Metabolically active components of fat free mass and resting
energy expenditure in nonobese adults
Kirsten
Illner1,2,
Gisbert
Brinkmann2,
Martin
Heller2,
Anja
Bosy-Westphal1, and
Manfred J.
Müller1
1 Institut für Humanernährung und
Lebensmittelkunde und 2 Klinik für
Radiologische Diagnostik, Christian-Albrechts-Universität zu
Kiel, D-24105 Kiel, Germany
 |
ABSTRACT |
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 |
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 |
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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(1)
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(2)
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(3)
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(4)
|
The indexes clarify which method was used by the investigators
to measure or calculate FFM (Calc, calulated from weight and age; Dens,
densitometry; ANTHRO, anthropometry, from skinfold measurements).
Formulas 1-4 were used because they assume body composition (i.e., FFM ± fat mass) as a predictor of REE. The data
were therefore compared with our approach (i.e., calculating REE from
FFM and/or individual organ masses) to assess the differences between
estimated and measured values.
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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(5)
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(6)
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(7)
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(8)
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In addition, whole body REE (REEc) was calculated as the sum of
seven individual organs (brainMRI, liverMRI,
skeletal muscleDEXA, fat massDEXA, plus
residual organs) times organ-tissue metabolic rates, on the basis of
organ-tissue metabolic rates reported by Elia et al. (10)
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(9)
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The agreement between calculated and measured REE was
analyzed by plotting the difference between measured and calculated REE vs. the average of REE measured and REE calculated, as
described by Bland and Altman (2).
 |
RESULTS |
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).
DEXA was used to assess the composition of different regions of the
body (i.e., trunk = t, legs = l, arms = a). The different segments
contributed to body masses by 44 and 45% (t), 39 and 36% (l), and 11 and 13% (a) in females and males,
respectively. Proportions of fat for each region were greater in
females than in males (t: 23.7 ± 6.7 and 15.0 ± 5.2%, l:
38.1 ± 4.5 and 17.6 ± 4.7%, a: 36.1 ± 7.3 and 15.7 ± 4.0% segmental weight for females and males, respectively;
P < 0.01 for sex differences). By contrast, the proportions
of weight of trunk, arms, and legs consisting of LBMDEXA
were increased in males (t: 74.0 ± 6.6 and 82.8 ± 5.0%; l: 58.0 ± 4.4 and 77.6 ± 4.5; a: 59.1 ± 7.0 and 79.3 ± 3.9% segmental weight, in females and males,
respectively, P < 0.01 for sex differences). Regional
proportions of bones were similar between sexes (data not shown).
The relationships between different body and tissue masses are given in
Table 2. There were significant
correlations between FFMBIA and all other organs except the
brain. Fat massDEXA did not reach significant associations
with body weight in normal-weight subjects. Plotting the ratio of
skeletal muscle massDEXA and the sum of organ
massesMRI against FFMBIA showed significant and
positive association (Fig. 1).
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Table 2.
Pearson correlation coefficients among body compartment sizes in 26 healthy subjects as assessed by different methods
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Fig. 1.
Ratios of high energy (sum of organs = organ massMRI)- vs.
low energy-requiring organs (= skeletal muscleDEXA) plotted
against FFMBIA. BIA, bioelectrical impedance analysis;
DEXA, dual-energy X-ray absorptiometry; MRI, magnetic resonance
imaging; FFM, fat-free mass.
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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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Fig. 2.
Metabolically active components of FFM plotted against resting energy
expenditure (REE) by BIA, DEXA, and MRI-DEXA.
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REE was significantly associated with FFMBIA (r =
0.92), muscle massDEXA (r = 0.89), and the sum of
organsMRI (r = 0.90) (Fig. 2, Table
3). There were no sex-dictated differences
between the slopes of the REE regression lines when FFMBIA,
muscle massDEXA, or organ massMRI was used as
the variable. REE
(kJ · day
1 · kg
FFM
1) 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).
In a multiple stepwise regression analysis, FFMBIA alone
explained 85% of the variance in REE (SE of the estimate 423 kJ/day). Additionally, including the sum of internal organsMRI into
the model increased the r2 to 0.89 with a SE of 381 kJ/day. In a stepwise multiple regression analysis, only skeletal
muscleDEXA and liver massMRI significantly contributed to REE, as indicated in the following regression
equation
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(8a)
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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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Fig. 4.
REE plotted against REE calculated according to formula 9 (REEc). REEc was calculated from organ masses times constant tissue
respiration rates as reported from the literature (see
METHODS for details of underlying assumptions).
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Fig. 5.
Bland-Altman Plot exploring the agreement between calculated (according
to formula 9) and measured REE. Dotted
lines are given for the first SE values.
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DISCUSSION |
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 |
A preliminary report of this work was presented in abstract form at
the 16th International Congress of Nutrition, Montreal, 1997 (PW
14.3).
 |
FOOTNOTES |
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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