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1 Institut für Humanernährung und Lebensmittelkunde und 2 Klinik für Radiologische Diagnostik, Christian-Albrechts-Universität zu Kiel, D-24105 Kiel, Germany
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Resting energy expenditure (REE) and components of fat-free mass (FFM) were assessed in 26 healthy nonobese adults (13 males, 13 females). Detailed body composition analyses were performed by the combined use of dual-energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), bioelectrical impedance analysis (BIA), and anthropometrics. We found close correlations between REE and FFMBIA (r = 0.92), muscle massDEXA (r = 0.89), and sum of internal organsMRI (r = 0.90). In a multiple stepwise regression analysis, FFMBIA alone explained 85% of the variance in REE (standard error of the estimate 423 kJ/day). Including the sum of internal organsMRI into the model increased the r2 to 0.89 with a standard error of 381 kJ/day. With respect to individual organs, only skeletal muscleDEXA and liver massMRI significantly contributed to REE. Prediction of REE based on 1) individual organ masses and 2) a constant metabolic rate per kilogram organ mass was very close to the measured REE, with a mean prediction error of 96 kJ/day. The very close agreement between measured and predicted REE argues against significant variations in specific REEs of individual organs. In conclusion, the mass of internal organs contributes significantly to the variance in REE.
body composition; muscle mass; organ mass; dual-energy X-ray absorptiometry; magnetic resonance imaging; bioelectrical impedance analysis; anthropometrics
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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VARIATION IN THE SIZE of fat-free mass (FFM) has been shown to explain 65-90% of the between-subject variation in resting energy expenditure (REE) (1, 3, 9, 13, 25, 28). REE per unit FFM is not constant, and the ratio of REE to FFM varies with body weight. REE per unit FFM decreases with increasing body mass, which suggests that subjects with higher FFM have lower REEs per kilogram FFM, whereas the opposite is true for individuals with lower FFM (25). This finding consigns the utility of indexing REE to body weight or FFM in subjects with different body masses. A potential explanation for this problem is the composition of the metabolically active FFM. Skeletal muscle and internal organ mass substantially differ with respect to their individual rates of energy expenditure. Estimates of REE per kilogram organ mass based mainly on in vitro measurements vary between 837 and 1,841 kJ for internal organs and 54 and 63 kJ for skeletal muscle (10, 24). Thus, from a metabolic point of view, different ratios of high vs. low energy-requiring organs may explain an unknown part of the variation in REE (25, 29).
Techniques of computed tomography (CT), magnetic resonance imaging (MRI), and dual-energy X-ray absorptiometry (DEXA) can be used for the in vivo measurement of metabolically active components of FFM. Up to now, only four studies have combined these measurements with measurements of REE (6, 12, 21, 27). Using CT in combination with densitometry, Deriaz et al. (6) measured REE and body composition (i.e., skeletal muscle mass and the sum of tissue masses) in 22 men before and after a 100-day overfeeding period. They found significant correlations between REE and muscle, as well as nonmuscle, compartments of FFM. Using these two variables of body composition did not improve the prediction of REE over that provided by muscle mass alone. After overfeeding, body mass, lean body mass, and skeletal muscle mass were the best correlates of REE. While the present study was under investigation, Sparti et al. (27) simultaneously used CT, DEXA, and echocardiography for the assessment of the composition of FFM in 20 females and 20 males, respectively. These authors also concluded that the composition of FFM could not improve the prediction of REE compared with FFM alone. In a further preliminary report, McNeill et al. (21) also provided no evidence that liver, kidney, and spleen masses (as determined by MRI in 30 women) explain any of the variance in REE between individuals. Taken together, the results of three studies (6, 21, 27) suggest that the variance in REE is more dominated by the energy expenditure of the individual organs than by the variance in the internal organ size. This idea is contrary to a very recent paper by Gallagher et al. (12). These authors measured body cell mass (as measured by total body potassium) and organ-tissue volumes by MRI and echocardiography. They found strong associations between REE and individual or combined organ weights. Moreover, calculating REE from individual organ masses and previously reported organ metabolic rates closely predicted measured REE [i.e., the difference between measured and calculated REE was less than 84 kJ/day (12)]. These data suggest a constant organ-tissue respiration rate.
After reviewing the available literature, we reached two conclusions. First, the data base on concomitant in vivo measurements of organ size and REE is very small. Second, the available data on the association between body composition and REE are in part contradictory. Faced with these discrepancies and the limited information from concomitant in vivo measurements of organ size and REE, we reassessed the relationship of metabolically active components of FFM to REE in a homogeneous group of healthy adults by use of DEXA, MRI, bioelectrical impedance analysis (BIA), and anthropometrics for detailed body composition analysis. Our data support the idea that organ sizes are important determinants of REE. The use of metabolically active components of FFM instead of total FFM reduced the variance in REE prediction.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Study population. Twenty-six healthy subjects (13 females, 13 males) participated in the study (see Table 1). The study protocol was approved by the ethical committee of the Christian-Albrechts-Universität zu Kiel. Before participation, each subject provided written consent. All subjects were healthy and weight stable. None had a history of recent illness, obesity, diabetes, hyperlipidemia, hypertension, or endocrinopathy. Each subject had a normal physical examination. Dieting or physical exhaustion were avoided during the 7 days before examination.
Measurements and estimations of REE.
In females, measurements of REE and body composition were performed in
the follicular phase of the menstrual cycle (i.e., <10 days since
last menstruation). REE was measured as described elsewhere (23).
Briefly, REE was measured by an open-circuit indirect calorimeter
(Deltatrac Metabolic Monitor, Datex Instruments, Helsinki, Finland).
Measurements were performed between 7:00 and 8:00 AM in a metabolic
ward at constant humidity (55%) and room temperature
(22°C). The day before testing, the subjects had eaten their last
evening meal between 6:00 and 7:00 PM. For 
1 h, gas exchange
measurements were done continuously. The first 20 min of data were
omitted. For the residual time of investigation (i.e., for a period of
40 min), data were integrated for 5-min intervals. The means of 40
measurements were presented as individual values. Calibrations of the
gas analyzers were performed immediately before and after the
measurements. Variation caused by the technique was calculated on the
basis of five repeated measurements of propane combustion and
was found to be <4%. The within-day coefficent of
variation of the 5-min oxygen consumption
(
O2) values was <7.5%. Intraindividual variances in REE were assessed in a
subgroup of 10 weight-stable men, who performed test-retest
measurements on three different days within a 14-day period. The
intraindividual variances were <6%. REE was calculated as described
by Weir (30): kcal/min = O2
(l/min) × [3.9 + (1.1 × RQ)], where
RQ is respiratory quotient. REE (kcal/day) was also calculated using
various prediction formulas as proposed by the following investigators
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(2) |
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(3) |
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(4) |
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Body composition analysis. By use of an electronic scale, weight was measured without shoes, with light clothing, and after voiding, at an accuracy of 0.1 kg (SECA, Modell 709, Vogel & Halke, Hamburg, Germany). Height was assessed to the nearest 0.5 cm with a stadiometer. Body composition was assessed by the use of anthropometrics (ANTHRO), BIA, DEXA, and MRI. ANTHRO and BIA were performed immediately after gas exchange measurements. DEXA and MRI were both performed on the following day. ANTHRO was used to assess body fat (measurements of 4 skinfolds) and arm muscle area (arm circumference). Skinfold thickness was measured by the use of caliper (Lafayette Instruments, Lafayette, IN). Fat mass was calculated according to Durnin and Wormersley (8), and arm muscle area was calculated according to Heymsfield et al. (17). BIA was performed as described previously (22) as a measure of total body water (TBW). We used a body impedance analyzer at a frequency of 50 kHz and the manufacturer's software for data analyses (BIA 2000-S, Data Input, Frankfurt, Germany). FFMBIA was calculated assuming a water content of FFM of 73.2% (FFM = TBW/0.732). Bone mineral content and whole body and regional lean body mass (LBM) were measured by DEXA (Hologic QDR 4500A, Hologic, Waltham, MA). Limb lean mass was used as a measure of muscle mass, as suggested by Heymsfield et al. (18). DEXA scans were analyzed with the manufacturer's whole body Version 5.54 (Hologic).
The volume of internal organs (brain, heart, liver, kidneys, spleen) was measured by transversal MRI images. The scans were made by use of a 1.5-Tesla Magnetom Vision scanner (Siemens, Erlangen, Germany). All organs were examined native and without distance factors. The slice thickness for the brain was 6 mm, for the heart, 7 mm, and for abdominal organs, 8 mm. The brain and the abdominal organs of the participants were examined by T1-weighted breathhold FLASH sequences (repetition time, TR: 174.9 ms; echo time, TE: 4.1 ms/echo). Because of the movement of the heart and to prevent artifacts, ultra-short scans were made by electrocardiogram (ECG)-triggered, T2-weighted HASTE sequences that were taken in breathhold technique (acquisition time: 20 ms). The volume of the organs was calculated from the sum of cross-sectional areas as determined "by hand" (i.e., by exactly drawing a line at the external limits of the organs) and multiplied by the scan's slice thickness. All scans were read by the same trained observer (K.I.); the coefficient of variation, based on three test-retest measurements, was <1%. Volume data were transformed into organ weights by use of the following densities: 1.036 g/cm3 for brain, 1.06 g/cm3 for heart and liver, 1.05 g/cm3 for kidneys, and 1.054 g/cm3 for spleen (7). Total body mass was considered as the sum of organ masses. Residual mass was calculated as body mass minus the sum of skeletal muscleDEXA, brainMRI, heartMRI, liverMRI, kidneysMRI, spleenMRI, bone mineralDEXA, and fat massDEXA (= residual 1 in Tables 1-3). Because skeletal muscleDEXA assesses appendicular, and not whole body skeletal muscle mass, residual mass was reanalyzed using skeletal muscleANTHRO (= residual 2 in Tables 1-3). Residual mass was also calculated assuming constant contributions of blood volume (i.e., 7.9% body wt), skin (3.7% body wt), connective tissue (2.3% body wt), lung tissue (1.4% body wt), intestine (1.7% body wt) to body weight, as given in Ref. 9 (= residual 3 in Tables 1-3).Methodological limitations.
Calculation of organ masses from data obtained by imaging techniques is
based on 1) the sum of cross-sectional areas multiplied by the
distance between scans [coefficient of variation (CV)
<1%] and 2) approximate organ densities taken from the
literature (7). In vivo organ weight was measured to the nearest 0.1 kg. This is within the order of the accuracy reached for radiographic
volume and mass determination by use of excised human cadaver organs (i.e., ±3-5%; Refs. 15, 16). If a standard deviation
for organ weights of 
0.4 kg and a mean specific energy expenditure of
~1,255 kJ/kg organ weight are assumed (15), the precision of the
imaging techniques may introduce a considerable error (i.e.,
7-9% of REE). This may contribute to the residual
variance in REE and also limit the value of modeling REE from
metabolically active tissue mass. There is also evidence that our
approach to assessing metabolically active components of FFM left a
considerable amount of so-called "residual masses" unexplained.
Residual mass is a heterogeneous component that includes intestine,
pancreas, lung, skin, blood volume, endocrine glands, and connective
tissue. It is evident that different calculation procedures result in
different amounts of residual mass (see residuals 1-3,
Tables 1-3). These differences are in part explained by the
methods used to assess skeletal muscle mass (e.g., skeletal muscle
massDEXA measures appendicular instead of whole body muscle
mass). The close association between the different measures of residual
mass and 1) FFMBIA and thus 2) REE suggests
that residual mass indirectly reflects metabolic active tissues (Table
2-3).
Data analyses.
All data were recorded in a database system with a personal computer;
statistical analyses were performed by SPSS for Windows 5.0.2. Data are
presented as means ± SD. The Mann-Whitney U-test or Fisher's
Exact Test was used for comparisons between groups. Pearson's
correlation coefficients were calculated to test for relationships
among different parameters. In addition, a multivariate stepwise
regression analysis was performed using REE as dependent variable. A
probability value of 0.05 was accepted as the limit of significance.
The specific contributions of body weight, FFM or muscle mass, and
organ masses (i.e., slopes or k values) were calculated from
the individual regression lines. The model assumes that whole body REE
is the sum of respiration of the individual tissues. Then REE was
predicted by different formulas
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(6) |
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(7) |
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(8) |
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(9) |
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Body composition data.
Body masses, as well as the organ sizes of the subjects, are shown in
Table 1. Significant sex-dictated
differences were seen for weight, height, total body
waterBIA, FFMBIA, fat massDEXA, fat
massANTHRO, and the different organs measured except for
brainMRI and liverMRI. The water content of
LBMDEXA was calculated from TBWBIA and
LBMDEXA to be 0.73 ± 0.03 in females and 0.73 ± 0.00 in
males, respectively. Residual mass accounted for 32 and 37% of
body weight in males and females, respectively (Table 1). Calculating
residual mass based on the assumption of constant contributions of
blood volume, skin, intestine, connective tissue, and lungs gave
residual masses of 13.1 ± 1.7 kg (male) and 10.7 ± 1.6 kg (female),
respectively (= residual 3). There were significant and
sex-independent differences between residual mass
1 and 2 or 3, respectively (P < 0.01 or < 0.05). We also found significant differences between
residual masses 2 and
3 (P < 0.01).
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REE and body composition.
Measured REE varied from 4.77 to 8.62 MJ/day. REE values were higher in
males than in females (+27%; P < 0.001; Table 1). Adjusting REE on the basis of group mean REE plus the measured REE
minus predicted REE (as predicted from the individual
FFMBIA in the linear regression equation generated in our
population) (see Fig. 2 and Ref. 25 for
details of the underlying assumptions) gave similar values for both
sexes [m, 6.45 ± 0.51 vs. f, 6.58 ± 0.30 MJ/day;
not significant (NS)].
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1 · kg
FFM1) was 126, 121, and 114 for subjects
with different FFM values, i.e., FFM <50 kg (n = 10), FFM
50-60 kg (n = 9), and FFM >60 kg (n = 7),
respectively (P < 0.01 for the difference in REE/FFM between
subjects <50 kg FFM vs. >60 kg FFM). Plotting REE on the ratio of
skeletal muscle massDEXA to the sum of organ
massesNMR resulted in a positive and significant
association (data not shown). Plotting REE per kilogram
FFMBIA on the ratio of skeletal muscle massDEXA
per sum of organsMRI gave a negative and significant correlation (Fig. 3).
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(8a) |
Prediction of REE.
Predicting whole body REE from FFMBIA ± FMDEXA by use of formulas
1-4 (METHODS) resulted in a very close
agreement between measured and predicted values (mean deviations
between +155 and 
485 kJ/day). There were no systematic errors in
groups of subjects differing with respect to their body mass index
(BMI, <21 vs. 21-23 vs. >23 kg/m2) or
their FFMBIA (<50 vs. 50-60 vs. >60 kg) (data not
shown). Calculating REE (REEc) on the basis of measured organ masses
times constant organ tissue respiration rates, as reported in the
literature (formula 9), a mean
prediction error of 96 kJ/day was observed. There was a very close
correlation between theoretically calculated (according to
formula 9) and measured REE (Fig.
4). We found significant differences in REE
and REEc (according to formula 9:
FFMBIA <50 kg, n = 10, vs. FFMBIA
50-60 kg, n = 9, vs. FFMBIA >60 kg,
n = 7; REE, 5.4 ± 0.4 vs. 6.7 ± 0.5 vs. 7.8 ± 0.7 MJ/day;
REEc, 5.7 ± 0.4 vs. 6.7 ± 0.4 vs. 7.7 ± 0.9 MJ/day; P < 0.01 vs. FFMBIA 50-60 kg) between groups differing
with respect to FFMBIA. A Bland-Altman plot showed no
significant trend (r = 0.19; P = 0.357) between measured and calculated REE difference (i.e., the difference between REE measured and REE calculated according to
formula 9 vs. the average of REE
measured and REE calculated; Fig. 5),
suggesting that there is no systematic error.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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REE varies among individuals. Body size, body composition, age, sex, hormones, and genetic factors explain most of its variability (1, 24, 28). It has been speculated that the relative proportion of high and low metabolically active tissues independent of differences in FFM significantly adds to the residual variance in REE (24, 28, 29).
Our present knowledge regarding the contribution of individual organs
to REE in humans is mainly based on 1) in vitro measurements of
tissue respiration (5, 9, 19) and 2) postmortem analysis of
body composition (10, 14, 15). These studies suggest that 1)
O2 per gram tissue is
relatively constant and 2) organ size is a major determinant of
REE. Tissue O2 can be
directly estimated in vivo by measurements of the arteriovenous
(a-v) differences of O2 together with blood flow
measurements. Methodological problems may limit the in vivo assessment
as well as the interpretation of organ-tissue metabolic rates (11).
However, the in vivo data suggest that the sum of regional
O2 exceeds whole body REE (3, 5). The comparison of in vivo with in vitro data is inconclusive (5).
Thus our knowledge on energy expenditure-body composition relationships
in humans is limited.
On the basis of data obtained from 1,598 autopsies and with the assumption of a constant mass-specific energy expenditure, Garby and co-workers (14, 15) calculated that the composition of FFM may explain 5% of the variation in the between-subjects variation in REE. This is close to our data, as well as to the recent results of others (12, 27), which were all based on direct and concomitant in vivo assessments of organ masses and REE. By contrast, Deriaz et al. (6) and McNeill et al. (21) provided no evidence that the composition of FFM explains any of the variance in whole body REE. Regarding the role of metabolically very active organs (i.e., muscle, brain, liver) contributing to REE, the different authors also came out with different results. We found that skeletal muscle and liver are the major determinants of REE in young, healthy, and nonobese subjects (RESULTS). By contrast, Gallagher et al. (12) found that brain and skeletal muscle were the major determinants of REE. The discrepancy between the results of these two studies may be explained by differences in the database (e.g., the number and age of the subjects differ between the two studies). In the two other studies, only skeletal muscle mass (6) or muscle plus fat plus heart mass (27) significantly contributed to the prediction of REE. However, in their multiple regression analysis, Deriaz et al. used only skeletal muscle mass and nonmuscular LBM (which is the sum of internal organs). Thus these authors could not differentiate among individual organs. In addition, in the study by Deriaz et al., only a limited number of CT scans at nine selected sites were performed, and the relationship between REE and FFM was poor (i.e., r = 0.56 for REE vs. LBMCT, or r = 0.49 for REE vs. FFM, as determined by densiometry; Ref. 6). Compared with Deriaz et al., Sparti et al. measured liver but not brain by serial CT images (27). In addition, appendicular skeletal muscle mass was measured by DEXA. With use of simple correlation coefficients, muscle and liver showed significant associations with REE (r = 0.84 and 0.75, respectively), which is very close to our data (r = 0.94 and 0.77, respectively; see Table 3). Because a homogeneous and comparable group of subjects was studied in both studies (27, this study) and similar methods have been used (CT, echocardiography, DEXA in Ref. 27; MRI, DEXA, BIA in this study), it is unclear why muscle but not liver reached statistical significance in Sparti's regression analysis. It should be mentioned that both studies (Ref. 27 and this study), although very similar with respect to the physical variables of the subjects, differ with respect to the magnitude of some internal organs (i.e., kidney mass was higher but left ventricular mass was lower in Ref. 27 compared with our data). Some of the differences in organ masses given in the different studies (12, 27, this study) are due to methodological problems. For example, Sparti et al. (27) as well as Gallagher et al. (12) assessed left ventricular mass by echocardiography, whereas ECG-triggered MRI was used in our study. In contrast with MRI, echocardiography measures only left ventricular mass, which accounts for approximately two-thirds of heart weight in healthy adults.
Organ contribution to whole body energy expenditure can also be
assessed by direct measures of organ energy metabolism. Regarding direct in vivo measurements of muscle and liver
O2 obtained by use of a-v
difference techniques, both organs together contribute ~50%
of REE (10, 22, 31). However, the a-v difference technique cannot
differentiate between nutritive and nonnutritive blood flow and thus
may overestimate organ-tissue respiration (11, 22). At present, there
are only limited in vivo data on organ-tissue O2. Suitable methods (e.g.,
150 or positron emission tomography, Ref. 26) for the in
vivo assessment of regional
O2 should be applied in
future studies. These techniques will contribute to the development of
new energy expenditure-body composition estimation models.
Body size-related variations in REE are explained by 1) the proportional contributions of different organs to FFM, as well as 2) tissue O2 consumption. In a stepwise multiple regression analysis, FFM alone explains 85% of the variance in REE, leaving an SE of the estimate of 423 kJ/day (RESULTS). Calculating REE as the sum of individual organ-tissue masses times a constant organ-tissue respiration rate, on the basis of data published in the literature (10), reduces the variance in REE and results in small differences between measured and calculated REE of 83 (12) or 96 kJ/day (this paper), respectively. However, it should be mentioned that the use of standard formulas for the prediction of REE also reaches a very high precision in our homogeneous group of young, healthy, and nonobese subjects. It is tempting to speculate that the accuracy of prediction may differ in a more heterogeneous sample of subjects (e.g., in patients with chronic diseases or obese patients).
In conclusion, the proportions of metabolically active components of REE contribute to the variance in REE and also explain the relation between REE and FFM. The essential findings of the present study are that 1) the contribution of the mass of the metabolically more active internal organs to the variance in REE is ~5%; only skeletal muscleDEXA and liverMRI significantly contribute to REE; 2) the decrease in REE per kilogram FFM with increasing FFM is explained by the changing proportions of metabolically active compounds of FFM; and 3) predictions of REE on the basis of individual organ masses were very close to measured REE. Our data support the assumptions (3, 24, 28, 29) and the data of some authors (12) but are contrary to the results of others (6, 21, 27).
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ACKNOWLEDGEMENTS |
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A preliminary report of this work was presented in abstract form at the 16th International Congress of Nutrition, Montreal, 1997 (PW 14.3).
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Address for reprint requests and other correspondence: M. J. Müller, Institut für Humanernährung und Lebensmittelkunde, Christian-Albrechts-Universität zu Kiel, Düsternbroker Weg 17, D-24105 Kiel, Germany (E-mail: mmueller{at}nutrfoodsc.uni-kiel.de).
Received 4 January 1999; accepted in final form 20 September 1999.
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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