International Journal of Obesity (1999) 23, 715±722
ß 1999 Stockton Press All rights reserved 0307±0565/99 $12.00
http://www.stockton-press.co.uk/ijo
Determinants of energy expenditure and fuel
utilization in man: effects of body composition,
age, sex, ethnicity and glucose tolerance in 916
subjects
C Weyer1*, S Snitker1, R Rising1, C Bogardus1 and E Ravussin1
1
Clinical Diabetes and Nutrition Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health,
Phoenix, AZ
BACKGROUND: 24-h energy expenditure (24-EE) and 24-h respiratory quotient (24-RQ) are important measurements
in obesity research, but their accurate assessment is limited to few specialized laboratories.
OBJECTIVES: 1) To provide comprehensive prediction equations for 24-EE, sleeping metabolic rate (SMR) and 24-RQ,
based on a large number of Caucasian and Pima Indian subjects, covering a wide range of body weight and
composition, body fat distribution, and age and 2) to test whether Pima Indians have lower metabolic rate and=or
higher 24-RQ than Caucasians.
SUBJECTS AND METHODS: 916 non-diabetic subjects, aged 31.5 11.9 y, body weight 90.5 26.1 kg (mean s.d.),
(561 males, 355 females; 416 Caucasians, 500 Pima Indians; 720 with normal (NGT) and 196 with impaired (IGT)
glucose tolerance) spent 24 h in a respiratory chamber for measurements of 24-EE, SMR and 24-RQ. Fat-free mass
(FFM) and fat mass (FM) were assessed by either hydrodensitometry or DEXA. Waist circumference and waist-to-thigh
ratio (WTR) were determined as measures of body fat distribution.
RESULTS: In a stepwise multiple regression analysis, FFM, FM, sex, age, WTR, and ethnicity were signi®cant
independent determinants of 24-EE (2258 422 kcal=d), explaining 85% of its variability (24-EE
(kcal=d) ˆ 696 ‡ 18.9 FFM (kg) ‡ 10.0 FM (kg) ‡ 180 male 7 1.9 age (y) ‡ 7.1 WTR (per decimal) ‡ 44 Pima Indian).
SMR (1623 315 kcal=d) was determined (78% of variability) by FFM, FM, sex, age, WTR, and glucose tolerance
(SMR (kcal=d) ˆ 443 ‡ 14.6 FFM (kg) ‡ 6.9 FM (kg) ‡ 79 male 7 1.0 age (y) ‡ 5.8 WTR (per decimal) ‡ 38 IGT), but not by
ethnicity. Adjustment for the respective variables reduced the variance in 24-EE from 422 to 162 kcal=d and in SMR
from 315 to 146 kcal=d. 24-RQ (0.854 0.026) was determined by waist circumference and energy balance (24RQ ˆ 0.88429 ± 0.00175 waist circumference (cm) ‡ 0.00004 energy balance (%)), but not by sex, ethnicity or glucose
tolerance. With this equation only 13% of the variability in 24-RQ could be explained (residual variance 0.024).
Compared to Caucasians, Pima Indians had higher 24-EE, but similar SMR and 24-RQ.
CONCLUSIONS: This analysis provides comprehensive prediction equations for 24-EE, SMR and 24-RQ from their
major known determinants. It con®rms the previous ®ndings that, even after adjustment for body composition, age,
sex, ethnicity, and glucose tolerance, there is still considerable variability in energy expenditure and substrate
oxidation that may, in part, be genetically determined. In adult Pima Indians, we found no evidence for lower
metabolic rate or impaired fat oxidation that could explain the propensity towards obesity in this ethnic group.
Keywords: energy metabolism; respiratory chamber; respiratory quotient; metabolic rate; Caucasians; Pima Indians
Introduction
Measurements of energy expenditure and substrate
oxidation have been important in our understanding
of the pathophysiology of human obesity,1 ± 5 but the
accurate assessment of both variables over 24 h
requires sophisticated methodology that is available
in only a small number of laboratories.6 ± 10 The
human whole body respiratory chamber allows measurement of the 24-h energy expenditure (24-EE), and
*Correspondence: Christian Weyer, MD, Clinical Diabetes and
Nutrition Section, National Institutes of Health, 4212 N 16th
Street, Rm 5-14, Phoenix, AZ, 85016, USA.
E-mail: [email protected]
Received 12 October 1998, revised 20 January 1999, accepted
19 February 1999
of the 24-h respiratory quotient (24-RQ) which Ð in
conjunction with measurement of 24-h urinary nitrogen excretion Ð allows calculation of the 24-h oxidation rates of fat, carbohydrate and protein.6,7,10
Several standard equations have been developed for
the prediction of energy expenditure on the basis of
body size and composition, age and sex.11 ± 13 However, considerable variability in energy expenditure
and substrate oxidation remains after adjustment for
differences in body size.3,6,14,15 In longitudinal studies, we have shown that this variability is important
for the development of obesity in that a low relative
energy expenditure3 and a high relative 24-RQ, that is
a low rate of fat-to-carbohydrate oxidation16 are predictive of body weight gain in humans. To minimize
the remaining variability in predictive equations, it is
important to identify additional determinants and
Determinants of energy metabolism
C Weyer et al
716
include them into these equations. Over the past
decade, we and others have shown that energy expenditure is not only determined by body composition,6,17
age18 and sex,19 but also by ethnicity,20 body fat
distribution,21 and glucose tolerance22 and that it is a
familial trait.17,23 It has also been reported that the 24RQ is determined by energy balance, age and sex and
that it is a familial trait.14,24,25 However, most of these
®ndings were based on rather small sample sizes. To
assess whether a given variable is an independent
determinant of any measurement of energy metabolism, it is crucial to adjust for all other co-variables
known to determine this measurement.14 The ®rst aim
of the present analysis, therefore, is to provide comprehensive equations for the estimation of 24-EE,
sleeping metabolic rate, and 24-RQ as derived from
a large number of subjects covering a wide range of
body weight and age, with different ethnic background and glucose tolerance.
More than half of the individuals included in this
analysis were Pima Indians of Arizona, an ethnic
group with a very high prevalence of obesity. Since
a reduced rate of 24-EE and a high 24-RQ are
predictors of weight gain,3,16 it could be hypothesized
that Pima Indians have lower rates of energy expenditure and=or fat oxidation, compared to Caucasians.
The second aim of the analysis was to test this
hypothesis.
Methods
Subjects
Of the 1229 subjects who ever spent 24 h in the
respiratory chamber of the Clinical Diabetes and
Nutrition Section of the National Institutes of Health
in Phoenix, AZ between 1985 and 1998, 916 were
included into this analysis (561 males (M), 355
females (F)), (416 Caucasians, 500 Pima Indians;
720 with normal glucose tolerance (NGT) and 196
with impaired glucose tolerance (IGT)), (Table 1).
Criteria that led to the exclusion of the other 313
subjects were age < 18 y, diabetes (as per 75 g oral
glucose tolerance test)26 or other medical conditions
or use of medications known to affect energy metabolism (as per patient history, physical examination
and routine laboratory tests). Also, subjects of other
ethnicity (African ± Americans (n ˆ 41), Hispanics
(n ˆ 4)) were excluded as were subjects from studies
in which exercise was involved or in which the diet
differed from our standard weight maintaining diet.27
Finally, only subjects who were in energy balance
(energy intake 20% of expenditure) during the stay
in the respiratory chamber were included in the
analysis. Prior to their stay in the respiratory chamber,
all subjects spent at least 3 d on the metabolic ward of
the Clinical Diabetes and Nutrition Section of the
National Institutes of Health (Phoenix, AZ), during
which they were fed a weight-maintaining diet (50%
carbohydrate, 30% fat, and 20% protein)27 and
abstained from strenuous exercise. The different protocols of the studies included in this analysis were all
approved by the Institutional Review Board of the
National Institute of Diabetes and Digestive and
Kidney Diseases and by the Tribal Council of the
Gila River Indian Community for those including
Pima Indians.
Body composition and fat distribution
Body composition was estimated by underwater
weighing until January 199628,29 (n ˆ 866) and by
total body dual energy X-ray absorptiometry thereafter (DPX-1; Lunar Radiation Corp., Madison, WI,
USA)30 (n ˆ 50). A conversion equation, derived from
comparative analysis in our unit, was used to make
measurements of body composition comparable
between the two methods.31 Waist-to-thigh ratio was
calculated as a measure of body fat distribution, with
circumferences measured at the level of the umbilicus
(waist) and the gluteal fold (thigh) in the supine and
standing position, respectively.21
Energy Metabolism
The measurement of energy expenditure and substrate
oxidation in the respiratory chamber has previously
been described.6 In brief, volunteers entered the
Table 1 Physical characteristics of the 916 subjects, in total, and divided by sex, ethnicity and glucose tolerance.
Sex
n
Males=Females
age (y)
body weight (kg)
% body fat
FM (kg)
FFM (kg)
WC (cm)
WTR ratio
Ethnicity
Glucose tolerance
All
Males
Females
Caucasians
Pima Indians
Normal
Impaired
916
561=355
32 12
90.5 26.1
29 11
32.6 19.0
57.9 12.3
101 20
1.58 0.18
561
355
31 11
91.5 25.9
24 9
28.0 17.8
63.5 10.5
99 20
1.60 0.18
33 13*
88.9 26.3
37 8**
39.8 18.5**
49.0 9.5**
104 21**
1.56 0.19*
416
250=166
35 14
86.1 26.9
26 12
28.1 20.1
58.0 12.6
95 21
1.51 0.18
500
311=189
29 9**
94.2 24.8**
32 9**
36.3 17.2**
57.9 12.2
106 18**
1.64 0.16**
720
465=255
31 12
7.9 24.9
28 11
0.1 18.2
57.8 11.9
98 19
1.56 0.18
196
96=100
34 13**
100.2 28.2**
35 9**
41.7 19.3**
8.4 13.7
111 20**
1.65 0.17**
Asterisks indicate signi®cant differences between the 2 sexes, ethnic groups and glucose tolerance groups, respectively * P < 0.05, **
P < 0.01, unpaired t-test). FM ˆ fat mass, FFM ˆ fat free mass, WC ˆ Waist circumference, WTR ˆ waist to thigh ratio.
Determinants of energy metabolism
C Weyer et al
chamber at 0745 h after an overnight fast and
remained therein until 0700 h the following morning.
Subjects were fed an amount of energy according to
previously determined equations,27 with meals provided at 0800 h, 1130 h, 1700 h and an evening snack
(2000 h).6 The rate of energy expenditure was measured continuously, calculated for each 15 min interval within the 23 h in the chamber, and then
extrapolated to 24 h (24-h energy expenditure, 24EE).6 Spontaneous physical activity (SPA) was
detected by radar sensors and expressed as percent
time over the 24 h period, in which activity was
detected.6 Sleeping metabolic rate (SMR) was de®ned
as the average energy expenditure of all 15 min
periods between 2330 h and 0500 h during which
SPA was < 1.5%.6 Carbon dioxide production
(VCO2) and oxygen consumption (VO2) were calculated for every 15 min interval within the 23 h in the
chamber and then extrapolated to 24 h. The 24-h
respiratory quotient (24-RQ) was calculated as the
ratio of 24-h VCO2 and 24-h VO2.
717
24-h energy expenditure
24-EE ˆ 696
(kcal=d)
Statistical Analyses
Statistical analyses were performed using the software
of the SAS Institute (Cary, NC).32 The analysis was
done in two steps.
First, stepwise linear regression models were used
to identify the determinants of 24-EE, SMR and 24RQ. The coef®cients of determination (R2), derived
from this analysis for each step, represent the percentage of variance in 24-EE, SMR or 24-RQ, explained
by the variables entered into the model.32
Second, the determinants of 24-EE, SMR and 24RQ identi®ed in the stepwise regression analysis were
entered one by one into a general linear regression
model to obtain the residual variances (pMSE-roots
of the mean square errors) which remain after adjusting for every new variable.32 When the models ®nally
included all variables, the intercepts and slopes for
the respective regression equations were obtained
(Figures 1 ± 3).
Determinant
Total variance
(s.d.) ˆ 422
Residual variance
pMSE
P-value
‡ 18.9 FFM (kg)
‡ 10.0 FM (kg)
‡ 180 male
7 1.9 age (y)
‡ 7.1 WTR (decimal)
‡ 44 Pima Indian
224
178
165
164
163
162
< 0.0001
< 0.0001
< 0.0001
< 0.001
< 0.05
< 0.001
Figure 1 Upper panels: Relationship between 24-h energy
expenditure (24-EE) and fat-free mass in 916 subjects of different
age, body weight and composition, sex, ethnicity and glucose
tolerance. 24-EE was signi®cantly related to fat-free mass
(r ˆ 0.84, P < 0.0001), but at any given body size, there was a
high inter-individual variability, corresponding to a standard
deviation (s.d.) of 422 kcal=d.
Lower panels: Prediction equation for 24-EE including fat-free
mass, fat-mass, sex, age, waist-to-thigh ratio (WTR), and ethnicity. Adjustment for these variables explained 88% of the variability in 24-EE, reducing the variance from 422 to 162 kcal=d (p
MSE ˆ root of the mean square error). All variables were independent determinants of 24-EE in a multivariate regression
analysis.
24-h energy expenditure
24-h energy expenditure (24-EE) varied considerably
between subjects with a range from 1251 to
4225 kcal=d and a standard deviation of 422 kcal=d
(Table 2, Figure 1). In the stepwise multiple regression analysis, fat-free mass (FFM) was the strongest
determinant of 24-EE explaining 72% of its variance
(Figure 1). Further determinants of 24-EE, in descend-
Results
Sleeping metabolic rate
Physical characteristics
The physical characteristics of the subjects are shown
in Table 1. The study population covered a wide range
of age (18 ± 85 y), body weight (42.2 ± 215.2 kg) and
body composition (3 ± 53 % body fat). Note that age
and body composition differed signi®cantly between
males and females, between Caucasians and Pima
Indians, and between subjects with normal and
impaired glucose tolerance (Table 1).
SMR ˆ 443
(kcal=d)
Determinant
Total variance
(s.d.) ˆ 315
Residual variance
pMSE
P-value
‡ 14.6 FFM (kg)
‡ 6.9 FM (kg)
‡ 79 male
7 1.0 age (y)
‡ 5.7 WTR (decimal)
‡ 38 IGT
184
150
147
147
146
146
< 0.0001
< 0.0001
< 0.0001
< 0.05
< 0.05
< 0.01
Determinants of energy metabolism
C Weyer et al
718
Figure 2 Upper panels: Relationship between sleeping metabolic rate (SMR) and fat-free mass in 916 subjects of different
age, body weight and composition, sex, ethnicity and glucose
tolerance. SMR was signi®cantly related to fat-free mass
(r ˆ 0.80, P < 0.0001), with a standard deviation (s.d.) of
315 kcal=d.
Lower panels: Prediction equation for SMR including fat-free
mass, fat-mass, sex, age, WTR, and glucose tolerance. Adjustment for these variables explained 78% of the variance in SMR,
reducing the variability from 315 to 146 kcal=d (pMSE ˆ root of
the mean square error).
ing order, were fat mass (FM, sex, age, waist-to-thigh
ratio (WTR), and ethnicity: 24-EE (kcal=d) ˆ
696 ‡ 18.9 FFM (kg) ‡ 10.0 FM (kg) ‡ 180 male
7 1.9 age (y) ‡ 7.1 WTR (per decimal) ‡ 44 Pima
Indian (Figure 1). Adjustment for these variables
explained another 13%, that is a total of 85% of the
variability in 24-EE. The residual variance after
adjustment for these determinants was 162 kcal=d,
which is 260 kcal=d lower than the standard deviation
of 24-EE (Figure 1). Spontaneous physical activity
(SPA, P < 0.0001) was an additional independent
determinant of 24-EE, explaining another 3 % of the
variability (residual variance 149 kcal=d) (24-EE
(kcal=d) ˆ 550 ‡ 19.0 FFM (kg) ‡ 9.8 FM (kg) ‡ 19.5
SPA (%) ‡ 175 M 7 2.2 age (y) ‡ 7.5 WTR (per
decimal) ‡ 36 Pima Indian). SPA was higher in
males compared to females (7.9 3.2 vs 7.4 2.8%,
P < 0.05) and in Pima Indians compared to Caucasians (7.9 3.0 vs 7.5 3.0 %, P < 0.05), but even
after adjustment for these differences, 24-EE was
higher in males compared to females ( ‡ 175
19 kcal=d, P < 0.0001) and in Pima Indians compared
to Caucasians ( ‡ 36 12 kcal=d, P < 0.01). Subjects
with IGT tended to have higher 24-EE than subjects
with NGT ( ‡ 20 13 kcal=d), but this trend was not
signi®cant.
24-h respiratory quotient
Total variance
(s.d.) ˆ 0.026
Residual
variance
pMSE
P-value
Determinant
24-RQ ˆ 0.88429 7 0.00175 WC (cm)
(kcal=d)
‡ 0.00004 E-balance (%)
0.025
0.024
< 0.0001
< 0.0001
24-RQ ˆ 0.86505 7 0.00045 body fat (%)
‡ 0.00004 E-balance (%)
‡ 0.00013 age (y)
0.025
0.024
0.024
< 0.0001
< 0.0001
< 0.05
Figure 3 Upper panels: Relationship between 24-h respiratory
quotient (24-RQ) and % body fat in 916 subjects of different age,
body weight and composition, sex, ethnicity and glucose tolerance. 24-RQ was signi®cantly related to % body fat (r ˆ 7 0.21,
P < 0.0001), with a standard deviation (s.d.) of 0.026.
Lower panels: Prediction equations for 24-RQ including waist
circumference and energy balance or % body fat, energy balance
and age, respectively. Note that adjustment for these variables
reduced the variance by only 0.002 and that sex, glucose tolerance, and ethnicity were not independent determinants of 24RQ. (pMSE ˆ root of the mean square error).
variance (Figure 2). Fat mass, sex, age, WTR, and
glucose tolerance were further determinants of SMR
(Figure 2), while spontaneous physical activity and
ethnicity were not. After adjustment for all determinants, 78% of the variability in SMR could be
explained resulting in a residual variance of
146 kcal=d (Figure 2). Adjusted SMR was higher in
males compared to females ( ‡ 79 18 kcal=d,
P < 0.001), and in subjects with IGT compared to
those with NGT ( ‡ 38 12 kcal=d, P < 0.01) (Figure
2). Unlike 24-EE, there was no difference in adjusted
Table 2 Energy metabolism in the 916 subjects
mean s.d.
Sleeping metabolic rate
Sleeping metabolic rate (SMR) ranged from 876 to
3728 kcal=d with a standard deviation of 315 kcal=d
(Table 2, Figure 2). The strongest determinant of
SMR was fat-free mass, which explained 66% of its
24-h energy intake (kcal=d)
2217 372
24-h energy expenditure (kcal=d)
2258 422
24-h energy balance (kcal=d)
7 45 187
spontaneous physical activity (%)
7.7 3.0
sleeping metabolic rate (kcal=d)
1623 315
24-h respiratory quotient
0.854 0.026
range
1115 ± 3837
1251 ± 4225
7 581 ± 418
2.1 ± 23.7
876 ± 3728
0.771 ± 0.931
Determinants of energy metabolism
C Weyer et al
SMR between Pima
(0 0 kcal=d, n.s.).
Indians
and
Caucasians
24-h respiratory quotient
24-h respiratory quotient (24-RQ) ranged from 0.771
to 0.931 with a standard deviation of 0.026 (Table 2).
Waist circumference and energy balance were independent determinants of 24-RQ, explaining 13% of its
variability (residual variance 0.024) (Figure 3). A
comparable percentage of variance in 24-RQ (12 %)
could be explained, when % body fat was used instead
of waist circumference. In this case, age became an
additional independent determinant of 24-RQ (Figure
3). Adjusted 24-RQ did not differ between males and
females or between subjects with NGT and IGT or
between Pima Indians and Caucasians.
Discussion
A low rate of 24-h energy expenditure and a high 24-h
respiratory quotient, that is a low rate of fat oxidation,
predict weight gain in humans3,16 indicating the
importance of energy expenditure and substrate oxidation in the pathophysiology of human obesity. Since
the exact measurement of these parameters requires
sophisticated methodology, they are often approximated by use of population-derived predictive equations based upon body composition, age and sex.11 ± 13
More recently, evidence has accumulated that energy
metabolism is additionally determined by other variables such as body fat distribution,21,24 ethnicity,20
and glucose tolerance.22 However, some of these
variables are related to each other. For example,
body composition and body fat distribution differ
between men and women, among different populations, and change with increasing age.33 ± 35 To assess
whether different variables are determinants of energy
metabolism independent of each other and to integrate
them into more comprehensive equations, a large
number of subjects of different body size, age, sex,
ethnicity and glucose tolerance is required. The present analysis of 916 respiratory chamber measurements over 24 h provides such comprehensive
equations for three major measurements of energy
metabolism, namely 24-h energy expenditure, sleeping metabolic rate and 24-h respiratory quotient.
Our ®nding that fat-free mass is the single best
predictor of 24-h energy expenditureship agrees
with previous reports from our group6,14 and
others.17,24 The percentage of variance in 24-EE that
was explained by fat-free mass in our analysis (72%)
is similar to the values reported by Astrup et al 24 and
Toubro et al17 of 75% and 82%, respectively. It has
also been previously reported that fat mass and
spontaneous physical activity (SPA) are independent
additional determinants of 24-h energy expenditure.6,14,17 A low level of SPA is predictive of
weight gain in males over three years36 and after 8
weeks of overfeeding37 underscoring the importance
of this variable. On the other hand, assessment of SPA
is not standardized to one commonly accepted measure and inclusion of our data on SPA, as assessed by
a radar system, would be of little use for others. We
therefore provided here two prediction equations for
24-EE, one with and one without SPA. With the
smaller number of subjects (n ˆ 177) in our previous
analysis we found no effect of age and sex on 24-h
energy expenditure.6 Both are now found to be additional independent determinants. After adjustment for
body composition, the effect of age was marginal, but
signi®cant, with a decrease of 2 kcal=d per year. In
contrast, a larger difference in 24-h energy expenditure of 180 kcal=d was found between men and
women after adjustment for body composition. This
difference is higher than the one we previously
reported for resting energy expenditure of approximately 100 kcal=d.19 A new and surprising ®nding
was that adjusted 24-h energy expenditure was higher
( ‡ 44 kcal=d) in Pima Indians compared to Caucasians. Since Pima Indians are prone to obesity and a
relatively low metabolic rate predicts weight gain in
this population,3 a lower 24-h energy expenditure
might have been anticipated. Several aspects are
important to consider in this context. First, the
higher 24-h energy expenditure in Pima Indians was
largely due to higher spontaneous physical activity
and no ethnic difference was found for resting energy
expenditure (sleeping metabolic rate). Since 24-h
energy expenditure and physical activity in a respiratory chamber are measured under restricted conditions, the results are not necessarily re¯ective of freeliving conditions and further studies such as with the
doubly labelled water method are warranted to con®rm the present ®ndings. Secondly, our analysis
included only adult Pima Indians of whom the majority were already obese. Therefore, our ®ndings do not
exclude the possibility that reduced energy expenditure at a younger age may contribute to the development of obesity in this population. We have
previously reported that individuals with type 2 diabetes have higher 24-h energy expenditure than those
with normal glucose tolerance.22 Since this could in
part be a secondary effect of the metabolic consequences of poor glycaemic control, diabetic subjects
were excluded from the present analyses. Interestingly, 24-h energy expenditure tended to be higher
( ‡ 20 kcal=d) in subjects with impaired, compared to
those with normal glucose tolerance, a ®nding that
became signi®cant when the waist-to-thigh ratio was
excluded from the regression model.
As with 24-h energy expenditure, fat-free mass was
the single best determinant of sleeping metabolic
rate, explaining 66% of its variability. This result is
in good agreement with ®ndings of another group
using a respiratory chamber17,24 and with our previous
®ndings on basal metabolic rate, as measured by a
ventilated hood system.14,15 In contrast to our previous
719
Determinants of energy metabolism
C Weyer et al
720
®ndings in a smaller number of subjects,14 age was an
independent determinant of sleeping metabolic rate in
the present analysis. The sex difference in sleeping
metabolic rate ( ‡ 79 kcal=d in men compared to
women) was similar to the one found for resting
metabolic rate15 and lower than for 24-h energy
expenditure as already discussed. In contrast to 24-h
energy expenditure, sleeping metabolic rate was signi®cantly higher ( ‡ 38 kcal=d) in subjects with
impaired, compared to those with normal glucose
tolerance, irrespective of whether or not the waistto-thigh ratio was included in the model. To our
knowledge, this is a novel ®nding, which suggests
that the increase in energy expenditure found in
individuals with type 2 diabetes22 occurs at an early
stage of the disease. This notion has recently been
con®rmed in a longitudinal study of our group,38
demonstrating that resting metabolic rate progressively increases during the transition from normal to
impaired to diabetic glucose tolerance over 5 y, independently of changes in body composition.
The exact mechanisms by which age, sex, ethnicity,
and glucose tolerance affect energy expenditure
remain unknown. Also, and as previously pointed
out,6,14,15, a rather large variability of about
150 kcal=d persists for both 24-h energy expenditure
and sleeping metabolic rate that cannot be explained
solely by the variability of the method or by intraindividual variations.6 Several physiological factors
were found to be related to metabolic rate include
sympathetic nervous system activity,17,39 body temperature,40 skeletal muscle metabolism,41 plasma concentrations of free T3,17,24 free-fatty acids, and
insulin,38 endogenous glucose production38 and the
expression of the novel uncoupling protein 3.42 These
factors may play a role in mediating the effects of age,
sex, ethnicity, and glucose tolerance on energy expenditure and may also explain part of the residual
variance. Also, and as pointed out,15 fat-free mass,
the single best known determinant of energy expenditure in humans, is not metabolically uniform, but
composed to different extents of inactive components
such as intercellular ¯uids and minerals on the one
hand and by metabolically highly active tissues=organs on the other.15,43 Finally, and of great
importance, we23 and others17,24 have previously
demonstrated that 24-h energy expenditure and resting
metabolic rate, respectively, are familial traits suggesting that much of the remaining variability may be
due to genetically determined factors not yet fully
identi®ed.
Compared to energy expenditure, the determinants
of substrate oxidation in man are less well understood.
It is known that the 24-h respiratory quotient is
mainly determined by two variables: dietary composition and energy balance. However, other factors have
been identi®ed that determine substrate oxidation
even when these two variables are controlled. These
factors include age,14,44 sex,14 waist circumference,24
and the plasma concentrations of insulin,14,17,24 free-
fatty acids17,24 and, in premenopausal women, of
estradiol.24 Furthermore, we14 and others17 have
demonstrated that 24-h respiratory quotient is a familial trait. The present results agree with our previous
®ndings in that 24-h respiratory quotient was positively related to age and energy balance. However, in
this larger group of 916 subjects, we could not con®rm
our previous observation of a sex difference in 24-h
respiratory quotient. Our ®nding that percent body fat
and waist circumference were negatively correlated
with 24-h respiratory quotient agrees with previous
®ndings of other groups.24,45,46 How adiposity and
waist circumference, respectively, affect 24-h substrate oxidation independently of energy balance
remains to be elucidated. The increased fat oxidation
seen with increasing adiposity may result simply from
the greater fat mass on the one hand,46 but may also be
due to metabolic abnormalities of obesity such as
increased sympathetic activity47 or increased adipose
tissue lipolysis.48 The ®nding further supports the
notion that enlarged body fat stores are an adaptation
to a high fat diet.49 It needs to be pointed out that the
equation for the approximation of 24-h respiratory
quotient derived from our data is of far less predictive
value than for the approximation of energy expenditure, as only 12 ± 13% of the variance in 24-h respiratory quotient could be explained. A greater proportion
of the variance in 24-h substrate oxidation may be
explained when biochemical variables such as plasma
hormone concentrations are taken into account.24
However, such variables are often not routinely
assessed and their inclusion into the equation would
thus not be useful for most investigators. Finally, we
found no difference in 24-h respiratory quotient
between adult Pima Indians and Caucasians suggesting that fat oxidation is not impaired in the former.
Conclusion
The present study provides comprehensive equations
for energy expenditure and fuel utilization in humans,
assessed in 916 subjects of different age, sex, body
size, ethnicity and glucose tolerance. In this large
number of subjects, we demonstrate that body composition, sex, age, fat distribution, ethnicity and glucose tolerance are independent determinants of energy
expenditure, while 24-h respiratory quotient is determined by energy balance, adiposity and age. Even
after adjustment for these variables, a considerable
inter-individual variability in energy expenditure, and
particularly substrate oxidation, remains that cannot
be explained and may in part be genetically determined. In adult Pima Indians, we found no evidence
of reduced energy expenditure or impaired fat oxidation that could be proposed to contribute to the
propensity towards obesity in this ethnic group.
Determinants of energy metabolism
C Weyer et al
Acknowledgements
We thank for the excellent technical support of Tom
Anderson and the assistance of the clinical and dietary
staff of the Clinical Diabetes and Nutrition Section,
NIDDK, Phoenix, AZ over the past 15 years. The
assistance of all NIH staff in the Gila River Indian
Community and the help and co-operation of the
volunteers is gratefully acknowledged.
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