FAO/WHO/UNU equations overestimate resting metabolic rate in ...

European Journal of Clinical Nutrition (2005) 59, 1099–1104

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ORIGINAL COMMUNICATION FAO/WHO/UNU equations overestimate resting metabolic rate in Vietnamese adults BT Nhung1,2, NC Khan2, LT Hop2, DTK Lien2, DSNT Le3, VTT Hien1,2, D Kunii1, T Sakai1, M Nakamori1 and S Yamamoto1* 1

Department of Nutrition, School of Medicine, The University of Tokushima, Tokushima, Japan; 2National Institute of Nutrition, Hanoi, Vietnam; and 3National Institutes of Health, Phoenix, AZ, USA

Objective: To evaluate the FAO/WHO/UNU equations for predicting resting metabolic rate (RMR) in Vietnamese adults. Design: A cross-sectional study with healthy subjects was carried out at the Basic Nutrition Department, National Institute of Nutrition, Vietnam. RMR was measured by indirect calorimetry, and anthropometric indices were recorded. Equations derived by linear regression of RMR vs body weight were compared to the FAO/WHO/UNU 1985 predictive equations. Subjects: A total of 188 subjects (98 males and 90 females) had a normal body mass index (BMI) and were divided into four groups by sex and age (male and female subjects 18–29 and 30–60 y old). Results: Mean RMR (MJ/kg/day) in males was lightly significant by higher than that in female subjects in the 18–29 y old age group (0.107470.0100 vs 0.096570.0123) and the same result was seen in the 30–60 y old group (0.101870.0114 vs 0.092270.0129). However, differences were not statistically significant in the two age groups. Compared to the FAO/WHO/ UNU equation, our findings were 7.4, 9.0, 11.7, and 13.5% lower in the four groups, respectively (Po0.001). Conclusion: Our findings suggest that the FAO/WHO/UNU equations may overestimate RMR in Vietnamese adults. Further studies examining the relationship between body weight and RMR are needed, and establishing new predictive equations for RMR in Vietnamese should be a priority.

European Journal of Clinical Nutrition (2005) 59, 1099–1104. doi:10.1038/sj.ejcn.1602199; published online 6 July 2005 Keywords: resting metabolic rate; Vietnamese; equation

Introduction Noncommunicable lifestyle-related chronic diseases such as obesity, type II diabetes, hypertension, and coronary heart disease are increasing around the world, even in developing countries (Ko et al, 1999; Deurenberg et al, 1999). The best way to prevent and control these diseases is through appropriate dietary and physical activity. Thus, understanding the *Correspondence: S Yamamoto, International Public Health Nutrition, Tokushima University Graduate School of Health Biosciences, The University of Tokushima (The former Applied Nutrition, Department of Nutrition, School of Medicine, The University of Tokushima), 3 Kuramoto, Tokushima 770-8503, Japan. E-mail: [email protected] Guarantor: S Yamamoto. Contributors: BTN, NCK, LTH, DTKL, DSNTL, VITH, DK, TS, MN, SY codesigned the study and contributed to the preparation of the manuscript. DK, TS, MN, SY provided resources used in data collection, and technical support. BTN, SY managed data collection, data analysis, and prepared the first and the final draft of the manuscript. Received 1 June 2004; revised 8 March 2005; accepted 29 March 2005; published online 6 July 2005

energy expenditure of individuals or a population is important because it is a major determinant of food and energy requirements. It is well known that basal metabolic rate (BMR) constitutes about 60–70% of total energy expenditure, and it has thus been used in estimating the energy requirements of populations (Ismail et al, 1998). BMR is the minimum energy requirement to sustain vital functions during absolute rest. BMR includes the energy expended in ventilation, blood circulation, intestinal contraction, the activities of internal organs, and maintenance of thermal equilibrium. Resting metabolic rate (RMR) is the energy expended while an individual is resting quietly in a supine position. Although there are some small differences between BMR and RMR, they are sometimes used interchangeably in community studies. One of the methods for estimating BMR is from predictive equations based on sex and weight. Since 1985, equations developed by Schofield and colleagues have formed the basis of equations used by FAO/WHO/UNU (Schofield, 1985; Hayter & Henry, 1994; Shetty et al, 1996). These equations

FAO/WHO/UNU equations, resting metabolic rate of Vietnamese adults BT Nhung et al

1100 have been adopted as a basic reference for the development of the recommended dietary allowance (RDA) in many countries around the world. The Schofield equations predict BMR accurately in many individuals from temperate climates, but they seem to be less accurate in predicting BMR in populations in the tropics (Piers & Shetty, 1993; Ismail et al, 1998) and in some populations in North America (Clark & Hoffer, 1991). They also appear to overestimate BMR in many populations (Hayter & Henry, 1993; Piers & Shetty, 1993; Soares et al, 1993). Several studies have examined these predictive formulas for BMR in people in tropical countries (Valencia et al, 1994; Shetty et al, 1996). According to Henry, the FAO/ WHO/UNU 1985 predictive equations overestimate BMR in people living in the tropics by an average of 8%, and by up to 11.5% for males over 30 y old (Henry & Rees, 1991). Similar findings were reported in studies in Japan, India, and China (Henry & Rees, 1991; Hayter & Henry, 1994). A study in Malaysia showed that the FAO/WHO/UNU 1985 predictive equations overestimate by an average of 13% in males and 9% in female subjects (Ismail et al, 1998). Furthermore, Japan no longer uses the FAO/WHO/UNU predictive equations in developing the RDA for energy. In Vietnam, obesity and noncommunicable diseases related to nutrition are increasing, particularly in big cities, even though energy intake has not changed much in the last 10 y (Khoi, 1996; NIN, 2001; Khoi et al, 2003). To this day, the FAO/WHO/UNU 1985 predictive equations are still used to calculate RMR for Vietnamese people (Giay et al, 1997), and no validation studies on predictive equations for RMR have been carried out in Vietnam. Furthermore, there is little data on the relationship between RMR, energy expenditure, and nutritional status in Vietnam. Thus, the purpose of this study was to obtain RMR values in Vietnamese adults and compare these values with those determined by the FAO/ WHO/UNU 1985 predictive equations.

Materials and methods Participants The study was carried out in Hanoi, a city of over two million in the northern region of Vietnam. A total of 450 adults aged 18–60 y were randomly selected from suburban and urban communes for a screening study. Details of the study were explained carefully to all subjects. To recruit these subjects, a listing of all families including adults aged 18–60 y was constructed. Family codes were also noted. From each selected family, all adults aged 18–60 y were invited to participate. From this list, the first family was selected by randomly picking a family code. From the first family, using the ‘random walking’ method, we approached another family and added subjects to obtain 450 adults (Levy & Lemeshow, 1999). In the screening, subjects’ height and weight were measured and a short questionnaire about occupation, disease history, and physical activity (defined by WHO criteria) was completed (William et al, 1991). After European Journal of Clinical Nutrition

screening, a total of 188 healthy subjects (98 males, 90 females) who were without disabilities, hypertension, chronic diseases of the heart or lungs, were not heavy smokers (MOH, 2003), had a normal BMI (18.5–24.9 kg/m2) and light, moderate, or high physical activity levels were invited to participate. In order to minimize bias by physical activity, the subjects were selected approximately equally from light, moderate, and high physical activity levels. Informed consent was obtained from each participant. The protocol of this study was approved by the Scientific Board of the National Institute of Nutrition of Vietnam.

Anthropometric measurement Body weight and height were measured in light clothing and without shoes to the nearest 0.1 kg and 0.1 cm, respectively. Body mass index (BMI) was calculated as weight per square of height (kg/m2).

Measurement of RMR Subjects were familiarized with the equipment and given a briefing on the experimental protocol before the day of the measurement. They were advised to avoid medications, coffee and other caffeine-containing beverages, smoking, heavy meals, alcohol, and strenuous exercise the evening before testing. In order to check the adherence to instructions, local health staff came to each household the day before the measurement to obtain written consent from each subject, and reminded them to avoid nicotine, alcohol, caffeine, and food. In addition, on this day, every subject was asked to record physical activity and all foods and beverages consumed over 24 h. Female subjects were asked about the date of their menstrual cycle to ensure testing at the same phase of the menstrual cycle (10 days before menstruation). On the test day, subjects came to the Basic Nutrition Department in the early morning after a 12 h fast, in a nonstrenuous manner. Subjects lay quietly and relaxed for 30 min prior to measurement. All measurements were carried out between 0600–0900 h in a quiet room with an ambient temperature of 22–241C and barometric pressure of 760– 770 mmHg. RMR was measured using an open-circuit indirect calorimeter (Oxycon Delta ERICHJAEGER BV, Bunnick, Netherlands). Calibration of the calorimeter in the early morning was performed according to the manufacturer’s instructions. First, the test subject was adapted to a tight-fitting breathing mask with attached Triple V sensor, which measures volume. The gas exchange measurement was carried out via the extremely fast O2 and CO2 analyzers. RMR was determined over a 15-min period while resting with the ergospirometry measurement program ‘Breath by Breath.’ RMR was calculated from the oxygen consumption and carbon dioxide production from minutes 5 to 15 to avoid the unstable condition at the beginning of each measurement. In addition, in order to minimize the bias from measurement, any subject who had relatively high or


1101 low RMR, or deviations of RMR greater than 10% or spontaneous movement was remeasured on the same day or on another day. RMR was derived using the Weir equations (Weir, 1949, 1990). In addition to the measured values, RMR was predicted using the equations of FAO/ WHO/UNU 1985. Statistical analyses Data are presented as means7s.d. The paired t-test was used to test differences between measured and predicted values. Linear regression equations were derived for groups of subjects according to sex and age. Correlation analysis was performed to determine relationships between variables. A P-value o0.05 was considered significant. All data were analyzed using the SPSS software (SPSS/Windows version 9.0, Chicago, IL, USA).

Results Among the 188 participants, 52 male and 40 female subjects were 18–29 y old, and 46 male and 50 female subjects were 30–60 y old. Table 1 shows the anthropometric indices of the participants. Compared to the young participants, older male subjects were significantly shorter and had a higher BMI, and older female subjects were heavier and had a higher BMI. The means, standard deviations, and differences between measured RMR (MJ/kg/day) and predicted RMR by the FAO/ Table 1 Physical characteristics of subjects Age-group

n

Age (y)

Weight (kg)

Height (cm)

BMI (kg/rn2)

Male 18–29 30–60

52 46

23.473.2a 41.978.2

55.875.6b 56.976.1b

166.775.6a,b 163.974.6b

20.171.5a 21.271.8

Female 18–29 30–60

40 50

23.673.la 44.979.3

47.873.8a 51.275.3

154.875.1 154.374.3

19.971.6a 21.571.9

a

Po0.05 when compared between age groups of the same sex. Po0.05 when compared between different sexes.

b

WHO/UNU equation are shown in Table 2. The RMR values were 0.107470.0100 and 0.096570.0123 in young males and female subjects, respectively, and 0.101870.0114 and 0.092270.0129 in older males and female subjects, respectively. The average coefficient of variation in VO2 and VCO2 on the 10 min was 7.5, 8.5, 7.9, and 9.3% in the four groups, respectively. Differences between male and female subjects between the two age groups were not statistically significant. In comparison to predicted values using FAO/WHO/UNU predictive equations, the RMR of the study participants were 7.4, 11.7, 9.0, and 13.5% lower than the FAO/WHO/UNU predictive equations in the four age–sex groups, respectively (Po0.001). In 55% of young male subjects and 36.9% of older male subjects predicted values were within 10% of measured RMR, while in 45% of the young male subjects and in 60.1% of the older male subjects they were greater than 10% of measured RMR. No subject had an RMR measurement greater than 10% of predicted values in either young or older male subjects. The maximum underestimation and overestimation was 5.9 and 24% in young male subjects, and 8.3 and 25.8% in older male subjects, respectively. However, in young and older female subjects, 42.5 and 36% had predicted values within 10% of measured RMR, while in 55% of the young and 64% of the older female subjects they were greater than 10% of measured RMR. Of the young female subjects, 1% had an RMR measurement greater than 10% of predicted values. In older females, no subject had an RMR measurement greater than 10% of predicted values. The maximum underestimation and overestimation was 15.6 and 30% in young female subjects, and 8.3 and 29% in older female subjects, respectively. Figures 1a–d show comparisons of linear regressions of RMR generated by the two methods in all age–sex groups. The two equations were approximately parallel but the regression for the measured values was lower. The individual difference between measured and predicted values of RMR plotted against the measured values using the technique of Bland and Altman (1986) in all age–sex groups is shown in Figures 2a–d. The 95% limits of agreement of RMR were 1.101 to 0.4014, 1.3832 to 0.6816, 1.6066 to 0.4582 and 1.5778 to 0.5310 MJ/day in the four age-sex

Table 2 Measured and predicted values of resting metabolic rate by FAO/WHO/UNU 1985 Measured RMR Age-group

Predicted RMR by WHO (1985)

(MJ/day)

(MJ/kg/day)

(MJ/day)

(MJ/kg/day)

Differences (Overestimated percentage of WHO equations)

Males 18–29 y

5.968570.5749

0.107470.0100

6.415070.3576

0.115470.0053

30–60 y

5.765770.6617

0.101870.0114

6.440670.2982

0.113970.0068

7.4% P ¼ 0.0005 11.7% P ¼ 0.0005

Females 18–29 y

4.601970.6294

0.096570.0123

5.016770.2364

0.105370.0035

30–60 y

4.696970.6565

0.092270.0129

5.333170.1912

0.104970.0072

9.0% P ¼ 0.0005 13.5% P ¼ 0.0005

European Journal of Clinical Nutrition


1102

a

b

7.5

6.0

R square=0.271

R square=0.152 5.5

7.0

P=0.05

P=0.01 5.0 RMR (Mj/day)

RMR (Mj/day)

6.5

6.0

4.5

4.0

5.5

5.0

RMR WHO (1985) y=0.0640x+2.84

3.5

RMR WHO (1985) y=0.0615x+2.08

4.5

RMR STUDY y=0.0535x+2.978

3.0


40

50

60

30

70

c

40

50

60

Body weight (kg)

Body weight (kg) 7.5

R square=0.154

7.0

d

P=0.05

6.0 R square=0.230 P=0.01

5.5

6.5 RMR (Mj/day)

RMR (Mj/day)

5.0 6.0 5.5

4.5

4.0

5.0 4.5 4.0 40

50

60

70

RMR WHO (1985) y=0.0485x+3.67

3.5

RMR WHO (1985) y=0.0364x+3.47


3.0


80

Body weight (kg)

40

50

60

70

Body weight (kg)

Figure 1 (a) Comparison between measured and predicted RMR by FAO/WHO/UNU (1985) in males aged 18–29 y. (b) Comparison between measured and predicted RMR by FAO/WHO/UNU (1985) in female subjects aged 18–29 y. (c) Comparison between measured and predicted RMR by FAO/WHO/UNU (1985) in males aged 30–60 y. (d) Comparison between measured and predicted RMR by FAO/WHO/UNU (1985) in female subjects aged 30–60 y.

groups, respectively. In addition, we used the standard errors and confidence interval (CI) to see how precise our estimates were. Hence, the 95% confidence interval for the bias of RMR was 0.4651 to 0.2353, 0.5282 to 0.1733, 0.7367 to 0.4116, and 0.6898 to 0.3569 MJ/day, respectively. The standard error of limits d72s.d. was 0.0875, 0.0985, 0.1534, 0.1351 MJ/day. The 95% confidence interval for the lower limit of agreement was 1.2865 to 0.9155, 1.5920 to 1.17438, 1.9318 to 1.2813, and 1.8642 to 1.2913 MJ/ day. For the upper limit of agreement the 95% confidence intervals were 0.2159 to 0.5869, 0.4727 to 0.8904, 0.1329 to 0.7834, and 0.2445 to 0.8174 MJ/day, respectively.

Discussion Our study found significantly lower RMRs than predicted by FAO/WHO/UNU equations for all age–sex groups. This European Journal of Clinical Nutrition

suggests the FAO/WHO/UNU predictive equations may overestimate energy needs for Vietnamese people. Such overestimation by the FAO/WHO/UNU predictive equations has been suggested in other reports, and may be explained by a disproportionately large group of Italian subjects in Schofield’s database (Shetty et al, 1996). Hayter and Henry (1994) reported, in a comparison of RMR data from Italians, North Europeans, Americans, Indians, and Chinese that the Italian regression equations were higher compared to other populations groups. We applied the ‘Italian’ and ‘Chinese’ predictive equations of Hayter and Henry (1994) to the young group of this population and found 0.119670.0062 and 0.117570.0062 (‘Italian’ equation) and 0.105470.0050 and 0.103770.005 (‘Chinese’ equation) for young male and female subjects, respectively. The predicted values using the ‘Italian’ equation were significantly higher (11.7 and 21.6%) than the measured values in our study. Compared to values by the ‘Chinese’ predictive equations, there was no


1103

1.0

Mean+2SD 0.5

0.0

Mean −0.5

Mean -2SD

−1.0

1.5 Difference in RMR (Measurement−Prediction)

b


a

1.0

Mean+2SD

0.5

0.0

Mean −0.5 −1.0

Mean -2SD −1.5

−1.5 5.2

5.4

5.6

5.8

6.0

6.2

6.4

6.6

6.8

7.0

3.8

RMR average of Measurement and Prediction

1.5

Mean+2SD

0.5 0.0

Mean −0.5 −1.0

Mean -2SD

−1.5 −2.0 5.0

5.5

6.0

6.5

7.0

4.4

4.6

4.8

5.0

5.2

5.4

5.6


Difference in RMR (Measurement−Prediction)

d

1.0

4.2


2.0

c

4.0

7.5


1.5

Mean+2SD

1.0 0.5 0.0

Mean

−0.5 −1.0

Mean -2SD

−1.5 −2.0 4.2

4.4

4.6

4.8

5.0

5.2

5.4

5.6

5.8


Figure 2 Resting metabolic rate. (a) Difference (measurement vs prediction of FAO/WHO/UNU 1985) for males aged 18–29 y with 95% limits of agreement. (b) Difference (measurement vs prediction of FAO/WHO/UNU 1985) for female subjects aged 18–29 y with 95% limits of agreement. (c) Difference (measurement vs prediction of FAO/WHO/UNU 1985) for males aged 30–60 y with 95% limits of agreement. (d) Difference (measurement vs prediction of FAO/WHO/UNU 1985) for males aged 30–60 y with 95% limits of agreement.

difference for young male subjects; however, there were slightly higher values (7.3%) for young female subjects in our study. Our results were similar to findings in Malaysians, Indians, and North Americans (Clark & Hoffer, 1991; Ismail et al, 1998). The individual differences between measured and predicted values were in ranges normally found by other authors (De Lorenzo et al, 2000). The individual error of the prediction is too high to be of practical use in individuals. In circumstances where individual values are required, the measurement instead of the prediction of RMR is recommended. Values from the Italian subjects in Schofield’s study are high, and some reports suggest they are higher than measured values of other Italian populations (De Lorenzo

et al, 2000). Hayter and Henry, (1994) argue that the subjects in the Schofield study were primarily young people with relatively high activity levels. If so, such bias could lead to inaccurate calculation of RMR in Vietnamese people, if using the FAO/WHO/UNU equations. These findings suggest that, if using these equations, the lower energy needs of Vietnamese with similar body weight may put them at greater risk for developing obesity, especially in overweight people. We conducted our RMR measurements under carefully controlled conditions, with consideration of many factors. Studies of RMR after periods of therapeutic low-energy intake suggest that RMR changes in relation to metabolic adjustment. Furthermore, there is a direct correlation between BMI and bias of predictive equations. Thus, to minimize such bias European Journal of Clinical Nutrition


1104 (Siervo et al, 2003), we selected subjects with BMI in the normal range. In addition, RMR for female subjects was measured in the same phase of the menstrual cycle in order to minimize any effects of menstruation on RMR. However, our study was carried out over a short period of time, and on a small scale. Thus, our results may not apply to the whole Vietnamese population. Further studies validating the RMR of Vietnamese on a large scale should be implemented. In conclusion, the present study shows that RMR in Vietnamese adults is lower than predicted by the FAO/WHO/ UNU 1985 equations by an average of 7.4 and 11.7% in male subjects (18–29 old, and 30–60 y old, respectively) and 9.0 and 13.5% in female subjects (18–29 and 30–60 y old, respectively). These differences may be explained by bias introduced by the dominance of young, active subjects in the Schofield database. These findings suggest that the lower energy needs of Vietnamese with similar body weight may put them at greater risk for developing obesity, especially in overweight people. Larger-scale studies should be conducted in Vietnam to determine which equations are appropriate for Vietnamese people.

Acknowledgements We thank the participants for their cooperation and the staff of the National Institute of Nutrition for their assistance. We are grateful to Mika Murata for her technical assistance in this survey. We also thank Jonathan Siekmann for help with the manuscript.

References Bland JM & Altman DG (1986): Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307–310. Clark HD & Hoffer LF (1991): Reappraisal of the resting metabolic rate of normal young men. Am. J. Clin. Nutr. 53, 21–26. De Lorenzo A, Andreoli A, Bertoli S, Testolin G, Oriani G & Deurenberg P (2000): Resting metabolic rate in Italians: relation with body composition and anthropometric parameters. Acta Diab. 37, 77–81. Deurenberg YM, Tan BY, Chew SK, Deurenberg P & van Staveren W (1999): Manifestation of cardiovascular risk factors at low level of body mass index and waist-hip ratio in Singaporean Chinese. Asia Pacific J. Clin. Nutr. 8, 177–183.

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Giay T, Khoi HH, Lien DTK, Lap CQ & Ngu T (1997): The Recommended Nutrition Allowance for Vietnamese, pp 10–11. Hanoi: Medical Publishing House. Hayter JE & Henry CJK (1993): Basal metabolic rate in human subjects migrating between tropical and temperate regions: a longitudinal study and review of previous work. Eur. J. Clin. Nutr. 47, 724–734. Hayter JE & Henry CJK (1994): A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subjects in sample selection. Eur. J. Clin. Nutr. 48, 702–707. Henry CJK & Rees DG (1991): New predictive equations for the estimation of basal metabolic rate in tropical peoples. Eur. J. Clin. Nutr. 45, 177–185. Ismail MN, Ng KK, Chee SS, Roslee R & Zawiah H (1998): Predictive equations for the estimation of basal metabolic rate in Malaysian adult. Malay. J. Nutr. 4, 81–90. Khoi HH (1996): Problems of Nutrition in Transition Period, pp 153–266. Hanoi: Medical Publishing House. Khoi HH, Khan NC, Mai LB & Tuyen LD (2003): General Nutrition Survey 2000, pp 21–37. Hanoi: Medical Publishing House. Ko GTC, Chan JCN, Cockram CS & Woo J (1999): Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese. Int. J. Obes. Relat. Metab. Disord. 23, 1136–1142. Levy PS & Lemeshow S (1999): Sampling of Populations. Methods and Applications, 3rd Edition. New York: John Wiley & Sons, INC. Ministry of Health-Vietnam (MOH) (2003): Manual for the Control and Prevention of the Affection of Tobacco in Vietnam. Hanoi: Medical Publishing House. National Institute of Nutrition (NIN) (2001): National Nutrition Strategy, pp 14–19. Hanoi: Medical Publishing House. Piers LS & Shetty PS (1993): Basal metabolic rates of Indian women. Eur. J. Clin. Nutr. 47, 586–591. Schofield WN (1985): Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. Clin. Nutr. 39 (Suppl 1), 5–41. Shetty PS, Henry CJK, Black AE & Prentice AM (1996): Energy requirements of adults: an update on basal metabolic rates (BMRs) and physical activity levels (PAL). Eur. J. Clin. Nutr. 50 (Suppl 1), S21–S23. Siervo M, Boschi V & Falconi C (2003): Which REE prediction equation should we use in normal-weight, overweight and obese women? Clin. Nutr. 22 (2), 193–204. Soares MJ, Francis DG & Shetty PS (1993): Predictive equations for basal metabolic rates of Indian males. Eur. J. Clin. Nutr. 47, 389–394. Valencia ME, Moya SY, McNeill G & Haggarty P (1994): Basal metabolic rate and body fatness of adult men in northern Mexico. Eur. J. Clin. Nutr. 48 (3), 205–211. Weir JBD (1949): New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 109, 1–9. Weir JBD (1990): New methods for calculating metabolic rate with special reference to protein metabolism. Nuture 6, 213–223. William DM, Frank IK & Victor LK (1991): Exercise Physiology, 3rd Edition. London: Lea & Febiger.