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This study examined gender differences in resting metabolic rate (RMR) across a broad age spectrum after controlling for differences in body composition and aerobic fitness. Three hundred twenty-eight healthy men (17-80 yr) and 194 women (18-81 yr) volunteers were characterized for RMR, body composition, physical activity, peak oxygen consumption (peak VO2), anthropometrics, and energy intake. Measured RMR was 23% higher (P < 0.01) in men (1,740 +/- 194 kcal/day) than in women (1,348 +/- 125 kcal/day). Multiple regression analysis showed that 84% of individual variation in RMR was explained by fat-free mass, fat mass, peak VO2, and gender. After controlling for differences in fat-free mass, fat mass, and peak VO2, a lower RMR (3%; P < 0.01) persisted in women (1,563 +/- 153 kcal/day) compared with men (1,613 +/- 127 kcal/day). Adjusted RMR in premenopausal (P < 0.01) and postmenopausal (P < 0.05) women was lower than in men of a similar age. Our results support a lower RMR in women than in men that is independent of differences in body composition and aerobic fitness.
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Resting metabolic rate is lower in women than in men
PAUL J. ARCIERO, MICHAEL I. GORAN, AND ERIC T. POEHLMAN
Baltimore Veterans Affairs Medical Center, Division
of
Gerontology and Geriatric Research Education and
Clinical Center, University
of
Maryland, Baltimore, Maryland
21201;
and Division
of
Endocrinology,
Metabolism, and Nutrition, Department
of
Medicine, College
of
Medicine, University
of
Vermont,
Burlington, Vermont
05405
ARCIERO,PAUL J., MICHAEL I. GORAN,ANDERIC T. POEHL-
MAN. Resting metabolic rate is lower in women than in men. J.
Appl. Physiol. 75(6): 2514-2520, 1993.-This study examined
gender differences in resting metabolic rate (RMR) across a
broad age spectrum after controlling for differences in body
composition and aerobic fitness. Three hundred twenty-eight
healthy men (17-80 yr) and 194 women (W-81 yr) volunteers
were characterized for RMR, body composition, physical activ-
ity, peak oxygen consumption (peak VO,), anthropometrics,
and energy intake. Measured RMR was 23% higher (P -c 0.01)
in men (1,740 & 194 kcal/day) than in women (1,348 t 125
kcal/day). Multiple regression analysis showed that 84% of indi-
vidual variation in RMR was explained by fat-free mass, fat
mass, peak VO,, and gender. After controlling for differences in
fat-free mass, fat mass, and peak VO,, a lower RMR (3%; P <
0.01) persisted in women (1,563 t 153 kcal/day) compared with
men (1,613 t 127 kcal/day). Adjusted RMR in premenopausal
(P < 0.01) and postmenopausal (P < 0.05) women was lower
than in men of a similar age. Our results support a lower RMR
in women than in men that is independent of differences in
body composition and aerobic fitness.
gender; fat-free mass; peak oxygen consumption; fat mass
RESTING METABOLIC RATE
(RMR) accounts for the larg-
est component (60-75%) of total daily energy expendi-
ture (13) and therefore plays a significant role in the regu-
lation of energy balance. A low RMR has been shown to
be a significant predictor for subsequent weight gain in
Southwestern American Indians (30), which underscores
its important role in the regulation of body energy re-
serves.
To our knowledge, it is unknown whether gender influ-
ences RMR. Men generally display a higher absolute
RMR than women because of their larger quantity of
fat-free mass. The question of interest, however, is
whether RMR is different in men and women indepen-
dent of differences in body composition.
Although several studies have examined potential
gender differences in RMR, they have been limited by
small sample sizes (3, 11, 16, 18, 20, 29, 34). A recent
study by Ferraro et al. (9) in 114 men and 121 women,
however, did find that 24-h sedentary energy expendi-
ture, but not RMR, was lower in the women than in the
men after adjusting for differences in body composition.
The primary purpose of this study was to retrospectively
analyze data to examine gender differences in RMR in a
large cohort of healthy men and women spanning a broad
range of age, body mass, aerobic fitness, and adiposity. A
secondary goal was to compare RMR in pre- and post-
menopausal women with men of a similar age to examine
the possibility that menopausal status influences
gender-related differences in RMR.
MATERIALS AND METHODS
Subjects
Three hundred twenty-eight healthy men (17-80 yr)
and 194 healthy women (18-81 yr) were examined in this
study. Some of the data from this cohort have been previ-
ously published (22,
24),
although gender differences in
RMR have not been examined. Subjects were excluded
from participation in the study for the following reasons:
1)
clinical evidence of coronary heart disease (e.g., ST
segment depression
>l
mm at rest or exercise) or cardio-
myopathy, 2) hypertension (resting blood pressure
>140/90 mmHg), 3) medications that could affect cardio-
vascular function or metabolic rate, 4) medical history of
diabetes, 5) instability of body weight during the preced-
ing year (a change of >2 kg), 6) exercise-limiting noncar-
disc disease (arthritis, peripheral vascular disease, cere-
bral vascular disease), or 7) history of oophorectomy.
Menopausal status for each female volunteer was deter-
mined by questionnaire and was assigned a dummy value
based on three levels (1 = premenopausal, 2 = perimeno-
pausal, 3 = postmenopausal) as previously performed (1,
24). No women were presently taking estrogen replace-
ment therapy. All premenopausal women were tested be-
tween days
5-12
during the follicular phase to standard-
ize measurements during the same phase of the men-
strual cycle. Menstrual status was not determined by
chemical analysis. The experimental procedures used in
this study were approved by the Committee on Human
Research for the Medical Sciences at the University of
Vermont. Written informed consent was obtained from
each subject before investigation.
Outline
of
Experimental Protocol
All volunteers were admitted to the Clinical Research
Center the afternoon before their metabolic testing be-
tween 1400 and 1600 h. Subjects were fed a standardized
l,OOO-kcal mixed meal (15% protein, 30% fat, 55% carbo-
hydrate) at -1730 h and thereafter were given practice
with the ventilated hood to reduce any concern or appre-
hension with testing conditions. After a 12-h overnight
fast in which volunteers slept in the Clinical Research
Center, the following tests were performed the next
2514 0161-7567/93 $2.00
Copyright 0 1993 the American Physiological Society
RESTING METABOLIC RATE IN MEN AND WOMEN
2515
morning in sequence: RMR, underwater weighing for
body composition determination, anthropometrics, and
test of peak oxygen consumption (peak
Oo,).
These
methods, as well as their reproducibility in our labora-
tory, have been previously described (23). However, be-
cause the data were collected over several years, updated
values for the reproducibility of the major outcome vari-
ables are presented.
Subject Characterization
RMR was established for each subject by indirect calo-
rimetry for
45
min using the ventilated hood technique. If
volunteers regularly participated in exercise, metabolic
tests were performed 36-48 h after the last exercise bout.
RMR was measured in the same room in which the sub-
jects slept. Recent work from our laboratory has shown
that outpatient measurements of RMR are 8% higher
when compared with measurements performed under in-
patient conditions (4). Thus, to obtain the lowest RMR
value in normal volunteers, inpatient measurement pro-
cedures are preferred. The intraclass correlation and co-
efficient of variation (CV) for RMR determined using
test-retest in 17 male volunteers was 0.90 and 4.3%, re-
spectively, in our laboratory performed in volunteers be-
tween 1988 and 1990. These values compare favorably
with recent test-retest data in eight older male volun-
teers, yielding an intraclass correlation of 0.91 and a CV
of 3.9% recently obtained in our laboratory.
Body fat was estimated from b
by underwater weighing, with
OdY
density as measured
ultaneous measure-
ment of residual lung volume by the helium dilution
method using the formula of Siri (32). Fat-free mass was
estimated as total body weight minus fat weight. Pre-
vious reproducibility measures for the estimation of per-
cent body fat reached 0.98, and the CV was 4.9% (25).
Recent test-retest conditi .ons of six older female volu n-
teers yi .elded an intraclass car relation of 0.94 and a CV of
4.1%. Fat distribution was estimated from the ratio of the
waist and hip circumference.
The energy cost of leisure time physical activity within
the past year was assessed in a structured interview using
the Minnesota Leisure Time Physical Activity Question-
naire (33). Peak
Voz
was measured by a progressive and
continuous treadmill test to volitional fatigue in all 522
volunteers. The highest
00,
for 1 min during the test was
recorded as the peak
VO,.
Earlier (1988-1990) test-retest
conditions for peak
VO,
in men
(n
= 25) yielded an intra-
class correlation of 0.94 and a CV of 3.8% in our labora-
tory (25). More recent test-retest data in seven older men
yielded an intraclass correlation of 0.95 and a CV of 3.9%.
Energy and macronutrient intakes were estimated from
3-day (2 weekdays and 1 weekend day) food diaries (21).
However, it is likely that individuals underreported their
true energy intake, as previously shown in our (13) and
other (15) laboratories.
Statistical Analysis
Means, SDS, and ranges for each v pariable were calcu-
lated. Differences between men and women for the de-
TABLE
1.
Physical characteristics in total group
of
men and women
Men Women
(n = 328) P (n = 194)
Age, Yr
42.0t19.2 NS 45.0t16.9
(17-80) (18-81)
Height, cm
177.0t7.1 <O.Ol 163.8t6.6
(162-200) (146-182)
Weight, kg
77.6tll.l <O.Ol 61.9t8.6
(55.9-122.5) (45.4-106.5)
Body mass index, kg/m2
24.823.2 <O.Ol 23.lk3.0
(18.2-37.9) (17.5-37.4)
Body fat, % 15.9k7.3
co.01 24.6t7.7
(2.3-39.4) (8.4-47.6)
Fat-free mass, kg
64.8~18.2 co.01 46.3t5.4
(48.4-98.3) (35.1-66.0)
Fat mass, kg
12.7t7.3 <O.Ol 15.626.5
(1.7-47.3) (4.9-50.7)
Waist-to-hip ratio 0.89t0.06 <O.Ol
0.7720.07
(0.78-1.07) (0.57-1.28)
Leisure time activity, kcal/day 434t241 <O.Ol 36lt231
(loo-1,517) (77-1,278)
Peak VO,, llmin 3.50t0.94 <O.Ol
2.20t0.62
(1.3-6.1) (1.0-4.0)
Self-reported energy intake,
kcallday 2,808+801 <O.Ol 1,830+455
(1,144-7,019) (994-3,376)
Values are means ~fr SD; ranges given in parentheses. Peak i702, peak
oxygen consumption; body composition was estimated from hydroden-
sitometry; leisure time activity was assessed from a structured inter-
view; self-reported energy intake was estimated from a 3day food
diary.
pendent variables were determined by an independent
t
test. Pearson product-moment correlation coefficients
were used to assess the degree of association between
pairs of variables. Stepwise multiple regression analysis
using all measured variables was applied to the total
group of men and women volunteers to determine the
variables contributing to the variation in RMR.
After the independent factors that contribute to varia-
tion in RMR were determined, analysis of covariance was
employed to test for differences in the adjusted means of
RMR between men and women. The parallelism of the
regression lines between men and women using fat-free
mass, fat mass, peak VO,,~and body fat distribution as
covariates were compared by a test of homogeneity of
slopes, and no violations were noted.
The influence of menopausal status on gender-related
differences in RMR was analyzed by subdividing women
into premenopausal and postmenopausal subsets. Briefly,
all premenopausal women
(n
= 105) were assigned a
dummy value of 1, which corresponded to women 147 yr,
whereas postmenopausal women
(n = 75)
were given a
value of 3, which corresponded to women >48 yr. There-
after, the cutoff age points for women were applied to the
men so that appropriate age-matched comparisons be-
tween men and women could be made. Perimenopausal
women
(n
= 14) were excluded from this subanalysis.
RESULTS
Total Group
Subject characteristics.
Table
1
shows differences in the
physical characteristics of the total group of men and
2516
RESTING METABOLIC RATE IN MEN AND WOMEN
TABLE
2. Pearson product correlation coefficients
of men, women, and total group
Resting Metabolic Rate
Men Women Total group
(n = 328) (n = 194) (n = 522)
Fat-free mass, kg
0.74* 0.90* 0.90*
Peak 60,, llmin
0.66* 0.55* 0.79*
Weight, kg
0.57* 0.54" 0.74*
Energy intake, kcal/day
0.31* 0.27* 0.5F
Waist-to-hip ratio -0.08
0.07 0.46*
Age,
Yr
-0.40* -0.35* -0.31*
Leisure time activity, kcal/day
0.20* 0.22* 0.24*
Fat mass, kg
0.03 -0.02 -0.13*
Fat-free mass and fat mass were estimated from hydrodensitometry;
resting metabolic rate was measured from indirect calorimetry.
*P < 0.01.
women. These volunteers represent a wide range of age,
body composition, peak
VO,,
physical activity level, and
energy intake. There was no age difference between the
men and women volunteers. Body weight was
20%
greater in men than in women, primarily due to a greater
quantity of fat-free mass. Women were
19%
fatter (P <
0.01)
and
17%
less physically active in their leisure time
as measured from a physical activity questionnaire (P <
0.01).
Men displayed a
37%
higher absolute peak
VO,
(P <
0.01)
than women. Women reported an energy in-
take of
-1,000
kcal/day less than men (P <
0.01).
RMR. Table 2 displays the Pearson product-moment
correlation coefficients among the physical characteris-
tics and metabolic variables with measured RMR in the
total group and when separated by gender. In general,
the magnitude of association between RMR and the inde-
pendent variables is similar in both the sexes. As ex-
pected, fat-free mass was the highest correlate with
RMR in the total group of 522 volunteers. Figure
1
shows
the significant linear relationship between RMR and fat-
free mass in the total group.
Stepwise regression analysis was performed to deter-
mine which factor(s) explains significant amounts of vari-
ation in RMR. In the total data set, variation in RMR
was best explained by four variables: fat-free mass (? =
81%;
P <
O.Ol),
peak
vo2
(partial ? = 2%; P < 0.05), fat
mass (partial 1-2 = 0.05%; P < 0.05), and gender (partial
? = 0.05%; P < 0.05). The regression equation to predict
RMR in our total cohort of men and women is as follows:
RMR (kcal/day) = 13.7 (fat-free mass, kg) + 3.3 (fat
mass, kg) +
74
(peak
VO,,
l/min) -
50
(gender; 0 = men,
1
= women) + 596 (? = 0.84, P < 0.01, standard error of
the estimate = 106.5 kcal/day).
Adjusted RMR. Figure 2 shows the results of analysis
of covariance that was performed on the total group (n =
522) using gender as the grouping variable. Figure 2
shows the comparison between men and women for the
absolute measured RMR and the adjusted RMR values
after controlling. for differences in fat-free mass, fat
mass, and peak
VO,.
The rationale for using these vari-
ables as covariates were their identification as indepen-
dent factors that accounted for a significant amount of
variation in RMR.
As expected, men showed a 23% higher measured
RMRthanwomen (Fig.
2A; 1,740 t 199vs.1,348 t 125
kcal/day; P < 0.01). A 3% higher adjusted RMR (1,613 t
127 vs. 1,563 t 153 kcal/day; P < 0.01) persisted in men
after controlling for differences in fat-free mass, fat
mass, and peak
VO,
(Fig. 2B). There was no difference in
fasting respiratory quotient between men (0.810 t 0.046)
and women (0.816 t 0.044) (P >
0.05).
Subset Analysis
A subset analysis was performed to examine whether
gender differences in RMR persisted in pre- and post-
menopausal women when compared with a group of men
of similar age. In the subset analysis, premenopausal
women showed a 24% lower measured RMR compared
with younger men (1,377 t 115 vs. 1,811 t 198 kcal/day;
P < 0.01; Fig.
3A).
A 4% lower adjusted RMR persisted in
the premenopausal women compared with the group
of men (1,618 t 143 vs. 1,681 t 125 kcal/day;
P < 0.01; Fig. 3B, Table 3). Similarly, the postmeno-
pausal women exhibited a 21% lower measured RMR
(1,291 t 106 kcal/day for women vs. 1,638 t 171 kcal/day
for men; P < 0.01; Fig. 3C) and a 5% lower adjusted RMR
(1,469 t 199 kcal/day for women vs. 1,539 t 174 kcal/day
for men; P < 0.05; Fig. 30, Table 3) than their male coun-
terparts. There were no differences in RMR between the
pre- and postmenopausal women after adjusting for dif-
ferences in body composition and peak
VO,
(1,348 t 61
vs. 1,344 t 69 kcal/day; P = 0.74). Thus, the lower RMR
in women persisted throughout the age range in this
study.
DISCUSSION
The purpose of this study was to examine differences
in RMR between men and women after controlling for
differences in body composition and aerobic fitness level.
The major findings of this study are that 1) RMR is 3%
lower (50 kcal/day) in women than in men after differ-
ences in body composition and peak
VO,
are taken into
account and 2) a lower RMR was found in both premeno-
pausal and postmenopausal women when compared with
men of similar ages.
As expected, measured RMR was 23% higher in men
than in women. This difference can be explained primar-
ily by the larger quantity of fat-free mass in the men
compared with the women (Table 1, Fig. 1). Our labora-
tory (22, 24) and others (9, 29) have consistently shown
that fat-free mass accounts for the greatest source of
variation in RMR in humans. Furthermore, the slope
(20.3 t 0.4 kcal/day) and y-intercept (418 t 26 kcal/day)
of our regression equation of RMR and fat-free mass
(Fig. 1) compares favorably with those of others (5,6,8,
17, 19, 28).
The present study found that the quantity of fat mass
and the aerobic fitness level of the individual were also
important factors in explaining additional variation in
RMR independent of fat-free mass. The independent
contribution of fat mass is in agreement with previous
work from our laboratory (22) and others (9,12,16) but
RESTING METABOLIC RATE IN MEN AND WOMEN
2517
2500
RMR (kcal/d) = 418 + 20.3 (FFM) v
4
2
/
r =
0.81 0
/
0
1000
3; 6-b 9;
Fat-free mass (kg)
. 1
in contrast with yet others (29). Furthermore, this study
confirms our previous work that showed that aerobic fit-
ness (peak
VO,)
is an additional independent factor ex-
plaining variation in RMR (22).
FIG. 1. Linear relationship between
resting metabolic rate (RMR) and fat-
free mass in 522 healthy men and
women (17-81 yr). Fat-free mass was de-
termined by underwater weighing, and
RMR was measured by indirect calorime-
try using ventilated hood system. P <
0.01; standard error of estimate = 115
kcallday.
v
0
males (
females
t-1=328)
(n=19
4)
110
The question of interest, however, is whether RMR is
different between men and women independent of differ-
ences in body composition and cardiovascular fitness. In
our large data set, women showed a 3% lower adjusted
RMR compared with men after controlling for differ-
ences in body composition and peak
00,.
Because this is
the largest study
(n
= 522) to date that has examined
gender differences in RMR across a broad age range (18-
81
yr), it is likely that we could detect small, but biologi-
cally meaningful, differences in RMR that otherwise
might have been ignored in studies using smaller sample
sizes. We performed a power analysis and found that 102
males and 102 females are needed to detect the 50-kcal/
day (3%) difference at an alpha error of 0.05 and statisti-
cal power of 80%. Previous investigations (3, 11, 16, 18,
20, 29, 34), using smaller sample sizes, have not found
gender differences in RMR. Because the seven afore-
mentioned studies did not test large numbers of subjects
(range of
n =
lo-177), it is possible that a type II error
contributed to the absence of small, but important,
gender differences in RMR.
The question of interest is whether a difference of 50
kcal/day has significant clinical implications in the long-
term regulation of energy balance. Because of the large
contribution of RMR to total daily energy expenditure
(13), it is possible that small gender-related differences
may have a significant long-term effect on the regulation
of body weight and composition. For example, Ravussin
et al. (30) showed that a lower adjusted RMR of 71 kcal/
day in a group of 15 subjects resulted in a weight gain of
>lO kg over a 4-yr time span. It is interesting to note that
our observed gender difference of 50 kcal/day approxi-
mates that found in the individuals that subsequently
gained weight in the study by Ravussin et al. However,
because our data are cross sectional and not prospective,
we cannot address the issue of subsequent gain in body
weight and adipose tissue stores in our female popula-
tion.
Recently, Ferraro et al. (9) found a 44-kcal/day lower
adjusted RMR in females after data were normalized for
fat-free mass, fat mass, and age. However, the difference
in RMR in the Ferraro et al. study was not significant
despite a large sample size
(n
= 235). Although these
findings may appear inconsistent with our power analy-
sis, the variation (SE = 49 kcal/day) of the adjusted
RMR value was actually larger than the mean RMR
value (44 kcal/day) in the work of Ferraro et al. The large
variation in RMR measurement may reflect their rela-
tively short measurement period (9-15 min) compared
with that of the present study (45 min). The study of
Cunningham (6) also failed to detect a gender-related
difference despite a large sample size
(n = 233).
However,
the data were obtained from the work of Harris and Ben-
edict (14), which has since been found to significantly
overestimate (7, 17) and underestimate (2, 10,ZO) RMR
in today’s current populations. Furthermore, in the study
of Cunningham, lean body mass was estimated from a
prediction equation and not directly measured.
2518
2000
s
:
a
0
r 1000
a
>
a
500
0
RESTING METABOLIC RATE IN MEN AND WOMEN
T
(PcO.01)
I
MEASURED RMR ADJUSTED RMR
A B
Because previous studies (1,9) have shown that meno-
pausal status influences RMR, we divided our female
population into two subsets based on menopausal status
to compare pre- and postmenopausal women with men of
similar ages. As expected, measured RMR was lower in
the women in both groups compared with the men. How-
Although this investigation cannot elucidate the mech-
anism(s) for the lower RMR in women compared with
men, several possibilities should be considered. Na+-
K+-ATPase activity has been shown to be reduced in
ever, the lower adjusted RMR in pre- and postmeno- women compared with men (31 ), and we h .ave recently
pausal women persisted even after normalizing for body reported that a lower Na+-K+ activity is related to a
composition and fitness. In addition, there were no dif- lower RMR, independent of difference& fat-free weight
ferences in RMR between pre- and postmenopausal (26). Our laboratory has previously shown that a re-
women after adjustments for differences in body compo- strained eating pattern in females was a significant fac-
sition and physical fitness were taken into account. Col- tor contributing to a reduced RMR and higher levels of
lectively, these findings confirm that the lower RMR in body fat (27). Differences in skeletal muscle metabolism
women compared with men is independent of meno- may also be implicated in gender-related differences in
2250
$1500
x
a
> 1250
a
1000
750
500
FIG. 2. Difference in RMR between
men and women for total group (n =
522). A:
measured RMR for men
[1,740 t 199 (SD) kcal/day] vs. women
(1,348 & 125 kcal/day). B: adjusted RMR
values after controlling for fat-free
mass, fat m-ass, and peak oxygen con-
sumption
(VO,)
for men (1,613 t 127
kcal/day) vs. women (1,563 t 153 kcal/
day).
pausal status and body composition and persists
throughout a large age range.
‘PcO.01) (P< FIG. 3.
A:
difference in measured
0.05) RMR between men [l&311 k 198 (SD)
kcal/day] and premenopausal women of
similar age (1,377 & 115 kcal/day) (n =
299).
B:
adjusted RMR after statistical
adjustment for fat-free mass, fat mass,
and peak VO, in younger men (1,681 *
125 kcal/day) vs. women (1,618 t 143
kcallday)
(n =
299). C: difference in
measured RMR between older men
(1,638 -t 171 kcal/day) and postmeno-
pausal women of similar age (1,291 t
106 kcal/day)
(n =
209). D: RMR after
statistical adjustment for fat-free mass,
fat mass, and peak VO, in older men
(1,539 t 174 kcal/day) vs. women
(1,469 t 199 kcal/day)
(n = 209).
MEASURED ADJUSTED MEASURED ADJUSTED
A B C D
RESTING METABOLIC RATE IN MEN AND WOMEN
2519
TABLE 3. Comparison
of
adjusted resting metabolic rate
Total group 522 1,613&127 -co.01 1,563+153
Younger group 299 1,681+125 <O.Ol 1,618+143
Older group 209 1,539+174 -co.05 1,469&199
Values are adjusted means t SD of resting metabolic rate in kcal/
day. Adjusted resting metabolic rate refers to resting metabolic rate
values after controlling for differences in body composition and peak
VO, between men and women volunteers using analysis of covariance.
Younger group was comprised of premenopausal women (147 yr), and
older group was comprised of postmenopausal women (248 yr). Cutoff
age points for women were applied to men to allow for appropriate
comparisons.
RMR, although previous findings do not support a
gender effect when adjustments were made for skeletal
muscle mass (35).
It is important to point out several aspects of our study
that reinforce the validity of our findings. These proce-
dures include 1) the measurement of RMR on an inpa-
tient basis removed at least 48 h from the last bout of
exercise, 2) the standardization of measurement proce-
dures during the follicular phase of the menstrual cycle,
3) the habituation of all subjects to the ventilated hood,
and 4) a large sample size of well-characterized healthy
individuals.
We conclude that women have a lower metabolic rate
than men, which does not appear to be explained by dif-
ferences in body composition, fitness level, menopausal
status, or age. A lower RMR in women for their meta-
bolic size represents a gender-specific difference in rest-
ing energy expenditure.
We thank all the volunteers who participated in this study. We also
thank Phil Ades, the Cardiac Rehabilitation staff, Shane Katzman-
Rooks, Dr. Andrew Gardner, and the General Clinical Research Center
staff for their assistance.
E. T. Poehlman is supported by National Institute of Aging Grant
AG-07857, National Institute of Aging Research Career and Develop-
ment Award K04-AG-00564, the American Association of Retired Per-
sons Andrus Foundation, and a Biomedical Research Grant from the
University of Vermont. M. I. Goran is supported by a grant from Ameri-
can Diabetes Association, the National Institute of Child Health and
Human Development, and US Department of Agriculture. This work
was supported in part by General Clinical Research Center Grant RR-
109.
Address for reprint requests: E. T. Poehlman, Baltimore Veterans
Affairs Medical Center, Division of Gerontology/GRECC, Univ. of
Maryland, Baltimore, MD 21201.
Received 1 June 1993; accepted in final form 20 July 1993.
REFERENCES
1. ARCIERO, P. J., M. I. GORAN, A. W. GARDNER, P. A. ADES, R. S.
TYZBIR, AND E. T. POEHLMAN. A practical equation to predict rest-
ing metabolic rate in older females. J. Am. Geriatr. Sot. 41: 389-
395,1993.
2. ARCIERO, P. J., M. I. GORAN, A. W. GARDNER, P. A. ADES, R. S.
TYZBIR, AND E. T. POEHLMAN. A practical equation to predict rest-
ing metabolic rate in older males. Metabolism 42: 950-957, 1993.
3. ASTRUP, A., G. THORBEK, J. LIND, AND B. ISAKSSON. Prediction of
24-h energy expenditure and its components from physical charac-
teristics and body composition in normal-weight humans. Am. J.
Clin. Nutr. 52: 777-783, 1990.
5. BERNSTEIN, R. S., J. C. THORNTON, M. U. YANG, J. WONG, A. M.
4. BERKE, E. M., A. W. GARDNER, M. I. GORAN, AND E. T. POEHL-
MAN. Effect of pre-testing environment on resting metabolic rate
measurements. Am. J. CZin. Nutr. 55: 626-629, 1992.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
REDMOND, R. N. PIERSON, F. X. PI-SUNYER, AND T. B. VAN ITAL-
LIE. Prediction of the resting metabolic rate in obese patients. Am.
J. CZin. Nutr. 37: 595-602, 1983.
CUNNINGHAM, J. J. A reanalysis of the factors influencing basal
metabolic rate in normal adults. Am. J. CZin. Nutr. 33: 2372-2374,
1980.
DALY, J. M., S. B. HEYMSFIELD, A. HEAD, L. P. HARVEY, D. W.
NIXON, H. KATZEFF, AND G. D. GROSSMAN. Human energy require-
ments: overestimation by widely used prediction equation. Am. J.
CZin. Nutr. 42: 1170-1174, 1985.
DORE, C., R. HESP, D. WILKINS, AND J. S. GARROW. Prediction of
requirements of obese patients after massive weight loss. Hum.
Nutr. CZin. Nutr. 36: 41-48, 1982.
FERRARO, R., S. LILLIOJA, A. M. FONTEVIELLE, R. RISING, C. Bo-
GARDUS, AND E. RAVUSSIN. Lower sedentary metabolic rate in
women compared to men. J. CZin. Inuest. 90: l-5, 1992.
FREDRIX, E. W. H. M., P. B. SOETERS, I. M. DEERENBERG, A. D.
M. KESTER, M. F. VON MEYENFELDT, AND W. H. M. SARIS. Rest-
ing and sleeping energy expenditure in the elderly. Eur. J. CZin.
Nutr. 44:741-747,199O.
FUKAGAWA, N. K., L. G. BANDINI, AND J. B. YOUNG. Effect of age
on body composition and resting metabolic rate. Am. J. Physiol.
259 (Endocrinol. Metab. 22): E233-E238, 1990.
GARBY, L., 0. LAMMERT, AND E. NIELSEN. Energy expenditure
over 24 hours on low physical activity programmes in human sub-
jects. Hum. Nutr. CZin. Nutr. 40: 141-150, 1986.
GORAN, M. I., AND E. T. POEHLMAN. Total energy expenditure and
energy requirements in healthy elderly persons. Metabolism 41:
744-753,1992.
HARRIS, J. A., AND F. G. BENEDICT. A Biometric Study of BasaZ
Metabolism in Man. Washington, DC: Carnegie Inst. of Washing-
ton, 1919. (Carnegie Inst. of Washington Publ. 279)
LICHTMAN, S. W., K. PISARSKA, E. R. BERMAN, M. PESTONE, H.
DOWLING, E. OFFENBACHER, H. WEISEL,
S.
HESHKA, D. W.
MATTHEWS, AND S. B. HEYMSFIELD. Discrepancy between self-re-
ported and actual caloric intake and exercise in obese subjects. N.
Engl. J. Med. 327: 1893-1898,1992.
MEIJER, G. A. L., K. R. WESTERTERP, W. H. M. SARIS, AND F. TEN
HOOR. Sleeping metabolic rate in relation to body composition and
the menstrual cycle. Am. J. CZin. Nutr. 55: 637-640, 1992.
MIFFLIN, M. D., S. T. ST. JEOR, L. A. HILL, B. J. SCOTT, S. A.
DAUGHERTY, AND Y. 0. KOH. A new predictive equation for resting
energy expenditure in healthy individuals. Am. J. CZin. Nutr. 51:
241-247,199O.
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.
OWEN,
0. E. Resting metabolic requirements of men and women.
Mayo Clin. Proc. 63: 503-510, 1988.
OWEN, 0. E., J. L. HOLUP, D. A. D’ALESSIO, E. S. CRAIG, M. Po-
LANSKY, K. J. SMALLEY, E. C. KAVLE, M. C. BUSHMAN, L. R.
OWEN,
M. A. MOZZOLI, Z. V. KENDRICK, AND G. H. BODEN. A
reappraisal of the caloric requirements of men. Am. J. CZin. Nutr.
46:875-885,1987.
POEHLMAN, E. T., P. J. ARCIERO, C. L. MELBY, AND S. F. BADY-
LAK. Resting metabolic rate and postprandial thermogenesis in veg-
etarians and nonvegetarians. Am. J. CZin. Nutr. 48: 209-213,1988.
POEHLMAN, E. T., E. M. BERKE, J. R. JOSEPH, A. W. GARDNER,
S. M. KATZMAN-ROOKS, AND M. I. GORAN. Influence of aerobic
capacity, body composition, and thyroid hormones on the age-re-
lated decline in resting metabolic rate. Metabolism 41: 915-921,
1992.
POEHLMAN, E. T., AND E. DANFORTH, JR. Endurance training in-
creases resting metabolic rate and norepinephrine appearance into
circulation in older individuals. Am. J. Physiol. 261 (EndocrinoZ.
Metub. 24): E233-E239, 1991.
POEHLMAN, E. T., M. I. GORAN, A. W. GARDNER, P. A. ADES, P. J.
ARCIERO, S. M. KATZMAN-ROOKS, S. M. MONTGOMERY, M. J.
TOTH, AND
P.
T. SUTHERLAND. Metabolic determinants of the de-
cline in resting metabolic rate in aging females. Am. J. Physiol. 267
(Endocrinol. Metab. 30): E450-E455, 1993.
I ,
POEHLMAN, E. T., T. MCAULIFFE, D. R. VAN HOUTEN, AND E.
DANFORTH, JR. Influence of age and endurance training on meta-
bolic rate and hormones in healthy men. Am. J. Physiol. 259 (Endo-
crinol. Metab. 22): E66-E72. 1990.
2520
RESTING METABOLIC RATE IN MEN AND WOMEN
26,
POEHLMAN,
E. T., M. J.
TOTH, AND
G. D.
WEBB.
Erythrocyte Na-
K pump activity contributes to the age-related decline in resting
metabolic rate. J. CZin. EndocrinoZ. Metab. 76: 1054-1057, 1993.
27. POEHLMAN,
E. T., H. F.
VIERS, AND
M.
DETZER.
Influence of physi-
cal activity and dietary restraint on resting energy expenditure in
young nonobese females. Can. J. Physiol. Pharmacol. 69: l-6,1991.
28. RAVUSSIN,
E.,
AND
C.
BOGARDUS.
Relationship of genetics, age,
and physical fitness to daily energy expenditure and fuel utiliza-
tion. Am, J. CZin. Nutr. 49: 968-975, 1989.
29.
RAVUSSIN,
E., S.
LILLIOJA,
T. E.
ANDERSON,
L.
CHRISTIN, AND C.
BOGARDUS.
Determinants
of 24-hour energy expenditure in man.
J. CZin. Inuest. 78: 1568-1578, 1986.
30.
RAVUSSIN,
E., S.
LILLIOJA,
W.
C. KNOWLER,
L.
CHRISTIN,
D.
FREYMOND,
W. G. H.
ABBOTT,
V.
BOYCE,
B. V.
HOWARD, AND
C.
BOGARDUS.
Reduced rate of energy expenditure as a risk factor for
body-weight gain. N. EngZ. J. Med. 318: 467-472, 1988.
31.
SIMAT,
B. M., J. E.
MORLEY,
A. H. L.
FROM,
J. E.
BRIGGS,
F. E.
KAISER,
A. S.
LEVINE, AND
K.
AHMED.
Variables affecting measure-
ment of human red cell Naf, K+ ATPase activity: technical factors,
feeding, aging.
Am. J. Ctin. Nutr. 40: 339-345, 1984.
32.
SIRI,
W. E. Body composition from fluid spaces and density: analy-
sis of methods. In: Techniques for Measuring Body Composition,
edited by J. Brozek and A. Henschel. Washington, DC: Natl. Acad.
Sci., 1961, p. 223-244.
33.
TAYLOR,
H. L., D. R.
JACOBS,
B.
SCHUCKER,
J.
KNUDSEN,
A. S.
LEON, AND
G.
DEBACKER.
A questionnaire for the assessment of
leisure time physical activities. J. Chronic Dis. 31: 741-755, 1978.
34.
WEBB,
P. Energy expenditure and fat-free mass in men and
women. Am. J. CZin. Nutr. 34: 1816-1826, 1981.
35.
ZURLO,
F., K.
LARSON,
C,
BOGARDUS, AND
E.
RAVUSSIN.
Skeletal
muscle metabolism is a major determinant of resting energy expen-
diture. J. CZin. Inuest. 86: 1423-1427, 1990.
... All of these components decrease with weight loss as a result of decreasing total body mass, decreasing metabolically active tissue mass, and lower energy intake. 47 Given that lean mass is the major determinant of metabolic rate in humans, typically explaining about 80% of the total variance in RMR and total energy expenditure, 48,49 and that individuals without obesity lose relatively more lean mass than individuals with obesity in response to the same diet-induced weight loss (see "Composition of weight loss"), 32,38,39 the obvious question arises: Do the effects of weight loss on metabolic rate differ by baseline level of adiposity? Results from a previously published analysis suggested that basal metabolic rate decreases after calorie restriction and weight loss in individuals with obesity, but considerably less than in individuals without obesity after prolonged fasting. ...
... It must be emphasized, however, that even though the difference in lean mass loss between individuals with and without obesity is considerable in relative terms, ie, as percentage of total weight loss (21%-25% and 35%-48%, respectively), in absolute terms it is only approximately 0.75 kg after 5% weight loss 32,38 and approximately 2.0 kg after 10% weight loss. 36 Such differences in absolute lean mass would result in estimated differences of 15 kcal/d-40 kcal/d in RMR 48 and 25 kcal/d-66 kcal/d in total energy expenditure, 49 which are likely too small to be detected with the available methodologies. Therefore, in practice, all components of daily energy expenditure decrease to the same extent in the presence or absence of obesity. ...
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Calorie restriction regimens are popular for their purported health-promoting effects. However, it is unclear whether chronic reduction in energy intake and subsequent weight loss have beneficial effects in the absence of obesity. To this end, the results of studies that examined the effects of the same diet-induced weight loss in individuals with and without obesity were reviewed. The contribution of lean mass to the total amount of weight lost is greater in participants without obesity than in those with obesity, but the reductions in resting, nonresting, and total energy expenditure are of similar magnitude. Both in the presence as well as in the absence of obesity, weight loss decreases visceral adipose tissue and liver fat, increases insulin sensitivity in skeletal muscle (insulin-mediated whole-body glucose disposal rate) and in adipose tissue (meal-induced or insulin-induced suppression of plasma free fatty acid concentration), and augments insulin clearance rate, without affecting pancreatic insulin secretion. These effects are of similar magnitude in participants with and without obesity and result in reductions in fasting plasma glucose and insulin concentrations. These data suggest that the same degree of calorie restriction and the same amount of weight loss have multiple beneficial effects on health outcomes in individuals without obesity, similar to those observed in individuals with obesity.
... Older adults (> 60 years) are particularly poorly represented. Alongside a representative age group, it is important that validation protocols include an equal sample across both sexes, given the variance in metabolic rate [6,25], and report on body size and composition. Finally, skin tone has previously been noted as having an effect on the accuracy of heart rate measures [17]. ...
... All five studies in the systematic literature review validating consumer wearables in a free-living context utilised the DLW method [10,[36][37][38][39], while 3 (10%) of the 28 semi-free-living protocols utilised a metabolic chamber [10,37,40]. The remaining studies with a semi-free-living protocol (25) and all 52 laboratory protocols selected indirect calorimetry in the form of a portable system or metabolic cart. The criterion device should be placed according to manufacturer's instructions and reported in the study write-up, alongside laboratory-specific data on the quality of the criterion measure where possible (i.e. percentage coefficient of variation [reliability]), and any calibration details. ...
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... Although, there is no evidence related to gender impacts on the MC4R gene, gender affects the metabolic rate [32]. A low metabolic rate has been suggested as a likely cause for the elevated adiposity commonly observed in women compared with men [33]. Thus, to precisely investigate and remove gender-specific associations, the present study was undertaken to assess the interactions of MC4R rs17782313 and dietary carbohydrate, GI, and GL on BMR and general and central obesity in overweight/obese women. ...
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... We used this model to evaluate the in vivo effects of Klf9 KO on obesity/adiposity and related parameters including hepatic lipid content, circulating adipokine concentrations, and hepatic and systemic oxidative stress, all of which are considered to positively influence HCC risk. Given established significant differences in hepatic metabolism and gene expression between sexes of both humans and mice [44,45], we conducted the study in mice of both genders. Our results provide support to the emerging concept of KLF9 as an important regulator of local and systemic oxidative stress and ROS-induced cell responses including inflammation [27,46], and which may underlie its tumor-suppressive effects in liver and other tissues. ...
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... Since FFM is the major determinant of REE, typically explaining ~ 80% of the total variability between individuals during weight-stable conditions [32,33], it is tempting to speculate that studies observing greater FFM loss with rapid than gradual weight loss would also find greater decreases in REE [16,17], whereas studies observing no differences in FFM loss would also find no differences in REE [21,25]. However, this has not always been the case; Coutinho et al. [19] reported a greater decrease in REE with rapid than gradual weight loss, but no corresponding differences in FFM loss. ...
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Women have a longer life expectancy than men in the general population. However, it has remained unclear whether this advantage is maintained in patients undergoing maintenance hemodialysis. The aim of this study was to compare the risk of mortality, especially infection-related mortality, between male and female hemodialysis patients. A total of 3065 Japanese hemodialysis patients aged ≥ 18 years old were followed up for 10 years. The primary outcomes were all-cause and infection-related mortality. The associations between sex and these outcomes were examined using Cox proportional hazards models. During the median follow-up of 8.8 years, 1498 patients died of any cause, 387 of whom died of infection. Compared with men, the multivariable-adjusted hazard ratios (95% confidence interval) for all-cause and infection-related mortality in women were 0.51 (0.45–0.58, P < 0.05) and 0.36 (0.27–0.47, P < 0.05), respectively. These findings remained significant even when propensity score-matching or inverse probability of treatment weighting adjustment methods were employed. Furthermore, even when the non-infection-related mortality was considered a competing risk, the infection-related mortality rate in women was still significantly lower than that in men. Regarding all-cause and infection-related deaths, women have a survival advantage compared with men among Japanese patients undergoing maintenance hemodialysis.
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The accuracy of previous equations for predicting resting metabolic rate (RMR) in healthy older men is questionable because they are based on limited sample sizes and the absence of cross-validation procedures. The purposes of this study were to (1) examine biological predictors of RMR in healthy older men; (2) develop a practical equation to predict RMR from easily measured variables and examine its accuracy using cross-validation procedures; and (3) test the validity of existing equations in the literature to predict RMR in older men by comparison with measured RMR values. RMR, body composition, anthropometric measurements, leisure time activity (LTA), maximal aerobic power (VO2max), energy intake, and plasma thyroid hormone levels were determined in 89 healthy older men aged 50 to 78 years. Stepwise regression analysis showed that RMR was best predicted by fat-free weight ([FFW] R2 = 85%), free 3,5,3'-triiodothyronine (T3) level (R2 = 1%), and VO2max (R2 = 1%); these variables predicted RMR with a residual error of +/- 30 kcal/d. A practical equation was developed in a randomly selected subsample (N = 61) using easily measured variables as potential predictors, and was successfully cross-validated in a random subsample of older men (N = 28). The pooled equation to predict RMR is as follows: RMR (in kilocalories per day) = 9.7 (weight in kilograms) - 6.1 (chest skinfold thickness in millimeters) - 1.8 (age in years) + 0.1 (leisure time activity [LTA] in kilocalories per day) + 1,060. These variables accounted for 76% (R2) of the variation, and predicted RMR with a residual error of +/- 42 kcal/d.(ABSTRACT TRUNCATED AT 250 WORDS)
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