Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among Adult Obese Filipino Patients with Prediabetes or Type 2 Diabetes Mellitus

Authors

Keywords:

basal metabolic rate, Harris-Benedict equation, bioelectrical impedance analysis, indirect calorimetry

Abstract

Objective. To compare mean basal metabolic rate (BMR) estimated using Harris-Benedict equation (HB) and Bioelectrical Impedance Analysis (BIA) and the BMR measured using Indirect Calorimetry (IC) among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus (T2DM).

Methodology. This was a multi-center, cross-sectional study based on review of outpatient medical records of adult, obese Filipino patients with pre-diabetes or type 2 diabetes mellitus who were seen prior to weight loss intervention at the Outpatient Clinic of St. Luke’s Medical Center-Quezon City and the Metabolic and Diabetes Center of Providence Hospital from August 2017 to January 2018. BMR was derived using three methods: Harris-Benedict equation, Bioelectrical Impedance Analysis and Indirect Calorimetry.

Results. A total of 153 subjects were included in the study. Eighty subjects (52%) have pre-diabetes while 73 subjects (48%) were diagnosed with T2DM. The mean BMR measured using IC is 1299±252 kcal/day while estimated mean BMR predicted using HB equation and BIA were 1628±251 kcal/day and 1635+260 kcal/day, respectively. Compared to measurement by IC, HBE and BIA significantly overestimated the mean BMR by 329 and 336 kcal/day, respectively (p value=<0.0001). IC measured BMR showed strong positive correlation with weight and moderate positive correlation with height. Multiple stepwise regression analysis yielded the BMR prediction equation: BMR (kcal/day)=-780.806 + (11.108 x weight in kg) + (7.164 x height in cm).

Conclusion. Among obese Filipinos with T2DM or prediabetes, HB equation and BIA tend to overestimate the BMR measured using IC.

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Author Biography

Sybil Claudine Luy, Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, St. Luke’s Medical Center, Quezon City

Fellow-in-Training, Section of Endocrinology, Diabetes, and Metabolism, St. Luke's Medical Center-Quezon City

References

Menon S, Mishra MK, Rathore VS. Prediction of basal metabolic rate on the basis of body composition variable and obesity indicators in physically active postmenopausal women. IJPESH. 2016;3(5):427-30. http://www.kheljournal.com/archives/2016/vol3issue5/PartH/3-5-74-954.pdf.

Frankenfield DC, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy non obese and obese adults: A systematic review. J Am Diet Assoc. 2005;105(5): 775-89. PMID: 15883556 https://doi.org/10.1016/j.jada.2005.02.005.

Pinheiro Volp AC, Esteves de Oliveira FC, Duarte Moreira Alves R, Bressan J. Energy expenditure: Components and evaluation methods. Nutr Hosp. 2011;6(3): 430-40. PMID: 21892558. https://doi.org/10.1590/S0212-16112011000300002.

Sabounchi NS, Rahmandad H, Ammerman A. Best-fitting prediction equations for basal metabolic rate: Informing obesity interventions in diverse populations. Int J Obes (Lond). 2013;37(10):1364-70. PMID: 23318720. PMCID: PMC4278349. https://doi.org/10.1038/ijo.2012.218.

Frankenfield DC, Rowe WA, Smith JS, Cooney RN.. Validation of several established equations for resting metabolic rate in obese and non-obese people. J Am Diet Assoc. 2003;103(9):1152-9. PMID: 12963943. https://doi.org/10.1053/jada.2003.50575.

Jia H, Meng Q, Shan C. Study on energy expenditure in healthy adults. Chin J Clin Nutr. 1999;7:70-3.

Valliant, MW, Tidwell DK, Hallam JS, et al. A resting metabolic rate equation including bioelectrical impedance-derived lean body mass provides a better prediction in premenopausal African American women across a spectrum of body mass indices. Top Clin Nutr. 2009;24(2):145-51. https://doi.org/ 10.1097/TIN.0b013e3181a6b98d.

Liu HY, Lu YF, Chen WJ. Predictive equations for basal metabolic rate in Chinese adults: A cross-validation study. J Am Diet Assoc. 1995;95(12):1403-8. PMID: 7594142. https://doi.org/10.1016/S0002-8223(95)00369-X.

Case KO, Brahler CJ, Heiss C. Resting energy expenditures in Asian women measured by indirect calorimetry are lower than expenditures calculated from prediction equations. J. Am. Diet. Assoc. 1997;97(11):1288-92. PMID: 9366867. https://doi.org/10.1016/S0002-8223(97)00308-8.

Nieman DC, Austin MD, Benezra L, et al. Validation of Cosmed’s FitMate in measuring oxygen consumption and estimating resting metabolic rate. Res Sports Med. 2006;14(2):89-96. PMID: 16869134. https://doi.org/10.1080/15438620600651512.

Yang X, Li M, Mao D, et al. Basal energy expenditure in southern Chinese healthy adults: Measurement and development of a new equation. Br J Nutr. 2010; 104(12):1817-23. PMID: 20804631. https://doi.org/10.1017/S0007114510002795.

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(2):241-7. PMID: 2305711. https://doi.org/10.1093/ajcn/51.2.241.

Owen OE, Holup JL, D’Alessio DA, et al. A reappraisal of the caloric requirements of men. Am J Clin Nutr. 1987;46(6):875-85. PMID: 3687821. https://doi.org/10.1093/ajcn/46.6.875.

Ikeda K, Fujimoto S, Goto M, et al. A new equation to estimate basal energy expenditure of patients with diabetes. Clin Nutr. 2013;32(5):777-82. PMID: 23267745. https://doi.org/10.1016/j.clnu.2012.11.017.

Pagsisihan D, Sandoval MA, Pacheco EP, Jimeno CA. Low indices of overweight and obesity are associated with cardiometabolic diseases among adult Filipinos in a rural community. J ASEAN Fed Endocr Soc. 2016;31(2):97-105. https://doi.org/10.15605/jafes.031.02.04.

Bland JM, DG Altman. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307-10. PMID:2868172.

Camps SG, Wang NX, Tan WSK, Henry CJ. Estimation of basal metabolic rate in Chinese: Are the current prediction equations applicable? Nutr J. 2016; 15(79):1-8. PMID: 27581329. PMCID: PMC5007802. https://doi.org/10.1186/s12937-016-0197-2.

Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev. 2002;3(3):141-6. PMID: 12164465.

Wouters-Adriaens MP, Westerterp KR. Low resting energy expenditure in Asians can be attributed to body composition. Obesity (Silver Spring). 2008;16(10):2212-6. PMID: 18719650. https://doi.org/10.1038/oby.2008.343.

Douglas CC, Lawrence JC, Bush NC, Oster RA, Gower BA, Darnell BE. Ability of the Harris Benedict formula to predict energy requirements differs with weight history and ethnicity. Nutr Res. 2007;27(4):194-9. PMID: 19081830. PMCID: PMC2598419. NIHMSID: NIHMS21916.

Miller S, Milliron BJ, Woolf K. Common prediction equations overestimate measured resting metabolic rate in young hispanic women. Top Clin Nutr. 2013; 28(2):120-35. PMID: 24058263. PMCID: PMC3779143. https://doi.org/10.1097/TIN.0b013e31828d7a1b.

Sun MX, Zhao S, Mao H, Wang ZJ, Zhang XY, Yi L. Increased BMR in overweight and obese patients with type 2 diabetes may result from an increased fat-free mass. J Huazhong Univ Sci Technol Med Sci. 2016;36(1):59-63. PMID: 26838741. https://doi.org/10.1007/s11596-016-1542-6.

Poehlman ET, Toth MJ, Ades PA, Calles-Escandon J. Gender differences in resting metabolic rate and noradrenaline kinetics in older individuals. Eur J Clin Invest. 1997;27(1):23-8. PMID: 9041373.

Molnár D, Schutz Y. The effect of obesity, age, puberty and gender on resting metabolic rate in children and adolescents. Eur J Pediatr. 1997;156(5):376-81. PMID: 9177980.

Johnstone AM, Murison SD, Duncan JS, Rance KA, and Speakman JR. 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(5):941-8. PMID: 16280423. https://doi.org/10.1093/ajcn/82.5.941.

Piers LS, Soares MJ, McCormack LM, O’Dea K. Is there evidence for an age-related reduction in metabolic rate? J Appl Physiol (1985). 1998;85(6):2196-204. PMID: 9843543. https://doi.org/10.1152/jappl.1998.85.6.2196.

Wang Z, Heshka S, Heymsfield SB, Shen W, Gallagher D. A cellular-level approach to predicting resting energy expenditure across the adult years. Am J Clin Nutr. 2005;81(4):799–806. PMID: 15817855. https://doi.org/10.1093/ajcn/81.4.799.

Lazzer S, Bedogni J, Lafortuna CL, et al. Relationship between basal metabolic rate, gender, age, and body composition in 8,780 white obese subjects. Obesity (Silver Spring). 2010;18(1):71-8. PMID: 19478787. https://doi.org/10.1038/oby.2009.162.

Wang Z, Heshka S, Wang J et al. Metabolically active portion of fat-free mass: A cellular body composition level modeling analysis. Am J Physiol Endocrinol Metab. 2007;292(1):E49-53. PMID: 16882929. PMCID: PMC2723740. NIHMSID: NIHMS132234. https://doi.org/ 10.1152/ajpendo.00485.2005.

Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(Suppl 1):5-41. PMID: 4044297.

Published

2018-09-10

How to Cite

Luy, S. C., & Dampil, O. A. (2018). Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among Adult Obese Filipino Patients with Prediabetes or Type 2 Diabetes Mellitus. Journal of the ASEAN Federation of Endocrine Societies, 33(2), 152. Retrieved from https://asean-endocrinejournal.org/index.php/JAFES/article/view/477

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Original Articles