Obesity Research & Clinical Practice (2014) 8, e283—e290
ORIGINAL ARTICLE
Validation of prediction equations for resting energy expenditure in Singaporean Chinese men Tammy Song a,∗, Kavita Venkataraman b, Peter Gluckman c, Chong Yap Seng b, Khoo Chin Meng a, Eric Yin Hao Khoo a, Melvin Khee Shing Leow c, Lee Yung Seng c, Tai E. Shyong a a
Department of Medicine, National University of Singapore, Singapore Department of Obstetrics and Gynaecology, National University of Singapore, Singapore c Singapore Institute for Clinical Sciences, Singapore b
Received 8 October 2012 ; received in revised form 17 May 2013; accepted 19 May 2013
KEYWORDS Prediction equation; Resting energy expenditure; Validity; Ethnicity; Chinese
Summary Accurate prediction of resting energy expenditure (REE) is important in establishing adequate dietary intake goals for effective weight management. Previous studies have shown that the validity of an energy prediction equation may depend on the ethnicity of the population. Validation studies are lacking in the Singaporean Chinese population. A total of 96 healthy Singaporean Chinese males of age 21—40 years and body mass index (BMI) 18.5—30.0 kg/m2 participated in this study. REE was measured by indirect calorimetry and compared with REE predicted using existing equations. Validity was evaluated on the basis of mean bias and percentage of subjects predicted within ±10% of REE measured. In addition, Bland and Altman analyses were performed. No significant difference was observed between the mean levels of measured and predicted REE derived from the Owen equation. The Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Harris—Benedict and Mifflin equations significantly overestimated the mean measured REE by 7.5%, 6.0% and 2.4% respectively. Percentage of valid predictions for FAO/WHO/UNU, Harris—Benedict, Mifflin and Owen equations were 60%, 67%, 75% and 73% respectively. Bland and Altman analyses demonstrated poor agreement for all equations. The Owen equation provided a valid estimation of REE in Singaporean Chinese men at a group level. However, the individual errors of the
∗ Corresponding author at: Centre for Translational Medicine, MD 6, National University of Singapore #10-01, 14 Medical Drive, 117599, Singapore. Tel.: +65 94895982. E-mail address: [email protected] (T. Song).
1871-403X/$ — see front matter © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.orcp.2013.05.002
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T. Song et al. equations were unacceptable high and may have limited utility in making clinical decisions on nutritional requirements. © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction The accurate determination of total energy expenditure (TEE) is important for establishing dietary intake goals in weight management of both normal weight and obese individuals and nutritional management of hospitalised patients to avoid the adverse effects of overfeeding and underfeeding [1,2]. It is also used in estimating the energy requirements of a population [1]. Since the routine use of indirect calorimetry (the criterion standard for measuring REE) is not feasible in daily clinical practice, predictive equations are often used to estimate REE [3]. The validity of any predictive equation is crucial as REE is often used as the predictor of TEE as it accounts for 60—70% of TEE for people leading a sedentary lifestyle [4]. Any bias in REE assessment would be amplified when it is multiplied by the factor that is used to estimate the TEE of a population or individuals. The Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) 1985 predictive equation developed using data from Schofield and colleagues have been adopted as a basic reference for the development of energy requirements in Singapore [5—7]. However, it is unclear whether this equation can be applied successfully to the Singaporean population. The FAO/WHO/UNU equations, developed using predominantly European subjects, seemed to be less accurate in predicting REE in Asian populations [8—13]. In a study by Ismail et al., FAO/WHO/UNU equations overestimated the REE of adult Malays, Chinese, Indians and Dayaks living in Malaysia by 13% in male and 9% in female subjects [11]. Recently, Tseng et al. reported that the measured REE was significantly lower than REE calculated by the equation by 271 ± 311 kcal/day in Chinese subjects living in Taiwan [13]. Researchers have also queried the continued applicability of the FAO/WHO/UNU equations in our modern population, with changes in body size and composition, physical activity level and diet [14,15]. Several studies have shown that these predictive equations overestimated REE in Caucasian as well as in Asian populations [16—18]. Muller et al. reported that the WHO equations overestimated REE from the measured values by up to 14.3% in males and 7.5% in females in Germany [16]. According to Piers et al., measured REE of
Australian men and women were significantly lower than the predicted REE using FAO/WHO/UNU equations by 406 ± 513 kJ/day and 124 ± 348 kJ/day respectively [17]. No validation studies on prediction equations for REE have been conducted in Singapore. Therefore, it is crucial to identify the most accurate equation appropriate for predicting energy needs in this population. The objective of this study was to evaluate the validity of FAO/WHO/UNU [19] equation and other commonly used prediction equations, Harris—Benedict, Owen et al. and Mifflin et al. from the literature in a sample of healthy Singaporean Chinese men [20—22]
Materials and methods Subjects A total of 96 healthy Singaporean Chinese men participated in this cross-sectional study. The subjects were selected from a larger study of 260 subjects designed to evaluate the effect of genomic variation, ethnicity and developmental factors on disease risk in Singaporean men with obesity. Only adults aged 21—40 yrs and body mass index (BMI) 18.5—30.0 kg/m2 , with sedentary lifestyle (defined as exercise less than once a week of not more than 30 min duration) were included. We have used the adjusted BMI definitions for the Asian population according to WHO expert consultation in 2002, where 18.5—22.9 kg/m2 represents low risk, 23.0—27.4 kg/m2 represents moderate risk and ≥27.5 kg/m2 represents high risk [23]. Exclusion criteria included hypertension, diabetes mellitus, dyslipidemia, altered thyroid function, chronic kidney, liver disease and the usage of medications known to influence REE, for example sibutramine. Subjects with significant recent changes in body weight of more than 5% over the past 6 months or who were actively attempting to lose weight through dieting, bariatric surgery or anti obesity drugs were excluded in view of the need to recruit participants with stable weight of a given narrow distribution of BMI. The subjects were recruited by advertisement. The study was approved by the Domain Specific Review Board of the National Healthcare Group and written informed consent was obtained from each subject.
Validation of prediction equations for resting energy expenditure in Singaporean Chinese men Table 1
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Predictive equations for resting energy expenditure (kcal/day).
Prediction equation FAO/WHO/UNU (1985) [19] Harris and Benedict [22] Mifflin et al. [20] Owen et al. (1986) [21]
Formula 18—29 yr: REE = 15.3 × W + 679 30—59 yr: REE = 11.6 × W + 879 REE = 66.473 + 13.7516 × weight + 5.0033 × Height − 6.755 × Age REE = 10 × weight + 6.25 × height − 5 × age + 5 REE = 879 + 10.2 × weight
Resting energy expenditure measurements On the test day, subjects reported to the National University Hospital at 0830 h. They were instructed to transport themselves to the hospital in a vehicle to avoid undue exertion. They were required to undergo a 12 h overnight fast and refrain from intensive physical activity for 24 h prior to measurement. They were also advised to abstain from coffee and other nicotine containing food or beverage, heavy meals, and alcohol in the evening prior to measurement. Subjects were then allowed to lie supine quietly and relaxed for 15 min before measurement commenced. REE was measured continuously by open-circuit indirect calorimetry using a ventilated hood system (Quark CPET, COSMED, Italy) for 60 min. For this, a transparent Perspex ventilated hood was placed over the subjects head, through which outside air was drawn by a pump. The flow rate (20—40 L/min) was measured and adjusted to keep the difference in carbon dioxide readings between inspired and expired air within the range of 0.8—1.2%. A small sample of air leaving the hood was analysed for oxygen (O2 ) and carbon dioxide (CO2 ) by a paramagnetic analyzer and infrared analyzer respectively. Calibration of flow and gas analyzers were done before each measurement according to the manufacturer’s instructions. Flow calibration was performed by a 3-litre calibration syringe and gas analyzers were calibrated with dried standard gas mixture (16.01% O2 , 4.98% CO2 ) and dried atmospheric air (20.93% O2 , 0.03% CO2 ). The validity of the ventilated hood system was further tested by ethanol combustion tests conducted biweekly. The mean oxygen recovery was 98.8 ± 1.9 (95% confidence interval 98.7 and 99.0) and the mean carbon dioxide recovery was 98.3 ± 1.6 (95% confidence interval 98.2 and 98.4) (unpublished results). Correction factors were applied when necessary. All measurements were carried out in a quiet room with an ambient temperature of 23—25 ◦ C, barometric pressure of 750—770 mmHg and constant humidity of 60%. During the measurements, subjects were lying in a semi-supine position, quiet, motionless and were kept awake by showing them non-stressing movies, which were vetted for violent
or sexual content that may excite them. O2 and CO2 concentrations of the outflowing air and airflow rate through the hood were measured every 5-s to obtain oxygen consumption (VO2 ), carbon dioxide production (VCO2 ), REE and respiratory quotients. To eliminate effects of subject habituation to the testing procedure, the respiratory measurements during the first 5 min were discarded. Only steady state periods of measurement of at least 30 min were used to calculate REE. REE was calculated from VO2 and VCO2 by the Weir formula [24]. In addition to measured values, REE was predicted by the FAO/WHO/UNU, Harris—Benedict, Owen et al. and Mifflin et al. equations (Table 1) [19—22].
Anthropometric characteristics Body weight was measured in light clothing to the nearest 0.1 kg using an electronic scale (SECA, Vogel & Halke, Germany). Height was measured without shoes to the nearest 0.1 cm using a wall-mounted stadiometer. BMI was calculated as weight divided by height squared (kg/m2 ).
Statistical analysis Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 20 [25]. Pearson’s correlation coefficients were used to examine the association between measured and predicted REE for each of the predictive equations. Measured and predicted REE were compared at a group level using student’s paired t test. Individual prediction accuracy was defined as the percentage of subjects that had an REE predicted within ±10% of REE measured [26,27]. A prediction below 90% of REE measured was classified as under-prediction and a prediction above 110% of REE measured was classified as over-prediction [28,29]. Bland and Altman [30] plots were used to determine agreement both graphically and numerically between measured REE and predicted REE by calculating mean bias (difference between measured and predicted REE) and limits of agreement (mean bias ± 2 s.d). For good agreement, it is expected that bias will be close to zero, clinically acceptable limits of agreement
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Table 2
the subjects falling within ±10% of measured REE followed by the Owen equation with an individual prediction accuracy of 73% (Table 4). However, whereas the Mifflin equation more commonly overestimated REE than underestimating it, the Owen equation had similar numbers in which REE was over- or underestimated. REE could not be accurately predicted by the FAO/WHO/UNU and Harris—Benedict equations in more than 30% of the subjects
Characteristics of the subjects (n = 96).
Variables
Mean
S.D (range)
Age (y) Weight (kg) Height (cm) Body mass index (kg/m2 )
28.4 69.4 172 23.4
6 (21—40) 10.3 (52.5—96.9) 5.8 (157.6—187.5) 2.9 (18.8—29.8)
that are within ±10% of measured REE and that there is no clear evidence of a relationship between difference and mean of measured and predicted REE [27,30,31]. Values are given as mean ± s.d. The threshold for significance in all tests was set at P < 0.05 (two sided).
Discussion In our study, the measured REE was significantly lower than that predicted by the FAO/WHO/UNU, Harris—Benedict and Mifflin equations by 7.5%, 6.0% and 2.4% respectively, in a group of adult Singaporean Chinese men. However, the Owen equation provided valid mean estimates of REE with only slight underestimation (0.4%). Several researchers have also reported overestimation of REE in Asians using FAO/WHO/UNU and Harris—Benedict equations, and found percentage differences comparable to those of our study [32—35]. According to Nhung et al., predicted values using the FAO/WHO/UNU equations overestimated REE in Vietnamese adults by 7.4—13.5% [32]. Case et al. reported that REE predicted by Harris—Benedict and FAO/WHO/UNU equations were significantly higher than measured REE by 8.5% and 5.4% respectively in Asian subjects. In a study by Liu et al., the Harris—Benedict equation overpredicted measured REE by 8% in Chinese [35]. On the other hand, the Liu equation which was derived from Chinese adults, predicted the REE of Asians more accurately [12,33]. These are similar to the 7.5% (FAO/WHO/UNU equation) and 6.0% (Harris—Benedict equation) observed in our study. It has been suggested that such overestimation of REE by these prediction equations in Singaporean Chinese may be largely explained by ethnic
Results Characteristics of the study population are summarised in Table 2. A significant correlation was found between measured REE and REE derived from the FAO/WHO/UNU equation (R = 0.68), Harris—Benedict equation (R = 0.71), Mifflin equation (R = 0.70) and Owen equation (R = 0.67) (Table 3). However, the FAO/WHO/UNU, Harris—Benedict and Mifflin equations significantly overestimated mean measured REE by 120 ± 140 kcal/day, 96 ± 137 kcal/day and 38 ± 136 kcal/day respectively. The Owen equation did not show a significant mean bias in the prediction of REE. The individual differences between measured and predicted values of REE plotted against the average of measured and predicted values are shown in Fig. 1. The limits of agreement were wide for all equations. The individual differences between measured and predicted REE ranged from −161 kcal/day to 400 kcal/day. Focusing on the distribution of individual measured REE values revealed that Mifflin equation had the highest percentage REE predictive accuracy, with 75% of
Table 3 Differences and Pearson correlation coefficients between measured and predicted resting energy expenditure (kcal/day). REE
Measured FAO/WHO/UN [19] Harris and Benedict [22] Mifflin et al. [20] Owen et al. [21] *
P < 0.05.
Difference
95% CI
Mean
S.D
Mean
S.D
Lower
Upper
1594 1714 1689 1632 1587
191 137 158 126 105
120* 96* 38* −7
140 137 136 144
91 68 11 −36
148 123 66 22
Correlation coefficient
Limits of agreement
0.68* 0.71* 0.70* 0.67*
−161 −179 −235 −295
to to to to
400 371 311 281
Validation of prediction equations for resting energy expenditure in Singaporean Chinese men
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Figure 1 Bland Altman plots for comparison of agreement between resting energy expenditure measured by indirect calorimetry and predicted resting energy expenditure derived from prediction equations.
differences in REE related to body composition [36—38]. The Schofield database was dominated by Italian data which constituted 3388 out of a total of 7173 REE data points [39,40]. Hayter and Henry further reported that Italian subjects had a higher REE per kilogram of body weight compared to other population groups (North Europeans, Americans, Indians and Asians) in the Schofield database [41]. The Harris—Benedict equation was also developed using predominantly white subjects [39,42]. On the other hand, the Owen equation was
Table 4
developed from a group of Caucasian, Negro and Oriental male subjects, and hence, may be more accurate in predicting energy needs in Singaporean Chinese men [21]. In addition, the subjects used in the Schofield study were primarily young people with relatively high activity levels. Labourers and miners, Italian military, Neapolitan police, Italian navy and Burmese hill dwellers made up the largest groups of men in the Schofield database [7,43]. In contrast, the subjects used in our study were sedentary. Poehlman et al. reported a higher REE
Percentage of subjects with accurate predictions, under-predictions and over-predictions.
FAO/WHO/UNU [19] Harris and Benedict [22] Mifflin et al. [20] Owen et al. [21]
Accurate prediction (%)
Under-prediction (%)
Over-prediction (%)
60.4 66.7 75.0 72.9
1.0 1.0 5.2 14.6
38.5 32.3 19.8 12.5
e288 in physically active individuals compared to those with sedentary lifestyles, even after adjustment for body composition [44] At the individual level, Bland Altman plots of all equations showed lack of agreement with measured REE with wide limits of agreement. Errors of REE measurement may be introduced by air leaks, incorrect calibration of the calorimeter, involuntary alternating periods of hyper and hypoventilation, fluctuating levels of fractional inspired O2 concentration, or acid—base disturbances [45]. Although REE measurements were performed under strictly standardised conditions, biological intra-individual variation in REE might also be due to variations in eating and activity pattern in the days before the REE measurements. Limits of agreement within ±10% of measured REE are considered clinically acceptable as intraindividual variation in REE was reported to be 2—10% explained by methodological and biological variability in REE [31]. However, in our study, only 70% of subjects had limits of agreement < 10%. Furthermore, for all equations, Bland Altman plot showed that bias was not consistent across the range of REE values because bias correlated significantly with the mean of measured and predicted REE (data not shown). Predicted REE typically deviated more profoundly in the lower and upper REE ranges, with the equations systematically over-estimating at lower REE and underestimating at high REE. This was also observed in other studies [32,46—48]. Considering that the average REE was about 1600 kcal/day, an error of 10% would translate into a 160 kcal/day deviation from true REE. However, the Owen equation demonstrated individual differences between measured and predicted energy values that ranged from −295 to 281 kcal/day which is about −19 to 18% of REE. If these equations were closely followed to establish energy intake in a controlled setting over the long term, energy needs could be over-predicted or under-predicted by the physiologic weight change equivalent of 0.3 kg/week or 14.4 kg/year. This is especially detrimental in the clinical setting where overfeeding or underfeeding may have adverse effects such as electrolyte imbalance and gastrointestinal problems a [49]. And in weight management for overweight and obese people, prescribed dietary plans might be ineffective in producing weight loss. A limitation of our study is that the REE measurements were made on an outpatient basis and could not be performed earlier in the morning. However, subjects were allowed to rest comfortably for at least 15 min before measurement, during which they became acclimated to the environment,
T. Song et al. therefore ensuring resting conditions. Also, the standard deviation found in our study is comparable to other studies that had REE measurements conducted at 0700 h with the subjects staying overnight in the facility [37,50]. Therefore this limitation did not add to the variability of the REE measured and is unlikely to affect the results reported in this paper. These findings show that the WHO/FAO/UNU, Harris—Benedict and Mifflin predictive equations significantly overestimated energy needs for Singaporean Chinese men, therefore usage of these equations may put them at a greater risk for developing obesity, especially in overweight people. We recommend the Owen equation for estimation of energy needs in Singaporean Chinese adult males at a group level due to small mean prediction error. However, the individual errors of the equation are too high to be of practical use in individuals, in situations where accurate estimation of REE is clinically relevant (such as when prescribing nutrition in the setting of treatment for the critically ill or during the treatment of obesity). Although the Owen equation predicted REE accurately in 73% of the subjects, it displayed wide limits of agreement and directional bias was observed across the range of REE. These wide limits of agreement were especially prominent in those with more extreme levels of REE. Therefore in individuals where a precise determination of REE is indicated, measurement by indirect calorimetry instead of the prediction of REE using equations is highly recommended.
Conflict of interest policy The authors report no conflict of interest. The authors alone are responsible for the content and writing of the paper.
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ORIGINAL ARTICLE
Validation of prediction equations for resting energy expenditure in Singaporean Chinese men Tammy Song a,∗, Kavita Venkataraman b, Peter Gluckman c, Chong Yap Seng b, Khoo Chin Meng a, Eric Yin Hao Khoo a, Melvin Khee Shing Leow c, Lee Yung Seng c, Tai E. Shyong a a
Department of Medicine, National University of Singapore, Singapore Department of Obstetrics and Gynaecology, National University of Singapore, Singapore c Singapore Institute for Clinical Sciences, Singapore b
Received 8 October 2012 ; received in revised form 17 May 2013; accepted 19 May 2013
KEYWORDS Prediction equation; Resting energy expenditure; Validity; Ethnicity; Chinese
Summary Accurate prediction of resting energy expenditure (REE) is important in establishing adequate dietary intake goals for effective weight management. Previous studies have shown that the validity of an energy prediction equation may depend on the ethnicity of the population. Validation studies are lacking in the Singaporean Chinese population. A total of 96 healthy Singaporean Chinese males of age 21—40 years and body mass index (BMI) 18.5—30.0 kg/m2 participated in this study. REE was measured by indirect calorimetry and compared with REE predicted using existing equations. Validity was evaluated on the basis of mean bias and percentage of subjects predicted within ±10% of REE measured. In addition, Bland and Altman analyses were performed. No significant difference was observed between the mean levels of measured and predicted REE derived from the Owen equation. The Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Harris—Benedict and Mifflin equations significantly overestimated the mean measured REE by 7.5%, 6.0% and 2.4% respectively. Percentage of valid predictions for FAO/WHO/UNU, Harris—Benedict, Mifflin and Owen equations were 60%, 67%, 75% and 73% respectively. Bland and Altman analyses demonstrated poor agreement for all equations. The Owen equation provided a valid estimation of REE in Singaporean Chinese men at a group level. However, the individual errors of the
∗ Corresponding author at: Centre for Translational Medicine, MD 6, National University of Singapore #10-01, 14 Medical Drive, 117599, Singapore. Tel.: +65 94895982. E-mail address: [email protected] (T. Song).
1871-403X/$ — see front matter © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.orcp.2013.05.002
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T. Song et al. equations were unacceptable high and may have limited utility in making clinical decisions on nutritional requirements. © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction The accurate determination of total energy expenditure (TEE) is important for establishing dietary intake goals in weight management of both normal weight and obese individuals and nutritional management of hospitalised patients to avoid the adverse effects of overfeeding and underfeeding [1,2]. It is also used in estimating the energy requirements of a population [1]. Since the routine use of indirect calorimetry (the criterion standard for measuring REE) is not feasible in daily clinical practice, predictive equations are often used to estimate REE [3]. The validity of any predictive equation is crucial as REE is often used as the predictor of TEE as it accounts for 60—70% of TEE for people leading a sedentary lifestyle [4]. Any bias in REE assessment would be amplified when it is multiplied by the factor that is used to estimate the TEE of a population or individuals. The Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) 1985 predictive equation developed using data from Schofield and colleagues have been adopted as a basic reference for the development of energy requirements in Singapore [5—7]. However, it is unclear whether this equation can be applied successfully to the Singaporean population. The FAO/WHO/UNU equations, developed using predominantly European subjects, seemed to be less accurate in predicting REE in Asian populations [8—13]. In a study by Ismail et al., FAO/WHO/UNU equations overestimated the REE of adult Malays, Chinese, Indians and Dayaks living in Malaysia by 13% in male and 9% in female subjects [11]. Recently, Tseng et al. reported that the measured REE was significantly lower than REE calculated by the equation by 271 ± 311 kcal/day in Chinese subjects living in Taiwan [13]. Researchers have also queried the continued applicability of the FAO/WHO/UNU equations in our modern population, with changes in body size and composition, physical activity level and diet [14,15]. Several studies have shown that these predictive equations overestimated REE in Caucasian as well as in Asian populations [16—18]. Muller et al. reported that the WHO equations overestimated REE from the measured values by up to 14.3% in males and 7.5% in females in Germany [16]. According to Piers et al., measured REE of
Australian men and women were significantly lower than the predicted REE using FAO/WHO/UNU equations by 406 ± 513 kJ/day and 124 ± 348 kJ/day respectively [17]. No validation studies on prediction equations for REE have been conducted in Singapore. Therefore, it is crucial to identify the most accurate equation appropriate for predicting energy needs in this population. The objective of this study was to evaluate the validity of FAO/WHO/UNU [19] equation and other commonly used prediction equations, Harris—Benedict, Owen et al. and Mifflin et al. from the literature in a sample of healthy Singaporean Chinese men [20—22]
Materials and methods Subjects A total of 96 healthy Singaporean Chinese men participated in this cross-sectional study. The subjects were selected from a larger study of 260 subjects designed to evaluate the effect of genomic variation, ethnicity and developmental factors on disease risk in Singaporean men with obesity. Only adults aged 21—40 yrs and body mass index (BMI) 18.5—30.0 kg/m2 , with sedentary lifestyle (defined as exercise less than once a week of not more than 30 min duration) were included. We have used the adjusted BMI definitions for the Asian population according to WHO expert consultation in 2002, where 18.5—22.9 kg/m2 represents low risk, 23.0—27.4 kg/m2 represents moderate risk and ≥27.5 kg/m2 represents high risk [23]. Exclusion criteria included hypertension, diabetes mellitus, dyslipidemia, altered thyroid function, chronic kidney, liver disease and the usage of medications known to influence REE, for example sibutramine. Subjects with significant recent changes in body weight of more than 5% over the past 6 months or who were actively attempting to lose weight through dieting, bariatric surgery or anti obesity drugs were excluded in view of the need to recruit participants with stable weight of a given narrow distribution of BMI. The subjects were recruited by advertisement. The study was approved by the Domain Specific Review Board of the National Healthcare Group and written informed consent was obtained from each subject.
Validation of prediction equations for resting energy expenditure in Singaporean Chinese men Table 1
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Predictive equations for resting energy expenditure (kcal/day).
Prediction equation FAO/WHO/UNU (1985) [19] Harris and Benedict [22] Mifflin et al. [20] Owen et al. (1986) [21]
Formula 18—29 yr: REE = 15.3 × W + 679 30—59 yr: REE = 11.6 × W + 879 REE = 66.473 + 13.7516 × weight + 5.0033 × Height − 6.755 × Age REE = 10 × weight + 6.25 × height − 5 × age + 5 REE = 879 + 10.2 × weight
Resting energy expenditure measurements On the test day, subjects reported to the National University Hospital at 0830 h. They were instructed to transport themselves to the hospital in a vehicle to avoid undue exertion. They were required to undergo a 12 h overnight fast and refrain from intensive physical activity for 24 h prior to measurement. They were also advised to abstain from coffee and other nicotine containing food or beverage, heavy meals, and alcohol in the evening prior to measurement. Subjects were then allowed to lie supine quietly and relaxed for 15 min before measurement commenced. REE was measured continuously by open-circuit indirect calorimetry using a ventilated hood system (Quark CPET, COSMED, Italy) for 60 min. For this, a transparent Perspex ventilated hood was placed over the subjects head, through which outside air was drawn by a pump. The flow rate (20—40 L/min) was measured and adjusted to keep the difference in carbon dioxide readings between inspired and expired air within the range of 0.8—1.2%. A small sample of air leaving the hood was analysed for oxygen (O2 ) and carbon dioxide (CO2 ) by a paramagnetic analyzer and infrared analyzer respectively. Calibration of flow and gas analyzers were done before each measurement according to the manufacturer’s instructions. Flow calibration was performed by a 3-litre calibration syringe and gas analyzers were calibrated with dried standard gas mixture (16.01% O2 , 4.98% CO2 ) and dried atmospheric air (20.93% O2 , 0.03% CO2 ). The validity of the ventilated hood system was further tested by ethanol combustion tests conducted biweekly. The mean oxygen recovery was 98.8 ± 1.9 (95% confidence interval 98.7 and 99.0) and the mean carbon dioxide recovery was 98.3 ± 1.6 (95% confidence interval 98.2 and 98.4) (unpublished results). Correction factors were applied when necessary. All measurements were carried out in a quiet room with an ambient temperature of 23—25 ◦ C, barometric pressure of 750—770 mmHg and constant humidity of 60%. During the measurements, subjects were lying in a semi-supine position, quiet, motionless and were kept awake by showing them non-stressing movies, which were vetted for violent
or sexual content that may excite them. O2 and CO2 concentrations of the outflowing air and airflow rate through the hood were measured every 5-s to obtain oxygen consumption (VO2 ), carbon dioxide production (VCO2 ), REE and respiratory quotients. To eliminate effects of subject habituation to the testing procedure, the respiratory measurements during the first 5 min were discarded. Only steady state periods of measurement of at least 30 min were used to calculate REE. REE was calculated from VO2 and VCO2 by the Weir formula [24]. In addition to measured values, REE was predicted by the FAO/WHO/UNU, Harris—Benedict, Owen et al. and Mifflin et al. equations (Table 1) [19—22].
Anthropometric characteristics Body weight was measured in light clothing to the nearest 0.1 kg using an electronic scale (SECA, Vogel & Halke, Germany). Height was measured without shoes to the nearest 0.1 cm using a wall-mounted stadiometer. BMI was calculated as weight divided by height squared (kg/m2 ).
Statistical analysis Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 20 [25]. Pearson’s correlation coefficients were used to examine the association between measured and predicted REE for each of the predictive equations. Measured and predicted REE were compared at a group level using student’s paired t test. Individual prediction accuracy was defined as the percentage of subjects that had an REE predicted within ±10% of REE measured [26,27]. A prediction below 90% of REE measured was classified as under-prediction and a prediction above 110% of REE measured was classified as over-prediction [28,29]. Bland and Altman [30] plots were used to determine agreement both graphically and numerically between measured REE and predicted REE by calculating mean bias (difference between measured and predicted REE) and limits of agreement (mean bias ± 2 s.d). For good agreement, it is expected that bias will be close to zero, clinically acceptable limits of agreement
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Table 2
the subjects falling within ±10% of measured REE followed by the Owen equation with an individual prediction accuracy of 73% (Table 4). However, whereas the Mifflin equation more commonly overestimated REE than underestimating it, the Owen equation had similar numbers in which REE was over- or underestimated. REE could not be accurately predicted by the FAO/WHO/UNU and Harris—Benedict equations in more than 30% of the subjects
Characteristics of the subjects (n = 96).
Variables
Mean
S.D (range)
Age (y) Weight (kg) Height (cm) Body mass index (kg/m2 )
28.4 69.4 172 23.4
6 (21—40) 10.3 (52.5—96.9) 5.8 (157.6—187.5) 2.9 (18.8—29.8)
that are within ±10% of measured REE and that there is no clear evidence of a relationship between difference and mean of measured and predicted REE [27,30,31]. Values are given as mean ± s.d. The threshold for significance in all tests was set at P < 0.05 (two sided).
Discussion In our study, the measured REE was significantly lower than that predicted by the FAO/WHO/UNU, Harris—Benedict and Mifflin equations by 7.5%, 6.0% and 2.4% respectively, in a group of adult Singaporean Chinese men. However, the Owen equation provided valid mean estimates of REE with only slight underestimation (0.4%). Several researchers have also reported overestimation of REE in Asians using FAO/WHO/UNU and Harris—Benedict equations, and found percentage differences comparable to those of our study [32—35]. According to Nhung et al., predicted values using the FAO/WHO/UNU equations overestimated REE in Vietnamese adults by 7.4—13.5% [32]. Case et al. reported that REE predicted by Harris—Benedict and FAO/WHO/UNU equations were significantly higher than measured REE by 8.5% and 5.4% respectively in Asian subjects. In a study by Liu et al., the Harris—Benedict equation overpredicted measured REE by 8% in Chinese [35]. On the other hand, the Liu equation which was derived from Chinese adults, predicted the REE of Asians more accurately [12,33]. These are similar to the 7.5% (FAO/WHO/UNU equation) and 6.0% (Harris—Benedict equation) observed in our study. It has been suggested that such overestimation of REE by these prediction equations in Singaporean Chinese may be largely explained by ethnic
Results Characteristics of the study population are summarised in Table 2. A significant correlation was found between measured REE and REE derived from the FAO/WHO/UNU equation (R = 0.68), Harris—Benedict equation (R = 0.71), Mifflin equation (R = 0.70) and Owen equation (R = 0.67) (Table 3). However, the FAO/WHO/UNU, Harris—Benedict and Mifflin equations significantly overestimated mean measured REE by 120 ± 140 kcal/day, 96 ± 137 kcal/day and 38 ± 136 kcal/day respectively. The Owen equation did not show a significant mean bias in the prediction of REE. The individual differences between measured and predicted values of REE plotted against the average of measured and predicted values are shown in Fig. 1. The limits of agreement were wide for all equations. The individual differences between measured and predicted REE ranged from −161 kcal/day to 400 kcal/day. Focusing on the distribution of individual measured REE values revealed that Mifflin equation had the highest percentage REE predictive accuracy, with 75% of
Table 3 Differences and Pearson correlation coefficients between measured and predicted resting energy expenditure (kcal/day). REE
Measured FAO/WHO/UN [19] Harris and Benedict [22] Mifflin et al. [20] Owen et al. [21] *
P < 0.05.
Difference
95% CI
Mean
S.D
Mean
S.D
Lower
Upper
1594 1714 1689 1632 1587
191 137 158 126 105
120* 96* 38* −7
140 137 136 144
91 68 11 −36
148 123 66 22
Correlation coefficient
Limits of agreement
0.68* 0.71* 0.70* 0.67*
−161 −179 −235 −295
to to to to
400 371 311 281
Validation of prediction equations for resting energy expenditure in Singaporean Chinese men
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Figure 1 Bland Altman plots for comparison of agreement between resting energy expenditure measured by indirect calorimetry and predicted resting energy expenditure derived from prediction equations.
differences in REE related to body composition [36—38]. The Schofield database was dominated by Italian data which constituted 3388 out of a total of 7173 REE data points [39,40]. Hayter and Henry further reported that Italian subjects had a higher REE per kilogram of body weight compared to other population groups (North Europeans, Americans, Indians and Asians) in the Schofield database [41]. The Harris—Benedict equation was also developed using predominantly white subjects [39,42]. On the other hand, the Owen equation was
Table 4
developed from a group of Caucasian, Negro and Oriental male subjects, and hence, may be more accurate in predicting energy needs in Singaporean Chinese men [21]. In addition, the subjects used in the Schofield study were primarily young people with relatively high activity levels. Labourers and miners, Italian military, Neapolitan police, Italian navy and Burmese hill dwellers made up the largest groups of men in the Schofield database [7,43]. In contrast, the subjects used in our study were sedentary. Poehlman et al. reported a higher REE
Percentage of subjects with accurate predictions, under-predictions and over-predictions.
FAO/WHO/UNU [19] Harris and Benedict [22] Mifflin et al. [20] Owen et al. [21]
Accurate prediction (%)
Under-prediction (%)
Over-prediction (%)
60.4 66.7 75.0 72.9
1.0 1.0 5.2 14.6
38.5 32.3 19.8 12.5
e288 in physically active individuals compared to those with sedentary lifestyles, even after adjustment for body composition [44] At the individual level, Bland Altman plots of all equations showed lack of agreement with measured REE with wide limits of agreement. Errors of REE measurement may be introduced by air leaks, incorrect calibration of the calorimeter, involuntary alternating periods of hyper and hypoventilation, fluctuating levels of fractional inspired O2 concentration, or acid—base disturbances [45]. Although REE measurements were performed under strictly standardised conditions, biological intra-individual variation in REE might also be due to variations in eating and activity pattern in the days before the REE measurements. Limits of agreement within ±10% of measured REE are considered clinically acceptable as intraindividual variation in REE was reported to be 2—10% explained by methodological and biological variability in REE [31]. However, in our study, only 70% of subjects had limits of agreement < 10%. Furthermore, for all equations, Bland Altman plot showed that bias was not consistent across the range of REE values because bias correlated significantly with the mean of measured and predicted REE (data not shown). Predicted REE typically deviated more profoundly in the lower and upper REE ranges, with the equations systematically over-estimating at lower REE and underestimating at high REE. This was also observed in other studies [32,46—48]. Considering that the average REE was about 1600 kcal/day, an error of 10% would translate into a 160 kcal/day deviation from true REE. However, the Owen equation demonstrated individual differences between measured and predicted energy values that ranged from −295 to 281 kcal/day which is about −19 to 18% of REE. If these equations were closely followed to establish energy intake in a controlled setting over the long term, energy needs could be over-predicted or under-predicted by the physiologic weight change equivalent of 0.3 kg/week or 14.4 kg/year. This is especially detrimental in the clinical setting where overfeeding or underfeeding may have adverse effects such as electrolyte imbalance and gastrointestinal problems a [49]. And in weight management for overweight and obese people, prescribed dietary plans might be ineffective in producing weight loss. A limitation of our study is that the REE measurements were made on an outpatient basis and could not be performed earlier in the morning. However, subjects were allowed to rest comfortably for at least 15 min before measurement, during which they became acclimated to the environment,
T. Song et al. therefore ensuring resting conditions. Also, the standard deviation found in our study is comparable to other studies that had REE measurements conducted at 0700 h with the subjects staying overnight in the facility [37,50]. Therefore this limitation did not add to the variability of the REE measured and is unlikely to affect the results reported in this paper. These findings show that the WHO/FAO/UNU, Harris—Benedict and Mifflin predictive equations significantly overestimated energy needs for Singaporean Chinese men, therefore usage of these equations may put them at a greater risk for developing obesity, especially in overweight people. We recommend the Owen equation for estimation of energy needs in Singaporean Chinese adult males at a group level due to small mean prediction error. However, the individual errors of the equation are too high to be of practical use in individuals, in situations where accurate estimation of REE is clinically relevant (such as when prescribing nutrition in the setting of treatment for the critically ill or during the treatment of obesity). Although the Owen equation predicted REE accurately in 73% of the subjects, it displayed wide limits of agreement and directional bias was observed across the range of REE. These wide limits of agreement were especially prominent in those with more extreme levels of REE. Therefore in individuals where a precise determination of REE is indicated, measurement by indirect calorimetry instead of the prediction of REE using equations is highly recommended.
Conflict of interest policy The authors report no conflict of interest. The authors alone are responsible for the content and writing of the paper.
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