Validation of predictive equations for resting energy expenditure in treatment-seeking adults with overweight and obesity: Measured versus estimated
Main Article Content
Keywords
obesity, resting energy expenditure, indirect calorimetry, predictive equations
Abstract
The quantification of resting energy expenditure (REE) in patients with obesity is an important measure. We aimed to evaluate the validity of predictive equations in estimating REE compared with indirect calorimetry (IC) in treatment-seeking Arab adults with overweight or obesity. Twenty-three predictive equations were compared with REE values measured by IC (Vmax Encore 229) in 89 adult participants with overweight or obesity (mean age = 40.62 ± 15.96 years and mean body mass index [BMI] = 35.02 ± 4.60 kg/m2) referred to the Department of Nutrition and Dietetics of Beirut Arab University (Lebanon). The accuracy of the predictive equations was evaluated on the basis of whether the percentage prediction was within 10% of the measured REE, and the mean difference between predicted and measured values (bias). The Bland–Altman method was used to assess the agreement between the predicted and measured values. The equations that demonstrated the closest agreement with IC were the De La Cruz equation in males (accurate predictions: 68.2%; bias: ?19.52 kcal/day) and the Mifflin equation in females (accurate prediction: 61.2%; bias: ?36.43 kcal/day). In conclusion, we suggest that these two equations produce the least biased estimations for REE in this population.
References
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30. Weijs PJ, Vansant GA. Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women. Clin Nutr 2010;29:347–51. https://doi.org/10. 1016/j.clnu.2009.09.009
31. de Luis DA, 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;50:193–6. https://doi.org/10.1159/000090740
32. Willis EA, Herrmann SD, Ptomey LT, et al. Predicting resting energy expenditure in young adults. Obes Res Clin Pract 2016;10:304–14. https://doi.org/10.1016/j.orcp.2015.07.002
33. Roza AM, Shizgal HM. The Harris Benedict equation reevaluated: Resting energy require-ments and the body cell mass. Am J Clin Nutr 1984;40:168–82. https://doi.org/10.1093/ajcn/40. 1.168
34. Bernstein RS, Thornton JC, Yang MU, et al. Prediction of the resting metabolic rate in obese patients. Am J Clin Nutr 1983;37:595–602. https:// doi.org/10.1093/ajcn/37.4.595
35. Owen OE, Kavle E, Owen RS, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr 1986;44:1–19. https://doi.org/10.1093/ ajcn/44.1.1
36. Livingston EH, Kohlstadt I. Simplified resting met-abolic rate-predicting formulas for normal-sized and obese individuals. Obes Res 2005;13:1255–62. https://doi.org/10.1038/oby.2005.149
37. Lazzer S, Agosti F, Silvestri P, Derumeaux-Burel H, Sartorio A. Prediction of resting energy expen-diture in severely obese Italian women. J Endocrinol Invest 2007;30:20–7. https://doi. org/10.1007/BF03347391
38. Lazzer S, Agosti F, Resnik M, Marazzi N, Mornati D, Sartorio A. Prediction of resting energy expen-diture in severely obese Italian males. J Endocrinol Invest 2007;30:754–61. https://doi.org/10.1007/ BF03350813
39. Weijs PJ. Validity of predictive equations for rest-ing energy expenditure in US and Dutch over-weight and obese class I and II adults aged 18–65 y. Am J Clin Nutr 2008;88:959–70. https://doi. org/10.1093/ajcn/88.4.959
40. FAO/WHO/UNU Expert Consultation. Energy and protein requirements. World Health Organ Tech Rep Ser 1985;724:1–206.
41. Ikeda K, Fujimoto S, Goto M, et al. A new equa-tion to estimate basal energy expenditure of patients with diabetes. Clin Nutr 2013;32:777–82. https://doi.org/10.1016/j.clnu.2012.11.017
42. El Ghoch M, Calugi S, Dalle Grave R. Weight cycling in adults with severe obesity: A longitudinal study. Nutr Diet 2018;75:256–62. https://doi. org/10.1111/1747-0080.12387
43. Giavarina D. Understanding Bland Altman anal-ysis. Biochem Med (Zagreb) 2015;25:141–51. https://doi.org/10.11613/BM.2015.015
44. Hanneman SK. Design, analysis, and interpretation of method-comparison studies. AACN Adv Crit Care 2008;19:223–34. https://doi.org/10.1097/01. AACN.0000318125.41512.a3
45. MedCalc Statistical Software Version 19.1, MedCalc Software Bvba, Ostend. [Internet]. [cited 2019 ]. Available from: http://www.medcalc.org.
46. Porter J, Nguo K, Collins J, et al. Total energy expenditure measured using doubly labeled water compared with estimated energy requirements in older adults (>/=65 y): Analysis of primary data. Am J Clin Nutr 2019;110(6):1353–61. https://doi. org/10.1093/ajcn/nqz200
47. Frankenfield DC, Muth ER, Rowe WA. The Harris-Benedict studies of human basal metabo-lism: History and limitations. J Am Diet Assoc 1998;98:439–45. https://doi.org/10.1016/S0002-8223(98)00100-X
48. Tershakovec AM, Kuppler KM, Zemel B, Stallings VA. Age, sex, ethnicity, body composi-tion, and resting energy expenditure of obese African American and white children and adoles-cents. Am J Clin Nutr 2002;75:867–71. https:// doi.org/10.1093/ajcn/75.5.867
49. Henry CJ. Basal metabolic rate studies in humans: Measurement and development of new equations. Public Health Nutr 2005;8:1133–52. https://doi. org/10.1079/PHN2005801
50. El Ghoch M, Alberti M, Capelli C, Calugi S, Dalle Grave R. Resting energy expenditure in Anorexia Nervosa: Measured versus estimated. J Nutr Metab 2012;2012:652932. https://doi.org/ 10.1155/2012/652932
51. El Ghoch M, Alberti M, Capelli C, et al. Resting energy expenditure assessment in anorexia ner-vosa: Comparison of indirect calorimetry, a mul-tisensor monitor and the Muller equation. Int J Food Sci Nutr 2012;63:796–801. https://doi.org/1 0.3109/09637486.2012.658761
2. El Ghoch M, Calugi S, Dalle Grave R. The effects of low-carbohydrate diets on psychosocial outcomes in obesity/overweight: A systematic review of randomized, controlled studies. Nutrients 2016;8:1–13. https://doi.org/10.3390/ nu8070402
3. Itani L, Calugi S, Dalle Grave R, et al. The asso-ciation between body mass index and health-re-lated quality of life in treatment-seeking Arab adults with obesity. Med Sci (Basel) 2018;6:1–9. https://doi.org/10.3390/medsci6010025
4. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index cate-gories: A systematic review and meta-analysis. JAMA 2013;309:71–82. https://doi.org/10.1001/ jama.2012.113905
5. Al Nohair S. Obesity in gulf countries. Int J Health Sci (Qassim) 2014;8:79–83. https://doi. org/10.12816/0006074
6. Badran M, Laher I. Obesity in Arabic-speaking countries. J Obes 2011;2011:686430. https://doi. org/10.1155/2011/686430
7. Kreidieh D, Itani L, El Kassas G, et al. Long-term lifestyle-modification programs for overweight and obesity management in the Arab States: Systematic review and meta-analysis. Curr Diabetes Rev 2018;14:550–8. https://doi.org/10.2174/1573399813 666170619085756
8. Ravussin E, Bogardus C. A brief overview of human energy metabolism and its relationship to essential obesity. Am J Clin Nutr 1992;55:242S– 5S. https://doi.org/10.1093/ajcn/55.1.242s
9. Carneiro IP, Elliott SA, Siervo M, et al. Is obesity associated with altered energy expenditure? Adv Nutr 2016;7:476–87. https://doi.org/10.3945/an. 115.008755
10. Matarese LE. Indirect calorimetry: Technical aspects. J Am Diet Assoc 1997;97:S154–60. https://doi.org/10.1016/S0002-8223(97) 00754-2
11. Dickerson RN. Specialized nutrition support in the hospitalized obese patient. Nutr Clin Pract 2004;19:245–54. https://doi.org/10.1177/01154265 04019003245
12. Levine JA. Measurement of energy expenditure. Public Health Nutr 2005;8:1123–32. https://doi. org/10.1079/PHN2005800
13. Mtaweh H, Tuira L, Floh AA, Parshuram CS. Indirect calorimetry: History, technology, and application. Front Pediatr 2018;6:257. https://doi. org/10.3389/fped.2018.00257
14. Amaro-Gahete FJ, Jurado-Fasoli L, De-la OA, Gutierrez A, Castillo MJ, Ruiz JR. Accuracy and validity of resting energy expenditure predictive equations in middle-aged adults. Nutrients 2018;10:1–13. https://doi.org/10.3390/nu10111635
15. Cancello R, Soranna D, Brunani A, et al. Analysis of predictive equations for estimating resting energy expenditure in a large cohort of morbidly obese patients. Front Endocrinol (Lausanne) 2018;9:367. https://doi.org/10.3389/ fendo.2018.00367
16. Alves VG, da Rocha EE, Gonzalez MC, da Fonseca RB, Silva MH, Chiesa CA. Assessement of resting energy expenditure of obese patients: Comparison of indirect calorimetry with formu-lae. Clin Nutr 2009;28:299–304. https://doi. org/10.1016/j.clnu.2009.03.011
17. Hassan A, Mahdi A, Hamade L, Kerkadi A, Yousif A. Resting energy expenditure in a con-trolled group of young Arab females: Correlations with body composition and agreement with pre-diction equations. Food Nutr Sci 2013;4:385–91. https://doi.org/10.4236/fns.2013.44049
18. Frankenfield DC, Ashcraft CM, Wood C, Chinchilli VM. Validation of an indirect calorimeter using n-of-1 methodology. Clin Nutr 2016;35:163–8. https://doi.org/10.1016/j.clnu.2015.01.017
19. Owen OE, Holup JL, D'Alessio DA, et al. A reap-praisal of the caloric requirements of men. Am J Clin Nutr 1987;46:875–85. https://doi.org/10. 1093/ajcn/46.6.875
20. Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci U S A 1918;4:370–3. https://doi.org/10.1073/pnas.4.12.370
21. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equa-tion for resting energy expenditure in healthy indi-viduals. Am J Clin Nutr 1990;51:241–7. https:// doi.org/10.1093/ajcn/51.2.241
22. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985;39 Suppl 1:5–41.
23. Muller MJ, Bosy-Westphal A, Klaus S, 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 analy-sis of a German database of resting energy expendi-ture. Am J Clin Nutr 2004;80:1379–90. https://doi. org/10.1093/ajcn/80.5.1379
24. De Lorenzo A, Tagliabue A, Andreoli A, Testolin G, Comelli M, Deurenberg P. Measured and pre-dicted resting metabolic rate in Italian males and females, aged 18–59 y. Eur J Clin Nutr 2001;55:208–14. https://doi.org/10.1038/sj.ejcn. 1601149
25. Johnstone AM, Rance KA, Murison SD, Duncan JS, Speakman JR. Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects. Eur J Clin Nutr 2006;60:1437–44. https://doi.org/10.1038/sj.ejcn. 1602477
26. Korth O, Bosy-Westphal A, Zschoche P, Gluer CC, Heller M, Muller MJ. Influence of methods used in body composition analysis on the predic-tion of resting energy expenditure. Eur J Clin Nutr 2007;61:582–9. https://doi.org/10.1038/sj. ejcn.1602556
27. Frankenfield DC, Rowe WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc 2003;103:1152–9. https:// doi.org/10.1016/S0002-8223(03)00982-9
28. Frankenfield DC. Bias and accuracy of resting metabolic rate equations in non-obese and obese adults. Clin Nutr 2013;32:976–82. https://doi. org/10.1016/j.clnu.2013.03.022
29. de la Cruz Marcos S, de Mateo Silleras B, Camina Martin MA, et al. [Proposal for a new formula for estimating resting energy expenditure for healthy Spanish population]. Nutr Hosp 2015;32:2346–52.
30. Weijs PJ, Vansant GA. Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women. Clin Nutr 2010;29:347–51. https://doi.org/10. 1016/j.clnu.2009.09.009
31. de Luis DA, 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;50:193–6. https://doi.org/10.1159/000090740
32. Willis EA, Herrmann SD, Ptomey LT, et al. Predicting resting energy expenditure in young adults. Obes Res Clin Pract 2016;10:304–14. https://doi.org/10.1016/j.orcp.2015.07.002
33. Roza AM, Shizgal HM. The Harris Benedict equation reevaluated: Resting energy require-ments and the body cell mass. Am J Clin Nutr 1984;40:168–82. https://doi.org/10.1093/ajcn/40. 1.168
34. Bernstein RS, Thornton JC, Yang MU, et al. Prediction of the resting metabolic rate in obese patients. Am J Clin Nutr 1983;37:595–602. https:// doi.org/10.1093/ajcn/37.4.595
35. Owen OE, Kavle E, Owen RS, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr 1986;44:1–19. https://doi.org/10.1093/ ajcn/44.1.1
36. Livingston EH, Kohlstadt I. Simplified resting met-abolic rate-predicting formulas for normal-sized and obese individuals. Obes Res 2005;13:1255–62. https://doi.org/10.1038/oby.2005.149
37. Lazzer S, Agosti F, Silvestri P, Derumeaux-Burel H, Sartorio A. Prediction of resting energy expen-diture in severely obese Italian women. J Endocrinol Invest 2007;30:20–7. https://doi. org/10.1007/BF03347391
38. Lazzer S, Agosti F, Resnik M, Marazzi N, Mornati D, Sartorio A. Prediction of resting energy expen-diture in severely obese Italian males. J Endocrinol Invest 2007;30:754–61. https://doi.org/10.1007/ BF03350813
39. Weijs PJ. Validity of predictive equations for rest-ing energy expenditure in US and Dutch over-weight and obese class I and II adults aged 18–65 y. Am J Clin Nutr 2008;88:959–70. https://doi. org/10.1093/ajcn/88.4.959
40. FAO/WHO/UNU Expert Consultation. Energy and protein requirements. World Health Organ Tech Rep Ser 1985;724:1–206.
41. Ikeda K, Fujimoto S, Goto M, et al. A new equa-tion to estimate basal energy expenditure of patients with diabetes. Clin Nutr 2013;32:777–82. https://doi.org/10.1016/j.clnu.2012.11.017
42. El Ghoch M, Calugi S, Dalle Grave R. Weight cycling in adults with severe obesity: A longitudinal study. Nutr Diet 2018;75:256–62. https://doi. org/10.1111/1747-0080.12387
43. Giavarina D. Understanding Bland Altman anal-ysis. Biochem Med (Zagreb) 2015;25:141–51. https://doi.org/10.11613/BM.2015.015
44. Hanneman SK. Design, analysis, and interpretation of method-comparison studies. AACN Adv Crit Care 2008;19:223–34. https://doi.org/10.1097/01. AACN.0000318125.41512.a3
45. MedCalc Statistical Software Version 19.1, MedCalc Software Bvba, Ostend. [Internet]. [cited 2019 ]. Available from: http://www.medcalc.org.
46. Porter J, Nguo K, Collins J, et al. Total energy expenditure measured using doubly labeled water compared with estimated energy requirements in older adults (>/=65 y): Analysis of primary data. Am J Clin Nutr 2019;110(6):1353–61. https://doi. org/10.1093/ajcn/nqz200
47. Frankenfield DC, Muth ER, Rowe WA. The Harris-Benedict studies of human basal metabo-lism: History and limitations. J Am Diet Assoc 1998;98:439–45. https://doi.org/10.1016/S0002-8223(98)00100-X
48. Tershakovec AM, Kuppler KM, Zemel B, Stallings VA. Age, sex, ethnicity, body composi-tion, and resting energy expenditure of obese African American and white children and adoles-cents. Am J Clin Nutr 2002;75:867–71. https:// doi.org/10.1093/ajcn/75.5.867
49. Henry CJ. Basal metabolic rate studies in humans: Measurement and development of new equations. Public Health Nutr 2005;8:1133–52. https://doi. org/10.1079/PHN2005801
50. El Ghoch M, Alberti M, Capelli C, Calugi S, Dalle Grave R. Resting energy expenditure in Anorexia Nervosa: Measured versus estimated. J Nutr Metab 2012;2012:652932. https://doi.org/ 10.1155/2012/652932
51. El Ghoch M, Alberti M, Capelli C, et al. Resting energy expenditure assessment in anorexia ner-vosa: Comparison of indirect calorimetry, a mul-tisensor monitor and the Muller equation. Int J Food Sci Nutr 2012;63:796–801. https://doi.org/1 0.3109/09637486.2012.658761
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Jan 18, 2020
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Itani, L. ., Tannir, H. ., Kreidieh, D. ., El Masri, D. ., & El Ghoch, M. (2020). Validation of predictive equations for resting energy expenditure in treatment-seeking adults with overweight and obesity: Measured versus estimated . Journal of Population Therapeutics and Clinical Pharmacology, 27(1), e32-e47. https://doi.org/10.15586/jptcp.v27i1.653
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