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
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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 (VO2) 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 = VO2 (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
Cunningham (Ref. B3)  (1)

REE = 21.6 × FFM<SUB>Calc</SUB> + 501.6

Garby et al. (Ref. B13) (2)

 REE = 27.88 × FFM<SUB>Dens</SUB> + 6.4 × FM

Elia (Ref. B10)   REE = 21.11 × FFM<SUB>ANTHRO</SUB> + 405 (3)

Ravussin and Bogardus (Ref. B25)  (4)

REE = 20.8 × FFM<SUB>Dens</SUB> + 471
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 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
<IT>a</IT> + (<IT>k</IT>1 × body wt.) (5)

<IT>b</IT> + (<IT>k</IT>2 × kg FFM<SUB>BIA</SUB>) (6)

<IT>c</IT> + (<IT>k</IT>2 × kg FFM<SUB>BIA</SUB> + k3 × kg FM<SUB>DEXA</SUB>) (7)

<IT>d</IT> + (<IT>k</IT>4 × kg muscle mass<SUB>DEXA</SUB>  (8)

+ <IT>k</IT>5 × kg liver weight<SUB>MRI</SUB>)
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)


REEc = (1.008 × brain mass<SUB>MRI</SUB>) + (840 × liver mass<SUB>MRI</SUB>) + (1.848 × heart mass<SUB>MRI</SUB>) + (1.848 × kidney mass<SUB>MRI</SUB>) 

+ (55 × skeletal muscle mass<SUB>DEXA</SUB>) + (19 × fat mass<SUB>DEXA</SUB>) + (50 × residual mass 1) (9)

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
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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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Table 1.   Characterization of study population

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.

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.

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).

                              
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Table 3.   Pearson correlation coefficients between REE and age, body, or organ sizes



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Fig. 3.   REE per kg FFMBIA plotted against different ratios of high- vs. low energy-requiring organs.

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
REE = 359 + 29 × skeletal muscle mass<SUB>DEXA</SUB> 

 + 319 × liver mass<SUB>MRI</SUB> (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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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.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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) VO2 per gram tissue is relatively constant and 2) organ size is a major determinant of REE. Tissue VO2 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 VO2 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 VO2 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 VO2. Suitable methods (e.g., 150 or positron emission tomography, Ref. 26) for the in vivo assessment of regional VO2 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).


    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.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

1.   Astrup, A., B. Buemann, N. J. Christensen, J. Madsen, C. Gluud, P. Bennett, and B. Svenstrup. The contribution of body composition, substrates, and hormones to the variability in energy expenditure and substrate utilization in premenopausal women. J. Clin. Endocrin. Metab. 74: 279-286, 1992[Abstract].

2.   Bland, M. J., and D. G. Altman. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1: 307-310, 1986[ISI][Medline].

3.   Cunningham, J. C. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am. J. Clin. Nutr. 54: 963-969, 1991[Abstract].

4.   Cunningham, J. J. A reanalysis of the factors influencing metabolic rate in normal adults. Am. J. Clin. Nutr. 33: 2372-2374, 1980[Abstract].

5.   Davies, M. On body size and tissue respiration. J. Cell. Comp. Physiol. 57: 135-147, 1961[ISI].

6.   Deriaz, O., G. Fournier, A. Tremblay, J.-P. Despres, and C. Bouchard. Lean-body mass composition and resting energy expenditure before and after long-term overfeeding. Am. J. Clin. Nutr. 56: 840-847, 1992[Abstract].

7.   Duck, F. A. Physical Properties of Tissue. New York: Academic, 1990.

8.   Durnin, J. V. G. A., and J. Wormersley. Body fat assessed from body density and 1st estimation from skinfold thickness: measurement on 481 men and women from 16 to 72 years. Br. J. Nutr. 32: 77-97, 1974[ISI][Medline].

9.   Elia, M. Energy expenditure in the whole body. In: Energy Metabolism: Tissue Determinants and Cellular Corollaries, edited by J. M. Kinney, and H. N. Tucker. New York: Raven, 1992, p. 19-59.

10.   Elia, M. Organ and tissue contribution to metabolic rate. In: Energy Metabolism: Tissue Determinants and Cellular Corollaries, edited by J. M. Kinney, and H. N. Tucker. New York: Raven, 1992, p. 61-77.

11.   Frayn, K. N., P. Lund, and M. Walker. Interpretation of organ and carbon dioxide exchange across tissue beds in vivo. Clin. Sci. (Colch). 85: 373-384, 1993[ISI][Medline].

12.   Gallagher, D., D. Belmonte, P. Deurenberg, Z. Wang, N. Krasnow, F. X. Pi-Sunyer, and S. B. Heymsfield. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am. J. Physiol. Endocrinol. Metab. 275: E249-E258, 1998[Abstract].

13.   Garby, L., J. S. Garrow, and B. Jörgensen. Reexamination between energy expenditure and body composition in man: specific energy expenditure in vivo of fat and fat free tissue. Eur. J. Clin. Nutr. 42: 301-305, 1988[ISI][Medline].

14.   Garby, L., and O. Lammert. Between-subjects variation in energy expenditure: estimation of the effect of variation in organ size. Eur. J. Clin. Nutr. 48: 376-378, 1994[ISI][Medline].

15.   Garby, L., O. Lammert, K. F. Kock, and B. Thobo-Carlsen. Weights of brain, heart, liver, kidneys and spleen in healthy and apparently healthy adult Danish subjects. Am. J. Hum. Biol. 5: 291-296, 1993[ISI].

16.   Heymsfield, S. B., T. Fulenwider, B. Nordlinger, R. Baröow, P. Stones, and M. Kutner. Accurate measurement of liver, kidney, and spleen volume and mass by computerized axial tomography. Ann. Intern. Med. 90: 185-187, 1979[ISI][Medline].

17.   Heymsfield, S. B., C. McManus, J. Smith, V. Stevens, and D. W. Nixon. Anthropometric measurement of muscle mass: revised equations for calculating bone free arm muscle area. Am. J. Clin. Nutr. 36: 680-690, 1982[Abstract].

18.   Heymsfield, S. B., R. Smith, M. Aulet, B. Bensen, S. Lichtman, J. Wang, and R. N. Piersen, Jr. Appendicular skeletal muscle mass. Measurement by dual photon absorptiometry. Am. J. Clin. Nutr. 52: 214-218, 1990[Abstract].

19.   Holliday, M. A., D. Potter, A. Jarrah, and S. Bearg. The relation of metabolic rate to body weight and organ size. Pediatr. Res. 1: 185-195, 1967[ISI][Medline].

20.   Krebs, H. A. Body size and tissue respiration. Biochim. Biophys. Acta 4: 249-268, 1950[ISI].

21.   McNeill, G., M. A. Foster, J. Love, and V. Antfang. Liver and kidney volume and their relationship to metabolic rate at rest. Proc. Nutr. Soc. 54: 151A, 1995.

22.   Müller, M. J. Hepatic energy and substrate metabolism: a possible metabolic basis for early nutritional support in cirrhotic patients. Nutrition 14: 30-38, 1998[ISI][Medline].

23.   Müller, M. J., A. von zur Mühlen, H. U. Lautz, F. W. Schmidt, M. Daiber, and P. Hürter. Energy expenditure in children with type 1 diabetes: evidence for increased thermogenesis. Br. Med. J. 299: 487-491, 1989[ISI][Medline].

24.   Nelson, K. M., R. L. Weinsier, C. L. Long, and Y. Schutz. Prediction of resting energy expenditure from fat-free mass and fat mass. Am. J. Clin. Nutr. 56: 848-856, 1992[Abstract].

25.   Ravussin, E., and C. Bogardus. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am. J. Clin. Nutr. 49: 968-975, 1989[ISI][Medline].

26.   Redies, C., L. J. Hoffer, C. Beil, E. B. Marliss, A. C. Evans, F. Lariviere, S. Marrett, E. Meyer, M. Diksic, A. Gjedde, and A. M. Hakim. Generalized decrease in brain glucose metabolism during fasting in humans studied by PET. Am. J. Physiol. Endocrinol. Metab. 256: E805-E810, 1989[Abstract/Free Full Text].

27.   Sparti, A., J. P. DeLany, J. De La Bretonne, G. E. Sander, and G. A. Bray. Relationship between resting metabolic rate and the composition of the fat-free mass. Metabolism 46: 1225-1230, 1997[ISI][Medline].

28.   Tataranni, P. A., and E. Ravussin. Variability in metabolic rate: biological sites of regulation. Int. J. Obes. 19, Suppl.4: S102-S106, 1995[ISI].

29.   Weinsier, R. L., Y. Schutz, and D. Bracco. Reexamination of the relationship of resting metabolic rate to fat-free mass and to metabolically active components of fat free mass in humans. Am. J. Clin. Nutr. 55: 790-794, 1992[Abstract].

30.   Weir, J. B., and V. De. New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. (Lond.) 109: 1-9, 1949[ISI].

31.   Zurlo, F., K. Larson, C. Bogardus, and E. Ravussin. Skeletal muscle metabolism is a major determinant of resting energy expenditure. J. Clin. Invest. 86: 1423-1427, 1990[ISI][Medline].


Am J Physiol Endocrinol Metab 278(2):E308-E315
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