Body-size dependence of resting energy expenditure can be
attributed to nonenergetic homogeneity of fat-free mass
Steven B.
Heymsfield,
Dympna
Gallagher,
Donald P.
Kotler,
Zimian
Wang,
David B.
Allison, and
Stanley
Heshka
New York Obesity Research Center, St. Luke's-Roosevelt Hospital,
and Institute of Human Nutrition, Columbia University, College of
Physicians and Surgeons, New York, New York 10025
 |
ABSTRACT |
An
enduring enigma is why the ratio of resting energy expenditure (REE) to
metabolically active tissue mass, expressed as the REE/fat-free mass
(FFM) ratio, is greater in magnitude in subjects with a small FFM than
it is in subjects with a large FFM. This study tested the hypothesis
that a higher REE/FFM ratio in subjects with a small body mass and FFM
can be explained by a larger proportion of FFM as high-metabolic-rate
tissues compared with that observed in heavier subjects. REE was
measured by indirect calorimetry, FFM by dual-energy X-ray
absorptiometry (DEXA), and tissue/organ contributions to FFM by whole
body magnetic resonance imaging (MRI) in healthy adults. Four tissue
heat-producing contributions to FFM were evaluated, low-metabolic-rate
fat-free adipose tissue (18.8 kJ/kg), skeletal muscle (54.4 kJ/kg), and
bone (9.6 kJ/kg); and high-metabolic-rate residual mass (225.9 kJ/kg).
Initial evaluations in 130 men and 159 women provided strong support
for two key, developed models, one linking DEXA FFM with MRI FFM
estimates and the other linking REE predicted from the four MRI-derived components with measured REE. There was an inverse association observed
between measured REE/FFM and FFM (r2 = 0.17, P < 0.001). Allometric models revealed a similar
pattern of tissue change relative to body mass across males and females with greater proportional increases in fat-free adipose tissue and
skeletal muscle than in FFM and a smaller proportional increase in
residual mass than in FFM. When examined as a function of FFM, positive
slopes were observed for skeletal muscle/FFM and pooled low-metabolic-rate components, and a negative slope for residual mass.
Our linked REE-body composition models and associations strongly
support the hypothesis that FFM varies systematically in the proportion
of thermogenic components as a function of body mass and FFM. These
observations have important implications for the interpretation of
between-individual differences in REE expressed relative to
metabolically active tissue mass.
body composition; metabolic rate; phenotyping
 |
INTRODUCTION |
AN INTENSE INTEREST
of early workers in the field of energy metabolism (7,
28), one maintained to the present time (1, 6, 9,
24), is establishing the factors that account for between-individual differences in resting heat production. Exploring these multiple factors required adjusting for observed differences in
body size, and typically an individual's resting energy expenditure (REE) was normalized for body mass and surface area (7, 20, 21,
28). An important observation, however, was that the ratio of
REE to body mass or related body surface area was not constant but
rather decreased with greater human body weight (7).
Because the proportion of body mass as "energetically inert" fat
increases with greater body mass in adults, later investigators advanced body composition compartments such as fat-free mass and body
cell mass as improved measures of "metabolically active" tissue
(1, 6, 9, 19). However, an enduring enigma is why REE is
still not observed as constant across adult humans when expressed as a
ratio to fat-free mass (or body cell mass) but rather decreases with
greater fat-free mass (27, 31, 32). Subjects with a small
fat-free mass have a greater REE-to-fat-free mass ratio than do
subjects with a large fat-free mass, suggesting a body size difference
in relative energy expenditure and requirements. The recognition of the
"fat-free mass dependence" of the REE/fat-free mass ratio extends
back more than a decade, and investigators now typically use
alternative statistical methods (e.g., regression analysis) to adjust
REE for fat-free mass when exploring between-individual thermogenic
differences (9, 17, 27). However, the question remains:
why do small subjects have a greater REE/fat-free mass ratio than do
large subjects?
Animal studies suggest that with greater mammal size, the proportion of
both body mass and fat-free mass as high-metabolic-rate organs and
tissues (e.g., brain) decreases (3, 5). In contrast, with
greater mammal size, the proportion of both body mass and fat-free mass
as low-metabolic-rate tissues (e.g., bone, adipose tissue, etc.)
increases (3, 5). However, the means of exploring on a
large scale in humans the interrelations between body weight, heat-producing organs and tissues, and fat-free mass in vivo was lacking until the introduction of magnetic resonance imaging (MRI) in
the mid-1980s (11). With MRI, investigators can quantify the volumes of all major heat-producing tissues and organs in healthy
subjects without exposure to ionizing radiation (13).
A higher REE/fat-free mass ratio and thus a relatively higher metabolic
rate in low-body-weight human subjects could be explained by a larger
proportion of fat-free mass as high-metabolic-rate tissues compared
with heavy subjects with a greater fat-free mass (31, 32).
The aim of the present study was to test this hypothesis in a large
cohort of healthy adults with the use of advanced imaging methods.
 |
METHODS |
Experimental design.
Our strategy was first to quantify with whole body MRI the volumes of
major high- and low-metabolic-rate compartments in healthy adult men
and women. In a subsequent analysis phase, we established whether these
collective measured components could account for measured REE with the
use of previously reported tissue-specific heat production rates
(7, 8). We also sought to confirm the close association
between the molecular level component fat-free mass and the
corresponding tissue/organ level component (measured by MRI)
adipose tissue-free mass, as defined by the formula: fat-free mass = adipose tissue-free mass + 0.15 × adipose tissue mass. This model assumes that 85% of adipose tissue is fat (16)
and 15% of adipose tissue is the remaining calculated fat-free
component. The ratio of fat to adipose tissue is variable
(16), but the impact of observed differences in this model
have only a small influence on the analyses that follow. Hence,
fat-free mass can be calculated as: fat-free mass = adipose
tissue-free mass + fat-free adipose tissue. This confirmatory
procedure was required to subsequently consider fat-free mass in
relation to REE and body composition compartments by using MRI-derived
adipose tissue-free mass and adipose tissue mass.
After this initial evaluation phase, we tested the hypothesis that the
proportion of fat-free mass as high- and low-metabolic-rate tissues and
organs varies as a function of fat-free mass. Our experiment thus
critically evaluated the long-standing but often challenged assumption
that fat-free mass is a metabolically homogeneous compartment.
Four major tissue/organ-level compartments were evaluated with MRI and
dual-energy X-ray absorptiometry (DEXA): adipose tissue, skeletal
muscle, bone, and residual mass. Residual mass, the difference between
body mass and other measured components (i.e., adipose tissue, skeletal
muscle, and bone), includes all of the high-metabolic-rate tissues and
organs such as heart, brain, liver, kidneys, spleen, and
gastrointestinal tract. In this study, we consider residual mass a
high-metabolic-rate compartment (225.9 kJ/kg), whereas low-metabolic-rate compartments include adipose tissue (18.8 kJ/kg), skeletal muscle (54.4 kJ/kg), and bone (9.6 kJ/kg). The
four-compartment REE values are based on the tissue/organ-specific
metabolic rates reported earlier by Elia (7, 8), Holliday
et al. (18), and Grande (14, 15)
along with REE and body composition data from Reference Man
(30). These specific metabolic rate values reflect
literature-derived coefficients based upon limited human and animal
studies. The specific metabolic rate values do not consider the
metabolic effects of over- and underfeeding, various hormonal/genetic
factors that might alter tissue energy flux, or purported interactive
metabolic effects of tissues and organs. Earlier studies from our
laboratory support the use of these tissue/organ-specific metabolic
rates in healthy, weight-stable, young adults (13).
Fat-free mass was calculated as the difference between body mass and
fat mass as measured by DEXA and related adipose tissue-free mass as
the difference between body mass and adipose tissue mass as measured by
MRI. As noted, fat-free mass was also calculated from adipose tissue
and adipose tissue-free mass. The associations between the molecular
level fat and fat-free mass components and the tissue/organ calculated
fat-free mass component are summarized in Fig.
1.

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Fig. 1.
Associations between molecular and tissue/organ-level
components evaluated in the present study. AT, adipose tissue; FFAT,
fat-free AT component; ATFM, MRI-measured adipose tissue-free mass;
FFM, fat-free mass; RM, residual mass; SM, skeletal muscle.
|
|
Subjects.
Subjects were healthy adults with body mass index (BMI) <40
kg/m2. Each subject, recruited through newspaper
advertisements and from a local university, completed a medical history
and physical examination to ensure his/her good health. Subjects were
excluded from the study if they were age <18 yr, were involved in a
structured physical activity program (2), had medical
conditions or medication use known to affect body composition, or
reported recent weight loss or weight gain (>10% of body weight
within past year). The study was approved by the Institutional Review
Board of St. Luke's-Roosevelt Hospital, and each subject signed an
informed consent before participation.
REE and body composition studies were carried out either on the same
day or within 1 day of each other, and subjects were asked to fast
overnight before each evaluation day.
Body composition.
Body weight and height were measured using a digital scale and a
wall-mounted stadiometer, respectively.
MRI of the whole body was carried out as previously reported by
Gallagher et al. (13). Whole body MRI scans were prepared using a 1.5 Tesla scanner (General Electric, 6X Horizon, Milwaukee, WI). The MRI data were obtained using a T1-weighted, spin-echo sequence
with 210-ms repetition time and a 17-ms echo time. Subjects rested
quietly in the magnet bore in a prone position with their arms extended
overhead. With the use of the intervertebral space between the fourth
and fifth lumbar vertebrae as the origin point, transverse images with
10-mm slice thickness were obtained every 40 mm from hand to foot,
resulting in a total of ~40 images for each subject. A 26-s breath
hold was required during abdominal slice imaging.
All MRI scans were segmented into the four components by highly trained
analysts using image analysis software (Tomovision, Montreal, QC,
Canada). A multiple-step procedure was used to identify specific tissue
areas (cm2) for a given MRI image. A threshold was selected
for adipose tissue and lean tissue on the basis of the image gray-level
histogram, or a filter was used to distinguish between different
gray-level regions on the images, and lines were drawn around the
selected regions by use of a Watershed algorithm. Thereafter, the
analyst labeled the tissues of interest by assigning them different
color codes. Images were then reviewed by an interactive slice editor program that allowed for verification and, where necessary, correction of the segmented component results. The original gray level was superimposed on the binary-segmented image by means of a transparency mode to facilitate the corrections. The respective tissue areas in each
image were automatically calculated by summing the specific tissue
pixels and then multiplying by the individual pixel surface area. The
volume per slice (in cm3) of each selected tissue was
calculated by multiplying tissue area (cm2) by slice
thickness. The volume of each tissue for the space between two
consecutive slices was calculated via a mathematical algorithm reported
by Ross (26). Volume estimates were converted to mass
units (kg) by taking the product of volume (liters) and reported tissue
density (i.e., adipose tissue = 0.92 kg/l; skeletal muscle = 1.04 kg/l) (30).
DEXA was used to measure fat, fat-free mass, and bone mineral mass.
Subjects were scanned using a whole body DPX (Lunar Radiation, Madison,
WI; version 3.6 software) with a cerium filtered X-ray source. The DEXA
system software first divides pixels into bone mineral mass and soft
tissue compartments. Soft tissue is then further separated by system
software into lean soft tissue and fat (22, 23, 25). Bone
mass was calculated from bone mineral mass with theassumption of a
stable proportion of bone mass as mineral (bone mineral mass/bone = 0.54) (30).
REE.
REE was measured in postabsorptive subjects by means of the Columbia
Respiratory Chamber-Indirect Calorimeter (13). After entering the metabolic chamber, the supine subject rested quietly in
the thermoneutral environment, and a transparent plastic ventilated hood was placed over the head for 40-60 min. Rates of oxygen
consumption and carbon dioxide production were analyzed using
magnetopneumatic oxygen (Magnos 4G) and carbon dioxide (Magnos 3G)
analyzers (Hartmann & Braun, Frankfurt, Germany), respectively. Gas
exchange results were evaluated during the stable measurement phase and
converted to REE in megaJoules per day using the Weir equation
(33).
REE (kJ/day) was also calculated from measured adipose tissue, skeletal
muscle, bone, and residual mass (kg) on the basis of published
tissue/organ-specific metabolic rates as follows (7, 8, 18,
30): REE = 18.8 × adipose tissue + 54.4 × skeletal muscle + 9.6 × bone + 225.9 × residual
mass, where adipose tissue and skeletal muscle mass are from MRI
analysis, bone is DEXA bone mineral mass/0.54, and residual mass is
calculated as body weight
(skeletal muscle + adipose
tissue + bone).
Statistical methods.
Descriptive statistics are reported as means ± SD. Between-gender
differences were evaluated via t-tests at the two-tailed 0.05
-level.
Regression analysis was used to examine the relationships between
measured and calculated REE and between DEXA-measured fat-free mass and
fat-free mass from MRI-derived adipose tissue-free mass and adipose
tissue mass. Potential measurement bias was explored as suggested by
Bland and Altman (4). Mean differences between measured
and calculated REE and between measured and calculated fat-free mass
were evaluated using paired t-tests.
Associations between relative change in the measured components (e.g.,
skeletal muscle) and change in body mass, sometimes referred to as
differential growth, were examined in our cross-sectional sample by use
of the standard allometric model: component = a × body weightb, where b is the scaling
exponent (5, 29). Men and women were analyzed separately,
because large between-gender differences in adiposity are recognized
and the relationship between body fat (i.e., adipose tissue) and body
weight differs significantly between males and females (10,
12).
The hypothesis was tested in the last analysis phase by examining the
associations between high- and low-metabolic-rate compartments and
fat-free mass with the use of linear regression methods. Specifically, we sought to establish whether the fraction of fat-free mass as high-
and low-metabolic-rate components varies significantly as a function of
fat-free mass. Men and women were pooled in these analyses as is the
practice for exploring REE/fat-free mass associations (27).
 |
RESULTS |
Subjects.
As shown in Table 1, there were 130 men
and 159 women in four ethnic groups: African-American
(n = 82), Asian (n = 42), Caucasian (n = 122), and Hispanic (n = 43). Men
had greater body weight, skeletal muscle, bone, and residual mass than
women (all P < 0.01). Women were shorter and had a
larger adipose tissue mass (both P < 0.01) than men.
Model validations.
The group mean predicted REE was 6.57 ± 1.35 MJ/day and was not
significantly different from the measured REE of 6.49 ± 1.36 MJ/day. The group mean predicted REEs for females and males were 5.64 ± 0.77 and 7.70 ± 1.28 MJ/day, respectively, whereas
their measured counterparts were 5.72 ± 0.82 and 7.42 ± 1.49 MJ/day, respectively [both P = not significant
(NS)]. Predicted and measured REE were highly correlated
(r2 = 0.56, P < 0.001;
Fig. 2), and there was no significant
bias detected by Bland-Altman analysis (4). However,
multiple regression analysis revealed a small but statistically
significant (P = 0.004) age contribution to the
measured vs. predicted REE relationship (r2
increase from 0.56 to 0.58). The age effect can be seen when the
residual REE (i.e., the difference between measured and predicted REE)
is plotted against age, as shown in Fig.
3. The data, fit with a polynomial model,
suggests a close association between measured and predicted REE up to
~50 yr of age and an increasing small discrepancy thereafter reaching
~0.42 MJ/day by age 80 yr. These observations suggest that, after the
four tissue compartments are accounted for, older subjects have a lower
REE than their younger counterparts. However, the small age effect does
not measurably influence the results that follow, and we therefore
present findings for the composite group of 289 subjects.

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Fig. 2.
Predicted vs. measured resting energy expenditure (REE)
in 289 study subjects (P < 0.001). Units are MJ/day.
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Fig. 3.
Residual REE vs. age in 289 study subjects
(y = 0.0001x2 + 0.0006x + 0.110; r2 = 0.027, P = 0.005). REE is expressed as MJ/day.
|
|
Measured and calculated fat-free mass (52.2 ± 11.9 vs. 53.5 ± 11.9 kg) were not significantly different, and the two estimates were highly correlated with each other (r2 = 0.97, P < 0.001; Fig.
4). No bias was detected by Bland-Altman analysis, and no age or sex effects were observed with multiple regression analysis. All data analyses that follow involving fat-free mass are based on MRI-derived fat-free mass, as this component is
synchronous with the MRI-based calculated REE estimates.

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Fig. 4.
Calculated vs. measured FFM (kg) in 289 study subjects
(P < 0.001). Dashed line, the line of identity; white
line, the regression line.
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REE vs. fat-free mass.
Measured REE is plotted against fat-free mass in Fig.
5A. There was a strong
association, as expected, between REE and fat-free mass
(r2 = 0.64, P < 0.001).
The REE/fat-free mass ratio is plotted against fat-free mass in Fig.
5B, and the association was negative in slope and
statistically significant (r2 = 0.17, P < 0.001). The predicted REE/fat-free mass for a
subject with a 50-kg fat-free mass is 0.123 MJ · kg
1 · day
1 and for a
subject with an 80-kg fat-free mass is 105 MJ · kg
1 · day
1. The
results of the present study thus demonstrate the previously reported
lowering of REE relative to metabolically active tissue, as defined by
fat-free mass, in subjects with greater metabolically active tissue
mass (31, 32).

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Fig. 5.
A: measured REE (MJ/day) vs. FFM (kg)
(P < 0.001). B: REE/FFM
(MJ · kg 1 · day 1) vs. FFM
(P < 0.001) in 289 study subjects.
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The fractional contributions of adipose tissue, skeletal muscle,
bone, and residual mass to body mass and model-predicted REE are shown
for men and women in Fig. 6, A and B,
respectively. Collectively, skeletal muscle, bone, and adipose tissue
contributed to 69.8 and 73.4% of body mass in men and women, whereas
respective contributions to REE were 30.9 and 31.7%, respectively. The
small residual mass, 30.1% of body mass in men and 26.6% in women,
contributed to 69.1 and 68.3% of predicted REE, respectively.

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Fig. 6.
Four tissue/organ components in females and males expressed as a
fraction of body mass (A) and as their respective fractional
contributions to REE (B).
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Body composition: relationship to body mass.
All of the measured components and REE increased as a function of body
mass but did so with different scaling exponents (Table 2). The pattern of component increase
relative to body weight (i.e., the scaling exponent b of
body weightb) was identical in males and females
with the following sequence: adipose tissue > skeletal
muscle > REE = fat-free mass > residual mass.
In other words, with greater body mass, the relative increase in
adipose tissue and skeletal muscle exceeded that of REE; relative
increases in REE and fat-free mass were similar, and the relative
increase in residual mass was smaller than that of all other components
and REE. The one exception was the small bone component, which scaled
differently compared with the other components in males and females.
Hence, with increasing body mass, both males and females responded
similarly with relatively greater increases in low-metabolic-rate
tissues (i.e., adipose tissue and skeletal muscle) compared with the
high-metabolic-rate residual mass.
Fractional contributions to fat-free mass.
With increasing fat-free mass there were corresponding increases in
skeletal muscle, bone, residual mass, and fat-free adipose tissue for
pooled subjects, as shown in Fig.
7A. When each component was
expressed as a fraction of fat-free mass, the four components responded
differently, as shown in Fig. 7B. The proportion of fat-free
mass as skeletal muscle increased with greater fat-free mass
(slope = 0.0021) whereas there were corresponding reductions in
the proportions of fat-free mass as residual mass (slope =
0.001), bone (slope =
0.0002), and fat-free adipose tissue
(slope =
0.0009). Thus, relative to fat-free mass, greater
fat-free mass was associated with a larger proportion of
low-metabolic-rate tissues, skeletal muscle, and bone and a smaller
proportion of high metabolic residual mass. This effect can be seen in
Fig. 8, in which the fraction of fat-free
mass as low-metabolic-rate tissues (i.e., skeletal muscle + bone + fat-free adipose tissue) is plotted against fat-free mass.
With increasing fat-free mass, there was a corresponding increase in
low-metabolic-rate components.

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Fig. 7.
Four tissue/organ components in 289 study subjects plotted as
absolute mass against fat-free mass (A) (SM = 0.59 × FFM 6.33, r2 = 0.93; RM = 0.32 × FFM + 2.67, r2 = 0.78;
Bone = 0.08 × FFM + 0.70, r2 = 0.78; FFAT = 0.006 × FFM + 3.00, r2 = 0.0017) and as a
fraction of FFM (B) (SM/FFM = 0.0021 × FFM + 0.35, r2 = 0.35; RM/FFM = 0.001 × FFM + 0.43, r2 = 0.081;
Bone/FFM = 0.0002 × FFM + 0.11, r2 = 0.078; FFAT/FFM = 0.0009x + 0.11, r2 = 0.11). For all correlations, P < 0.001, except FFAT
vs. FFM, P = not significant.
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Fig. 8.
Ratio of low-metabolic-rate (LMR) tissues and organs to
FFM (LMR/FFM) vs. FFM in 289 study subjects. (P < 0.01).
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 |
DISCUSSION |
The present study strongly supports the hypothesis that
fat-free mass is not an energetically homogeneous compartment but varies systematically in heat-producing components as a function of
body mass and fat-free mass. Our allometric analyses in a
cross-sectional sample indicate that, with increasing body mass, there
is a corresponding relatively large increase in the adipose tissue
compartment and, to a lesser extent, the skeletal muscle compartment.
In contrast, our empirical cross-sectional models suggest that
increasing body mass is accompanied by a minimal expansion of the
high-metabolic-rate residual mass component. The result is that
composite fat-free mass experiences a change in composition with
expansion of body mass as a whole: skeletal muscle and fat-free adipose
tissue increase to a relatively greater extent than residual mass. The
net result is that, with greater body mass, there is an increase in the
proportion of fat-free mass as low-metabolic-rate tissues and a
decrease in the proportion as high-metabolic-rate tissues. The
anticipated effect, one that is actually observed in vivo, is a
lowering of REE relative to fat-free mass with increasing body size and
fat-free mass.
Our analysis is supported by close agreement between measured and
calculated values for both REE and fat-free mass. A reasonable qualitative group estimate of REE was obtained from only four tissue/organ components: adipose tissue, skeletal muscle, bone, and
residual mass. This four-component REE model may be useful for roughly
estimating how relative changes in one component or another influences
overall resting heat production. In an earlier report, Gallagher et al.
(13) confirmed the validity of a more complex tissue/organ
model in young adults. The reported model included brain, liver,
kidneys, and heart in addition to adipose tissue, skeletal muscle, and
bone. The residual mass was thus smaller in magnitude than the residual
mass as calculated in the present study. In accordance with the present
study, Gallagher et al., in a follow-up investigation, applied their
REE model to a group of older subjects and also found a
lower-than-expected resting heat production (12). These
observations extend earlier reports suggesting that, after controlling
for fat-free mass, the elderly have a reduced REE of unknown mechanism.
Our estimate of fat-free mass from adipose tissue mass and adipose
tissue-free mass was nearly identical to the fat-free mass measured by
DEXA. This observation demonstrates the strong and quantifiable links that exist between body composition levels, in this case the molecular and tissue/organ levels.
An immediate implication of the present study is that REE
"adjusted for fat-free mass" should be interpreted with caution. A
high or low REE after adjusting for fat-free mass is usually interpreted as meaning a high- or low-energy flux rate through metabolically active tissues. Our observations suggest an equally likely possibility: the existence of a relatively large or small proportion of fat-free mass as high-metabolic-rate tissues and organs.
This finding leads to the question of what the ideal method is of
adjusting REE for between-individual differences in body size.
Unfortunately, all potential combined body composition compartments (e.g., adipose tissue-free mass) share with fat-free mass the same
problem as representing a metabolically heterogeneous compartment. Ideally, future technological advances will allow direct measurement of
a tissue or organ's resting heat production while corresponding tissue/organ mass is estimated using methods such as multi-slice MRI.
Study limitations.
An important assumption in the present study is that
tissue/organ-specific metabolic rates are known and constant in healthy young adults. These coefficients reflect compiled summaries (7, 8, 14, 15, 18, 30) of small-scale animal and human experiments
with limited validation, as in this and earlier studies (12,
13), and should be interpreted and applied with caution. Many
factors may be responsible for causing individual variation in
tissue/organ-specific metabolic rates, and some of these factors can
now be studied in vivo with the use of newly developed metabolic study methods.
In conclusion, the present study results, taken collectively, suggest
that tissue/organ components associated with fat-free mass vary
relative to fat-free mass as a function of body size and fat-free mass.
The observed pattern of changes suggests that the greater magnitude
REE/fat-free mass ratio observed in low-body mass subjects can be
attributed to the high proportion of fat-free mass as residual mass and
low proportion as fat-free adipose tissue, skeletal muscle, and bone.
These observations have important implications for the evaluation and
interpretation of between-individual differences in resting heat
production and energy requirements.
 |
ACKNOWLEDGEMENTS |
This study was supported by National Institutes of Health Grant
RR-00645 and National Institute of Diabetes and Digestive and Kidney
Diseases Grants DK-42618, DK-51716, and DK-26687.
 |
FOOTNOTES |
Address for reprint requests and other correspondence: S. B. Heymsfield, Obesity Research Center, 1090 Amsterdam Ave., 14th Floor, New York, NY 10025 (E-mail: SBH2{at}Columbia.edu).
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. Section 1734 solely to indicate this fact.
Received 9 March 2001; accepted in final form 17 August 2001.
 |
REFERENCES |
1.
Arciero, PJ,
Goran MI,
and
Poehlman ET.
Resting metabolic rate is lower in women than in men.
J Appl Physiol
75:
2514-2520,
1993[Abstract].
2.
Baecke, JA,
Burema J,
and
Frijters JE.
A short questionnaire for the measurement of habitual physical activity in epidemiological studies.
Am J Clin Nutr
36:
936-942,
1982[Abstract].
3.
Brody, S.
Bioenergetics and Growth, With Special Reference to the Efficiency Complex in Domestic Animals. New York: Reinhold, 1945.
4.
Bland, JM,
and
Altman DG.
Statistical methods for assessing agreement between two methods of clinical measurement.
Lancet
8:
307-310,
1986.
5.
Calder, WAIII
Size, Function, and Life History. New York: Dover Publications, 1996.
6.
Cunningham, JJ.
A reanalysis of the factors influencing basal metabolic rate in normal adults.
Am J Clin Nutr
33:
2372-2374,
1980[Abstract].
7.
Elia, M.
Energy expenditure in the whole body.
In: Energy Metabolism: Tissue Determinants and Cellular Corollaries, edited by Kinney JM,
and Tucker HN. New York: Raven, 1992, p. 19-60.
8.
Elia, M.
Organ and tissue contribution to metabolic rate.
In: Energy Metabolism: Tissue Determinants and Cellular Corollaries, edited by Kinney JM,
and Tucker HN. New York: Raven, 1992, p. 61-80.
9.
Ferraro, R,
Lillioja S,
Fontvielle AM,
Rising R,
Bogardus C,
and
Ravussin E.
Lower sedentary metabolic rate in women compared to men.
J Clin Invest
90:
1-5,
1992[ISI][Medline].
10.
Forbes, G.
Human Body Composition. New York: Springer- Verlag, 1987, p. 28-49.
11.
Foster, MA,
Hutchison JMS,
Mallard JR,
and
Fuller M.
Nuclear magnetic resonance pulse sequence and discrimination of high- and low-fat tissues.
Magn Reson Imaging
2:
187-192,
1984[Medline].
12.
Gallagher, D,
Allen A,
Wang ZM,
Heymsfield SB,
and
Krasnow N.
Smaller organ tissue mass in the elderly fails to explain lower resting metabolic rate.
Ann NY Acad Sci
904:
449-455,
2000[Abstract/Free Full Text].
13.
Gallagher, D,
Belmonte D,
Deurenberg P,
Wang ZM,
Krasnow N,
Pi-Sunyer FX,
and
Heymsfield SB.
Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass.
Am J Physiol Endocrinol Metab
275:
E249-E258,
1998[Abstract].
14.
Grande, F.
Energy expenditure of organs and tissues.
In: Assessment of Energy Metabolism in Health and Disease, edited by Kinney JM. Columbus, OH: Ross Laboratories, 1989, p. 88-92.
15.
Grande, F.
Nutrition and energy balance in body composition.
In: Techniques for Measuring Body Composition, , edited by Brozek J,
and Herschel A. Washington, DC: National Academy of Sciences-National Research Council, 1961, p. 168-188.
16.
Garrow, JS.
Energy Balance and Obesity in Man. Oxford, UK: Elsevier Biomedical, 1978.
17.
Hoffmans, M,
Pfeifer WA,
Gundlach BL,
Nijkrake HGM,
Oude Ophuis AJM,
and
Hautvast JGAJ
Resting metabolic rate in obese and normal weight women.
Int J Obes
3:
111-118,
1979[ISI][Medline].
18.
Holliday, MA,
Potter D,
Jarrah A,
and
Bearg S.
The relation of metabolic rate to body weight and organ size.
Pediatr Res
1:
185-195,
1967[ISI][Medline].
19.
Kinney, JM,
Lister J,
and
Moore FD.
Relationship of energy expenditure to total exchangeable potassium.
Ann NY Acad Sci
10:
711-722,
1963.
20.
Kleiber, M.
The Fire of Life. An Introduction to Animal Energetics. New York: Wiley, 1961.
21.
Kleiber, M.
Body size and metabolism.
Hilgardia
6:
315-353,
1932.
22.
Kohrt, W.
Body composition by DEXA: tried and true?
Med Sci Sports Exerc
27:
1349-1353,
1995[ISI][Medline].
23.
Lohman, TG.
Dual energy x-ray absorptiometry.
In: Human Body Composition, edited by Roche AF,
Heymsfield SB,
and Lohman TG. Champaign, IL: Human Kinetics, 1996, p. 63-78.
24.
Nelson, KM,
Weinsier RL,
Long CL,
and
Schutz Y.
Prediction of resting energy expenditure from fat-free mass and fat mass.
Am J Clin Nutr
56:
848-856,
1992[Abstract].
25.
Pietrobelli, A,
Formica C,
Wang ZM,
and
Heymsfield SB.
Dual-energy X-ray absorptiometry body composition model: review of physical concepts.
Am J Physiol Endocrinol Metab
271:
E941-E951,
1996[Abstract/Free Full Text].
26.
Ross, R.
Magnetic resonance imaging provides new insights into the characterization of adipose and lean tissue distribution.
Can J Clin Pharmacol
74:
778-785,
1996.
27.
Ravussin, E,
and
Bogardus C.
Relationship of genetics, age and physical fitness to daily energy expenditure.
Am J Clin Nutr
49:
968-975,
1989[ISI][Medline].
28.
Rubner, M.
Uber den Einfluss der Korpergrosse auf Stoff- und Kraft-wechsel.
Z Biol
19:
535-562,
1883.
29.
Schmidt-Nielsen, K.
Scaling: Why Is Animal Size So Important? Cambridge, UK: Cambridge Univ. Press, 1984.
30.
Snyder, WS,
Cook MJ,
Nasset ES,
Karhausen LR,
Howells GP,
and
Tipton IH.
Report of the Task Group on Reference Man. Oxford, UK: Pergamon, 1975.
31.
Wang, ZM,
Heshka S,
Gallagher D,
Boozer CN,
Kotler DP,
and
Heymsfield SB.
Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling.
Am J Physiol Endocrinol Metab
279:
E539-E545,
2000[Abstract/Free Full Text].
32.
Weinsier, RL,
Schutz U,
and
Bracco D.
Reexamination of the relationship of resting metabolic rate to fat-free mass and to the metabolically active components of fat-free mass in humans.
Am J Clin Nutr
5:
790-794,
1992.
33.
Weir, JB.
New methods for calculating metabolic rate with special reference to protein metabolism.
J Physiol (Lond)
109:
1-9,
1949[ISI].
Am J Physiol Endocrinol Metab 282(1):E132-E138
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