Journal of Postgraduate Medicine
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Year : 2013  |  Volume : 59  |  Issue : 1  |  Page : 25-29  

Development of new equations for basal metabolic rate for adolescent student Indian population

SR Patil1, J Bharadwaj2,  
1 Department of Physiology, Dr. D.Y. Patil Medical College, Navi Mumbai, Mumbai, Maharashtra, India
2 Department of Physiology, S.S. Institute of Medical Sciences and Research Centre, Davangere, Karnataka, India

Correspondence Address:
S R Patil
Department of Physiology, Dr. D.Y. Patil Medical College, Navi Mumbai, Mumbai, Maharashtra


Background: Calculation of daily calorie needs is extremely essential in several aspects of public health nutrition. Aims: To check the applicability of the existing equations for the prediction of basal metabolic rate (BMR) for Indian adolescent population and to develop an appropriate equation for the estimation of BMR for Indian adolescent population. Materials and Methods: BMR was assessed in 152 healthy, adolescent student aged between 18 and 20 years. BMR is calculated from the measured skinfold parameters. Body density was determined by the equation suggested by Durnin and Wormley using the skinfold parameters (triceps, subscapula, biceps, and SIM). Siri«SQ»s equation is employed for calculating the percentage of body fat from the body density. Eventually, the BMR is calculated using Cunningham«SQ»s equation. The actual BMR«SQ»s were compared with values obtained from published prediction equations that used solely, or in various combinations, measures of height, weight, and age. Results: The equations suggested in the literature (Henry, Schofield, and Cole) are not able to predict the BMRs for Indian adolescent population. Hence, a new equation involving weight of an individual is suggested for Indian adolescent population. Conclusions: There is a need for generation of appropriate BMR prediction equations for Indian population for various age groups.

How to cite this article:
Patil S R, Bharadwaj J. Development of new equations for basal metabolic rate for adolescent student Indian population.J Postgrad Med 2013;59:25-29

How to cite this URL:
Patil S R, Bharadwaj J. Development of new equations for basal metabolic rate for adolescent student Indian population. J Postgrad Med [serial online] 2013 [cited 2023 Sep 26 ];59:25-29
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The estimation of daily energy requirements is extremely important in many aspects of public health nutrition. Daily energy requirement estimations would be useful in the prediction of food requirements of a country or population [1],[2] and in the determination of individuals who have chronic energy deficiency. [3] Hence, the accurate prediction of basal metabolic rate (BMR) of individual is an important issue in public health nutrition. BMR is defined as the daily rate of energy metabolism that needs to be sustained by an individual in order to preserve the integrity of vital functions. [4] BMR is used to gauge the physiological and biochemical integrity of the individual concerned.

The prediction of BMR has gained attention since the publication of the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) Expert Consultation Report in 1985, [1] which adopted the principle of relying on estimates of energy expenditure rather than energy intake to estimate human energy requirements. BMR forms the basis of this factorial approach because it constitutes between 60% and 75% of the total daily energy expenditure. The energy expenditure of different age and gender groups is currently estimated as multiples of BMR. These multiples of BMR are referred to as physical activity levels. Underestimation or overestimation of BMR could result in errors during the planning of population energy allowances and the calculation of the energy requirements of an individual.

Difficulties involved in direct calorimetry, indirect calorimetry, and doubly labeled water method have motivated several investigators in generating simple equations for the estimation of BMR based on age, body weight, height, and gender. [5],[6],[7],[8],[9],[10],[11] Most of the equations generated for the estimation of BMR are applicable for the temperate climate. However, in tropical countries like India, the BMR is reported to be lower than that predicted by the equations suggested by FAO and others. [12],[13] Soares et al.[13] undertook a study to determine the contribution of body composition to the differences in BMR between 96 Indians and 81 Caucasian Australians of both sexes, aged 18-30 years. Absolute BMR and BMR adjusted for body weight were significantly lower in Indians when compared with Australians of the corresponding sex. However, BMR adjusted for fat-free mass (FFM) were not significantly different between the two groups. This observation is further confirmed by a recent study conducted by Adriaens and Westerterp. [14] This is the basis of the present study for the estimation of BMR.

Present work was undertaken to develop equations for the estimation of BMR of an Indian adolescent individual. Cunningham's equation is used for the estimation of BMR based on fat free mass. Fat free mass is estimated using the measured skinfold parameters namely biceps, triceps, supra-iliac, and subscapula.

 Materials and Methods

The subjects considered for this study were confined to first year medical undergraduate students of G.S. Medical College. The proforma and plan of the study were submitted to the local Ethical Committee and were approved before undertaking this study. The purpose of the study was explained to students and informed consent was obtained from every student. BMR and energy requirements were evaluated in 152 healthy, adolescent medical student population whose age is ranging from 18 to 20 years. 152 subjects considered in this study comprised 98 male students and 54 female students. [Table 1] summarizes the number of groups of subjects in each gender, age, and BMI category.{Table 1}

Tools used in the present study were Crown to heel scale, Weighing machine, Skinfold caliper, and measuring tape. Age (years) and anthropometric parameters (height (cm) and weight (kg)) were noted. Height of the student was measured using a measuring scale whose least count is 0.1 cm. Weight was measured using a weighing machine whose least count was 0.5 kg. Skinfold parameters measured were biceps (mm), triceps (mm), subscapula (mm), and suprailiac (mm). Skinfold parameters were measured using skinfold caliper (make Anand Agencies) whose least count is 0.1 mm. Skinfold calipers is a device which measures the thickness of the fold of the skin with the underlying layer of fat.

During measurement, the subject should stand erect but relaxed through the shoulders and arms. The objective was to raise a double fold of skin and subcutaneous adipose leaving the underlying muscle undisturbed. All skinfold parameters were taken on the right side of the body. The caliper was held in the right hand and the pressure plates of the caliper were applied perpendicular to the fold and 1 cm below or to the right of the fingers depending on the direction of the raised skinfold. The caliper was held in position for 2 s prior to recording the measurement to the nearest 0.1 mm. The grasp was maintained throughout the measurement. All skinfolds were measured three times with at least a 2 min interval to allow the tissue to restore to its uncompressed form. The mean value was the accepted value. Midpoint between the acromion and the olecranon process was identified by using a measuring tape. At this midpoint, fold was taken in a vertical direction directly on the center of the back of the arm for triceps skinfold. Biceps skinfold was taken exactly the same as triceps, except it is on the center of the front of the upper arm. Subscapular skinfold measurement was taken at a 45° angle to the vertical located just below the shoulder blade at the back. Suprailiac was located just above the iliac crest, a little toward the front from the side of the waist. Suprailiac skinfold measurement was taken approximately horizontal in the mid-axillary line.

Predictive equations

The Schofield equations [10],[11] were derived from 7173 individual BMR data (4809 men, 2364 women), 0-100 years of age, from 114 published between 1914 and 1980 [Table 2]. In this database, 57% of men and 27% of women were from Italy; the Italian group appears to have a higher BMR for unit kg of body weight compared to any other Caucasian group. The Henry equations [10] were obtained from 10,502 individual BMR values (5794 men and 4708 women), 0-106 years of age, from published (1994-2001) and unpublished studies (Oxford database). The Cole equations [11] were also developed from this database [Table 2]. The Henry and Schofield equations assumed a linear association between BMR and weight. However, both the investigations reported by Henry and Ramirez-Zea [10],[11] demonstrated that the inclusion of height in the equation did not improve its prediction ability significantly. Cole [11] developed another set of equations only for adults 18-80 years old, which avoid any discontinuities between age groups. He used the Oxford database, as Henry did, but he did not exclude the Italian subjects. Cole's models predict the BMR using age, logarithm of weight and logarithm of height as independent variables, adjusted for differences in mean BMR (also adjusted for age, weight, and height) between studies.{Table 2}

Analysis procedure

Analysis procedure involves the calculation of BMR using skinfold parameters, height, age, gender, and body weight. This calculated BMR is considered as the golden standard. It is established in the literature by McCargar et al.[15] that the subcutaneous fat measurements by skinfold calipers matched with those of computed tomography. Durnin and Womersely [16] have suggested an equation relating the body density with the skinfold parameters from the data collected from large body of subjects. Siri's equation [16] is used to calculate percentage of body fat. Eventually, Cunningham's equation [5],[6] is used to relate the fat free mass with the BMR.

Durnin and Womersley [16] have suggested the following relation for calculating the body density (D b in kg/m 3 ) from the measured skinfold parameters


for male subjects.


for female subjects.

Percentage of body fat is calculated using the equation suggested by Siri [16] as given below:


Fat weight is estimated using the following equation:


Fat free mass is estimated by the following equation:

FFM = Body Weight (BW) - Fat weight (FW).

BMR is estimated using the equation suggested by Cunningham, [5],[6] which is given below:

BMR = 370 + 21.6 (FFM).

This actual BMR's were compared with values obtained from three published prediction equations [10],[11] that used solely, or in various combinations, measures of height, weight, and age. Details of the various equations are given in [Table 2].

Results are reported as mean±SD. Linear regression analysis (to test for precision) and analysis of variance (to test for accuracy) were used to compare predicted BMR by each set of equations with measured BMR. The best model was considered with the lowest mean squared prediction error (MSPR), a better indicator of how well the model predicts in another data set than standard error of the estimate (SEE). This methodology is in suggested by Neter et al.[17]


Variation of BMR for male subjects

[Figure 1] shows the variation of the computed BMR and the weight of the individual for male subjects. It may be inferred that the BMR is strongly dependent on the weight of the individual. Hence, a correlation relating the BMR of the male subject and the weight of the individual is generated on the basis of linear regression. Equation for BMR as a function of weight for male subjects is given by

BMR = 0.05147 (Weight) +3.49598.{Figure 1}

[Figure 2] shows the variation of the computed BMR and the weight of the individual for female subjects. It may be inferred that the BMR is strongly dependent on the weight of the individual. Hence, a correlation relating the BMR of the female subject and the weight of the individual is generated on the basis of linear regression. Equation for BMR as a function of weight for female subjects is given by

BMR = 0.0468 (Weight) +2.4285.{Figure 2}

Equation for BMR as a function of weight for female subjects is given by

BMR = 0.0468 (Weight) +2.4285.

[Table 3] presents the predictive equations generated for male and female subjects from the measured BMR. It may be observed that the BMR is highly dependent on weight of the male or female subject. Dependence of BMR on age and height is very minimal for both male and female subjects. [Table 4] presents the application of the existing predictive equations in the literature for the present data. It may be observed that the standard error and mean square prediction error are significantly higher for all other predictive equations namely Schofield equation, Henry equation, and Cole equation. [Figure 3] shows the scatter plots among measured and predictive BMR by each set of equations (Schofield, Henry, and Cole) for male and female subjects. It may be observed that the predictive equations suggested in the literature are not able to estimate the BMR for the Indian population. Hence, there is a need for measuring the BMR for Indian population independently.{Figure 3}{Table 3}{Table 4}


Existing equations reported in the literature are inadequate in predicting the BMR of Indian individuals. Hence, new equations are developed for BMR for male and female adolescent individuals. New equations developed for BMR for male and female adolescent individuals (18-20 years) are given as follows:

BMR = 0.05147 (Weight) +3.49598,

BMR = 0.0468 (Weight) +2.4285.

The present study indicates that there are no predictive equations for the estimation of BMR in Indian population. Since, many years, Indians have been using equations used for temperate climates using which the calorie needs would be overestimated. Previous study by the present authors [18] reaffirmed this conclusion. Hence, there is no data available in the literature on the estimation of BMR for tropical population. This study is limited to the age group of 18-20 years. BMR in adolescents should be calculated properly. This is essential because growth spurt occurs at this age. Obesity represents a major medical problem in urban areas. Obesity can be obviated by proper guidance of calorie needs estimated using rightly calculated BMR.


1FAO/WHO/UNU Expert Consultation, Energy and Protein Requirements, WHO Technical Report Series 724. Geneva: World Health Organization; 1985. p. 1-206.
2FAO Human Energy Requirements, Report of a Joint FAO/WHO/UNU Expert Consultation. FAO Food and Nutrition Technical Report Series 724. Rome: Food and Agriculture Organization of the United Nations; 2004. p. 1-106.
3James WP, Ferro-Luzzi A, Waterlow JC. Definition of chronic energy deficiency in adults. Report of a working party of the international dietary energy consultative group. Eur J Clin Nutr 1988:42:969-81.
4Schofield WN. Predicting basal metabolite rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985;39(Suppl 1):5-41.
5Cunningham JJ. A reanalysis of the factors influencing basal metabolic rate in normal adults. Am J Clin Nutr 1980;33:2372-4.
6Cunningham JJ. Body composition as a determinant of energy expenditure: A synthetic review and a proposed general prediction equation. Am J Clin Nutr 1991;54:963-9.
7ICMR, Report on Nutrient Requirements and Recommended Dietary Allowances for Indians. Hyderabad: National Institute of Nutrition; 1998. p. 18-20.
8Fredrix WW, Soeters PB, Deerenberg IM, Kester AD, Von Meyenfeldt MF, Saris WH. Resting and sleeping energy expenditure in the elderly. Eur J Clin Nutr 1990;44:741-7.
9Miffin 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;51:241-7.
10Henry CJ. Basal metabolic rate studies in humans: Measurement and development of new equations. Public Health Nutr 2005;8:1133-52.
11Ramirez-Zea M. Validation of three predictive equations for basal metabolic rate in adults. Public Health Nutr 2005;8:1213-28.
12Razalee S, Poh BK, Ismail MN. Predictive equation for estimating the basal metabolic rate of Malaysian Armed Forces naval trainees. Singapore Med J 2010;51:635-40.
13Soares MJ, Piers LS, O'Dea K, Shetty PS. No evidence for an ethnic influence on basal metabolism: An examination of data from India and Australia. Br J Nutr 1998;79:333-41.
14Wouters-Adriaens MP, Westerterp KR. Low resting energy expenditure in Asians can be attributed to body composition. Obesity (Silver Spring) 2008;16:2212-6.
15Orphanidou C, McCargar L, Birmingham CL, Mathieson J, Goldner E. Accuracy of subcutaneous fat measurement: Comparison of skinfold calipers, ultrasound and computed tomography. J Am Diet Assoc 1994;8:855-8.
16Durnin JV. Womersely J. Body fat and assessed from total body density and its estimation from skinfold thickness: Measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 1974;32:77-97.
17Neter J, Waserman W, Kutner MH. Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs, 3 rd ed. Illinois: Richard D Irwin Inc.; 1990.
18Patil SR, Bharadwaj J. Comparison of different methods to estimate BMR in adolescent student population. Indian J Physiol Pharmacol 2011;55:77-83.

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