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Relationship Between Basal Metabolic Rate, Gender, Age, and Body Composition in 8,780 White Obese Subjects

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The objective of the present study was to explore the relationship between basal metabolic rate (BMR), gender, age, anthropometric characteristics, and body composition in severely obese white subjects. In total, 1,412 obese white children and adolescents (BMI > 97 degrees percentile for gender and age) and 7,368 obese adults (BMI > 30 kg/m(2)) from 7 to 74 years were enrolled in this study. BMR was measured using an indirect calorimeter equipped with a canopy and fat free mass (FFM) were obtained using tetrapolar bioelectrical impedance analysis (BIA). Using analysis of covariance, we tested the effect of gender on the relationship between BMR, age, anthropometry, and body composition. In children and adolescents, the predictor x gender interaction was significant in all cases except for FFM x gender. In adults, all predictor x gender interactions were significant. A prediction equation based on body weight (BW), age, and gender had virtually the same accuracy of the one based on FFM, age, and gender to predict BMR in both children and adults (R(2)(adj) = 0.59 and 0.60, respectively). In conclusion, gender was a significant determinant of BMR in children and adolescents but not in adults. Our results support the hypothesis that the age-related decline in BMR is due to a reduction in FFM. Finally, anthropometric predictors of BMR are as accurate as body composition estimated by BIA.
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OBESITY | VOLUME 18 NUMBER 1 | JANUARY 2010 71
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INTRODUCTION
e increasing prevalence of obesity during the last decades (1)
is ascribed mainly to a mismatch between energy intake and
energy expenditure (EE) (2,3). e factors that inuence this
balance are numerous and complex, involving genes, environ-
ment, and their interaction. However, the rationale of weight
management strategies is to identify and modify the amount
of energy introduced and expended in order to regain normal
body weight (BW) (1). EE is a major determinant of energy bal-
ance and body composition. According to an usually accepted
scheme in human nutrition, daily EE (DEE) can be partitioned
between basal metabolic rate (BMR) extrapolated to 24 h,
which corresponds to the energy needed to sustain the body
functions at rest and which accounts for ~65% of DEE in sed-
entary subjects (4); EE associated with physical activity (oen
referred to as the thermic eect of activity), which accounts
for ~25% of DEE (5); and the thermic eect of food, which
includes EE due to digestion, absorption, and metabolism of
nutrients and which accounts for ~10% of DEE (5). Because
of its large contribution to DEE, especially in obese subjects,
BMR has frequently been the main focus of attention in the
studies on the development and treatment of obesity.
BMR can be considered as the sum of the EEs of tissues and
organs in a fasting and resting state and in thermoneutral con-
ditions. It depends on the mass and metabolic rate of tissues
and organs (6). For instance, EE is ~10, 15, 20, 35, and 35 times
higher in the digestive tract, liver, brain, heart, and kidney than
in resting muscle, whereas it is only ~1/3 of resting muscle in
white adipose tissues (7). us, although organs only account
for ~7% of BW, they contribute ~60% of BMR. In comparison,
skeletal and adipose tissues account for 35–40% of BW but
only 18–22% and 3–4% of BMR, respectively (8). Generally,
BMR depends on body composition as expressed by fat-free
mass (FFM) and fat mass (FM) and on gender, age, physical
activity, and nutritional status. e main determinant of BMR
is FFM (6), whereas FM is signicant only in obese subjects
(9). Gender is also a signicant determinant of BMR, with men
having a greater BMR than females aer adjustment for body
composition (9,10). In addition, BMR markedly decreases
with advancing age in sedentary populations (11) at a rate
Relationship Between Basal Metabolic Rate,
Gender, Age, and Body Composition
in 8,780 White Obese Subjects
Stefano Lazzer1,2, Giorgio Bedogni3, Claudio L. Lafortuna4, Nicoletta Marazzi1, Carlo Busti1,
Raffaela Galli1, Alessandra De Col1, Fiorenza Agosti1 and Alessandro Sartorio1,5
The objective of the present study was to explore the relationship between basal metabolic rate (BMR), gender, age,
anthropometric characteristics, and body composition in severely obese white subjects. In total, 1,412 obese white
children and adolescents (BMI > 97° percentile for gender and age) and 7,368 obese adults (BMI > 30 kg/m2) from 7
to 74 years were enrolled in this study. BMR was measured using an indirect calorimeter equipped with a canopy and
fat‑free mass (FFM) were obtained using tetrapolar bioelectrical impedance analysis (BIA). Using analysis of covariance,
we tested the effect of gender on the relationship between BMR, age, anthropometry, and body composition. In
children and adolescents, the predictor × gender interaction was significant in all cases except for FFM × gender. In
adults, all predictor × gender interactions were significant. A prediction equation based on body weight (BW), age, and
gender had virtually the same accuracy of the one based on FFM, age, and gender to predict BMR in both children
and adults (R2
adj = 0.59 and 0.60, respectively). In conclusion, gender was a significant determinant of BMR in children
and adolescents but not in adults. Our results support the hypothesis that the age‑related decline in BMR is due to a
reduction in FFM. Finally, anthropometric predictors of BMR are as accurate as body composition estimated by BIA.
Obesity (2009) 18, 71–78. doi:10.1038/oby.2009.162
1Istituto Auxologico Italiano, IRCCS, Laboratorio Sperimentale di Ricerche Auxo-endocrinologiche, Verbania, Italy; 2Dipartimento di Scienze e Tecnologie
Biomediche, Università degli Studi di Udine, Udine, Italy; 3Unità di Epidemiologia Clinica, Centro Studi Fegato, Trieste, Italy; 4Istituto di Bioimmagini e Fisiologia
Molecolare, Consiglio Nazionale delle Ricerche, Segrate, Italy; 5Istituto Auxologico Italiano, IRCCS, Divisione di Auxologia e Riabilitazione Funzionale, Verbania, Italy.
Correspondence: Alessandro Sartorio (sartorio@auxologico.it)
Received 8 October 2008; accepted 24 April 2009; published online 28 May 2009. doi:10.1038/oby.2009.162
72 VOLUME 18 NUMBER 1 | JANUARY 2010 | www.obesityjournal.org
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of ~1–2% per decade aer the age of 20 (ref. 12). Such a decline
in EE probably contributes to an impaired ability to regulate
energy balance with age. Several studies have addressed the
issue of whether EE decreases with age and whether females
have lower EE than males, but the literature is equivocal on
this topic concerning obese subjects. e aim of the present
study was therefore to explore the relationship between BMR,
gender, age, anthropometric characteristics, and body compo-
sition in a very large sample of severely obese white subjects.
METHODS AND PROCEDURES
Subjects
In total, 1,412 obese white children and adolescents (age range: 7–18
years) and 7,368 obese adults (age range: 18–74 years) were consecu-
tively enrolled into the study between January 2003 and December 2007
at the Division of Auxology and between January 1999 and December
2007 at the 3rd Division of Metabolic Diseases of the Italian Institute
for Auxology (Italy). e inclusion criteria were: (i) age between 7 and
74 years and (ii) BMI above the 97th percentile for gender and age
using Italian reference values for children and adolescents (13) and
BMI 30 kg/m
2
for adults. Subjects who had overt metabolic and/
or endocrine diseases (e.g., diabetes, hypothyroidism, hypertension,
amenorrhea), and those taking any drug known to inuence energy
metabolism were excluded from the study. e experimental proto-
col was approved by the Ethics Committee of the Italian Institute for
Auxology. e purpose and the objectives were carefully explained to
the subjects and written informed consent was obtained from them or
their legal guardians.
e measurements were performed during a stable BW period before
the beginning of a weight-reduction program at the Italian Institute of
Auxology. e fasting subjects were taken to the laboratory and BMR,
BW, height, and body composition were assessed.
Physical characteristics and body composition
BW was measured to the nearest 0.1 kg with a manual weighing scale
(Seca 709, Hamburg, Germany). Height was measured to the nearest
0.5 cm using a standardized wall-mounted height board (Wunder, Milan,
Italy). BMI was calculated as BW (kg)/height
2
(m) (14). e standard
deviation score of BMI was calculated applying the LMS method (15) to
Italian reference values for children and adolescents (13).
Body composition was measured using bioelectrical impedance
analysis (BIA) with a tetrapolar impedance meter (Human-IM Scan;
DS-Medigroup, Milan, Italy). Measurements were performed accord-
ing to the method of Lukaski (16) and the National Institutes of Health
guidelines (17). FFM was estimated using the prediction equations devel-
oped by Lazzer et al . (18) for children and adolescents, and those of Gray
et al. (19) for adults. FM was obtained by subtracting FFM from BW and
%FM as (FM/BW) × 100. e within-day coecient of variation for three
repeated assessments of FFM in 10 obese subjects (with repositioning of
electrodes) was 2.4%.
BMR
BMR was measured in the morning (between 8 and 10 AM) aer an
overnight fast and in thermoneutral conditions (in a 22–25 °C room)
using an open-circuit, indirect computerized calorimeter equipped
with a canopy (Vmax 29; Sensor Medics, Yorba Linda, CA). e medi-
cal charts of fertile females were reviewed for regularity of menses
and the date of last menstrual period. BMR was always determined
during the follicular phase of the menstrual cycle. e gas analyzers
were calibrated before each test using a reference gas mixture (15.0%
O2 and 5.0% CO2). Subjects were measured at rest in a supine position
for a period of at least 45 min, including a 10-min acclimation period
(20). Data from the initial 10 min of measurement, reecting adjust-
ment to the procedural environment and subjects adaptation, were
not considered for BMR calculation. Aer achieving a steady state, O2
consumption and CO2 production standardized for temperature, baro-
metric pressure, and humidity were recorded at 1-min intervals for a
minimum of 30 min and averaged over the whole measurement period.
EE was calculated from O2 uptake and CO2 output using the equation
of Weir (21).
Statistical analysis
Values of continuous variables are given as mean and standard devia-
tion and those of categorical variables as the number or percentage of
subjects with the characteristic of interest. Between-gender compari-
sons were performed using Student’s unpaired t-test. e univariable
relationships between BMR and continuous predictors (age, BW, height,
FFM, and FM) were rst studied using scatterplots and nonparametric
regression plots. A rst-degree linear model was as accurate as more
complex models to describe all the BMR-predictor relationships and
was thus chosen as the reference model for all univariable analyses. In
order to test the eect of gender on the BMR-predictor relationships,
we used analysis of covariance (22). Four prespecied models were
used to test the accuracy of anthropometry and body composition in
multivariable prediction of BMR. Model 1 was based on BW, age, and
gender; Model 2 added height to the predictors of Model 1; Model 3
was based on age, gender, and FFM; Model 4 added FM to the predic-
tors of Model 3. Standard diagnostic plots were used to test univariable
and multivariable model t (23). Regression residuals were normally
distributed for all univariable and multivariable models. e adjusted
coecient of determination (R2
adj) and the root mean squared error of
the estimate (RMSE) were used as measures of model t. e 95% con-
dence intervals (95% CI) of the regression coecients, R2
adj and RMSE
were calculated using bootstrap on 1,000 random samples of 1,412 chil-
dren and adolescents and 7,368 adults (24). Statistical analysis was per-
formed using STATA 10.0 (STATA, College Station, TX).
RESULTS
e physical characteristics of the 1,412 obese children and
adolescents and 7,368 obese adults are shown in Ta b l e 1 . In
both groups, 58% of the children and adolescents and 73%
of the adults were females. In both groups, mean age and
percent FM were signicantly higher in females than males,
whereas BW, height, FFM, and BMR were signicantly lower
in females.
Figure 1 shows the regression of BMR vs. age, BW, height,
FFM, and FM in children and adolescents and in adults strati-
ed by gender. On visual inspection of the graphs, males
have higher values of BMR for the same value of the predic-
tor. Table 2 shows the analysis of covariance models formally
testing the eect of gender on the regression lines mentioned
earlier. In children and adolescents (Table 2), the predictor ×
gender interaction was signicant in all cases except for
FFM × gender. However, when BW, FFM, or FM were used as
predictors, the eect of gender as main eect was not statis-
tically signicant. Judging from R2
adj and RMSE, the univari-
able predictions based on BW were as accurate as those based
on FFM (R2
adj: 0.59 vs. 0.59 and RMSE (kJ): 1,073 vs. 1,079,
respectively).
In adults (Table 2), all predictor × gender interactions terms
and main eects were signicant. As for children and adoles-
cents, the predictions based on BW were as accurate as those
based on FFM (R2
adj: 0.59 vs. 0.59 and RMSE (kJ): 1,065 vs.
1,059, respectively).
e four models of increasing complexity for the predic-
tion of BMR are shown in Table 3. Model 1 is the simplest one
OBESITY | VOLUME 18 NUMBER 1 | JANUARY 2010 73
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Table 1 Physical characteristics of subjects
Children and adolescents (n = 1,412) Adults (n = 7,368)
Females (n = 823) Males (n = 589) P valueaFemales (n = 5,368) Males (n = 2,000) P valuea
Age (years) 14.5 (2.1) 14.0 (2.3) 0.006 47.8 (13.9) 46.3 (13.8) <0.001
Body weight (kg) 94.1 (19.4) 102.4 (26.8) <0.001 105.8 (17.5) 123.9 (22.6) <0.001
Height (m) 1.60 (0.10) 1.70 (0.10) <0.001 1.60 (0.10) 1.70 (0.10) <0.001
BMI (kg/m2) 36.6 (6.0) 36.7 (6.6) 0.740 41.9 (6.5) 41.6 (6.8) 0.098
z-BMI (SDS) 3.0 (0.5) 3.0 (0.7) 0.056
FFM (kg) 44.4 (8.8) 51.3 (13.5) <0.001 53.4 (9.0) 78.2 (14.4) <0.001
FM (kg) 49.7 (10.7) 51.1 (13.6) 0.042 52.4 (8.6) 45.8 (8.5) <0.001
FM (%) 52.7 (1.5) 49.8 (1.9) <0.001 49.5 (1.0) 36.9 (1.4) <0.001
BMR (kJ) 7,652 (1,246) 9,101 (1,826) <0.001 7,418 (1,255) 9,409 (1,723) <0.001
Values are given as means and s.d.
BMR, basal metabolic rate; FFM, fat-free mass; FM, fat mass; SDS, standard deviation score; z-BMI, z-score of BMI.
aUnpaired t-test for males vs. females.
4,000
510
Age (years)
15 20
8,000
12,000
BMR (kJ)
16,000
4,000
50 100 150 200 250
Weight (kg)
8,000
12,000
BMR (kJ)
16,000
4,000
20 40 60 80 100 120
FFM (kg)
8,000
12,000
BMR (kJ)
16,000
4,000
20 40 60 80 100 120
FM (kg)
8,000
12,000
BMR (kJ)
16,000
4,000
120 140 160 180 200
Height (cm)
8,000
12,000
BMR (kJ)
16,000
a
4,000
20 40 60 80
Age (years)
8,000
12,000
BMR (kJ)
16,000
4,000
50 100 150 200 250
Weight (kg)
8,000
12,000
BMR (kJ)
16,000
4,000
40 60 80 100 120 140
FFM (kg)
8,000
12,000
BMR (kJ)
16,000
4,000
20 40 60 80 100
FM (kg)
8,000
12,000
BMR (kJ)
16,000
4,000
140 160 180 200
Height (cm)
8,000
12,000
BMR (kJ)
16,000
b
Figure 1 Basal metabolic rate (BMR, kJ/die) plotted as a function of age (years), body weight (kg), height (m), fat-free mass (FFM, kg) and fat mass
(FM, kg) for (a) children and adolescents (n = 1,412) and (b) adults (n = 7,368) (gray line = males; black line = females).
74 VOLUME 18 NUMBER 1 | JANUARY 2010 | www.obesityjournal.org
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and is based on BW, age, and gender; Model 2 adds height to
the predictors of Model 1; Model 3 is based on age, gender,
and FFM and Model 4 adds FM to the predictors of Model 3.
Because there was no meaningful improvement in model t
obtained by adding the predictor × gender interactions (data
not shown), we kept the models simpler by removing these
interactions also, when statistically signicant.
All models had virtually the same accuracy for predicting
BMR in children and adolescents as in adults (R2
adj from 0.59
to 0.60). In detail, height added nothing practically relevant
Table 2 Effect of gender on the relationship between BMR, anthropometry, and body composition
Age Body weight Height BMI FFM FM
Children and adolescents
Male −3,147***
(−4,105, −2,190)
133
(−366, 633)
−2,557*
(−4,740, −374)
−1,051**
(−1,810, −292)
419
(−92, 929)
−8
(−499, 483)
Age (years) 66** (21, 111)
Male × age 331*** (265, 398)
Body weight (kg) 41*** (37, 45)
Male × weight 10*** (5, 14)
Height (cm) 66*** (56, 77)
Male × height 22** (8, 35)
BMI (kg/m2) 107*** (94, 121)
Male × BMI 68*** (47, 88)
FFM (kg) 92*** (83, 100)
Male × FFM 8 (−3, 18)
FM (kg) 73*** (66, 80)
Male × FM 27*** (17, 36)
Intercept 6,693***
(6,032, 7,353)
3,771***
(3,408, 4,135)
−2,963***
(−4,612, −1,314)
3,723***
(3,210, 4,236)
3,574***
(3,196, 3,953)
4,001***
(3,649, 4,354)
N1,412 1,412 1,412 1,412 1,412 1,412
R2
adj 0.31 0.59 0.41 0.47 0.59 0.58
RMSE (kJ) 1,393 1,073 1,282 1,223 1,079 1,083
P model <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Adults
Male 2,605***
(2,361, 2,849)
744***
(431, 1,058)
−4,065***
(−5,568, −2,562)
799***
(407, 1,191)
796***
(488, 1,104)
940***
(624, 1,257)
Age (years) −21*** (−24, −19)
Male × age −14*** (−19, −9)
Body weight (kg) 46*** (45, 48)
Male × weight 3* (1, 6)
Height (cm) 58*** (53, 63)
Male × height 31*** (22, 39)
BMI (kg/m2) 98*** (93, 103)
Male × BMI 29*** (20, 39)
FFM (kg) 91*** (88, 94)
Male × FFM −14*** (−18, −9)
FM (kg) 91*** (88, 95)
Male × FM 36*** (30, 43)
Intercept 8,436***
(8,307, 8,565)
2,522***
(2,348, 2,697)
−1,745***
(−2,534, −957)
3,324***
(3,113, 3,536)
2,541***
(2,371, 2,711)
2,642***
(2,463, 2,820)
N7,368 7,368 7,368 7,368 7,368 7,368
R2
adj 0.33 0.59 0.38 0.47 0.59 0.57
RMSE (kJ) 1,351 1,065 1,306 1,209 1,059 1,088
P model <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
95% confidence intervals in brackets.
BMR, basal metabolic rate; FFM, fat-free mass; FM, fat mass; R2
adj, adjusted coefficient of determination; RMSE, root mean squared error.
*P < 0.05, **P < 0.01, ***P < 0.001.
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to the simpler model based on BW and age and gender and
FM added nothing practically relevant to the model based on
age, gender, and FFM (Ta bl e 3 ). us, a simple model based
on BW, age, and gender is as accurate as more complex mod-
els based on body composition. erefore, the new equations
for the prediction of BMR in children and adolescents (Eqs. 1
and 2) and adults (Eqs. 3 and 4) are the following:
BMR50BW57Age 1,007 gender 3,804
(R: 0.59; RMSE: 1,0
adj
2
−× +
774 kJ; accurate prediction: 59%)
(1)
BMR99FFM 28 Age 749 gender 3,640
(R: 0.59; RMSE: 1,07
adj
2
−× +
88 kJ; accurate prediction: 59%)
(2)
BMR46BW14Age 1,140 gender 3,252
(R: 0.60; RMSE: 1,0
adj
2
−× +
448 kJ; accurate prediction: 56%)
(3)
BMR82FFM 10 Age44gender3,517
(R: 0.59; RMSE: 1,054
adj
2
−× −× +
kJ; accurate prediction: 56%),
(4)
where gender = 1 for males and 0 for females, BMR is
expressed in kJ, age in years, BW and FFM in kg (R2
adj =
adjusted coecient of determination; RMSE: root mean
squared error; accurate prediction: percentage of all subjects
whose BMR predicted was within 90–110% of measured
BMR. ese equations are given in Appendix 1 with BMR
expressed as kcal).
DISCUSSION
We evaluated the relationship between BMR, gender, age,
anthropometry, and body composition in the largest sample of
obese white children, adolescents, and adults studied so far.
In children and adolescents, gender was a signicant predic-
tor of BMR. As shown in Table 3, gender entered all prediction
models, contributing from 749 to 1,007 kJ more in males than
in females. In agreement with previous studies performed in
obese children and adolescents (10,25), the higher BMR of our
male subjects can be explained mostly by their higher FFM as
compared to females. FFM, the metabolically active compo-
nent of the body, explained ~60% of the variability of BMR in
our children and adolescents, which suggests that other factors
inuence BMR. In particular, aer adjustment for FFM, gen-
der remained a signicant multivariable predictor of BMR in
children and adolescents (regression coecient = 749, 95% CI:
621–887 kJ), which may be explained by higher proportions of
Table 3 Comparison of different models for the prediction of basal metabolic rate
Model 1 Model 2 Model 3 Model 4
Children and adolescents
Body weight (kg) 50*** (46, 53) 44*** (40, 48)
Age (years) −57*** (−85, −28) −95*** (−126, −64) −28* (−55, −0) −50** (−81, −19)
Male 1,007*** (884, 1,131) 889*** (759, 1,018) 749*** (621, 877) 938*** (767, 1,109)
Height (cm) 24*** (16, 32)
FFM (kg) 99*** (92, 106) 63*** (39, 86)
FM (kg) 37** (14, 60)
Intercept 3,804*** (3,439, 4,169) 1,044* (92, 1,997) 3,640*** (3,268, 4,012) 3,759*** (3,385, 4,134)
N1,412 1,412 1,412 1,412
RMSE (kJ) 1,074*** (1,029, 1,118) 1,062*** (1,016, 1,107) 1,078*** (1,033, 1,123) 1,073*** (1,029, 1,118)
R2
adj 0.59*** (0.56, 0.62) 0.60*** (0.56, 0.63) 0.59*** (055, 0.62) 0.59*** (0.56, 0.62)
P model <0.001 <0.001 <0.001 <0.001
Adults
Body weight (kg) 46*** (44, 47) 44*** (42, 45)
Age (years) −14*** (−16, −12) −13*** (−14, −11) −10*** (−11, −8) −14*** (−16, −11)
Male 1,140*** (1,075, 1,206) 997*** (919, 1,074) −44 (−130, 43) 1,003*** (725, 1,282)
Height (cm) 13*** (10, 17)
FFM (kg) 82*** (79, 84) 50*** (41, 59)
FM (kg) 41*** (30, 51)
Intercept 3,252*** (3,076, 3,427) 1,270*** (679, 1,861) 3,517*** (3,340, 3,694) 3,270*** (3,097, 3,443)
N7,368 7,368 7,368 7,368
R2
adj 0.60*** (0.58, 0.61) 0.60*** (0.59, 0.62) 0.59*** (0.58, 0.61) 0.60*** (0.58, 0.61)
RMSE (kJ) 1,048*** (1,026, 1,070) 1,045*** (1,023, 1,067) 1,054*** (1,032, 1,076) 1,048*** (1,026, 1,070)
P model <0.001 <0.001 <0.001 <0.001
Bootstrapped 95% confidence intervals in brackets.
FFM, fat-free mass; FM, fat mass; R2
adj, adjusted coefficient of determination; RMSE, root mean squared error.
*P < 0.05, **P < 0.01, ***P < 0.001.
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skeletal glycolytic bers (26), higher Na+−K+ ATPase activity
(27), and dierent hormonal status (28).
However, male gender (regression coecient = −44, 95%
CI −130 to 43) did not enter the multivariable prediction
model for adults based on FFM. is is an agreement with pre-
vious studies showing that the confounding eect of gender is
eliminated when FFM is taken into account (29).
In all prediction models (Ta bl e 3 ), there was an inverse
relationship between age and BMR. As expected, the increase
of BMR for each year of age was higher in children and ado-
lescents than in adults (Ta bl e 2 ). ese results conrm pre-
vious observations that there is a reduction in BMR adjusted
for dierences in body composition in older subjects com-
pared with younger ones (30,31). Our results therefore sup-
port the hypothesis that the age-related decline in BMR is
mainly attributed to a reduction in FFM quantity. Gallagher
et al. (8,32) rst addressed the age-related decline in BMR in
normal weight subjects by applying a BMR-prediction model
based on seven organ/tissue components. Subsequently, Wang
et al. (6,31) conrmed that the decline in both the mass and
the cellular fraction of organs and tissues may account for the
lower BMR observed in elderly adults. Whereas body com-
position cannot fully explain the interindividual variability of
BMR, FFM explained ~60% of the variability of BMR in both
children and adolescents and in adults in the present study. It
is possible that there are other factors that may contribute to
predicting BMR in severely obese subjects. Ponderal history,
genetic factors, such as physical activity level (33) and dier-
ences in organ mass and metabolic rate (31,32), and hormonal
status (34) may also inuence BMR. Whether the addition of
these variables can improve the accuracy of predicting BMR in
the severely obese deserves further study. However, at present,
we cannot oer any plausible metabolic mechanism explaining
this observation, and further research is needed.
In the present study, the main predictors of BMR for children
and adolescents (Ta bl e 3 ) and adults (Ta bl e 3 ) were investi-
gated. e prediction equation based on anthropometric (BW,
height, gender, and age) and body composition measurements
(FFM, FM, gender, and age) had the same R2
adj and similar
RMSE. us, an estimation of BMR in obese subjects can be
obtained with the same accuracy using anthropometric or
body composition measurements. Clearly, the equations based
on anthropometric measurements are easier to use in clinical
practice because they are based on routine measurements. e
equations based on body composition (FFM and FM as assessed
by BIA) are also generally more population-specic than those
based on anthropometric measurements (10,25), and require
specic equipment and more time to assess body composition.
In addition, the new equations are characterized by good accu-
racy and better agreement between predicted and measured
BMR than that provided by WHO (35) and Miin et al. (36)
as well as independently from age and gender (Ta b le 4 ). As
BMR makes up more than 60% of EE in obese subjects, a better
understanding of the main factors inuencing it and its predic-
tion is necessary to develop a dietary treatment able to induce a
desired level of energy decit for obese subjects.
In the present study, body composition was measured using
BIA on the basis of the water content in the body (16). All
measurements of body composition in our study were per-
formed under strictly controlled conditions in accordance
with the National Institutes of Health guidelines (17). BIA is
a common, simple, rapid, and noninvasive method to estimate
total body water and FFM in healthy subjects as well as in
obese subjects (37). BIA has been cross validated in children
Table 4 Comparison between BMR and predicted BMR by WHO (35), Mifflin et al. (36) and new equations
Author BMRa (kcal/day) Differencea (kcal/day) Difference (%) Accurateb prediction P valuec
Measured REE (boys <18 years) 2,181 ± 437
WHO (boys <18 years) 2,416 ± 458 234 ± 315 11 36 <0.001
Mifflin (boys <18 years) 2,007 ± 322 −174 ± 288 −8 46 <0.001
New (boys <18 years) 2,191 ± 305 10 ± 290 0 57 0.377
Measured REE (girls <18 years) 1,825 ± 297
WHO (girls <18 years) 1,685 ± 174 −140 ± 227 −8 50 <0.001
Mifflin (girls <18 years) 1,708 ± 228 −117 ± 227 −6 50 <0.001
New (girls <18 years) 1,834 ± 223 9 ± 226 0 60 0.237
Measured REE (male >18 years) 2,244 ± 413
WHO (male >18 years) 2,299 ± 362 55 ± 345 2 45 <0.001
Mifflin (male >18 years) 2,087 ± 278 −157 ± 308 −7 47 <0.001
New (male >18 years) 2,269 ± 262 25 ± 310 1 54 0.342
Measured REE (female >18 years) 1,771 ± 301
WHO (female >18 years) 1,764 ± 210 −6 ± 240 0 53 0.021
Mifflin (female >18 years) 1,650 ± 221 −120 ± 228 −7 49 <0.001
New (female >18 years) 1,795 ± 204 25 ± 225 1 58 0.132
BMR, basal metabolic rate; REE, resting energy expenditure; WHO, World Health Organization.
aMean ± s.d. bPercentage of subjects whose predicted BMR is within 90–110% of measured BMR. cPaired t-test for predicted vs. measured BMR.
OBESITY | VOLUME 18 NUMBER 1 | JANUARY 2010 77
articles
integrative Physiology
and adolescents against measurements of total body water by
deuterium dilution (38) and total body potassium (39). Similar
validation studies are available for adults (40). e accuracy
of BIA is highly dependent on the equations used to calculate
FFM. e BIA prediction equations developed by our group
against dual-energy X-ray absorptiometry (18) allowed an
estimate of body composition in obese youths similar to those
studied here. Moreover, the fatness-specic prediction equa-
tions employed for adults have been cross-validated in adults
(19) within a wide range of BMI (up to 53.3 kg/m2). Das et al.
(40) reported that the BIA estimate of percent body fat obtained
with fatness-specic equations in extremely obese women was
within 1.1–1.5% of the value obtained using body density and
doubly labeled water as gold standards.
In conclusion, gender was a signicant determinant of BMR in
obese children and adolescents but not in obese adults. In chil-
dren and adolescents, gender remained signicant aer adjust-
ment for BW or FFM, with a BMR higher in males. In addition,
the present study supports the hypothesis that the age-related
decline in BMR is due to a reduction in FFM, which suggests that
physical activity is essential for obese subjects both to maintain
or increase BMR, as well as to increase DEE and contribute to
weight loss. Finally, anthropometric measurements (BW, height,
gender, and age) are as accurate as body composition estimated
by BIA for the prediction of BMR. e equations developed in
the present study may represent a useful tool for health care pro-
fessionals, who do not have access to indirect calorimetry equip-
ment, for the estimation of BMR in obese subjects.
ACKNOWLEDGMENTS
We are grateful to the patients for their cooperation, and to the head-
nurses and the nursing staff at the Division of Auxology and 3rd Division
of Metabolic Diseases, Italian Institute for Auxology, IRCCS, for their kind
assistance during the study. We thank Dr Vermorel (INRA, France) for his
valuable advice in improving the manuscript and Dr Buckler for the English
revision. The study was supported by Progetti di Ricerca Corrente, Italian
Institute for Auxology, Milan, Italy.
DISCLOSURE
The authors declared no conflict of interest.
© 2009 The Obesity Society
REFERENCES
1. World Health Organization. Obesity: preventing and managing the global
epidemic. Report of a WHO Consultation. Geneva, Switzerland, 2004.
2. Bouchard C, Blair SN. Introductory comments for the consensus on physical
activity and obesity. Med Sci Sports Exerc 1999;31(11 Suppl):S498–S501.
3. Flatt JP. Macronutrient composition and food selection. Obes Res
2001;9(Suppl 4):S256–S262.
4. Foster GD, McGuckin BG. Estimating resting energy expenditure in obesity.
Obes Res 2001;9(Suppl 5):S367–S372.
5. Goran MI. Energy metabolism and obesity. Med Clin North Am
2000;84:347–362.
6. Wang Z, Heshka S, Wang J et al. Metabolically active portion of fat-free
mass: a cellular body composition level modeling analysis. Am J Physiol
Endocrinol Metab 2007;292:E49–E53.
7. Elia M. Energy expenditure in whole body. In: Kinney MJ, Tucker HN (eds).
Energy Metabolism. Raven Press: New York, 1992, pp 19–60.
8. Gallagher D, Belmonte D, Deurenberg P et al. Organ-tissue mass
measurement allows modeling of REE and metabolically active tissue mass.
Am J Physiol 1998;275:E249–E258.
9. Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR.
Factors influencing variation in basal metabolic rate include fat-free mass,
fat mass, age, and circulating thyroxine but not sex, circulating leptin, or
triiodothyronine. Am J Clin Nutr 2005;82:941–948.
10. Goran MI, Kaskoun M, Johnson R. Determinants of resting energy
expenditure in young children. J Pediatr 1994;125:362–367.
11. Fukagawa NK, Bandini LG, Young JB. Effect of age on body composition
and resting metabolic rate. Am J Physiol 1990;259:E233–E238.
12. Keys A, Taylor HL, Grande F. Basal metabolism and age of adult man.
Metab Clin Exp 1973;22:579–587.
13. Cacciari E, Milani S, Balsamo A et al. Italian cross-sectional growth charts
for height, weight and BMI (6-20 y). Eur J Clin Nutr 2002;56:171–180.
14. Quetelet L, Adolphe J. A treatise on man and the development of his
faculties. In: Chambers WaR (ed). Comparative Statistics in the 19th
Century. Edinburgh, Scotland, 1842.
15. Cole TJ, Freeman JV, Preece MA. British 1990 growth reference centiles for
weight, height, body mass index and head circumference fitted by maximum
penalized likelihood. Stat Med 1998;17:407–429.
16. Lukaski HC, Bolonchuk WW, Hall CB, Siders WA. Validation of tetrapolar
bioelectrical impedance method to assess human body composition. J Appl
Physiol 1986;60:1327–1332.
17. NIHT. Bioelectrical impedance analysis in body composition measurement:
National Institutes of Health Technology Assessment Conference Statement.
Am J Clin Nutr 1996;64(3 Suppl):S524–S532.
18. Lazzer S, Bedogni G, Agosti F et al. Comparison of dual-energy X-ray
absorptiometry, air displacement plethysmography and bioelectrical
impedance analysis for the assessment of body composition in severely
obese Caucasian children and adolescents. Br J Nutr 2008;100:918–924.
19. Gray DS, Bray GA, Gemayel N, Kaplan K. Effect of obesity on bioelectrical
impedance. Am J Clin Nutr 1989;50:255–260.
20. Isbell TR, Klesges RC, Meyers AW, Klesges LM. Measurement reliability and
reactivity using repeated measurements of resting energy expenditure with
a face mask, mouthpiece, and ventilated canopy. JPEN J Parenter Enteral
Nutr 1991;15:165–168.
21. Weir JB. New methods for calculating metabolic rate with special reference
to protein metabolism. J Physiol (Lond) 1949;109:1–9.
22. Huitema BE. The Analysis of Covariance and Alternatives. Wiley: New York,
1980.
23. Chatterjee S, Hadi AS. Regression Analysis by Example. Wiley-Interscience:
New York, 2006.
24. Efron B, Tibshirani R. An Introduction to the Bootstrap. Chapman & Hall:
New York, 1993.
25. Weinsier RL, Schutz Y, 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 1992;55:790–794.
26. Simoneau JA, Bouchard C. Human variation in skeletal muscle fiber-type
proportion and enzyme activities. Am J Physiol 1989;257:E567–E572.
27. Simat BM, Mayrand RR, From AH et al. Is the erythrocyte sodium pump
altered in human obesity? J Clin Endocrinol Metab 1983;56:925–929.
28. Ferraro R, Lillioja S, Fontvieille AM et al. Lower sedentary metabolic rate in
women compared with men. J Clin Invest 1992;90:780–784.
29. Cunningham JJ. Body composition as a determinant of energy expenditure:
a synthetic review and a proposed general prediction equation. Am J Clin
Nutr 1991;54:963–969.
30. Piers LS, Soares MJ, McCormack LM, O’Dea K. Is there evidence for an
age-related reduction in metabolic rate? J Appl Physiol 1998;85:2196–2204.
31. Wang Z, Heshka S, Heymsfield SB, Shen W, Gallagher D. A cellular-level
approach to predicting resting energy expenditure across the adult years.
Am J Clin Nutr 2005;81:799–806.
32. Gallagher D, Allen A, Wang Z, Heymsfield SB, Krasnow N. Smaller organ
tissue mass in the elderly fails to explain lower resting metabolic rate. Ann N
Y Acad Sci 2000;904:449–455.
33. Lazzer S, Boirie Y, Bitar A et al. Assessment of energy expenditure
associated with physical activities in free-living obese and nonobese
adolescents. Am J Clin Nutr 2003;78:471–479.
34. Leibel RL, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting
from altered body weight. N Engl J Med 1995;332:621–628.
35. World Health Organization. Energy and protein requirements. Report of a
Joint FAO/WHO/UNU Expert Consultation. WHO Technical Report Series,
No. 724. Geneva, Switzerland, 1985.
36. Mifflin MD, St Jeor ST, Hill LA et al. A new predictive equation for resting
energy expenditure in healthy individuals. Am J Clin Nutr 1990;51:241–247.
37. Houtkooper LB, Lohman TG, Going SB, Howell WH. Why bioelectrical
impedance analysis should be used for estimating adiposity. Am J Clin Nutr
1996;64(3 Suppl):S436–S448.
78 VOLUME 18 NUMBER 1 | JANUARY 2010 | www.obesityjournal.org
articles
integrative Physiology
38. Wabitsch M, Braun U, Heinze E et al. Body composition in 5-18-y-old obese
children and adolescents before and after weight reduction as assessed
by deuterium dilution and bioelectrical impedance analysis. Am J Clin Nutr
1996;64:1–6.
39. Schaefer F, Georgi M, Zieger A, Schärer K. Usefulness of bioelectric
impedance and skinfold measurements in predicting fat-free mass derived
from total body potassium in children. Pediatr Res 1994;35:617–624.
40. Das SK, Roberts SB, Kehayias JJ et al. Body composition assessment in
extreme obesity and after massive weight loss induced by gastric bypass
surgery. Am J Physiol Endocrinol Metab 2003;284:E1080–E1088.
APPENDIX 1
New equations for the prediction of BMR (kcal) in children
and adolescents (Eqs. 5 and 6) and adults (Eqs. 7 and 8) are
the following:
BMR12BW14Age 241 gender 909
(R: 0.59; RMSE: 257 kca
adj
2
−× +
ll; accurate prediction: 59%)
(5)
(6)
BMR11BW3Age 272 gender 777
(R: 0.60; RMSE: 251 kcal
adj
2
−× +
;; accurate prediction: 56%)
(7)
BMR20FFM 2Age 11 gender 841
(R: 0.59; RMSE: 252 kcal
adj
2
−× −× +
;; accurate prediction: 56%)
(8)
where gender = 1 for males and 0 for females, age in years, BW
and FFM in kg (R2
adj = adjusted coecient of determination;
RMSE: root mean squared error; accurate prediction: percent-
age of all subjects whose BMR predicted was within 90–110%
of measured BMR).
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Half Title Series Information Title Copyright Dedication Contents Preface
Article
The 1981 FAO/WHO/UNU report on protein and energy requirements makes significant advances in the application of science. Here it is summarized and reviewed with emphasis on areas of uncertainty and differences in approach since 1971. The convenient procedures for making estimates of energy requirements leave out some important minor factors, thus allowing debate and reconsideration of when and how they can be accounted for properly. Judgements are made when appropriate data is unavailable, for example protein requirements in the elderly. A controversial issue may be a health judgement, which could be interpreted to mean that increased physical activity rather than low-calorie diets is the route to lowering the prevalence of obesity and improving long-term health.