Background: Various methods and equations are available to predict the basal metabolic rate (BMR). A published study comparing the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry (IC), was done among Filipinos, and was able to obtain a novel formula for BMR. The purpose of this study is to validate this novel formula.

Methods: This is a multi-center, cross-sectional, validation study of the novel BMR equation, done among adult overweight and obese Filipinos, who were seen at St. Luke’s Medical Center and Providence Hospital in Quezon City, Outpatient Clinics from August 2019 to March 2020. Purposive sampling was done, and upon giving consent, subjects had undergone interview, anthropometrics measurement, and IC.

Results: 174 samples were enrolled. Mean age is 43 years old, majority are females. 27% have no co-morbidities; of those with co-morbidities, half have diabetes mellitus (DM). Mean weight is 74.30 kg; mean BMI is 29.78 kg/m2. The mean computed BMR is 1174.70 kcal/day, which is 145.83 significantly lower than the BMR derived with calorimetry: 1320.53 kcal/day (P-value 0.000). However, the scatterplot reveals the linearity of positive direction for both values. 31% of the computed BMR fell within the +/-10% estimate of the actual BMR. Stratification of the results between those with DM and without, lowered the difference between the calculated and actual BMR to 46 kcal/day (from 145.83) among the DM subgroup, and increased the estimated accuracy to 38% falling within the +/- 10% estimate of the actual values.

Conclusion: The novel BMR formula is linearly reflective of the basal metabolism of adult overweight and obese Filipinos, but the numerical values are lower compared to actual calorimetry results, yielding more accuracy when applied among patients with diabetes.

Keywords: Basal metabolic rate; Indirect calorimetry; Obesity management  

1. World Health Organization. Obesity and Overweight Fact Sheet. [Internet]. October 2017. Accessed July 3, 2018, from http://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight.
2. 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, 18(1):71-78,2009.
3. Sabounchi NS, Rahmandad H, Ammerman A. Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations. Int J Obes (Lond), 37:1364-1370,2013.
4. Luy SC, Dampil OA. 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. J. ASEAN Fed. Endocr. Soc., 33(2),152,2018. Retrieved from https://asean-endocrinejournal.org/index.php/JAFES/article/view/477.
5. Frankenfield D, Rowe W, Smith JS, Cooney R. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc., 103:1151-1159,2003.
6. Leung R, Woo J, Chan D, Tang N. Validation of prediction equations for basal metabolic rate in Chinese subjects. Eur J Clin Nutr, 54:551-554,2000.
7. Ikeda K, Fujimoto S, Goto M, Yamada C, Hamasaki A, Ida M, et al. A new equation to estimate basal energy expenditure of patients with diabetes. Clin Nutr, 32:777-782,2013.
8. Alawad AO, Merghani TH, Ballal MA. Resting metabolic rate in obese diabetic and obese non-diabetic subjects and its relation to glycaemic control. BMC Res. Notes, 6:382,2013. Retrieved from http://www.biomedcentral.com/1756-0500/6/382.
9. Bitz C, Toubro S, Larsen TM, Harder H, Rennie KL, Jebb SA. et al. Increased 24-h Energy Expenditure in Type 2 Diabetes. Diabetes Care, 27(10):2416–2421,2004.
10. Ferreira MF, Detrano F, Coelho GMO, Barros ME, Lanzillotti RS, Neto JFN, et al. Body Composition and Basal Metabolic Rate in Women with Type 2 Diabetes Mellitus. J Nutr Metab, Vol. 2014,2014. Retrieved from http://dx.doi.org/10.1155/2014/574057.
11. Morino K, Kondo K, Tanaka S, Nishida Y, Nakae S, Yosuke Y, et al. Total energy expenditure is comparable between patients with and without diabetes mellitus: Clinical Evaluation of Energy Requirements in Patients with Diabetes Mellitus (CLEVER-DM) Study. BMJ Open Diab Res Care, 7:e000648. doi:10.1136/bmjdrc-2019-000648,2019.
12. Kumar AS, Maiya GA, Shastry BA, Vaishali K, Maiya S, Umakanth S. Correlation between basal metabolic rate, visceral fat and insulin resistance among type 2 diabetes mellitus with peripheral neuropathy. Diabetes Metab Syndr, 13:344e348,2019. Retrieved from https://doi.org/10.1016/j.dsx.2018.10.005.
13. Martin K, Wallace P, Rust PF, Garvey WT. Estimation of Resting Energy Expenditure Considering Effects of Race and Diabetes Status. Diabetes Care, 27(6):1405-1411,2004.
14. Vermorel M, Lazzer S, Bitar A, Ribeyre J, Montaurier C, Fellmann N, et al. Contributing Factors and Variability of Energy Expenditure in Non-Obese, Obese, and Post-Obese Adolescents. Reprod. Nutr. Dev., 45:129-142,2005.
15. Wang Z, Heshka S, Wang J, Gallagher D, Deurenberg P, Chen Z, et al. Metabolically Active Portion of Fat-Free Mass: A Cellular Body Composition Level Modeling Analysis. Am J Physiol Endocrinol Metab., 292(1): E49–E53. doi:10.1152/ajpendo.00485.2005,2007.
16. Weyer C, Snitker S, Rising R, Bogardus C, Ravussin E. Determinants of Energy Expenditure and Fuel Utilization in Man: Effects of Body Composition, Age, Sex, Ethnicity and Glucose Tolerance in 916 Subjects. Int J Obes (Lond), 23:715-722,1999.
17. Gupta RD, Ramachandran R, Venkatesan P, Anoop S, Joseph M, Thomas N. Indirect Calorimetry: From Bench to Bedside. Indian J Endocrinol Metab, 21:594-9,2017.