The Validity of Predicting the Basal Metabolic Rate of Young Australian Men and Women

The validity of predicting the basal metabolic rate of young Australian men and women

LS Piers1, B Diffey2; MJ Soares1;2, SL Frandsen2; LM McCormack2, MJ Lutschini2 and K O'Dea1

1Deakin Institute of Human Nutrition; and 2School of Nutrition & Public Health, Deakin University, 336 Glenferrie Road, Malvern 3144, Victoria, Australia

Objectives: To assess the accuracy of the Scho®eld, Scho®eld & James (1985) equations and those of Hayter & Henry (1994) for the prediction of the basal metabolic rate (BMR), of young Australians. Design: BMR was measured by indirect calorimetry, while fat free mass (FFM) and fat mass (FM) were measured by bioelectric impendence analysis (BIA) in 128 volunteers (39 men and 89 women), aged between 18 and 30 y. Setting: Deakin Institute of Human Nutrition, Deakin University, Melbourne, Australia. Results: The measured BMR of Australian men and women were signi®cantly lower (P 0.001) than the predicted BMR using the Scho®eld et al (1985) equation, with a mean (s.d.) bias (bias measured 7 predicted BMR) of 7406 (513) kJ/d in men and 7124 (348) kJ/d in women. The measured BMR of Australian men and women were similar to the predicted BMR using the equations of Hayter & Henry (1994) and bias was unrelated to body weight. BMR adjusted for FFM and FM was signi®cantly higher by three percent in women on oral contraceptive agents (OCA) as compared to those not on OCA. Conclusions: The Scho®eld et al (1985) equations are not valid for the prediction of BMR of young Australian men and women. The equations of Hayter & Henry (1994) for North Europeans and Americans, provide an accurate estimate of the BMR of Australian men and women at the group level. However, in young women not using OCA a correction factor of 0.97 applied to the predicted BMR provides a better estimate. Sponsorship: Deakin University, Australia. Descriptors: basal metabolic rate; body composition; energy requirements; predictive equations; oral contraceptive agents; Australians

Introduction the tropics, the disagreements between measured and predicted BMR were ascribed to ethnic differences in basal The publication of the FAO/WHO/UNU report (1985) on metabolism (Quenouille et al, 1951; Scho®eld et al, 1985). Energy and Protein Requirements has helped established However present day North Americans, Asian Indians and two principles for the assessment of energy requirements in other tropical populations have been shown to have similar humans; (a) that all estimates of energy requirements BMRs (Soares & Shetty, 1988; Soares et al, 1993; Piers & should be based on measures of energy expenditure rather Shetty, 1993), while Asians living in Britain had BMR than energy intake; (b) that the basal metabolic rate (BMR) similar to Britons (Henry et al, 1987). form the basis of the factorial method to estimate total A re-examination of the FAO database revealed that the energy expenditure. In situations where actual measures of BMR of Italian subjects, which made up over 50% of males BMR are not feasible, it could be predicted using equations and 16% of the females in the 18±30 y age group, were based on anthropometric measurements. For this purpose signi®cantly higher than Northern Europeans and Amer- the FAO/WHO/UNU (1985) recommended the use of age icans, with a higher BMR per kg body weight when and gender speci®c equations, based either on body weight compared to other Caucasian data (Scho®eld et al, 1985). alone or weight and height, to predict the BMR in all In addition, a sizeable number of the subjects were military populations. Modi®ed versions of these equations, based on cadets and miners who were not representative of the an expanded BMR data base (Scho®eld et al, 1985), were general Italian population (Hayter & Henry, 1994; Shetty recommended for use in Australians (Warwick, 1990). et al, 1996). It was, therefore, possible that the inclusion of Over the last decade, there has been a growing body of this Italian data resulted in the equations that overestimated evidence that the equations of Scho®eld et al (1985), do not the BMR of other population groups. New equations, accurately predict the BMR of some population groups excluding the Italian data, were derived by Hayter & (Soares & Shetty, 1984; McNeil et al, 1987; Henry & Rees, Henry (1994) to try and rectify the problem. 1988; Soares & Shetty, 1988; Minghelli et al, 1990; Soares Australian BMR data, collected over 50 y ago, forms a et al, 1993; Piers & Shetty, 1993; Valencia et al, 1993). As very small part of the FAO database (Scho®eld et al, 1985). these observations were mostly made in populations from The four studies included were based on a total of 42 Aboriginal women aged between 9 and 41 y (Hicks et al, Correspondence: Dr LS Piers 1931); four Australian men of European origin aged Received 20 September 1996; revised 17 December 1996; between 20 and 43 y (Wardlow et al, 1934) and 30 accepted 24 January 1997 Aboriginal men aged between 19 and 57 y (Wardlow & BMR of young Australians LS Piers et al 334 Horsley, 1928; Wardlow & Lawrence, 1932; Wardlow et Men aged 18±30 y. Scho®eld et al (1985) equation: al, 1934). We were, therefore, interested in validating the BMR (kJ/d) 63 6 weight (kg) 2896 use of the Scho®eld et al (1985) equations in a large group of young Australians of European origin, and have com- Hayter & Henry (1994) equation: pared the measured BMR to values predicted by the Scho®eld et al (1985) equations, as well as the equations BMR (kJ/d) 51 6 weight (kg) 3500. of Hayter & Henry (1994) for Northern Europeans and Americans. Women aged 18±30 y Scho®eld et al (1985) equation: BMR (kJ/d) 62 6 weight (kg) 2036 Subjects Hayter & Henry (1994) equation: One hundred and twenty eight subjects (39 men and 89 BMR (kJ/d) 47 6 weight (kg) 2880. women) participated in the study (Table 1). Subjects were recruited from amongst staff and students of Deakin Uni- versity, or from other residents of Melbourne by advertise- Measurement protocol ment or by personal approach. All subjects were of Subjects were required to follow the following instructions: European origin (assessed by family history), aged between (i) abstain from any strenuous exercise for 36 h prior to the 18 and 30 y, weight stable for at least three months prior to BMR measurement; (ii) on the day before the measurement measurements, and in good health as judged from a medical to complete their evening meal at a speci®ed time, to history and clinical examination that excluded signs and ensure a 12±14 h fast prior to the measurement, after symptoms of systemic illness. which they were to refrain from eating or drinking anything except water; (iii) to get a minimum of 8 h sleep; (iv) to refrain from eating or drinking anything to the morning of Methods the BMR measurement, to keep all physical activity to a minimum, and not bathe or shower. In addition, the few Anthropometry and body composition smokers in this study were instructed to refrain from Standing height was measured using a SECA standiometer smoking for at least 12 h prior to the BMR measurement. (model 708, Germany) and recorded to the nearest 0.1 cm. On arriving at the laboratory subjects were asked to Body weight was measured immediately after voiding, with empty their bladder, then lie down and rest for a minimum subjects wearing light indoor clothing and no shoes, on a of 30 min. During this time the Deltatrac was calibrated. digital weighing scale (SECA, model 708, Germany) and After the rest period, the canopy of the Deltatrac was recorded to the nearest 100 g. Estimates of FFM were placed over the head of the subject, who was then obtained for each subject from measurements of resistance instructed to remain awake and motionless, as far as and reactance, using a four-terminal impedance plethysmo- possible, for the following 35 min. Following the BMR graph (RJL Systems, model 101, Detroit, USA) as measurement, subjects were asked to empty their bladder described by Lukaski et al (1986) to estimate fat free and body weight, height, other anthropometric variables mass (FFM). Fat mass (FM) was determined from the and body composition were measured. All measurements difference between FFM and body weight. were made at the Toorak campus of Deakin University. Subjects were then questioned about their medical and Basal metabolic rate family histories. Women were also asked about the reg- The basal metabolic rate (BMR) was measured by indirect ularity and duration of their menstrual cycle and about the calorimetry using a Deltatrac II metabolic monitor (Datex, use of oral contraceptive agents. In those women not on Finland), an open-circuit ventilated canopy measurement oral contraceptive agents, who were certain about the system. The measurement was conducted under standar- commencement of their last menstrual period, the day of dised conditions (Schutz, 1984); subjects were lying (i) at the menstrual cycle (the day of onset of menstruation being complete physical rest after sleeping for a minimum of 8 h; designated day 1) on which the BMR was measured was (ii) in a thermally neutral environment; (iii) 12±14 h after noted. This enabled a retrospective division of the women their last meal; (iv) awake and emotionally undisturbed; into a follicular (day of cycle 14th day) and a luteal cycle and (v) without disease or fever. (day of cycle >14th day). The Deltatrac was calibrated each morning, prior to the BMR measurements. A calibration gas mixture of oxygen Statistics (95%) and carbon-dioxide (5%) (Datex, Finland) was used. All data is expressed as mean (s.d.), unless otherwise Air ¯ow rates through the canopy (46.5 l/min) were stated. Data analysis was carried out on an IBM compatible checked by means of ethanol burning tests as described computer using SPSS for Windows (Version 6.1, 1993, by the manufacturer and were conducted once each month, SPSS Inc., IL, USA). A two-tailed Student's paired t-test during data collection. Performance of the Deltatrac moni- was used to detect differences between measured and tor was also checked by monitoring the ratio of carbon- predicted BMR. Bias was calculated as the difference dioxide produced to oxygen consumed, during the ethanol between measured and predicted BMR. It was also burns. The mean (s.d.) ratio for the last 15 min of the tests expressed as a percentage of measured BMR. The relation- was 0.66 (0.2), which was within the manufacturers recom- ship of bias to body weight was obtained by regression mended rage of 0.64±0.69. analysis (Bland & Altman, 1986). An analysis of covar- BMR was predicted using the Scho®eld et al (1985) iance (ANCOVA) with either body weight or FFM and FM equations, as well as, the equations for North Europeans as covariates, was used to compare the BMR of women not and Americans derived by Hayter & Henry (1994). Both on OCA with those on OCA, and also to compare the BMR sets of equations are based on body weight and are gender of those women not on OCA measured in the follicular and age speci®c as follows: phase of the measured cycle to the BMR of those measured BMR of young Australians LS Piers et al 335 in the luteal phase. Statistical signi®cance was accepted at Table 1 Anthropometric and body composition data of young Australian the 5% level. men and women Men Women (n 39) (n 89) Ethics The study was approved by the Deakin University Ethics Variable Mean s.d. Mean s.d. Committee and all subjects gave informed and written Age (y) 25 3 24 3 consent. Weight (kg) 76.5 11.7 60.3 7.1 Height (cm) 178.2 7.4 165.8 6.7 BMI (kg/m2) 24.1 3.1 22.0 2.3 Fat mass (kg) 15.3 8.0 18.0 4.8 Results Fat free mass (kg) 61.2 6.4 42.4 4.4 Forty seven of the women in this study did not use oral contraceptive agents (OCA). Of these nine women were unsure of the exact day of their menstrual cycle. In the Table 2 Anthropometric data, body composition, measured and remainder, there was no signi®cant difference in the BMR predicted basal metabolic rates (BMR) of women not on oral adjusted for body weight (ANCOVA: df 1,35; F 0.75; contraceptive agents (OCA) and those on OCA NS) or adjusted for FFM (ANCOVA: df 1,35; F 0.80; Women not on Women on OCA NS) between women measured in the follicular phase OCA (n 19) and those measured in the luteal phase (n 19) (n 47) (n 42) of the menstrual cycle. Variable Mean s.d. Mean s.d. Forty two women had used OCA for six months or more. All subjects on OCA used either phasic or constant Age (y) 23 2 24 3 dose combined pills. The most common phasic dose pill Weight (kg) 60.7 8.2 59.9 5.8 BMI (kg/m2) 22.2 2.6 21.7 2.0 used was a combination of levonorgestrel (50±125 mg) and Fat mass (kg) 18.5 5.3 17.4 4.1 ethinyloestradiol (30±50 mg) per day. The most common Fat free mass (kg) 42.3 4.9 42.5 3.8 constant dose pill was levonorgestrel (125 mg) and Measured BMR (kJ/d) 5590 530 5725 485 ethinyloestradiol (50 mg) per day; other combinations adjusted for body weight (kJ/d) 5569 5748* adjusted for FFM and FM (kJ/d) 5580 5735* included desogesterl (150 mg) and ethynyloestradiol Predicted BMR (kJ/d) (30 mg); levonorgestrel (150 mg) and ethinyloestradiol Scho®eld et al (1985) equation 5801** 509 5751 360 (30 mg); and norethisterone (500 mg) and ethinyloestradiol Hayter & Henry (1994) equation 5734** 386 5696 273 (35 mg) per day. None of the subjects were on progesterone Note: FM fat mass; FFM fat free mass. only pills. *Signi®cantly different (P < 0.03) from women not on OCA on an There were no signi®cant differences in age, anthropo- ANCOVA metry, body composition or absolute BMR in those women **Signi®cantly different (P < 0.01) from measured BMR on a two tailed not on OCA as compared to those on OCA (Table 2). paired t-test. Measured BMR of women on OCA adjusted for body weight, was signi®cantly higher than that of women not on OCA (ANCOVA: df 1,86; F ratio 6.31, P 0.014). Table 3 Measured and predicted basal metabolic rates (BMR) of young When adjusted for FFM and FM, this difference in mea- Australian men and women sured BMR persisted (ANCOVA: df 1,85; F ratio 5.3; Men Women P 0.024). The Scho®eld et al (1985) equations as well as (n 39) (n 89) the Hayter & Henry (1994) equations correctly predicted the BMR of women using OCA (Table 2). Both equations Variable Mean s.d. Mean s.d. however, signi®cantly over predicted BMR of women not Measured BMR (kJ/d) 7312 775 5653 511 on OCA, with a mean bias of 73.8% [95% CI: 75.8, Predicted BMR (kJ/d) * * 71.8] and 72.6% [95% CI: 74.5, 70.7] respectively Scho®eld et al (1995) equation 7718 739 5778 443 Bias (kJ/d) 7406 513 7124 348 (Table 2). Hayter & Henry (1994) equation 7403 599 5716 336 In men the measured BMR was signi®cantly lower Bias (kJ/d) 791 493 762 344 (P < 0.0005) than the BMR predicted by the Scho®eld et al (1985) equation (Table 3). Mean bias was 75.5% [95% Note: Bias measured 7 predicted BMR. Signi®cantly different (P < 0.001) from measured BMR on a two-tailed CI: 77.8, 73.3%], but it was not related to body weight paired t-test. (r 70.28; P 0.09) (Figure 1). However, there was no signi®cant difference between the measured and predicted BMR using the equation of Hayter & Henry (1994) (Table Hayter & Henry (1994) (Table 3). Mean bias was 71.1% 3). The mean bias was 71.2% [95% CI: 73.5, 1.0%] and [95% CI: 72.4, 0.2%] and it was not related to body it was not related to body weight (r 0.00; P 0.99) weight (r 0.13; P 0.24) (Figure 2). (Figure 2). The relationship of BMR to body weight in this study On pooling the data for all women studied (both those was given by: on and not on OCA, N 89) measured BMR was signi®- cantly lower (P 0.001) than the BMR predicted by the Men: BMR (kJ/d) 51 6 Body weight 3415 equation of Scho®eld et al (1985) (Table 3). Mean bias was 72.2% [95% CI: 73.5%, 0.9%] and it was not related to [n 39; r 0.77; SEE 499; P < 0.00005] body weight (r 70.18; P 0.09) (Figure 1). However, Women: BMR (kJ/d) 53 6 Body weight 2447 the measured BMR of the women was not signi®cantly different from the predicted BMR using the equation of [n 89; r 0.74; SEE 344; P < 0.00005] BMR of young Australians LS Piers et al 336

Figure 1 Bias of the BMR predicted by the Scho®eld, Scho®eld & James Figure 2 Bias of the BMR predicted by the Hayter & Henry (1994) (1985) equation for men (d, Ð ) (r 7 0.28, P 0.09) and women equation for men (d, Ð) (r 7 0.00, P 0.99) and women (s,- - -) (s,- - -) (r 7 0.18, P 0.09) in relation to body weight. (r 7 0.13, P 0.24) in relation to body weight.

1984; Bisdee et al, 1989). In this study no objective Discussion assessment of the phase of the cycle was attempted and The results of this study indicate that the Scho®eld et al we depended on the accuracy of the menstrual history of (1985) equations overestimate the BMRs of Australian the subject. While acknowledging that our methodology men. Our results are consistent with previous reports was less than ideal, our cross sectional data showed that the from Australia (Warwick et al 1988), from the Indian phase of the menstrual cycle had no demonstrable effect on sub-continent (Soares & Shetty, 1988; Piers et al, 1993; BMR; an observation documented in some longitudinal Soares et al, 1993), other tropical locations (Henry & Rees, studies (Weststrate, 1993; Piers et al, 1995). An earlier 1988), the Americas (Owen et al, 1987; Clark & Hoffer, study in this laboratory had suggested that the use of oral 1991; Valencia et al, 1993 Piers & Shetty, 1993; Soares et contraceptive agents (OCA) may be another factor that al, 1993 and Europe (de Boer et al, 1988). There are several could in¯uence the BMR of women (Diffey et al, 1997). factors that are thought to be responsible for this inaccuracy The present study, in a larger group of women has con- in predicting BMR. These include the age of the data base, ®rmed those observations, as users of OCA had a signi®- as well as, the type of subjects included in the original FAO cantly higher BMR when adjusted for body weight or body data base (Soares & Shetty, 1988; Soares et al, 1993). A composition (Table 2). recent re-examination of the original FAO database has The Scho®eld et al (1985), equations, as well as the con®rmed that it was not truly representative of BMR data equations of Hayter & Henry (1994) correctly predicted the worldwide, as it was weighted by Italian data that was not BMR of women on OCA (Table 2). However, both sets of representative of the Italian population. This resulted in equations signi®cantly overestimated the BMR of women equations that over predicted the BMR of many population not on OCA; the magnitude of error in prediction being groups (Hayter & Henry, 1994; Shetty et al, 1996). The smaller with the use of Hayter & Henry (1994) equation applicability of predictions for BMR based on body weight, (Table 2). In the context of predicting energy requirements depend to a large extent on the similarity in body composi- for groups of individuals or large population groups, it was tion between the data used to generate these equations and important to assess whether these differences in the sub- study population they are applied to. It is likely that the groups of women, led to consistent errors in predicted Italian data in the Scho®eld database, many of whom were BMR. In Table 3 we have, therefore, examined the miners and military personnel with higher FFM, would pooled data on women. The Scho®eld et al (1995) equation have contributed to results obtained. signi®cantly overestimated BMR of women. The equation When the Italian data were excluded from the analysis, of Hayter & Henry (1994) provided an accurate estimate of the resultant equations of Hayter & Henry (1994) derived measured BMR and the bias was unrelated to body weight, only from Northern Europeans and American subjects, which lends support to the use of these equations. The accurately estimated the BMR of Australian men. Bias in result of this study clearly show that the over-prediction of prediction was unrelated to body weight (Figure 2) which measured BMR in men and women by the Scho®eld et al suggested a constant and close agreement between mea- (1985) equations, was due to the inclusion of the original sured and predicted BMR, over the body weight range Italian data. The decision of Hayter & Henry (1994) to studied. Similar conclusions were reached when examining generate new equations without the Italian data has led to the slope and intercept of the equation relating body weight an improved prediction of BMR in this population. to BMR for Australian men, and comparing the respective values obtained by Hayter & Henry (1994) in their equa- Conclusions tions for males. When measuring the BMRs of women, the in¯uence of The Scho®eld et al (1985) equations for predicting BMR the menstrual cycle is a factor that deserves consideration, from body weight consistently overestimated the BMR of in addition to standard measurement conditions (Schutz, young Australian men and women. These results, together BMR of young Australians LS Piers et al 337 with the overwhelming evidence in other populations, Owen OE, Holup JL, D'Alessio DA, Craig ES, Polansky M, Smalley KJ, indicate that these equations should no longer be recom- Kavle EC, Bushman MC, Owen LR, Mozzoli MA, Kendrick ZV & Boden GH (1987): A reappraisal of the caloric requirements of men. mended for the prediction of BMR in individuals aged Am. J. Clin. Nutr. 46, 875±885. between 18 and 30 y. The equations of Hayter & Henry Piers LS & Shetty PS (1993): Basal metabolic rates of Indian women. Eur. (1994) provided an accurate estimate of the BMR in young J. Clin. Nutr. 47, 586±591. 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