar 

  31. Orozco-Ruiz X, Pichardo-Ontiveros E, Tovar AR, Torres N, Medina-Vera I, Prinelli F, et al. Development and validation of new predictive equation for resting energy expenditure in adults with overweight and obesity. Clin Nutr. 2018;37:2198–205.

    Article  PubMed  Google Scholar 

  32. Lazzer S, Bedogni G, Lafortuna CL, Marazzi N, Busti C, Galli R, et al. Relationship between basal metabolic rate, gender, age, and body composition in 8,780 white obese subjects. Obesity. 2010;18:71–78.

    Article  PubMed  Google Scholar 

  33. Horie LM, Gonzalez MC, Torrinhas RS, Cecconello I, Waitzberg DL. New specific equation to estimate resting energy expenditure in severely obese patients. Obesity. 2011;19:1090–4.

    Article  PubMed  Google Scholar 

  34. Lamarca F, Vieira FT, Lima RM, Nakano EY, da Costa THM, Pizato N, et al. Effects of Resistance Training With or Without Protein Supplementation on Body Composition and Resting Energy Expenditure in Patients 2–7 Years PostRoux-en-Y Gastric Bypass: a Controlled Clinical Trial. Obes Surg. 2021;31:1635–46.

    Article  PubMed  Google Scholar 

  35. Berber LCL, Melendez-Araújo MS, Nakano EY, de Carvalho KMB, Dutra ES. Grazing Behavior Hinders Weight Loss in Long-Term Post Bariatric Surgery: a Cross-Sectional Study. Obes Surg. 2021;31:4076–82.

    Article  PubMed  Google Scholar 

  36. Berti LV, Campos J, Ramos A, Rossi M, Szego T, Cohen R. Position of the SBCBM - nomenclature and definition of outcomes of bariatric and metabolic surgery. ABCD Arq Bras Cir Dig. 2015;28:2–2.

    Article  PubMed  Google Scholar 

  37. da Silva FBL, Gomes DL, de Carvalho KMB. Poor diet quality and postoperative time are independent risk factors for weight regain after Roux-en-Y gastric bypass. Nutrition. 2016;32:1250–3.

    Article  PubMed  Google Scholar 

  38. Deitel M, Gawdat K, Melissas J. Reporting Weight Loss 2007. Obes Surg. 2007;17:565–8.

    Article  PubMed  Google Scholar 

  39. Compher C, Frankenfield D, Keim N, Roth-Yousey L. Best Practice Methods to Apply to Measurement of Resting Metabolic Rate in Adults: A Systematic Review. J Am Diet Assoc. 2006;106:881–903.

    Article  PubMed  Google Scholar 

  40. Fullmer S, Benson-Davies S, Earthman CP, Frankenfield DC, Gradwell E, Lee PSP, et al. Evidence Analysis Library Review of Best Practices for Performing Indirect Calorimetry in Healthy and Non-Critically Ill Individuals. J Acad Nutr Diet. 2015;115:1417–46.

    Article  PubMed  Google Scholar 

  41. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016;15:155–63.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Duvoisin C, Favre L, Allemann P, Fournier P, Demartines N, Suter M. Roux-en-Y Gastric Bypass: Ten-year Results in a Cohort of 658 Patients. Ann Surg. 2018;268:1019–25.

    Article  PubMed  Google Scholar 

  44. Jiménez A, Ibarzabal A, Moizé V, Pané A, Andreu A, Molero J, et al. Ten-year outcomes after Roux-en-Y gastric bypass and sleeve gastrectomy: an observational nonrandomized cohort study. Surg Obes Relat Dis. 2019;15:382–8.

  45. de Oliveira VLP, Martins GP, Mottin CC, Rizzolli J, Friedman R. Predictors of Long-Term Remission and Relapse of Type 2 Diabetes Mellitus Following Gastric Bypass in Severely Obese Patients. Obes Surg. 2018;28:195–203.

    Article  PubMed  Google Scholar 

  46. Hawkins RB, Mehaffey JH, McMurry TL, Kirby J, Malin SK, Schirmer B, et al. Clinical significance of failure to lose weight 10 years after roux-en-y gastric bypass. Surg Obes Relat Dis. 2017;13:1710–6.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Larjani S, Spivak I, Hao Guo M, Aliarzadeh B, Wang W, Robinson S, et al. Preoperative predictors of adherence to multidisciplinary follow-up care postbariatric surgery. Surg Obes Relat Dis. 2016;12:350–6.

    Article  PubMed  Google Scholar 

  48. Khorgami Z, Zhang C, Messiah SE, de la Cruz-Muñoz N. Predictors of Postoperative Aftercare Attrition among Gastric Bypass Patients. Bariatr Surg Pract Patient Care. 2016;10:79–83.

    Article  Google Scholar 

  49. Nuijten MAH, Monpellier VM, Eijsvogels TMH, Janssen IMC, Hazebroek EJ, Hopman MTE. Rate and Determinants of Excessive Fat-Free Mass Loss After Bariatric Surgery. Obes Surg. 2020;30:3119–26.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Cole AJ, Kuchnia AJ, Beckman LM, Jahansouz C, Mager JR, Sibley SD, et al. Long-Term Body Composition Changes in Women Following Roux-en-Y Gastric Bypass Surgery. J Parenter Enter Nutr. 2017;41:583–91.

    Article  Google Scholar 

  51. Lamarca F, Melendez-Araújo MS, Porto de Toledo I, Dutra ES, de Carvalho KMB. Relative Energy Expenditure Decreases during the First Year after Bariatric Surgery: A Systematic Review and Meta-Analysis. Obes Surg. 2019;29:2648–59.

    Article  PubMed  Google Scholar 

  52. Wolfe BM, Schoeller DA, McCrady-Spitzer SK, Thomas DM, Sorenson CE, Levine JA. Resting Metabolic Rate, Total Daily Energy Expenditure, and Metabolic Adaptation 6 Months and 24 Months After Bariatric Surgery. Obesity. 2018;26:862–8.

    Article  PubMed  Google Scholar 

  53. Nuijten MAH, Eijsvogels TMH, Monpellier VM, Janssen IMC, Hazebroek EJ, Hopman MTE. The magnitude and progress of lean body mass, fat-free mass, and skeletal muscle mass loss following bariatric surgery: A systematic review and meta-analysis. Obes Rev J Int Assoc Study Obes. 2022;23:e13370.

    Article  Google Scholar 

  54. Heshka S, Lemos T, Astbury NM, Widen E, Davidson L, Goodpaster BH, et al. Resting Energy Expenditure and Organ-Tissue Body Composition 5 Years After Bariatric Surgery. Obes Surg. 2020;30:587–94.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Dias Rodrigues JC, Lamarca F, Lacroix de Oliveira C, Cuppari L, Lourenço RA, Avesani CM. Agreement between prediction equations and indirect calorimetry to estimate resting energy expenditure in elderly patients on hemodialysis. ESPEN J. 2014;9:e91–6.

  56. Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform. 2022;159:104679.

    Article  PubMed  Google Scholar 

  57. Enodien B, Taha-Mehlitz S, Saad B, Nasser M, Frey DM, Taha A. The development of machine learning in bariatric surgery. Front Surg. 2023;10:1–8.

    Google Scholar 

  58. Bektaş M, Reiber BMM, Pereira JC, Burchell GL, van der Peet DL. Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives. Obes Surg. 2022;32:2772–83.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, et al. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg. 2022;32:2717–33.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are particularly thankful to Gabriela Sousa de Oliveira, Isabela Nogueira Martins Sena Rios, and Gustavo Neves de Souza Gomes for contributing to data collection.

Funding

This study was funded by the Foundation for Research Support of the Federal District (FAPDF; grant number 0193.001.462/2016), the Brazilian National Council for Scientific and Technological Development and the Ministry of Health (CNPq/MS; grant number 408340/2017-7). The authors RML and KMBC also thank CNPq (grant numbers 305746/2022-7 and 302740/2022-8) for the financial support.

Author information

Authors and Affiliations

  1. Department of Applied Nutrition, Institute of Nutrition, Rio de Janeiro State University (Uerj), Rio de Janeiro, Brazil

    Fernando Lamarca

  2. Graduate Program in Medical Sciences, Faculty of Medical Sciences, Rio de Janeiro State University (Uerj), Rio de Janeiro, Brazil

    Fernando Lamarca

  3. Graduate Program in Human Nutrition, University of Brasília (UnB), Brasília, Brazil

    Flávio T. Vieira, Mariana S. Melendez-Araújo, Ricardo M. Lima, Eliane Said Dutra & Kênia Mara Baiocchi de Carvalho

Authors
  1. Fernando Lamarca
    View author publications

    Search author on:PubMed Google Scholar

  2. Flávio T. Vieira
    View author publications

    Search author on:PubMed Google Scholar

  3. Mariana S. Melendez-Araújo
    View author publications

    Search author on:PubMed Google Scholar

  4. Ricardo M. Lima
    View author publications

    Search author on:PubMed Google Scholar

  5. Eliane Said Dutra
    View author publications

    Search author on:PubMed Google Scholar

  6. Kênia Mara Baiocchi de Carvalho
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, FL and ESD; Formal analysis, FL; Funding acquisition, KMBC; Investigation, FL, FTV and MSMA; Methodology, FL, FTV, MSMA, RML, ESD and KMBC; Project administration, KMBC; Supervision, RML, ESD and KMBC; Writing – original draft, FL; Writing – review & editing, FL, FTV, MSMA, RML, ESD and KMBC.

Corresponding author

Correspondence to Fernando Lamarca.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Research Ethics Committee of the Faculty of Health Sciences of the University of Brasília [protocol codes 2,052,734 (2017) and 3,595,291 (2018)]. Informed consent was obtained from all subjects involved in the study.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lamarca, F., T. Vieira, F., S. Melendez-Araújo, M. et al. Resting energy expenditure of females mid- to long-term after bariatric surgery: agreement between indirect calorimetry and predictive methods. Eur J Clin Nutr 79, 587–596 (2025). https://doi.org/10.1038/s41430-025-01577-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41430-025-01577-2

Search

Advanced search

Quick links