New equations to estimate resting energy expenditure in obese adults from body composition

  • Antonino De Lorenzo
  • Laura Di Renzo
  • Pietro Morini
  • Renata Costa de Miranda
  • Lorenzo Romano
  • Carmela Colica
Original Article
  • 123 Downloads

Abstract

Aims

The aims of this study were: to develop new equations for predicting resting energy expenditure (REE) in obese Italian subjects according to body composition parameters; to compare them with predicted values estimated by other REE prediction equations; and to cross-validate our equations using a validation set cohort.

Methods

Four hundred patients were enrolled and divided into three groups. Besides anthropometry and REE (indirect calorimetry), total body fat and lean were evaluated by dual X-ray absorptiometry, and fat mass and fat-free mass by bioelectrical impedance analysis.

Results

The subjects eligible to participate were 330. Group 1 (n = 174) was used to develop (R2 = 0.79) and (R2 = 0.77). Group 2 (n = 115) was used to generate (R2 = 0.85) and (R2 = 0.81). Group 3 (n = 41) was used to cross-validate the equations.

Conclusion

Equations 1 and 3 are reliable to measure REE from calorimetry and better than other equations that use anthropometric variables as predictors of REE. Further analysis in different populations is required before it can be applied in clinical practice.

Keywords

Prediction equation Resting energy expenditure Body composition Obese Adults 

Notes

Acknowledgements

We are beholden to all the subjects who volunteered in the study. We also thank the entire medical team from the Section of Clinical Nutrition and Nutrigenomic, University of Rome Tor Vergata, Rome. This study was supported by grants from Ministry of Agriculture, Food and Forestry (D.M.; 2017188).

Authors’ Contributions

De Lorenzo A conceived, designed the experiments and drafted the manuscript; Di Renzo L, Morini P, Romano L contributed to the interpretation of the data and drafted the manuscript; Romano L collected the data and performed the experiments; de Miranda RC analyzed the data; Colica C had primary responsibility for the final content. All the authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

All authors declare no conflict of interest.

Ethical approval

All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all participants included in this study.

References

  1. 1.
    Deed G, Barlow J, Kawol D, Kilov G, Sharma A, Hwa LY (2015) Diet and diabetes. Aust Fam Phys 44(5):192–196Google Scholar
  2. 2.
    American Diabetes Association, Bantle JP, Wylie-Rosett J, Albright AL, Apovian CM, Clark NG et al (2008) Nutrition recommendations and interventions for diabetes: a position statement of the American Diabetes Association. Diabetes Care 1:S61–S78. doi:10.2337/dc08-S061 CrossRefGoogle Scholar
  3. 3.
    Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ et al (2013) American Diabetes Association: nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care 36(11):3821–3842. doi:10.2337/dc13-2042 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Foster GD, McGuckin BG (2001) Estimating resting energy expenditure in obesity. Obes Res 5:367S–372S (discussion 373S–374S) Google Scholar
  5. 5.
    DeLany JP, Lovejoy JC (1996) Energy expenditure. Endocrinol Metab Clin North Am 25(4):831–846CrossRefPubMedGoogle Scholar
  6. 6.
    Hills AP, Mokhtar N, Byrne NM (2014) Assessment of physical activity and energy expenditure: an overview of objective measures. Front Nutr 16(1):5. doi:10.3389/fnut.2014.00005 Google Scholar
  7. 7.
    Abdel-Hamid TK (2002) Modeling the dynamics of human energy regulation and its implications for obesity treatment. Syst Dyn Rev 18:431–471CrossRefGoogle Scholar
  8. 8.
    Fullmer S, Benson-Davies S, Earthman CP, Frankenfield DC, Gradwell E, Lee PS et al (2015) Evidence analysis library review of best practices for performing indirect calorimetry in healthy and non-critically ill individuals. J Acad Nutr Diet 115(9):1417–1446. doi:10.1016/j.jand.2015.04.003 CrossRefPubMedGoogle Scholar
  9. 9.
    Lazzer S, Agosti F, Silvestri P, Derumeaux-Burel H, Sartorio A (2007) Prediction of resting energy expenditure in severely obese Italian women. J Endocrinol Investig 30(1):20–27CrossRefGoogle Scholar
  10. 10.
    Madden AM, Mulrooney HM, Shah S (2016) Estimation of energy expenditure using prediction equations in overweight and obese adults: a systematic review. J Hum Nutr Diet 29(4):458–476. doi:10.1111/jhn.12355 CrossRefPubMedGoogle Scholar
  11. 11.
    Wells JC, Fuller NJ, Dewit O, Fewtrell MS, Elia M, Cole TJ (1999) Four-component model of body composition in children: density and hydration of fat-free mass and comparison with simpler models. Am J Clin Nutr 69(5):904–912PubMedGoogle Scholar
  12. 12.
    De Lorenzo A, Andreoli A, Bertoli S, Testolin G, Orinani G, Deurenberg P (2000) Resting metabolic rate in Italians: relation with body composition and anthropometric parameters. Acta Diabetol 37(2):77–81CrossRefPubMedGoogle Scholar
  13. 13.
    Van der Ploeg GE, Gunn SM, Withers RT, Modra AC, Keeves JP, Chatterton BE (2001) Predicting the resting metabolic rate of young Australian males. Eur J Clin Nutr 55(3):145–152CrossRefPubMedGoogle Scholar
  14. 14.
    Roza AM, Shizgal HM (1984) The Harris-Benedict equation revaluated: resting energy requirements and the body cell mass. Am J Clin Nutr 40(1):168–182PubMedGoogle Scholar
  15. 15.
    De Lorenzo A, Tagliabue A, Andreoli A, Testolin G, Comelli M, Deurenberg P (2001) Measured and predicted resting metabolic rate in Italian males and females, aged 18–59 y. Eur J Clin Nutr 55(3):208–214CrossRefPubMedGoogle Scholar
  16. 16.
    Marra M, Cioffi I, Sammarco R, Montagnese C, Naccarato M, Amato V et al (2017) Prediction and evaluation of resting energy expenditure in a large group of obese outpatients. Int J Obes (Lond) 41(5):697–705. doi:10.1038/ijo.2017.34 CrossRefGoogle Scholar
  17. 17.
    Ikeda K, Fujimoto S, Goto M, Yamada C, Hamasaki A, Ida M et al (2013) A new equation to estimate basal energy expenditure of patients with diabetes. Clin Nutr 32(5):777–782. doi:10.1016/j.clnu.2012.11.017 CrossRefPubMedGoogle Scholar
  18. 18.
    Müller MJ, Bosy-Westphal A, Kutzner D, Heller M (2003) Metabolically active components of fat free mass (FFM) and resting energy expenditure (REE) in humans. Forum Nutr 56:301–303PubMedGoogle Scholar
  19. 19.
    Wang Z, Heshka S, Gallagher D, Boozer CN, Kotler DP, Heymsfield SB (2000) Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling. Am J Physiol Endocrinol Metab 279(3):E539–E545PubMedGoogle Scholar
  20. 20.
    Pourhassan M, Eggeling B, Schautz B, Johannsen M, Kiosz D, Glüer CC et al (2015) Relationship between submaximal oxygen uptake, detailed body composition, and resting energy expenditure in overweight subjects. Am J Hum Biol 27(3):397–406. doi:10.1002/ajhb.22666 CrossRefPubMedGoogle Scholar
  21. 21.
    Weir JB (1949) New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 109:1–9CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Horie LM, Gonzalez MC, Torrinhas RS, Cecconello I, Waitzberg DL (2011) New specific equation to estimate resting energy expenditure in severely obese patients. Obesity (Silver Spring) 19(5):1090–1094. doi:10.1038/oby.2010.326 CrossRefGoogle Scholar
  23. 23.
    De Lorenzo A, Soldati L, Sarlo F, Calvani M, Di Lorenzo N, Di Renzo L (2016) New obesity classification criteria as a tool for bariatric surgery indication. World J Gastroenterol 22(2):681–703. doi:10.3748/wjg.v22.i2.681.Review CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    De Lorenzo A, Bianchi A, Maroni P, Iannarelli A, Di Daniele N, Iacopino L et al (2013) Adiposity rather than BMI determines metabolic risk. Int J Cardiol 166:111–117CrossRefPubMedGoogle Scholar
  25. 25.
    Bruno E, Alessandrini M, Napolitano B, De Padova A, Di Daniele N, De Lorenzo A (2009) Dual-energy X-ray absorptiometry analysis of body composition in patients affected by OSAS. Eur Arch Otorhinolaryngol 266(8):1285–1290. doi:10.1007/s00405-008-0844-0 CrossRefPubMedGoogle Scholar
  26. 26.
    De Lorenzo A, Martinoli R, Carbonelli MG, Monteleone G, Di Lorenzo N, Di Daniele N (2004) Resting metabolic rate incremented by pulsating electrostatic field (PESF) therapy. Diabetes Nutr Metab 17(5):309–312PubMedGoogle Scholar
  27. 27.
    Cunningham JJ (1990) Calculation of energy expenditure from indirect calorimetry: assessment of the Weir equation. Nutrition 6(3):222–223PubMedGoogle Scholar
  28. 28.
    Di Renzo L, Carbonelli MG, Bianchi A, Domino E, Migliore MR, Rillo G et al (2012) Impact of the −174 G > C IL-6 polymorphism on bioelectrical parameters in obese subjects after laparoscopic adjustable gastric banding. J Obes 2012:208953. doi:10.1155/2012/208953 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Altman DG (1996) Relation between two continuous variables. Practical statistics for medical research. Chapman & Hall, London, pp 277–324Google Scholar
  30. 30.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310CrossRefPubMedGoogle Scholar
  31. 31.
    Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR (2005) 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 82(5):941–948PubMedGoogle Scholar
  32. 32.
    Nelson KM, Weinsier RL, Long CL, Schutz Y (1992) Prediction of resting energy expenditure from fat-free mass and fat mass. Am J Clin Nutr 56(5):848–856PubMedGoogle Scholar
  33. 33.
    Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM et al (2004) Bioelectrical impedance analysis—part I: review of principles and methods. Clin Nutr 23(5):1226–1243CrossRefPubMedGoogle Scholar
  34. 34.
    Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gómez J et al (2004) Bioelectrical impedance analysis—part II: utilization in clinical practice. Clin Nutr 23(6):1430–1453CrossRefPubMedGoogle Scholar
  35. 35.
    Zhu K, Briffa K, Smith A, Mountain J, Briggs AM, Lye S et al (2014) Gender differences in the relationships between lean body mass, fat mass and peak bone mass in young adults. Osteoporos Int 25(5):1563–1570. doi:10.1007/s00198-014-2665-x CrossRefPubMedGoogle Scholar
  36. 36.
    Ness-Abramof R, Apovian CM (2006) Diet modification for treatment and prevention of obesity. Endocrine 29(1):5–9CrossRefPubMedGoogle Scholar
  37. 37.
    Seidell JC, Halberstadt J (2015) The global burden of obesity and the challenges of prevention. Ann Nutr Metab 66(Suppl 2):7–12. doi:10.1159/000375143 CrossRefPubMedGoogle Scholar
  38. 38.
    Zoico E, Corzato F, Bambace C, Rossi AP, Micciolo R, Cinti S et al (2013) Myosteatosis and myofibrosis: relationship with aging, inflammation and insulin resistance. Arch Gerontol Geriatr 57(3):411–416. doi:10.1016/j.archger.2013.06.001 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Bredella MA, Ghomi RH, Thomas BJ, Torriani M, Brick DJ, Gerweck AV et al (2010) Comparison of DXA and CT in the assessment of body composition in premenopausal women with obesity and anorexia nervosa. Obesity (Silver Spring, Md.) 18(11):2227–2233. doi:10.1038/oby.2010.5 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l. 2017

Authors and Affiliations

  1. 1.Section of Clinical Nutrition and Nutrigenomic, Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
  2. 2.ICANS International Center for Assessment of Nutritional StatusUniversity of MilanMilanItaly
  3. 3.School of Applied Medical-Surgical SciencesUniversity of Rome Tor VergataRomeItaly
  4. 4.CAPES Scholarship (Proc N° BEX 13264/13-3). CAPES FoundationMinistry of Education of BrazilBrasíliaBrazil
  5. 5.Specialisation School of Food ScienceUniversity of Rome Tor VergataRomeItaly
  6. 6.CNR, IBFM UOS of GermanetoUniversity “Magna Graecia” of CatanzaroGermanetoItaly
  7. 7.Casa di Cura Madonna dello ScoglioCotronei (KR)Italy

Personalised recommendations