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World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure^1,2,3

¹ From the Christian-Albrechts-Universität zu Kiel, Institut für Humanernährung und Lebensmittelkunde, Kiel, Germany (MJM and AB-W); the Deutsches Institut für Ernährungsforschung, Abteilung Biochemie und Physiologie der Ernährung, Potsdam-Rehbrücke, Germany (SK and RN); the Universitätskrankenhaus Eppendorf, Medizinische Klinik, Hamburg, Germany (GK); the Justus-Liebig-Universität, Institut für Ernährungswissenschaft, Giessen, Germany (PML and MN-B); the Universität Trier, Forschungszentrum für Psychobiologie und Psychosomatik, Trier, Germany (KMP); the Universität Würzburg, Biologische und Klinische Psychologie, Würzburg, Germany (PP); the Medizinische Hochschule Hannover, Abteilung Gastroenterologie und Hepatologie, Hannover, Germany (OS); the Klinikum Berlin-Buch, Herbert-Krauß-Klinik, Berlin (JS)

² Supported by Deutsche Forschungsgemeinschaft (DFG Mü 8-1).

³ Address reprint requests to MJ Müller, Institut für Humanernährung und Lebensmittelkunde, Agrar- und Ernährungswissenschaftliche Fakultät, Christian-Albrechts-Universität zu Kiel, Düsternbrooker Weg 17-19, D-24105 Kiel, Germany. E-mail: mmueller{at}nutrfoodsc.uni-kiel.de.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Reference standards for resting energy expenditure (REE) are widely used. Current standards are based on measurements made in the first part of the past century in various races and locations.

Objective: The aim of the present study was to investigate the application of the World Health Organization (WHO) equations from 1985 in healthy subjects living in a modern, affluent society in Germany and to generate a new formula for predicting REE.

Design: The study was a cross-sectional and retrospective analysis of data on REE and body composition obtained from 2528 subjects aged 5–91 y in 7 different centers between 1985 and 2002.

Results: Mean REE varied between 5.63 and 8.07 MJ/d in males and between 5.35 and 6.46 MJ/d in females. WHO prediction equations systematically overestimated REE at low REE values but underestimated REE at high REE values. There were significant and independent effects of sex, age, body mass or fat-free mass, and fat mass on REE. Multivariate regression analysis explained up to 75% of the variance in REE. Two prediction formulas including weight, sex, and age or fat-free mass, fat mass, sex, and age, respectively, were generated in a subpopulation and cross-validated in another subpopulation. Significant deviations were still observed for underweight and normal-weight subjects. REE prediction formulas for specific body mass index groups reduced the deviations. The normative data for REE from the Institute of Medicine underestimated our data by 0.3 MJ/d.

Conclusions: REE prediction by WHO formulas systematically over- and underestimates REE. REE prediction from a weight group–specific formula is recommended in underweight subjects.

Key Words: Body composition • resting energy expenditure • fat-free mass • Harris-Benedict prediction • World Health Organization prediction

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Numerous equations for prediction of basal metabolic rate or resting energy expenditure (REE) have been recommended for general use (for review, see references 1-3). Of these, the Harris-Benedict prediction (4) and the Schofield formulas proposed by the FAO/WHO/UNU [World Health Organization (WHO) equation] (5) are and have been widely used. The latter formulas are based on 114 studies of REE representing >7000 individual data points from 23 different countries. However, whether these formulas adequately address REE in subjects living in modern, affluent societies is unclear. In fact, the 2 prediction formulas have been reported to overpredict but also to underestimate measured REE in more recent studies (6-15). In addition, the above-mentioned prediction formulas are considered unsuitable for predicting REE in obese (15) and underweight (16) subjects. These shortcomings are due in part to the heterogeneity of the reference study populations, methodologic drawbacks, and the variability of REE. A more recent and heterogeneous database of 574 energy expenditure measurements showed an SD of 20% of the mean values obtained in different age and sex groups (17). The interindividual CV of REE is 8–13% (18), which leads to a considerable number of over- and underestimations of REE with the use of a prediction formula.

Thus, it is desirable to critically reassess REE data and to generate regional and more homogeneous REE databases. These data should be based on stringent inclusion criteria and be capable of being used mathematically to derive suitable predictors and to generate new prediction formulas for REE (19). A reference database has to have a sample size with an acceptable statistical power and be reasonably representative of the variables tested. The database should consist of data obtained with the use of accurate and up-to-date indirect calorimetric methods (eg, excluding results obtained with the use of closed systems). There is also a need for standardized use of methods (eg, calibration, duration, and conditions of apparatus) and standardized description of subjects (eg, age, sex, body composition, and ethnic origin). There are biologically sound reasons that prediction of REE should use its major determinant, fat-free mass (FFM), instead of body weight (19-21). Although this idea has been questioned (1), it should be readdressed.

In the present study, we report an actual German database of REE. The database includes 2528 subjects with a wide age range. The goals of the present study were 1) to investigate the application of the most frequently used WHO equations in subjects living in a modern, affluent society, 2) to establish the average REE and the range of REE in different age, sex, and body mass index (BMI; in kg/m²) groups, and 3) to mathematically derive a new and validated prediction equation.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study populations
Data from 7 research centers in Germany in which subjects were enrolled in different investigations were collected over a period of 18 y (Table 1). The purpose of all investigations was identical (ie, metabolic exploration). The total number of subjects (children, adolescents, and adults) who served as the basis of this study was 2528 (1027 males and 1501 females). In 180 adults (78 females and 102 males), REE was measured with the use of a closed system. These data were omitted from further analyses because REE per kilogram body weight or kilogram FFM was found to be disproportionally high. There were no other selection criteria. All subjects were white, nonpregnant, and nonlactating. All subjects were healthy (defined as the absence of a clinical condition) except for 97 adults who were underweight (BMI < 18.5), with a mean (±SD) BMI of 16.2 ± 1.6 (range: 12.4–18.4). Forty-nine subjects were diagnosed with anorexia nervosa according to DSM IV criteria. None of the subjects took any medications known to influence REE. Smoking was not considered as an exclusion criterion. Informed consent to participate in the study was obtained from each subject at the beginning of the study, which was approved by the responsible local ethical committees.

Anthropometric data and body composition
Body weight was measured to the nearest 0.1 kg and standing height to the nearest 0.5 cm while the subject wore underwear and no shoes. BMI was calculated with weight (kg) and height (m) measurements. Underweight, normal weight, overweight, and obesity were determined with the use of corresponding actual German BMI percentiles (<10th, >90th, and >97th percentiles, respectively) for children and adolescents (32) and with the use of WHO criteria for adults (33).

In a subset of 2066 participants, body composition was assessed by either bioelectical impedance analysis (BIA; n = 1813 subjects) or skinfold-thickness measurements (n = 250 subjects). Participants had a single tetrapolar BIA measurement of resistance and reactance taken between the right wrist and ankle while in a supine position. BIA devices and references for prediction equations for conversion of BIA values into FFM are given in Table 1 (24-31). No author used a population-specific algorithm. Most authors used manufacturer's equations, which differed from each other, changed over time, and are partly unknown. By contrast, 2 groups of authors (28, 29) applied Segal's algorithm (22), and another group (30) applied Deurenberg et al's algorithm (23). A comparison of FFM calculated with the use of the manufacturer's algorithm (FFM_m) with FFM calculated with the use of either Segal's algorithm (FFM_s) or Deurenberg's algorithm (FFM_d) in a subgroup of 88 subjects from Kiel showed a very close association between FFM_m and FFM_s and between FFM_m and FFM_d (R² values of 0.967 and 0.966, respectively). However the 2 alternative regression equations (FFM_s = 1.0945 x FFM_m –4.671; FFM_d = 0.9415 x FFM_m –4.0787) suggested a >4-kg systematic bias between the 2 estimates of FFM. Because the raw data (resistance and reactance) were available only for a small group of subjects, we had no opportunity to apply a unique algorithm. Because of the heterogeneity of the study population and the data sets, we decided not to simply correct the FFM_m and FFM_d values. Fat mass (FM) was derived from the equation FM = body weight - FFM, and percentage FM (%FM) was derived from the equation %FM = FM/body weight. Triceps, subscapular, and suprailiac skinfolds were measured on the right side of the body to the nearest 0.5 mm with the use of a Lange Skinfold Caliper (Beta Technology Inc, Cambridge, MD; respective equations are given in reference 34). Each skinfold value represented the mean of 3 consecutive measurements taken by the same investigator.

Assessment of resting energy expenditure
REE was obtained by using indirect calorimetry with different ventilated hood systems, mouthpiece measurements, or a metabolic chamber (see Table 1 for the description of the individual measurement procedure, technical details about instrumentation, and its calibration; references 24-31). Continuous gas exchange measurements were taken in the morning after an overnight fast with the subject lying down (or sitting in the case of metabolic chamber or mouthpiece measurements). Energy expenditure was calculated by using the Weir equation (35) or in the case of chamber measurements by using REE (kJ) = 16.18O₂ + 5.02CO₂ –5.99N_excretion (29, 36). REE measured by using indirect calorimetry was compared with REE calculated from the WHO equations (5). Hypermetabolism was defined as a measured REE exceeding the predicted values by >20%. Hypometabolic subjects were below the –10% prediction level (25).

Statistics
All data were recorded in a database with the use of a personal computer. Descriptive statistics including means, SDs, and ranges were calculated for all variables for defined age and sex groups. Normalization of body-composition data for different bioelectrical impedance analyzers and equations used could not be performed (see Anthropometric data and body composition). Statistical analyses were performed by using SPSS for WINDOWS 8.0 (SPSS Inc, Chicago). Differences between sexgroups and subpopulations 1 and 2 were analyzed by using the Mann-Whitney U test. Wilcoxon's signed-ranks test was calculated for related samples (comparison of measured and predicted REE in the same group). Data from BMI groups were compared by using two-way analysis of variance and Bonferroni post hoc test. Pearson's correlation coefficients were calculated for relations between variables. Stepwise multivariate regression analysis was performed to obtain the prediction equations for REE. Potential predictors of REE considered were height, weight, BMI, age, sex, FFM, and FM. Two models were used to generate prediction formulas. Model 1 used weight, sex, and age as predictors. In model 2, FFM, FM, sex, and age were used to predict REE. Data were analyzed separately for a combined group of children and adolescents and for adults split into subpopulation 1 for development of the prediction equation and subpopulation 2 for model validation. In addition, BMI group–specific formulas were generated in subpopulation 1 and validated in subpopulation 2. All tests were two-tailed, and a P value of 0.05 was accepted as the limit of significance. REE was adjusted for FFM according to Ravussin and Bogardus (37) by using the following equation:

(1)

The slope is derived from the regression equation between REE and FFM. Adjustments for FM were performed accordingly.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Anthropometric data and body composition
The whole study population of 2528 subjects is characterized in Table 2. According to the WHO criteria (33), a high prevalence of overweight and obesity was found in the whole study population. Subpopulations 1 and 2 were matched in age, BMI, and REE (Table 3). This was also true for the different BMI groups in subpopulations 1 and 2 (Table 4).

Table 5

Figure 1

Figure 2

View larger version (38K): [in this window] [in a new window]   FIGURE 1. Resting energy expenditure (REE) plotted against body weight or fat-free mass (FFM) in children and adolescents and in adults (total n = 2348). For the adults, data from 180 subjects whose REE was measured with a closed system are depicted in the insets. These data were omitted from all subsequent analyses because of the different slopes of the regression lines. Interaction terms between sex and weight and between sex and FFM were significant for adults but not for children and adolescents. FFMBIA, FFM determined with the use of bioelectrical impedance analysis; FFMBIA+Anthro, FFM determined with the use of bioelectrical impedance analysis or skinfold-thickness measurements.

View larger version (35K): [in this window] [in a new window]   FIGURE 2. Mean (±SD) resting energy expenditure (REE), REE adjusted for fat-free mass (FFM), and REE adjusted for FFM and fat mass (FM) in underweight (n = 98 F, 9 M), normal-weight (n = 551 F, 375 M), overweight (n = 313 F, 220 M), and obese (n = 345 F, 194 M) women () and men (). Bars with different letters are significantly different, P < 0.05 (ANOVA and Bonferroni post hoc test). Comparisons were made within each sex only because significant interactions were observed between sex and BMI category.

Table 6

Table 7

(2)

or

(3)

Figure 3

Figure 3

Tables 8

9

Figure 4

View larger version (30K): [in this window] [in a new window]   FIGURE 3. Upper panels: measured resting energy expenditure (REEm) versus REE predicted according to World Health Organization (WHO) formulas (REEWHO) in female (n = 1307) and male (n = 798) subjects. Lower panels: respective Bland-Altman plots of REEm minus REEWHO versus the average of REEm and REEWHO in females and males.

View larger version (29K): [in this window] [in a new window]   FIGURE 4. Mean (±SD) differences between measured resting energy expenditure (REEm) and REE predicted according to World Health Organization (WHO) formulas (REEWHO) for underweight (n = 98 F, 9 M), normal-weight (n = 551 F, 375 M), overweight (n =313 F, 220 M), and obese (n = 345 F, 194 M) women () and men (). No significant interaction between sex and BMI category was observed. For comparison between BMI groups, data for men and women were combined. *,**Significant difference between REEm and REEWHO (Wilcoxon's signed-ranks test): *P < 0.05, **P < 0.001.

Table 9

Table 9

Table 7

Table 9

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
WHO formulas are widely used to predict REE. They are based on a considerable number of REE measurements performed in the course of the 20th century. Although more recent formulas have been provided by other authors, none of these algorithms was based on a comparably huge database (for review, see reference 1). The major finding of our study is that the widely used REE prediction equations are not adequate for a population living in a modern, affluent society in Germany. We found significant and systematic over- and underestimations between measured and predicted REE (Figures 3 and 4, Table 9). It is evident that the WHO prediction equations overestimated REE at low REE, whereas underestimation was observed at high REE (Figure 3). The generation of new REE prediction formulas showed no clear advantage of body composition–derived formulas compared with the use of body weight as a predictor (Tables 7 and 9). Our data also provide evidence that prediction could be improved with the use of BMI group–specific prediction formulas (Table 9).

WHO prediction, which uses body weight as one determinant, systematically overestimates REE at low metabolic rate (Figure 3) and thus low body mass. The data in the present study suggest that in comparison with the metabolic rate per kilogram body weight or FFM in overweight and obese subjects, that in underweight subjects is lower than expected, and thus REE cannot be predicted from body mass alone. This idea is somehow contradictory to the observation of the nonlinearity of the relation between REE and body mass or between REE and FFM (20). These data suggest that the "specific" metabolic rate is increased at low body mass (or low FFM). By contrast, a lower ratio of REE to body mass (or FFM) is observed in overweight and obese subjects (20, 37, 38).

The discrepancies between measured and predicted REE values may be explained in part by methodologic problems and biological factors. There is no doubt that huge databases suffer from several methodologic shortcomings. This point was discussed in detail by Elia (1). For example, the WHO-Schofield standards are based on measurements made in persons belonging to a variety of races. In addition, one third of the reference population had a BMI < 20. To compare the methodologic approaches used by the various groups of authors discussed in the present study, we tried to obtain as much information as possible from each group of authors. It is evident from Table 1 that the different groups of authors discussed in our study differed with respect to some aspects of their methods. This is true for measurements of REE as well as for assessment of body composition. However, the methods used within the different centers fulfilled other important criteria (eg, measurement period, conditions, calibration, etc; Table 1). It should be mentioned that none of the authors contributing to our database had started his or her measurements with the idea of creating a reference database. Our approach is a post hoc compilation and analysis of data. Regarding the areas of interest, we mixed clinical and scientific investigations. Because most of the authors were working in the area of obesity research, there was a high prevalence of overweight and obese subjects.

Regarding biological determinants of REE, FFM was found to be it's major determinant (see Results). FFM alone explained 61.7% of the variance in REE in adults. This number increased to 71% with the further inclusion of FM, age, and sex (Table 7). The differences between the explained variances observed in our study and in other studies may be explained in part by the methologic limitations of body composition analysis used in field studies (see Subjects and Methods). Most of our subjects were investigated with the use of standard BIA (Table 1). In addition, anthropometric measurements were used in a small subgroup of subjects.

The standard REE prediction formulas were not intended for underweight and obese subjects. However, both conditions are common in developing countries, and the incidence of obesity is also still growing in developed countries. Thus, in practice, WHO formulas are often applied to underweight subjects as well as to overweight and obese subjects. The WHO reference population also included a substantial number of subjects with a BMI < 17. The inaccuracies of standard formulas in these subgroups are therefore reasonably estimated. Because patients with anorexia nervosa are considered to be physically "healthy," wedecided to include these patients as a severely underweight subgroup. It became evident that BMI group–specific REE prediction is necessary in severely underweight subjects (see Results).

With respect to practical utility, we compared the measured REE of normal and overweight adults with dietary recommended intakes from the Institute of Medicine (IOM; reference 39) and the German (D), Austrian (A), and Swiss (CH) societies for nutrition (DACH; reference 40) (Figure 5). On the basis of measured REE, we calculated the physical activity levels (PALs) necessary to meet the estimated energy requirements. According to the IOM estimates, PALs between 1.59 and 1.72 were calculated; these PALs are within the physical activity recommendations (ie, PAL between 1.6 and 1.7). These numbers are 5.8% higher than the respective PALs derived from the DACH estimates (ie, 1.49–1.73). The mean difference accounts for 0.6 MJ/d. The PALs necessary to meet the IOM recommendations exceed the measured PALs of most subjects in Western Europe (ie, 1.48–1.70 in old and young females and 1.54–1.85 in old and young males; reference 17). The recent population goals for a healthy lifestyle in Europe (ie, a PAL of >1.75; reference 41) also exceed the calculated PAL values. However, previous recommendations of energy requirements were based on the most recent equations predicting REE (Schofield equations; references 3, 5). Because our data provide evidence that the Schofield equations overestimate REE at low REE but underestimate REE at high REE, the IOM recommendations may be too high at low REE (and thus at low body weight and high age) but too low at high REE (and thus in overweight and young subjects). The IOM physical activity recommendations were based on measurements of total energy expenditure (doubly labeled water) and predicted REE (in the case of children) or measured REE (values for adults). In a comparison of the normative REE data from the IOM (42) with our data, a mean deviation of –82 kcal/d (range: 0–187 kcal/d for the different age and sex groups) was observed. Higher differences were seen for children and adolescents (–99 and –158 kcal/d for boys and girls, respectively). These differences were slightly above or within the estimated SDs of individual estimated energy requirements (42). However, the US IOM REE data are lower than the present REE data for a German population.

View larger version (31K): [in this window] [in a new window]   FIGURE 5. Mean (±SD) measured resting energy expenditure (REE) and reference intake values for energy estimated by the Institute of Medicine (IOM) (40) or the German (D), Austrian (A), and Swiss (CH) societies for nutrition (DACH) (41) in 5 age groups of normal-weight and overweight women and men. Physical activity level (PALs) necessary to maintain body weight when following the recommendations from the IOM (PALIOM) or the DACH (PALDACH) were calculated as reference energy intake/REE.

	ACKNOWLEDGMENTS

 
MJM was responsible for the study design. Data were collected and discussed by all the authors. MJM and AB-W performed data analyses and wrote the manuscript. None of the authors had any conflicts of interest.

	REFERENCES

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

1. Elia M. Energy expenditure in the whole body. In: Kinney JM, Tucker HN, eds. Energy metabolism. Tissue determinants and cellular corollaries. New York: Raven Press, 1992:19-59.
2. Quenouille MH, Boyne AW, Fisher WB, Leitch I. Statistical studies of recorded energy expenditure of man. Part 1. Basal metabolism related to sex, stature, age, climate and race. Commonwealth Bureau of Animal Nutrition Communication No. 17. Bucksburn, United Kingdom: Commonwealth Agricultural Bureau, Rowett Institute, 1951.
3. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985;39(suppl):5-41.
4. Harris JA, Benedict FG. A biometric study of basal metabolism in man. Washington, DC: Carnegie Institute of Washington, 1919. (Carnegie Institute of Washington Publication 279.)
5. FAO/WHO/UNU. Energy and protein requirements. Report of a joint FAO/WHO/UNU Expert Consultation. World Health Organ Tech Rep Ser 1985;724:1-206.[Medline]
6. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990;51:241-7.[Abstract/Free Full Text]
7. Daly JM, Heymsfield SB, Head CA, et al. Human energy requirements: overestimation by widely used prediction equation. Am J Clin Nutr 1985;42:1170-4.[Abstract/Free Full Text]
8. Cunningham JJ. A reanalysis of the factors influencing basal metabolic rate in normal adults. Am J Clin Nutr 1980;33:2372-4.[Abstract/Free Full Text]
9. Owen OE, Kavle E, Owen RS, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr 1986;44:1-19.[Abstract/Free Full Text]
10. Owen OE, Holup JL, D'Alessio DA, et al. A reappraisal of caloric requirements in men. Am J Clin Nutr 1987;46:875-85.[Abstract/Free Full Text]
11. Clark HD, Hoffer LJ. Reappraisal of resting metabolic rate of normal young men. Am J Clin Nutr 1991;53:21-6.[Abstract/Free Full Text]
12. Hayter JE, Henry CJK. A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subject sample selection. Eur J Clin Nutr 1994;48:702-7.[Medline]
13. Vinken AG, Bathalon GP, Sawaya AL, Dallal GE, Tucker KL, Roberts SB. Equation for predicting resting energy requirements of healthy adults aged 18–81 y. Am J Clin Nutr 1999;69:920-6.[Abstract/Free Full Text]
14. Pullicino E, Copperstone C, Luzi L, McNeill G, Elia M. Relationship between anthropometric indices of body fat distribution and basal energy metabolism in healthy Maltese women. Acta Diabetol 1996;33:198-204.[Medline]
15. Horgan GW, Stubbs J. Predicting basal metabolic rate in the obese is difficult. Eur J Clin Nutr 2003;57:335-40.[Medline]
16. Shetty PS. Adaptation to low energy intakes: the responses and limits to low intakes in infants, children and adults. Eur J Clin Nutr 1999;53(suppl):S14-33.
17. Black AE, Coward WA, Cole TJ, Prentice AM. Human energy expenditure in affluent societies: an analysis of 574 doubly-labeled water measurements. Eur J Clin Nutr 1996;50:72-92.[Medline]
18. Shetty P, Henry CJK, Black AE, Prentice AM. Energy requirements of adults: an update on basal metabolic rates (BMRs) and physical activity levels (PALs). Eur J Clin Nutr 1996;50(suppl):S11-23.
19. Durnin JVGA. Energy requirements: general principles. Eur J Clin Nutr 1996;50(suppl):S1-10.
20. Nelson KM, Weinsier R, Long CL, Schutz Y. Prediction of resting energy expenditure from fat-free mass and fat mass. Am J Clin Nutr 1992;56:848-56.[Abstract/Free Full Text]
21. Wang Z, Heshka S, Gallagher D, Boozer CN, Kotler DP, Heymsfield SB. Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling. Am J Physiol 2000;279:E539-45.
22. Segal KR, van Loan M, Fitzgerald PI, Hodgdon JA, van Itallie TB. Lean body mass estimation by bioelectrical impedance analysis: a four-site cross validation study. Am J Clin Nutr 1988;47:7-14.[Abstract/Free Full Text]
23. Deurenberg P, Van der Kooy K, Leenen R, Weststrate JA, Seidell JC. Sex and age specific prediction formulas for estimating body composition from bioelectrical impedance: a cross-validation study. Int J Obes 1991;15:17-25.[Medline]
24. Bosy-Westphal A, Eichhorn C, Kutzner D, Illner K, Heller M, Müller MJ. The age-related decline in resting energy expenditure in humans is due to the loss of fat-free mass and to alterations in it's metabolically active components. J Nutr 2003;133:2356-62.[Abstract/Free Full Text]
25. Müller MJ, Böttcher J, Selberg O, et al. Hypermetabolism in clinically stable patients with liver cirrhosis. Am J Clin Nutr 1999;69:1194-201.[Abstract/Free Full Text]
26. Kreymann G, Grosser S, Buggisch P, Gottschall C, Matthaei S, Greten H. Oxygen consumption and resting metabolic rate in sepsis, sepsis syndrome, and septic shock. Crit Care Med 1993;21:1012-9.[Medline]
27. Kunz I, Schorr U, Rommling K, Klaus S, Sharma AM. Habitual fat intake and basal fat oxidation in obese and non-obese Caucasians. Int J Obes Relat Metab Disord 2002;26:150-6.[Medline]
28. Rieper H, Karst H, Noack R, Johnsen D. Intra- and inter-individual variations in energy expenditure of 14–15-year-old schoolgirls as determined by indirect calorimetry. Br J Nutr 1993;69:29-36.[Medline]
29. Steininger J, Noack R. Bestimmung von energie- und substratumsätzen mittels indirekter kalorimetrie. [Determining energy and substrate metabolism from indirect calorimtery.] Z Med Lab Diagn 1988;29:17-27 (in German).[Medline]
30. Lührmann PM, Herbert BM, Neuhäuser-Berthold M. Effects of fat mass and body fat distribution on resting metabolic rate in the elderly. Metabolism 2001;50:972-5.[Medline]
31. Platte P, Wurmser H, Wade SE, Mercheril A, Pirke KM. Resting metabolic rate and diet-induced thermogenesis in restrained and unrestrained eaters. Int J Eat Disord 1996;20:33-41.[Medline]
32. Kromeyer-Hauschild K, Wabitsch M, Kunze D, et al. Perzentile für den body-mass-index für das kindes- und jugendalter unter heranziehung verschiedener deutscher stichproben. [BMI percentiles for children and adolescents considering several German samples.] Monatsschr Kinderheilkd 2001;149:807-18 (in German).
33. WHO. Obesity: preventing and managing the global epidemic. World Health Organ Tech Rep Ser 2000;894:i–xii, 1–253.
34. Johnsen D. Ermittlung und beurteilung der nahrungsaufnahme und des ernährungszustandes. [Assessing and evaluating nutrition and nutritional status.] In: Ketz HA, ed. Grundriß der Ernährungslehre. [Outline of nutritional science.] Darmstadt, Germany: Steinkopf, 1990:S.385-401.
35. Weir JB. New methods for calculating metabolic rate with special reference to protein. J Physiol 1949;109:1-9.
36. Erbersdobler H, Petry H, Tiews J. Energiehaushalt. [Energy metabolism.] In: Scheunert A, Trautmann A, eds. Lehrbuch der veterinärphysiologie. [Textbook of veterinary physiology.] 6th ed. Berlin: P Parey, 1976:S.325-62 (in German).
37. Ravussin E, Bogardus C. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr 1989;49(suppl):968-75.
38. Müller MJ, Bosy-Westphal A, Kutzner D, Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: recent lessons from imaging technologies. Obes Rev 2002;3:113-22.[Medline]
39. Institute of Medicine. Dietary reference intakes: energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids. A report of the Panel on Micronutrients, Subcommittees on Upper Reference Levels of Nutrients and Interpretation and Uses of Dietary Reference Intakes, and the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. Washington, DC: The National Academies Press, 2002.
40. Deutsche Gesellschaft für Ernährung, Österreichische Gesellschaft für Ernährung, Schweizerische Gesellschaft für Ernährungsforschung, Schweizerische Vereinigung für Ernährung. Referenzwerte für die Nährstoffzufuhr. [Recommendations for dietary intake.] Frankfurt am Main: Umschau/Braus-Verlag, 2000 (in German).
41. Eurodiet Core Report. Nutrition & diet for healthy lifestyle in Europe. Science & policy implications. Public Health Nutr 2001;4:265-73.[Medline]
42. Brooks GA, Butte NF, Rand WM, Flatt JP, Caballero B. Chronicle of the Institute of Medicine physical activity recommendation: how a physical activity recommendation came to be among dietary recommendations. Am J Clin Nutr 2004;79:921S-30S.[Abstract/Free Full Text]

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