06 LUHRMANN_e_c:06 LUHRMANN_e_c

The Journal of Nutrition, Health & Aging© Volume 8, Number 3, 2004

RESTING METABOLIC RATE IN THE ELDERLY

ARE THE EQUATIONS PUBLISHED IN LITERATURE FOR PREDICTING RESTING METABOLIC RATE ACCURATE FOR USE IN THE ELDERLY? P.M. LUHRMANN, M. NEUHAUSER-BERTHOLD Institute of Nutritional Science, University of Giessen, Germany, Responsible for correspondence and requests for reprints: Monika Neuhäuser-Berthold, PhD, Professor of Human Nutrition, Institute of Nutritional Science, University of Giessen, Goethestr. 55, D-35390 Giessen, Germany, Phone: + 49-(0)641-99 39067, Fax: + 49-(0)641-99 39069, e-mail: [email protected]

Abstract: Purpose: Equations published in literature for predicting resting metabolic rate (RMR) in older individuals were derived from studies with small samples of this age group or extrapolated from data of younger adults. The aim of the present investigation was therefore to validate various predictive equations by comparing calculated RMR with measured RMR in a large group of elderly subjects. Subjects and methods: RMR was measured by indirect calorimetry after an overnight fast in 225 female (age 67.7 ± 5.7 y, BMI 26.7 ± 3.9 kg/m2) and 130 male (age 67.4 ± 5.4 y, BMI 26.7 ± 3.2 kg/m2) participants of the longitudinal study on nutrition and health status in an aging population of Giessen, Germany, who were at least 60 years old. Results: In females and males RMR was on average underestimated by 3.3% and 7.5% with the Schofield equation based on body weight, by 2.4% and 4.5% with the Schofield equation based on both weight and height, by 0.7% and 5.0% with the WHO equation based on body weight, and by 2.6% and 4.6% with the Harris-Benedict equation, respectively. RMR calculated with the WHO equation based on body weight and height was 1.8% higher in females and 3.9% lower in males compared to measured RMR. Regarding all predictive equations the difference between predicted and measured RMR were negatively correlated with measured RMR and were partly more pronounced in smokers and obese subjects than in non-smokers and subjects with a BMI < 30 kg/m2. Conclusion: At the group level all predictive equations used provide a valid estimation of RMR. However, on an individual basis estimation errors may be high. Thus in individuals RMR should be measured instead of being estimated. If measurements cannot be taken, population specific equations should be used for predicting RMR. Key words: Resting metabolic rate, elderly, predictive equations.

Introduction For estimating the energy requirement of adults the principle of estimating energy expenditure, rather than energy intake from dietary surveys, has been adopted (1-3). Because resting metabolic rate (RMR) constitutes between 60 and 75 % of total energy expenditure, it forms the basis of this factorial approach. Currently energy expenditure is estimated by multiplying RMR by an activity factor and RMR is either measured by indirect calorimetry or more frequently estimated from predictive equations in which height, weight, age, and sex may be considered. However, equations for predicting RMR in individuals who are older than 60 years of age were exclusively derived from studies with only small samples of this age group (1, 4) or from data extrapolated from younger adults (5-8). In most countries the recommendations for energy intake (13) are based on data for RMR calculated with the age and sex specific equations from the WHO (1) (Table 1). These equations were derived by Schofield et al. (4) on the basis of all 114 RMR studies that were available in literature at that time, representing data of about 7.000 subjects altogether. However, the equations for subjects who are older than 60 years of age rely solely on RMR data of only 38 elderly women and 50 elderly men. The analysis of RMR data was not completed by Schofield et al. (4) when the WHO equations were derived and 144

published. Therefore the WHO equations differ from those of Schofield et al. (4) and the sample of elderly subjects taken into account is probably even smaller than 38 women and 50 men. The most common equations used in the clinical setting are the Harris-Benedict equations (5). These equations are based on RMR data of 103 women and 136 men with a wide age range, but include only six women and three men who were over 60 years of age. Up to now these predictive equations have not been tested for accuracy in an appropriately large sample of women and men who are over 60 years old. Validation studies have only been performed in one moderate sample of elderly women (9), some very small samples of women and men over 60 (10, 11), or in younger subjects (12-14). Therefore the aim of our investigation was to validate the equations for the elderly of Schofield et al. (4), WHO (1), and Harris-Benedict (5) by comparing predicted with measured RMR in the largest group of subjects over 60 up to now studied.


THE JOURNAL OF NUTRITION, HEALTH & AGING© Table 1 Equations for estimating RMR [kJ/d] in the elderly (≥ 60 years) Authors

Equations

r

SEE

Study group

Females Schofield 1 (4) Schofield 2 (4) WHO 1 (1) WHO 2 (1) Harris-Benedict (5)

2755 + 38.0 BW 74 + 33.0 BW + 1917 BH 2491 + 43.9 BW - 1264 + 38.5 BW + 2665 BH 2742 + 40.0 BW + 774 BH - 19.6 A

0.68 0.73 0.74 0.82

451 429 452 393

38 women from 23 different countries: A = 66.4 ± 5.3 y; BMI = 23.7 ± 4.7 kg/m2; RMR = 4850 ± 605 kJ/d Subgroup of Schofield et al. (4), n = ?

Males Schofield 1 (4) Schofield 2 (4) WHO 1 (1) WHO 2 (1) Harris-Benedict (5)

2459 + 49.0 BW - 3491 + 38.0 BW + 4068 BH 2039 + 56.5 BW - 4481 + 36.8 BW + 4720 BH 278 + 57.6 BW + 2094 BH - 28.3 A

0.71 0.74 0.79 0.84

103 American women aged 15-74 y including 6 women over 60 y (A = 66.0 y; BMI = 21.7 kg/m2; RMR = 5138 kJ/d) 687 660 620 552

50 men from 23 different countries: A = 72.4 ± 10.5 y; BMI = 22.8 ± 3.6 kg/m2; RMR = 5590 ± 928 kJ/d Subgroup of Schofield et al. (4), n = ? 136 American men aged 16-63 y including 3 men over 60 y (A = 62.0 y; BMI = 24.6 kg/m2; RMR = 6450 kJ/d)

BW = body weight (kg), BH = body height (m), A = Age (y)

Subjects and methods Study design The present investigation is part of the longitudinal study on an aging population of Giessen, Germany (GISELA), in which the nutritional and health status of free-living elderly people has been investigated at annual or biannual intervals since 1994. Within the scope of the GISELA study, many points are considered including anthropometrical data, body composition, and RMR of the study participants. Measurements take place at the Institute of Nutritional Science in Giessen, Germany, from June to November between 6:00 and 11:00 a.m. after an overnight fast. Subjects were familiarized with the experimental procedure and a written informed consent was obtained from each study participant. The study protocol was approved by the Ethical Committee of the Faculty of Medicine at the Justus-Liebig University Giessen, Germany. Subjects Study participants had to be at least 60 years of age, physically mobile, and available around Giessen on a long term basis. Subjects were recruited by physicians, notices, senior citizens` meetings, advertisements in local newspapers, and by recruitment through subjects who had already been participants. From 1994 to 2000 a total of 375 female and 158 male Germans took part in the GISELA study. The present report includes the cross-sectional data from the baseline examinations in those participants with complete data on RMR, anthropometrical measurements, diseases, and medications. Data of subjects who suffered from hypothyroidism, hyperthyroidism, or took thyroid hormones were excluded because these diseases or medications may influence RMR. The results from 225 elderly females and 130 elderly males remained for further analysis. 145

Resting metabolic rate RMR was determined by an open-circuit indirect calorimeter (DeltatracTM MBM-100, Hoyer, Bremen, Germany). Oxygen uptake and carbon dioxide production were measured for 25-35 min at intervals of one minute by respiratory gas analysis using a ventilated-hood system, with the subjects in a supine position and completely at rest in a thermoneutral environment. Calibrations of the gas analyser were performed immediately before each measurement. Participants were allowed to acclimatize themselves appropriately before measurements were started. Data collected during the initial 10 min of the measurements were discarded. After this adaptation period coefficient of variation for oxygen uptake and carbon dioxide production was 0.7 % and 1.9 %, respectively. RMR was determined by using the equation derived by Weir (15). The Deltatrac metabolic monitor was shown to be accurate within 3 % for RMR (16). The mean coefficient of variation for measured RMR in our laboratory was 1.05 %. Anthropometrical data Body weight was determined with a calibrated digital scale (Seca, Vogel & Halke, Frankfurt, Germany) to the nearest 0.1 kg after shoes, coats, and sweaters had been removed. 0.5 to 1.0 kg was subtracted from the measured weight depending on the estimated weight of the remaining clothes. Body height was obtained by a height measurement device integrated in the scale to the nearest 0.5 cm with the subjects in a standing position without shoes. Subjects` characteristics Further data, such as age, diseases, medication and smoking behaviour were obtained from the study participants by questionnaire.


RESTING METABOLIC RATE IN THE ELDERLY Statistical analysis Statistical analyses were carried out with the SPSS/PC Statistical Package version 9.0 (SPSS Inc, Chicago, USA). Data were checked concerning normal distribution by KolmogorowSmirnow test. Deviations between measured RMR and RMR predicted with equations published in the literature were examined by t-test for paired samples. To test if deviations between measured and predicted RMR differ between separate BMI groups or between smokers and non-smokers ANOVA and unpaired t-test, respectively were used. To determine the associations between measured and predicted RMR, Pearson`s correlations (r) as well as Bland-Altman plots (17) were used. Data are presented as mean, standard deviation (SD) and 95 % confidence interval (95 % - CI). Results were considered statistically significant if P values were less than 0.05.

Figure 1 Bland-Altman plots for comparison RMR predicted with different equations from literature with measured RMR

Results The study group comprised 225 women and 130 men and is characterized in table 2. The subjects` age ranged from 60 to 85 years but most of the females (70.2 %) and males (74.6 %) were between 60 and 70 years old. Table 2 Characteristics of the subjects Females (n = 225) Age (y) Body height (cm) Body weight (kg) BMI (kg/m2) BMI (%) < 25.0 kg/m2 25.0 - 30.0 kg/m2 > 30.0 kg/m2 Smokers (%)

Males (n = 130)

Mean ± SD

Range

Mean ± SD

Range

67.7 ± 5.7 160.2 ± 5.5

60 -85 145.0 -174.5

67.4 ± 5.4 172.9 ± 6.3

60 - 85 159.5 - 190.5

68.5 ± 10.6

42.5 - 109.0

79.7 ± 10.3

60.0 - 112.0

26.7 ± 3.9

18.5 - 40.1

26.7 ± 3.2

18.3 - 36.5

36.9 44.9 18.2 6.8

33.8 53.1 13.1 14.0

Predicted and measured RMR as well as the differences between predicted and measured RMR are shown in table 3. Mean measured RMR was 5580 kJ/d in females and 6950 kJ/d in males. Measured RMR correlated significantly positively with RMR predicted with all five equations in women and men. In both sexes, RMR was significantly underestimated with all predictive equations, except with the WHO 2 equation in women. However, underestimations were larger in men (3.9-7.5 %) than in women (0.7-3.3 %). The largest underestimations were found for the Schofield equations based on weight only with 3.3 % and 7.5 % in females and males, respectively. Figure 1 shows that for all five predictive equations the difference between predicted and measured RMR depends on the absolute values for measured RMR in both women and men.

Figure 2 compares predicted to measured RMR in different BMI groups separately. With the WHO 2 and Schofield 2 equations the underestimation is significantly larger in obese women (P < 0.01 and P < 0.05, respectively) and men (P < 0.01 and P < 0.001, respectively) than in women and men with a BMI < 30 kg/m2. For the other three equations we did not observe any significant differences between the different BMI groups regarding the deviations between predicted and measured RMR in women and men. In males the underestimation of RMR is significantly larger in smokers than in non-smokers with all five predictive equations (P < 0.05) (Figure 3). Although figure 3 shows the same tendency in women, deviations between predicted and measured RMR did not significantly differ between female smokers and non-smokers.

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THE JOURNAL OF NUTRITION, HEALTH & AGING© Table 3 RMR predicted with different equations from literature compared to measured RMR RMR (kJ/d) Mean ± SD Females Measured RMR Schofield 1 (4) Schofield 2 (4) WHO 1 (1) WHO 2 (1) Harris-Benedict (5) Males Measured RMR Schofield 1 (4) Schofield 2 (4) WHO 1 (1) WHO 2 (1) Harris-Benedict (5)

Difference (kJ/d) Mean ± SD 95 % - CI

Difference (%) Mean ± SD 95 % - CI

Pearson correlation

5580 ± 690 5357 ± 402 5404 ± 396 5504 ± 465 5642 ± 475 5399 ± 472

-224 ± 464 * -176 ± 464 * -76 ± 452 * 61 ± 451 * -181 ± 450 *

-285 – -163 -237 – -115 -136 – -17 2 – 120 -240 – -122

-3.3 ± 7.6 -2.4 ± 7.6 -0.7 ± 7.7 1.8 ± 7.7 -2.6 ± 7.4

-4.3 – -2.3 -3.4 – -1.4 -1.7 – 0.3 0.8 – 2.8 -3.6 – -1.6

0.76 ** 0.77 ** 0.76 ** 0.76 ** 0.72 **

6950 ± 934 6366 ± 506 6573 ± 545 6545 ± 584 6614 ± 566 6585 ± 684

-583 ± -376 ± -405 ± -335 ± -365 ±

-703 – -464 -507 – -245 -522 – -287 -469 – -202 -482 – -247

-7.5 ± 8.1 -4.5 ± 9.4 -5.0 ± 8.4 -3.9 ± 9.7 -4.6 ± 8.6

-9.0 – -6.1 -6.1 – -2.9 -6.5 – -3.6 -5.6 – -2.2 -6.3 – -3.1

0.69 ** 0.59 ** 0.69 ** 0.57 ** 0.69 **

689 * 754 * 678 * 769 * 675 *

* Significant difference between predicted and measured RMR: P < 0.001; ** Pearson correlation between predicted and measured RMR: P < 0.001

Discussion

Figure 2 RMR predicted with different equations from literature compared to measured RMR for separate BMI groups

In the scope of the present investigation we determined RMR in 355 free-living German subjects who were over 60. To the best of our knowledge this is the largest group of older subjects investigated concerning RMR up to now. From this measured RMR data, we were aimed to validate various equations published in literature for predicting RMR in the elderly. Although the GISELA sample is not representative for the elderly German population, weight, height, and BMI of the subjects is comparable to those of the average elderly German population (18). All the published RMR predicting equations used in this study underestimate RMR significantly in females and males, except for the WHO 2 equation in women. However, deviations between predicted and our measured RMR are on the average very small, especially in women. The relatively good agreement is remarkable in so far as the predictive equations used rely on RMR data of small samples of elderly subjects and measurements that were made a long time ago. Whereas Harris and Benedict (5) performed their measurements at the end of the 19th century, the 114 studies evaluated by Schofield et al. (4) covered a very large period of time (1914-1983). Furthermore neither Schofield et al. (4) nor Harris and Benedict (5) considered diseases or medications that may have influenced their analysis of RMR. Our results are in accordance with the findings of two earlier studies by Arciero et al. (12, 13) who cross-validated the WHO 2 and the Harris-Benedict equations in a group of 75 female and 89 male Americans aged between 50 and 81. In males RMR was underestimated by 3.5% and 4.0% with the WHO 2 and the Harris-Benedict equation, respectively. In females RMR predicted with the WHO 2 equation was 3.0% higher and RMR predicted with the Harris-Benedict equation 4.0% lower than measured RMR. Taaffe et al. (9) showed that in 116 white American women, aged 60 to 82 years, RMR was

Figure 3 RMR predicted with different equations from literature compared to measured RMR for smokers and non-smokers

147


RESTING METABOLIC RATE IN THE ELDERLY overestimated by 8.7% with the Schofield 2 equation and by 3.1% with the Harris-Benedict equation. Findings from other studies are also inconsistent, but it must not be overlooked that validation samples were very small. Fredrix et al. (14) validated the Schofield 1 and the Harris-Benedict equations in 22 female and 18 male subjects from the Netherlands aged between 51 and 82 years. The Schofield 1 equations and the HarrisBenedict equations underpredicted measured RMR by 5.6% and 6.1%, respectively. Unfortunately the results were not differenciated by gender. Fuller et al. (11) showed that in 23 Caucasian males over 75 years of age, RMR was underestimated by about 6 % with the Harris-Benedict equation, overestimated by about 3 % with the Schofield 2 equation and in agreement with RMR predicted with the Schofield 1 equation. In contrast, Fukagawa et al. (10) did not observe any significant differences between measured RMR and RMR predicted with the WHO 1 equations in 20 elderly women and 24 elderly men (67-89 years). In our study of both women and men for all five predictive equations, the differences between predicted and measured RMR depended on the absolute values for measured RMR (Figure 1), indicating a systematic estimation error. This is probably due to the fact that predictive equations are always specific for the population from which they were derived. Subjects investigated by Schofield et al. (4) and by Harris and Benedict (5) differ from our study group. The mean weight, height, and BMI and as a consequence the mean RMR of the Schofield group as well as the Harris-Benedict group were lower than they were in our study group. So it is possible that the estimation error, in this case the underestimation is increased in subjects with comparatively higher RMR. In addition, in the samples of Schofield, WHO, and HarrisBenedict no obese subjects were included. This could be the reason why underestimation is more pronounced in obese subjects than in subjects with a BMI lower than 30 kg/m2, especially when predicted with the WHO and Schofield equations based on both weight and height. Furthermore, the general underestimation of RMR is probably connected with ethnic differences in RMR. The Schofield and WHO predictive equations are based on RMR data of subjects from 23 different countries including Asiatic countries especially India. It has been reported in literature that Asiatic subjects show lower values of RMR for the same body weight than subjects from North Europe and North America (4, 19, 20). However, we have no explanation why underestimation is more pronounced in men than in women of our study group as well as in the studies by Arciero et al. (12, 13). In males and tendentiously in females, RMR was more underestimated in smokers than in non-smokers with all predictive equations used, indicating that RMR is higher in smokers than in non-smokers. However, we do not know if the higher RMR of smokers is a chronic metabolic effect of smoking or an acute metabolic effect due to smoking immediately before the RMR measurement, because in our

study we could not check for smoking. Results from other studies investigating the chronic effect of smoking on RMR are inconsistent (21-24). It is unclear whether smoking is jointly responsible for the deviations between predicted and measured RMR, because neither Schofield et al. (4) or the WHO (1) nor Harris and Benedict (5) give any information about the smoking status of the subjects investigated. When comparing measured and predicted RMR we have to consider the measurement conditions in each study. In our study RMR measurements were made on an outpatient basis. RMR determined under outpatient conditions is reported to be higher than RMR measured under inpatient conditions by about 7-8 % (25). However, in the studies of Schofield et al. (4) and Harris and Benedict (5) RMR measurements were not made under basal conditions either, and experimental conditions were nearly the same as in our study (post-absorptive in the morning). Whereas the 114 studies evaluated by Schofield et al. (4) were conducted with different measurement techniques and types of devices mainly with spirometer, Harris and Benedict (5) took all the RMR measurements by spirometer. The ventilated hood technique as it was used in our study was not available at that time. However, various studies showed that, following an acclimation period, no significant differences in RMR determined with different measurement methods occur (26-28). Therefore we assume that the observed differences between measured and predicted RMR are not caused by variations in the methods. Conclusion In summary, on the group level the WHO, the Schofield, and the Harris-Benedict predictive equations results in rather accurate mean predicted values and therefore provide valid estimates of RMR. However, on an individual basis all predictive equations used demonstrate considerable variability between predicted and measured RMR. Thus for individuals RMR should be measured instead of estimated. Where measurements cannot be taken, population specific equations should be used for predicting RMR. For calculation RMR in elderly German subjects we recommend using an equation that was developed on the basis of the data determined in our large group of elderly subjects and that is both easy and accurate for use in practice (29). References 1.

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