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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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integrative Physiology
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 (
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 |
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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.
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
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
(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 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).
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,
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
integrative Physiology
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.
Age (years)
15 20
BMR (kJ)
50 100 150 200 250
Weight (kg)
BMR (kJ)
20 40 60 80 100 120
FFM (kg)
BMR (kJ)
20 40 60 80 100 120
FM (kg)
BMR (kJ)
120 140 160 180 200
Height (cm)
BMR (kJ)
20 40 60 80
Age (years)
BMR (kJ)
50 100 150 200 250
Weight (kg)
BMR (kJ)
40 60 80 100 120 140
FFM (kg)
BMR (kJ)
20 40 60 80 100
FM (kg)
BMR (kJ)
140 160 180 200
Height (cm)
BMR (kJ)
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 |
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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)
(−366, 633)
(−4,740, −374)
(−1,810, −292)
(−92, 929)
(−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)
(3,408, 4,135)
(−4,612, −1,314)
(3,210, 4,236)
(3,196, 3,953)
(3,649, 4,354)
N1,412 1,412 1,412 1,412 1,412 1,412
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
Male 2,605***
(2,361, 2,849)
(431, 1,058)
(−5,568, −2,562)
(407, 1,191)
(488, 1,104)
(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)
(2,348, 2,697)
(−2,534, −957)
(3,113, 3,536)
(2,371, 2,711)
(2,463, 2,820)
N7,368 7,368 7,368 7,368 7,368 7,368
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.
integrative Physiology
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
−× +
774 kJ; accurate prediction: 59%)
BMR99FFM 28 Age 749 gender 3,640
(R: 0.59; RMSE: 1,07
−× +
88 kJ; accurate prediction: 59%)
BMR46BW14Age 1,140 gender 3,252
(R: 0.60; RMSE: 1,0
−× +
448 kJ; accurate prediction: 56%)
BMR82FFM 10 Age44gender3,517
(R: 0.59; RMSE: 1,054
−× −× +
kJ; accurate prediction: 56%),
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).
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)
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
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
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.
76 VOLUME 18 NUMBER 1 | JANUARY 2010 |
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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.
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.
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.
The authors declared no conflict of interest.
© 2009 The Obesity Society
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78 VOLUME 18 NUMBER 1 | JANUARY 2010 |
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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
−× +
ll; accurate prediction: 59%)
BMR11BW3Age 272 gender 777
(R: 0.60; RMSE: 251 kcal
−× +
;; accurate prediction: 56%)
BMR20FFM 2Age 11 gender 841
(R: 0.59; RMSE: 252 kcal
−× −× +
;; accurate prediction: 56%)
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).
... Thus, it is worth exploring genetic factors correlated with obesity to develop an intervention for this condition. Obese individuals have been shown to have high fat mass (FM), skeletal muscle mass, and fat-free mass (FFM), which are important factors correlated with the resting metabolic rate (RMR) [8,9]. The RMR is the minimum energy that is required to maintain the essential homeostasis of life. ...
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This study investigated the associations between relative telomere length (RTL) and resting metabolic rate (RMR), resting fat oxidation (RFO), and aerobic capacity and whether oxidative stress and inflammation are the underlying mechanisms in sedentary women. We also aimed to determine whether the correlations depend on age and obesity. Sixty-eight normal weight and 66 obese women participated in this study. After adjustment for age, energy expenditure, energy intake, and education level, the RTL of all participants was negatively correlated with absolute RMR (RMRAB) and serum high-sensitivity C-reactive protein (hsCRP) concentration, and positively correlated with maximum oxygen consumption (V˙O2max) (all p < 0.05). After additional adjustment for adiposity indices and fat-free mass (FFM), RTL was positively correlated with plasma vitamin C concentration (p < 0.05). Furthermore, after adjustment for fasting blood glucose concentration, RTL was negatively correlated with age and positively correlated with V˙O2max (mL/kg FFM/min). We found that normal weight women had longer RTL than obese women (p < 0.001). We suggest that RTL is negatively correlated with RMRAB and positively correlated with aerobic capacity, possibly via antioxidant and anti-inflammatory mechanisms. Furthermore, age and obesity influenced the associations. We provide useful information for the management of promotion strategies for health-related physical fitness in women.
... A significant increased levels in BMR were detected in smokers compared to non-smoker subjects in gender-based groups. In females, BMR is lower than in age-matched male subjects [59]. ...
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Coronavirus 2019 (COVID-19) disease management is highly dependent on the immune status of the infected individual. An increase in the incidence of depression has been observed during the ongoing COVID-19 pandemic. Autoantibodies against in vitro reactive oxygen species (ROS) modified BSA and Lys as well as antibodies against receptor binding domain subunit S1 (S1-RBD) (S1-RBD-Abs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were estimated using direct binding and competition ELISA. Serum samples were also tested for fasting blood glucose (FBG), malondialdehyde (MDA), carbonyl content (CC), interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Significant structural changes were observed in ROS modified BSA and Lys. Female depressed subjects who were also smokers (F-D-S) showed the highest levels of oxidative stress (MDA and CC levels). Similarly, increased levels of autoantibodies against ROS modified proteins were detected in F-D-S subjects, in males who were depressed and in smokers (M-D-S) compared to the other subjects from the rest of the groups. However, contrary to this observation, levels of S1-RBD-Abs were found to be lowest in the F-D-S and M-D-S groups. During the pandemic, large numbers of individuals have experienced depression, which may induce excessive oxidative stress, causing modifications in circulatory proteins. Thus, the formation of neo-antigens is induced, which lead to the generation of autoantibodies. The concomitant effect of increased autoantibodies with elevated levels of IFN-γ and TNF-α possibly tilt the immune balance toward autoantibody generation rather than the formation of S1-RBD-Abs. Thus, it is important to identify individuals who are at risk of depression to determine immune status and facilitate the better management of COVID-19.
... Thus, female astronauts are set to play a signi cant role in future space exploration missions. Given that female astronauts are, on average, of shorter stature than their male counterparts [Moore et al., 2015], and the differences between males and females in terms of stature and body mass, aerobic tness [Sparling, 1980], body composition [Chumlea et al., 2002], and resting [Lazzer et al., 2010], exercise-related [Tarnopolsky, 2008] and post-prandial [Jensen, 1995] metabolism, as well as a spectrum of potential physiological and behavioural responses to the space ight environment and/or its analogues [Mark et al., 2014], it is critical to consider whether the sex of the crew has an operationally meaningful effect on upon estimated mission resources. ...
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Employing a methodology reported in a recent theoretical study on male astronauts, this study estimated the effects of body size and aerobic countermeasure (CM) exercise in a four-person, all-female crew composed of individuals drawn from a stature range (1.50- to 1.90-m) representative of current space agency requirements upon total energy expenditure (TEE), oxygen (O 2 ) consumption, carbon dioxide (CO 2 ) and metabolic heat (H prod ) production, and water requirements for hydration, during space exploration missions. Assuming geometric similarity across the stature range, estimates were derived using available female astronaut data (mean age: 40-y; BMI: 22.7-kg·m − 2 ; resting VO 2 and VO 2max : 3.3- and 40.5-mL·kg − 1 ·min − 1 ) on 30- and 1080-d missions, without and with, ISS-like countermeasure exercise (modelled as 2x30min aerobic exercise at 75% VO₂ max , 6d·wk ¹ ). Where spaceflight-specific data/equations were not available, terrestrial equivalents were used. Body size alone increased 24-h TEE (+ 30%), O₂ consumption (+ 60%), CO₂ (+ 60%) and H prod (+ 60%) production, and water requirements (+ 17%). With CM exercise, the increases were 25–31%, 29%, 32%, 38% and 17–25% across the stature range. Compared to the previous study of theoretical male astronauts, the effect of body size on TEE was markedly less in females, and, at equivalent statures, all parameter estimates were lower for females, with relative differences ranging from − 5% to -29%. When compared at the 50th percentile for stature for US females and males, these differences increased to -11% to -41% and translated to larger reductions in TEE, O 2 and water requirements, and less CO 2 and H prod during 1080-d missions using CM exercise. Differences between female and male theoretical astronauts result from lower resting and exercising O 2 requirements – based on available astronaut data of female astronauts who are lighter than male astronauts at equivalent statures, and having lower relative VO₂ max values. These data, combined with the current move towards smaller diameter space habitat modules point to a number of potential advantages of all-female crews during future human space exploration missions.
... The overall prevalence of overweight or obesity was 24.4 % among females and 23.0 % among males (IIPS and ICF, 2021). The above variation in the percentage of (Lazzer et al., 2010), which correlates with higher lean tissues among males and higher fat tissues among females (Jensen, 2020). ...
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Hypertension is a risk factor for cardiovascular disease, which remains poorly controlled due to low awareness. The present study assessed the awareness and prevalence of hypertension and associated factors among Indian adults aged 18 years and above with a minimum of 12 years of education through an online cross-sectional survey based on convenience sampling. Out of 1933 respondents, 891 provided information on blood pressure. The mean age of the respondents was 32.2 ± 12.3 years, with the age range of 18 to 77 years. The respondents’ average body mass index (BMI) and basal metabolic rate (BMR) were 23.9 ± 4.11 kg/m2 and 1441 ± 279 kcal/day, respectively. BMI increased with age, while a decline in BMR with age was observed. Males had significantly higher BMI than females (p < 0.001). More than half of the respondents (55.9 %) are overweight or obese. About 46.1 % of the respondents were aware of their blood pressure profile. The prevalence of prehypertension and hypertension was 21.0 % and 5.1 %, respectively. Males are more likely to be prehypertensive and hypertensive than females. An increase in the prevalence of prehypertension and hypertension with age was observed. Hypertension was positively associated with age, BMI, BMR, urban residence, monthly per capita income, social class, and educational level. Family size was negatively correlated with hypertension. Individuals with higher BMI, income, upper social class, and those in the prehypertensive age group of 35 to 55 years can be targeted through nutritional awareness campaigns to sensitize them regarding modifiable risk factors.
In order to understand the links between obesity and cancer, we must first understand how obesity develops and the systemic impact it has on the human body. To do so, we must recognize that adipose tissue has functions beyond fat storage. It is a living organ that communicates with the rest of the body to maintain homeostasis, and it becomes dysfunctional when it undergoes stress, which occurs in conditions like obesity. This chapter will provide an overview of metabolism and the functions of adipose tissue in non-obese individuals and then describe how these processes become deranged in the obese state, leading to systemic metabolic dysfunction and metabolic syndrome.
Lipids a source of energy and can also be stored in body cells for proper cellular functions. Defects in lipid metabolism can lead to a wide range of metabolic disorders. A number of risk factors are generally responsible for the dysregulation of lipid metabolism and subsequently the development of metabolic diseases. In this chapter, the pathophysiology of several lipid-metabolism-related diseases has been discussed, and the therapeutic potential of different phytochemicals or plant-derived phytonutrients in managing these disorders are highlighted. There are several medicinal secondary metabolites, which could be highly significant to manage the lipid profile and prevent the development of serious outcomes of lipid-metabolism abnormalities such as hyperchylomicronemia, hypercholesterolemia, atherosclerosis, cancer, obesity, insulin sensitivity, and resistance. Many phytonutrients isolated from fruits, vegetables, and plant sources have presented their broad spectrum of medicinal activities to modulate metabolic processes and are also involved in lipid metabolism and the management of cholesterol levels in body. Bioactive compounds such as small molecular phytonutrients from natural sources have suggested prospective treatments against lipid-metabolism-related abnormalities and have been defined in this chapter. Considering diverse physiochemical properties and therapeutic value of phytonutrients, it is highly recommended to introduce more vegetables, and fruits in the dietary regimen to intake food containing fewer fats and high fibers so that it could significantly aid the management of lipid-metabolism-related diseases.
The elaboration of therapeutic protocols using natural compounds can help in improving the outcomes of many human conditions such as malignant disorders, neurodegenerative diseases, and systemic disorders. Recently, the attention of scientists was more focused on nutraceuticals as potential candidates that can be administered in the management strategy of various pathologies. This rise in nutraceutical applications is due to their relative safety and their pleiotropic effects. Several studies suggest the use of dietary regimens and food-derived substances for the prevention and treatment of many metabolic disorders that affect the central nervous system. The neuroprotective actions offered by these substances are mediated by their pertinent antiapoptotic, antiinflammatory, and antioxidative potentials. Some compounds may also intervene in the promotion of individuals’ health via the regulation of the process of autophagy and via the enhancement of the functionality of intracellular organelles such as mitochondria. Furthermore, healthy diet and the use of dietary supplements can directly influence the functions and the progeny of neural stem cells and the metabolism of microglial cells and can influence the polarization of macrophages in the nervous tissue resulting in better outcomes in some pathologic situations. In this chapter, we review the different roles and applications of nutraceuticals in the treatment of the major brain disorders that can affect human beings.
Metabolic diseases are devastating abnormalities that address human lives toward death if they are not correctly managed. Obesity and diabetes mellitus are the prime factors that induce insulin resistance to signaling pathways and increase the risk of cardiovascular diseases. Phytonutrients are the biologically active agents derived from natural sources such as vegetables, fruits, grains, cereals, and medicinal plants, and present the ability to boost the immune system of patients with metabolic disease and also enhance the conditions by the management of lipid profiles, insulin resistance and glucose homeostasis, and chemopreventive events in case of cancer disease. This chapter highlights some phytonutrients that may have issues with the gene and produce healthy and unhealthy interactions. However, the interaction between genetic and environmental factors such as intake of particular healthy and sufficient diet plans with a good lifestyle encourages the development and pathogenesis of diseases of polygenic dietary components. Phytonutrients are critical tools for the modulation of gene expressions involved in signaling pathways and phenotypes linked with metabolic diseases. It is also noted that human health is also affected by dietary nutrients having carcinogens and aflatoxin attached with them and influence the genetic variants. As the knowledge of carcinogen and anticarcinogen increases, nutritional science leads to promising therapeutics for cancer management by healthy diet plans. This chapter has depicted essential aspects of phytonutrients and their interactions with genes in metabolic disease prevention and treatments.
In spite of the advanced researches, preventive measures, and treatment options, cancer remains a growing ailment all over the world and its prevalence is estimated to increase in future. Cellular metabolic alterations have been documented as a hallmark of cancer. Metabolic regulation is an intricately coupled process whose deregulation leads to tumor progression as well as metastasis. In order to thrive in the living system, cancer cells adapt different metabolic pathways (bioenergetics and biosynthesis). They replenish their metabolic demands by switching from normal metabolism to cancer metabolism by the process of metabolic rewiring. Recent researches suggest that starving cancer cells by the use of nontoxic chemical entities can give promising results regarding cancer proliferation. Natural products, especially those of plant origin, offer different chemical scaffolds to target cancer via modulation of multiple cell signaling cascades. Phytonutrients, the secondary metabolites from the plants, constitute edible phytochemicals which are abundantly found in vegetables, whole grains, and fruits. The growing numbers of evidences suggest that phytonutrients exhibit anticancer as well as chemopreventive activities of these bioactive molecules against several cancers by targeting the various significant enzymes of glycolysis, the PPP pathway, TCA cycle, and serine metabolism. This book chapter presents an update for the scientific community about targeting the cancer metabolism by phytonutrients. The alterations in the cancer metabolism in the context of bioenergetics, biosynthesis, and mitochondrial functions have been discussed while presenting the impact of phytonutrients as modulators of potential metabolic effectors in the cancer metabolism.
To update the British growth reference, anthropometric data for weight, height, body mass index (weight/height) and head circumference from 17 distinct surveys representative of England, Scotland and Wales (37,700 children, age range 23 weeks gestation to 23 years) were analysed by maximum penalized likelihood using the LMS method. This estimates the measurement centiles in terms of three age-sex-specific cubic spline curves: the L curve (Box–Cox power to remove skewness), M curve (median) and S curve (coefficient of variation). A two-stage fitting procedure was developed to model the age trends in median weight and height, and simulation was used to estimate confidence intervals for the fitted centiles. The reference converts measurements to standard deviation scores (SDS) that are very close to Normally distributed – the means, medians and skewness for the four measurements are effectively zero overall, with standard deviations very close to one and only slight evidence of positive kurtosis beyond ±2 SDS. The ability to express anthropometry as SDS greatly simplifies growth assessment. © 1998 John Wiley & Sons, Ltd.
Half Title Series Information Title Copyright Dedication Contents Preface
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.