Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32468
Agreement between Basal Metabolic Rate Measured by Bioelectrical Impedance Analysis and Estimated by Prediction Equations in Obese Groups

Authors: Orkide Donma, Mustafa M. Donma


Basal metabolic rate (BMR) is widely used and an accepted measure of energy expenditure. Its principal determinant is body mass. However, this parameter is also correlated with a variety of other factors. The objective of this study is to measure BMR and compare it with the values obtained from predictive equations in adults classified according to their body mass index (BMI) values. 276 adults were included into the scope of this study. Their age, height and weight values were recorded. Five groups were designed based on their BMI values. First group (n = 85) was composed of individuals with BMI values varying between 18.5 and 24.9 kg/m2. Those with BMI values varying from 25.0 to 29.9 kg/m2 constituted Group 2 (n = 90). Individuals with 30.0-34.9 kg/m2, 35.0-39.9 kg/m2, > 40.0 kg/m2 were included in Group 3 (n = 53), 4 (n = 28) and 5 (n = 20), respectively. The most commonly used equations to be compared with the measured BMR values were selected. For this purpose, the values were calculated by the use of four equations to predict BMR values, by name, introduced by Food and Agriculture Organization (FAO)/World Health Organization (WHO)/United Nations University (UNU), Harris and Benedict, Owen and Mifflin. Descriptive statistics, ANOVA, post-Hoc Tukey and Pearson’s correlation tests were performed by a statistical program designed for Windows (SPSS, version 16.0). p values smaller than 0.05 were accepted as statistically significant. Mean ± SD of groups 1, 2, 3, 4 and 5 for measured BMR in kcal were 1440.3 ± 210.0, 1618.8 ± 268.6, 1741.1 ± 345.2, 1853.1 ± 351.2 and 2028.0 ± 412.1, respectively. Upon evaluation of the comparison of means among groups, differences were highly significant between Group 1 and each of the remaining four groups. The values were increasing from Group 2 to Group 5. However, differences between Group 2 and Group 3, Group 3 and Group 4, Group 4 and Group 5 were not statistically significant. These insignificances were lost in predictive equations proposed by Harris and Benedict, FAO/WHO/UNU and Owen. For Mifflin, the insignificance was limited only to Group 4 and Group 5. Upon evaluation of the correlations of measured BMR and the estimated values computed from prediction equations, the lowest correlations between measured BMR and estimated BMR values were observed among the individuals within normal BMI range. The highest correlations were detected in individuals with BMI values varying between 30.0 and 34.9 kg/m2. Correlations between measured BMR values and BMR values calculated by FAO/WHO/UNU as well as Owen were the same and the highest. In all groups, the highest correlations were observed between BMR values calculated from Mifflin and Harris and Benedict equations using age as an additional parameter. In conclusion, the unique resemblance of the FAO/WHO/UNU and Owen equations were pointed out. However, mean values obtained from FAO/WHO/UNU were much closer to the measured BMR values. Besides, the highest correlations were found between BMR calculated from FAO/WHO/UNU and measured BMR. These findings suggested that FAO/WHO/UNU was the most reliable equation, which may be used in conditions when the measured BMR values are not available.

Keywords: Adult, basal metabolic rate, FAO/WHO/UNU, obesity, prediction equations.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 821


[1] M. X. Sun, S. Zhao, H. Mao, Z. J. Wang, X. Y. Zhang, and L. Yi, “Increased BMR in overweight and obese patients with type 2 diabetes may result from an increased fat-free mass,” J. Huazhong Univ. Sci. Technolog. Med. Sci., vol. 36, no. 1, pp. 59-63, Feb. 2016.
[2] /help-guides/products-manuals/ (Accessed on 01/ 12/ 2019).
[3] S. Aliasgharzadeh, R. Mahdavi, M. Asghari Jafarabadi, and N. Namazi, “Comparison of indirect calorimetry and predictive equations in estimating resting metabolic rate in underweight females,” Iran. J. Public Health, vol. 44, no. 6, pp. 822-829, Jun. 2015.
[4] E. Pavlidou, D. Petridis, M. Tolia, N. Tsoukalas, A. Poultsidi, A. Fasoulas, et al., “Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate,” Nutr. Metab. (Lond)., vol.15, pp. 41, Jun. 2018.
[5] S. C. Luy, and O. A. Dampil, “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., vol. 33, no. 2, pp. 152, Sep. 2018.
[6] T. E. Nightingale, and A. S. Gorgey, “Predicting basal metabolic rate in men with motor complete spinal cord injury,” Med. Sci. Sports Exerc., vol. 50, no. 6, pp. 1305-1312, Jun. 2018.
[7] M. F. Ferreira, F. Detrano, G. M. O. Coelho, M. E. Barros, R. S. Lanzilotti, J. F. N. Neto, et al., “Body composition and basal metabolic rate in women with type 2 diabetes mellitus,” J. Nutr. Metabol., vol. 2014, no. 574057, pp. 1-9, Nov. 2014.
[8] R. Miyake, K. Ohkawara, K. Ishikawa-Takata, A. Morita, S. Watanabe, and S. Tanaka, “Obese Japanese adults with type 2 diabetes have higher basal metabolic rates than non-diabetic adults,” J. Nutr. Sci. Vitaminol. (Tokyo), vol. 57, no. 5, pp. 348-354, 2011.
[9] R. Miyake, S. Tanaka, K. Ohkawara, K. Ishikawa-Takata, Y. Hikihara, E. Taguri, et al., “Validity of predictive equations for basal metabolic rate in Japanese adults,” J. Nutr. Sci. Vitaminol. (Tokyo), vol. 57, no. 3, pp. 224-232, 2011.
[10] S. Miller, B. J. Milliron, and K. Woolf, “Common prediction equations overestimate measured resting metabolic rate in young Hispanic women”, Top. Clin. Nutr., vol. 28, no. 2, pp. 120-135, Apr. 2013.
[11] T. Steemburgo, C. Lazzari, J. B. Farinha, T. P. Paula, L. V. Viana, A. R. Oliveira, et al., “Basal metabolic rate in Brazilian patients with type 2 diabetes: comparison between measured and estimated values,” Arch. Endocrinol. Metab., vol. 63, no. 1, pp. 53-61, Feb. 2019.
[12] J. A. Harris, and F. G. Benedict, A Biometric Study of Basal Metabolism in Man, Carnigie Institution of Washington, Boston, Mass, USA, 1919.
[13] Food and Agriculture Organization, World Health Organization, and United Nations University, “Energy and protein requirements,” WHO Technical Report Series 724, WHO, Geneve, Switzerland, 1985.
[14] O. E. Owen, E. Kavle, R. S. Owen, et al., “A reappraisal of caloric requirements in healthy women,” Am. J. Clin. Nutr., vol. 44, no. 1, pp. 1-19, 1986.
[15] M. D. Mifflin, S. T. Jeor, L. A. Hill, B. J. Scott, S. A. Daugherty, and Y. O. Koh, “A new predictive equation for resting energy expenditure in healthy individuals,” Am. J. Clin. Nutr., vol. 51, no. 2, pp. 241-247, 1990.
[16] J. M. Bland, and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet, vol. 327, no. 8476, pp. 307-310, 1986.
[17] M. R. Sgambato, V. Wahrlich, and L. A. D. Anjos, “Validity of basal metabolic rate prediction equations in elderly women living in an urban tropical city of Brazil,” Clin. Nutr. ESPEN, vol 32, pp. 158-164, Aug. 2019.
[18] M. L. Macena, I. R. O. M. Pureza, I. S. V. Melo, A. G. Clemente, H. S. Ferreira, T. M. M. T. Florêncio et al., “Agreement between the total energy expenditure calculated with accelerometry data and the BMR yielded by predictive equations v. the total energy expenditure obtained with doubly labelled water in low-income women with excess weight,” Br. J. Nutr., vol. 122, no. 12, pp. 1398-1408, Dec. 2019.