Are basal metabolic rate prediction equations
appropriate for female children and adolescents?
WILLIAM W. WONG, NANCY F. BUTTE, ALBERT C. HERGENROEDER,
REBECCA B. HILL, JANICE E. STUFF, AND E. O’BRIAN SMITH
United States Department of Agriculture/Agricultural Research Service Children’s Nutrition
Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030
whole body calorimetry; energy metabolism; Caucasians;
African-Americans
(BMR) is the minimal rate of
energy consumption necessary to support all cellular
functions and accounts for 50–70% of total energy
expenditure in humans. BMR is used routinely by
clinicians for estimation of energy requirements in
patient care as well as by governmental agencies and
health organizations in defining population energy
requirements.
BMR is measured by indirect calorimetry after the
subject has fasted ,12 h (usually overnight), then
rested motionless in a supine position 20–30 min in a
thermally neutral environment. The subject is instructed to remain still for the next 30–40 min while
the indirect calorimetry measurements are taken. The
procedure is not only time-consuming but requires
extensive subject cooperation as well as accurate and
precise flow and concentration measurements, using
sophisticated flow and gas analyzers.
Recognizing the significance of BMR in defining
energy requirements in humans, several authors have
generated simple equations for estimation of BMR
based on age, body weight, height, and gender (3, 9, 22,
26). Although these equations were formulated based
on BMR measurements performed between 1919 and
1952, many of these equations presently are employed
by clinicians, governmental agencies, and health orga-
THE BASAL METABOLIC RATE
nizations to estimate human energy requirements.
BMR measurements done .44 years ago were characterized by the use of inappropriate techniques; duplicate results and group means in the calculation; data
obtained from nonfasted, sleeping, or agitated subjects;
and data collected under nonstandardized conditions,
such as temperature and altitude, without appropriate
corrections. These concerns prompted the Food and
Agriculture Organization of the United Nations (FAO)/
World Health Organization (WHO)/United Nations University (UNU) (30) and Schofield (23) to scrutinize the
BMR results to exclude unacceptable data and derive
more appropriate prediction equations.
In a recent study, Dietz et al. (6) compared BMR
values obtained for 54 adolescents with the use of the
ventilation hood and indirect calorimetry with BMR
values calculated from prediction equations. These
authors concluded that the FAO/WHO/UNU equations
(30) yielded average BMR values which did not differ
significantly from the measured mean values. However, similar BMR measurements performed by Maffeis
et al. (15) on 33 obese and 97 nonobese prepubertal
children in Italy were found to be significantly lower
than those calculated using the prediction equations,
including those formulated by FAO/WHO/UNU.
We describe here the use of BMR measured by whole
body calorimetry to evaluate the applicability of 10
equations commonly used for prediction of BMR in
Caucasian and African-American female children and
adolescents of different body composition and stages of
sexual maturation. We hypothesize that the prediction
equations, which were derived primarily from BMR
data collected in adults and in Caucasians, are not
suitable for estimation of BMR in children and adolescents of different ethnic origins.
METHODS
Subjects. We studied 118 female children and adolescents
(76 Caucasians, 42 African-Americans) between 8 and 17 yr of
age. The subjects were recruited from schools in the greater
Houston metropolitan area. Based on medical history, vital
signs, standard clinical blood chemistries, and physical examination, all subjects were healthy at the time of the study. All
subjects tested negative for pregnancy by using the QuickVue
One-Step hCG-Urine test (Quidel, San Diego, CA). The
protocol met the Occupational Safety and Health Administration/Department of Health and Human Services guidelines
for human immunodeficiency virus/hepatitis B virus occupational safety and was approved by the Baylor Affiliates
Review Board for Human Subjects at Baylor College of
Medicine. After thorough explanation of the procedures to the
subjects and to their parents, all subjects and their parents
gave written informed consent.
0161-7567/96 $5.00 Copyright r 1996 the American Physiological Society
2407
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Wong, William W., Nancy F. Butte, Albert C. Hergenroeder, Rebecca B. Hill, Janice E. Stuff, and E. O’Brian
Smith. Are basal metabolic rate prediction equations appropriate for female children and adolescents? J. Appl. Physiol.
81(6): 2407–2414, 1996.—The basal metabolic rate (BMR),
which accounts for 50–70% of total energy expenditure, is
essential for estimation of patient and population energy
needs. Numerous equations have been formulated for prediction of human BMR. Most equations in current use are based
on measurements of Caucasians performed more than four
decades ago. We evaluated 10 prediction equations commonly
used for estimation of BMR in 76 Caucasian and 42 AfricanAmerican girls between 8 and 17 yr of age against BMR
measured by whole-body calorimetry. The majority of the
prediction equations (9 of 10) overestimated BMR by 60 6 46
kcal/day (range, 15–176 kcal/day). This overestimation was
found to be significantly greater (P , 0.05) for AfricanAmericans (77 6 17 kcal/day) than for Caucasians (25 6 17
kcal/day) in six equations, controlling for age, weight, and
sexual maturity. We conclude that ethnicity is an important
factor in estimation of the BMR and that the current prediction equations are not appropriate for accurate estimation of
the BMR of individual female children and adolescents.
2408
BMR EQUATIONS VS. INDIRECT CALORIMETRY
BMR (kcal / day) 5 3.942V̇O2 1 1.106V̇CO2
(1)
Sound-sleep metabolic rate. To ensure that BMR was
measured rather than sleeping metabolic rate, soundsleeping metabolic rate (SSMR) was extracted from total
sleeping metabolic rate. SSMR was the sleeping energy
expenditure with ,10 counts of physical movement, as
determined by the Doppler microwave sensor. A valid BMR
measurement must have a BMR-to-SSMR ratio .1.
Anthropometric measurements. The body weight of each
subject, dressed in a hospital gown and without socks or
shoes, was measured to the nearest 0.1 kg with a digital
balance (Scale-Tronix, Dallas, TX). Height was measured to
the nearest 1 mm with a stadiometer (Holtain, Crymmych,
Pembs, UK). All measurements were performed by one investigator. Body mass index (BMI) was calculated from body
weight (Wt, in kg) and height (Ht, in m) as
BMI (kg / m2) 5 Wt / Ht2
(2)
Sexual maturity. Tanner stages of breast and pubic hair
development (27) were used to classify sexual maturity based
on physical examination.
BMR prediction equations. The equations formulated by
Harris and Benedict (9), Boothby et al. (3), Talbot (26),
Robertson and Reid (22), FAO/WHO/UNU (30), Schofield (23),
and Maffeis et al. (15) were used to predict BMR of our female
subjects. These equations are summarized in Table 1.
Statistical analyses. The Bland-Altman comparison technique (2) was used to compare BMR values predicted by
equations with values measured by whole body calorimetry.
Differences between predicted and measured BMR were
plotted against the averages of predicted and measured BMR.
Regression analysis was used to test the relationship between
these differences and means. If the slope was not significant,
the relative bias (mean difference between methods) and the
95% limits of agreement (mean difference 6 2 SD of the
differences) were computed. If the slope relating the differences and means was significant, the 95% limits of agreement
were estimated as 2 SE of the estimate around the regression
line.
Table 1. Equations for prediction
of basal metabolic rate
Researchers
and Subjects
Ref.
Harris and Benedict
All ages
9
Boothby et al.
All ages
Talbot
All ages
3
BMR 5 9.6Wt 1 1.9Ht
2 4.7A 1 655
BMR 5 24BMRBSA BSA*
26
Taken from BMR tables
based on Wt and Ht
Robertson and Reid
All ages
22
FAO/WHO/UNU
3- to 10-yr old
10- to 18-yr old
3- to 18-yr old
Schofield
3- to 10-yr old
30
BMR 5 24BSA[30 1 30/
(1 1 eu /10)]†
BMR 5 22.5Wt 1 499
BMR 5 12.1Wt 1 746
BMR 5 7.4Wt 1 482Ht 1 217
23
BMR 5 20.3Wt 1 486
BMR 5 17.0Wt 1 1.6Ht 1 371
BMR 5 13.4Wt 1 693
BMR 5 8.4Wt 1 4.7Ht 1 200
10- to 18-yr old
Maffeis et al.
All ages
Equations
15
BMR 5 8.6Wt 1 3.7Ht
2 8.7A 1 371
Subjects, by age; Ref., reference numbers; FAO/WHO/UNU, Food
and Agricultural Organization/World Health Organization/United
Nations University; BMR, basal metabolic rate expressed as kcal/
day; Wt, body weight in kg; Ht, height in cm; A, age in yr; BMRBSA ,
BMR estimated from body surface area in kcal/m2; BSA, body surface
area in m2; u is a function calculated from age. * BSA is calculated,
using the Du Bois equation (7): BSA (m2 ) 5 0.007184Wt0.425Ht 0.725.
† The function, u 5 0.968304 2 0.22383820A 1 0.0685533A2 2
0.0040652313A3 1 0.000095117564A4 2 0.00000078506248A5, where
A 5 age.
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Whole body calorimetry. Two large (34 m3 ) and two small
(19 m3 ) room respiration calorimeters were used in the study
(18). Each calorimeter is equipped with a bed, table, video and
stereo equipment, exercise cycle, telephone, lavatory, commode, and intercom system for communication with the
calorimetry and nursing staff. Microprocessor controllers
(CMP 3244; Conviron, Winnipeg, Manitoba, Canada) maintained temperature and humidity to within 60.3°C and 65%
relative humidity, respectively. Air inflow and exhaust flow
rates of these chambers were controlled by thermal-mass flow
controllers to within 62% full scale or 60.5% for air flows
between 13 and 50 l/min (Sierra, Monterrey, CA). Concentrations of CO2 in these chambers were maintained at 0.45%.
The CO2 concentrations in the inflow and exhaust air streams
were measured with nondispersive infrared CO2 analyzers
(Ultramat 5E, Siemens, Karlsruhe, Germany). Oxygen concentrations in the inflow and exhaust air streams were measured
by paramagnetic oxygen gas analyzers (Oxymat 5E, Siemens). Water content of air samples from the inflow and
exhaust air streams was reduced to ,0.01% with perfluorosilicate membrane dryers (PD-625–48SS; Perma-Pure, Toms
River, NJ) before subjects entered the gas analyzers. Twentyfour-hour N2-CO2 infusion tests indicated that errors for CO2
production rate (V̇CO2) and oxygen consumption rate (V̇O2)
using these respiration calorimeters were 20.34 6 1.24 and
0.11 6 0.98%, respectively. Response times of these calorimeters were 2–6 min for V̇O2, which ranged from 100 to .4,000
ml/min. The gas analyzers and flow controllers were tested by
N2-CO2 infusion before each study. Physical movement and
heart rate of each subject inside the calorimetric chamber
were monitored continuously by Doppler microwave sensor
(D9/50; Microwave Sensors, Ann Arbor, MI) and by telemetry
(Dynascope 3300; Fukuda Denshi America, Redmond, WA),
respectively.
Each subject checked into the Metabolic Research Unit
(MRU) of the Children’s Nutrition Research Center 1 day
before the calorimetric measurement and received oral and
written instructions regarding the schedule, procedures, and
operations of the chamber. After an overnight stay in one of
the volunteer suites at the MRU, the subject was awakened
at 7:00 A.M. and took a shower; a heart-rate monitor was then
taped on the subject’s chest above the heart. Each subject
entered the chamber at 8:00 A.M. and ate breakfast at 8:30
A.M. Lunch was served at 12:00 P.M. and dinner at 5:30 P.M. All
subjects remained awake until bedtime at 10:00 P.M. No food,
other than caffeine-free beverages, was allowed after dinner.
BMR. At 6:50 A.M. the next day, each subject was awakened, allowed to urinate, and then returned to bed. At 7:20
A.M., the subject was awakened if she was asleep and instructed to find a comfortable position in bed and remain
awake and motionless for the next 40 min. The V̇O2 and V̇CO2
measurements per minute with the least movement (#50
counts) as indicated by the Doppler microwave sensor during
the 40-min measurement period were converted to BMR
using the Weir nonprotein equation (29)
2409
BMR EQUATIONS VS. INDIRECT CALORIMETRY
Table 2. Age, physical characteristics, and sexual
maturity of 76 Caucasian and 42 AfricanAmerican subjects
Caucasian
Age, yr
12.6 6 2.0
Weight, kg
46.9 6 12.7
Height, cm
153.0 6 12.0
2
BMI, kg/m
19.8 6 3.8
Sexual maturity, %
Stage 1
11.3 (18.3)
Stage 2
22.5 (18.3)
Stage 3
28.2 (18.3)
Stage 4
16.9 (29.6)
Stage 5
21.1 (15.5)
AfricanAmerican
P Values
13.5 6 1.7
59.9 6 18.7
159.4 6 8.9
23.4 6 6.2
,0.02
,0.01
,0.01
,0.01
0 (2.7)
2.7 (0)
21.6 (16.2)
8.1 (27.0)
67.6 (54.1)
,0.01 (,0.01)*
Values are means 6 SD. BMI, body mass index. Sexual maturity
according to Tanner stages of sexual maturation, shown as %subjects
with breast development; %subjects with pubic hair development in
parentheses. * P values by t-test and by x2 testing.
RESULTS
Age, physical characteristics, and sexual maturity.
Age, body weight, height, BMI, and sexual maturity of
our volunteers are summarized in Table 2. The AfricanAmerican girls on average were older than the Caucasian girls. Twelve subjects (11 Caucasians, 1 AfricanAmerican) were below the 15th percentile of BMI, 77
(48 Caucasians, 29 African-Americans) were between
the 15th and 85th percentiles, and 29 (16 Caucasians,
13 African-Americans) were above the 85th percentile
(19).
The African-American girls also were heavier, taller,
and had higher BMI than the Caucasian girls. After
Table 3. Whole body calorimetric data of 76 Caucasian
and 42 African-American subjects
Temperature, °C
Relative humidity, %
BMR, kcal/day
Duration of BMR, min
Sound-sleep metabolic rate
(SSMR), kcal/day
BMR/SSMR
Caucasian
AfricanAmerican
P
Values
23.9 6 0.4
43.6 6 4.9
1,350 6 106*
27.4 6 5.4
24.0 6 0.4
43.8 6 5.3
1,298 6 108*
27.9 6 4.0
0.28
0.82
,0.02
0.57
1,183 6 98*
1.15 6 0.07
1,130 6 100*
1.15 6 0.08
,0.01
0.60
Values are means 6 SD. * Adjusted for mean body weight of 2
ethnic groups.
%BMR by Whole
Body Calorimetry
Source of
Equations
Harris and Benedict
Boothby et al.
Talbot, by Wt
Talbot, by Ht
Robertson and Reid
FAO/WHO/UNU, by
Wt
FAO/WHO/UNU, by
Wt and Ht
Schofield, by Wt
Schofield, by Wt and
Ht
Maffeis et al.
Ref.
Caucasian
AfricanAmerican
P
Values
9
3
26
26
22
102.6 6 7.8
111.4 6 7.9
102.9 6 8.8
103.8 6 12.2
103.5 6 7.2
106.1 6 7.3
117.0 6 9.1
109.1 6 8.6
106.0 6 13.3
107.8 6 8.1
,0.02
,0.01
,0.01
0.37
,0.01
30
101.8 6 8.0
107.7 6 8.6
,0.01
30
23
100.5 6 8.1
101.5 6 7.9
104.0 6 7.8
108.5 6 9.1
,0.03
,0.01
23
15
100.7 6 8.2
95.2 6 7.1
105.3 6 7.8
98.9 6 7.0
,0.01
,0.01
Values are means 6 SD.
controlling for age, the African-American girls remained heavier and had higher BMI than the Caucasian girls (P , 0.01). According to the Tanner stages of
sexual maturation, the African-American girls were
more mature for age than the Caucasian girls by
Student’s t-test and by x2 testing (P , 0.01).
Whole body calorimetry. Room indirect calorimetric
results are summarized in Table 3. Minimal movement
during SSMR measurements was detected by Doppler
microwave sensor, with average physical movement of
2.8 6 3.3 counts for the Caucasian girls and 2.5 6 3.6
counts for the African-American girls. Physical movement as detected by the microwave sensor varied from
zero to 1,600 counts for our subjects, with average
counts of 10 during BMR measurements. Furthermore,
the BMR values for both ethnic groups were 15% higher
Table 5. Mean differences between predicted
and measured BMR values of 76 Caucasian
and 42 African-American subjects
Caucasian
Source of
Equations
Harris and
Benedict
Boothby et al.
Talbot, by Wt
Talbot, by Ht
Robertson and
Reid
FAO/
WHO/UNU,
by Wt
FAO/
WHO/UNU,
by Wt and Ht
Schofield, by Wt
Schofield, by Wt
and Ht
Maffeis et al.
African-American
Ref.
Mean
Difference,
kcal/day
P
Values
Mean
Difference,
kcal/day
P
Values
9
3
26
26
24 6 101
146 6 97
36 6 114
41 6 162
0.04
,0.01
,0.01
,0.03
79 6 98
230 6 118
119 6 109
66 6 181
,0.01
,0.01
,0.01
0.02
22
40 6 90
,0.02
104 6 109
,0.01
30
17 6 104
0.17
106 6 123
,0.01
30
23
22 6 111
15 6 105
0.85
0.22
48 6 105
118 6 132
,0.01
,0.01
23
15
1 6 110
270 6 102
0.91
,0.01
68 6 105
220 6 98
,0.01
0.18
Values are means 6 SD.
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Student’s t-test was used to test for differences in age,
anthropometric characteristics, sexual maturity, and calorimetric data between the African-American and Caucasian
girls. Because Tanner stages of sexual maturity are not
continuous variables, differences in sexual maturity between
the two ethnic groups at various stages of development were
done again by frequency analyses, using x2 testing (Minitab,
State College, PA). Univariate analysis was used to test for
significant effect of sexual maturation and ethnicity on the
agreement between the predicted and the estimated BMR
values. After identification of the best equations for prediction
of BMR, analysis of covariance (ANCOVA; Minitab) was used
to assess the effect of ethnicity on the magnitude of the
difference between methods while controlling for age, anthropometric characteristics, and sexual maturation.
Table 4. Comparison of BMR predicted by equations
and by whole body calorimetry of 76 Caucasian and 42
African-American subjects
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Fig. 1.
BMR EQUATIONS VS. INDIRECT CALORIMETRY
2410
BMR EQUATIONS VS. INDIRECT CALORIMETRY
indicated that the overestimation remained significantly greater (P , 0.05) for the African-American girls
(77 6 17 kcal/day) than for the Caucasian girls (25 6 17
kcal/day) in six of the 10 equations after controlling for
differences in age, weight, and sexual maturity between the two ethnic groups. However, the effect of
sexual maturation became insignificant in the ANCOVA.
Among these prediction equations, the Maffeis equation (15) underestimated BMR of both the Caucasian
and the African-American girls. Talbot’s BMR table (26)
based on height yielded BMR values with the largest
SD among the 10 equations. The equation of Boothby et
al. (3) overestimated BMR the most among the 10
prediction equations. According to the comparisons
shown in Table 4, the equations proposed by FAO/WHO/
UNU (30) and Schofield (23) using both body weight
and height in the calculation yielded the most accurate
mean BMR compared with mean BMR by whole body
calorimetry. The remaining equations by Harris and
Benedict (9), by Talbot based on body weight (26), by
Robertson and Reid (22), by FAO/WHO/UNU based on
weight (30), and by Schofield based on weight (23) also
yielded mean BMR similar to the mean BMR by whole
body calorimetry. However, agreement between the
mean BMR estimated by the prediction equations and
the mean BMR by whole body calorimetry was consistently poorer with the African-American girls than
with the Caucasian girls.
Detailed comparisons (2) of predicted and measured
BMR, as shown in Fig. 1, offer a different interpretation. With the exceptions of the BMR values derived
from the table of Talbot based on height (26; (Fig. 1D),
the equations of Robertson and Reid (22; Fig. 1E),
FAO/WHO/UNU based on weight (30; Fig. 1F), and
Schofield based on weight and height (23; Fig. 1I), the
majority of the equations showed significant relationships (P , 0.03) between the individual differences in
Fig. 1. Detailed comparisons of basal metabolism rate (BMR) predicted by using the equations shown in Table 1
with BMR by whole body calorimetry. A-J: solid line represents mean difference between predicted and measured
BMR values. The 2 dashed lines represent upper and lower limits of agreement, calculated as mean difference 6 2
SD of differences or as 2 SE of estimate around the regression line if slope relating the between-method differences
and the average BMR values was significant. Symbols represent individual differences between predicted and
measured BMR values of Caucasian (s)and African-American (l) girls, respectively. Numerical values above and
below the 2 dashed lines are upper and lower limits of agreement at corresponding BMR values of 900 and 2,100
kcal/day. P value is significance level for slope relating differences between predicted and measured BMR values to
average BMR.
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than the SSMR values, thus minimizing the possibility
that sleeping metabolic rate was mistaken for BMR
values. Among the 118 subjects, only one subject had a
BMR/SSMR ratio of 0.98. Because the African-American girls were heavier than the Caucasian girls, the
BMR and SSMR presented in Table 3 were adjusted for
the mean body weight of the two ethnic groups. The
adjusted means of BMR and SSMR of the Caucasian
girls were found to be significantly higher than those of
the African-American girls (P , 0.02).
BMR by prediction equations. The ratios of the
predicted to the measured BMR expressed in percentages are shown in Table 4. With the exception of the
Maffeis equation (15), the prediction equations on
average overestimated (101.7–113.4%) the measured
values of our subjects. With the exception of the BMR
estimated according to height using Talbot’s table (26)
and the equation of Maffeis et al. (15), the overestimation expressed as percentages of the measured BMR
values was significantly higher in the African-American girls than in the Caucasian girls (P , 0.03).
Expressed in absolute differences between the predicted and the measured BMR values (Table 5), Student’s t-test indicated that the average overestimation
was statistically significant in five of the 10 equations
among the Caucasian girls. Among the African-American girls, however, the overestimation of BMR by the
prediction equations was statistically significant in
nine of the 10 equations. Underestimation of BMR
using the Maffeis et al. equation (15) was found to be
statistically significant among the Caucasian girls but
not among the African-American girls.
Other than the BMR calculated using the equations
of Robertson and Reid (22) and Maffeis et al. (15), the
mean difference between the predicted and the measured BMR values was found to increase with sexual
maturation by univariate analysis (P # 0.053). ANCOVA
2411
2412
BMR EQUATIONS VS. INDIRECT CALORIMETRY
DISCUSSION
The majority of the equations for prediction of BMR
were formulated based on BMR measurements done
.40 years ago. After elimination of erroneous data due
to clerical errors, repeated measurements on the same
individual, duplication of data, ill subjects, and outliers, Schofield (23) produced revised equations for prediction of BMR. In his article, Schofield indicated that the
revised equations worked well with Caucasians but
overestimated the BMR of Indians. Other studies also
reported overestimation of BMR in adults, using the
available equations. For example, the Harris and Benedict equations, which were based on BMR measurements of Caucasians, have been shown to overestimate
BMR in healthy adults by 14.1 6 12.6% (4). Because
,45% of the BMR measurements used in the formulation of the Schofield (23) and the FAO/WHO/UNU (30)
equations were collected from young and physically
active Italian subjects, the applicability of these equations to prediction of BMR in other ethnic groups has
been questioned. Indeed, the Schofield and the FAO/
WHO/UNU equations have been shown to overestimate BMR of non-European adults (5, 11, 12, 17). For
children and adolescents, conflicting results were reported in four recent studies. In 1991, using the FAO/
WHO/UNU equations (30), Dietz et al. (6) reported
good agreement between the predicted and the measured BMR values of 54 adolescents. However, the
FAO/WHO/UNU equations were reported by Henry
and Rees (12) to overestimate the BMR of children and
adolescents living in the tropics. Using the Schofield
equations, Spurr et al. (25) reported overestimation of
BMR in mestizo boys but not in girls. The resting
metabolic rates (RMR) of 33 obese and 97 nonobese
Italian children between 6 and 10 yr of age were
reported by Maffeis et al. (15) to be overestimated when
the equations formulated by FAO/WHO/UNU (30),
Robertson and Reid (22), Talbot (26) and Boothby et al.
(3) for estimation of BMR were used. The overestimation was higher in the obese children than in the
nonobese children. The Robertson and Reid equations
(22) were based on BMR measurements done on normal
people between 3 and 80 yr of age in Britain. Presumably, these subjects were primarily of European descent. No specific information on the ethnic origins of
the children studied by Talbot (26) was given. The
Boothby et al. (3) equation was based on BMR data
collected from children attending the schools in Rochester, MN, employees of the Mayo Clinic, and patients at
the clinic, but without specificity as to their ethnic
origins. We assume Caucasians were the majority of
the subjects in their study.
Several explanations have been offered to the observed overestimation of BMR when the prediction
equations were used. In a study of nine men highly
trained in exercise and nine sedentary men (21), the
RMR of the trained men was found to be higher than
that of the untrained men. Because the BMR data used
in the formulation of the Schofield and the FAO/WHO/
UNU equations consisted of a significant portion of
young and physically active subjects, it is reasonable to
expect that these equations will overestimate the BMR
of sedentary subjects. Difference in racial abilities to
produce different degrees of muscular relaxation and
temperature-induced changes in thyroid gland activity
that might lower BMR have been postulated to be
responsible for the lower BMR in people living in the
tropics (12, 16). The ‘‘thrifty genotype’’ or increased
efficiency in intake and utilization of food also has been
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BMR (predicted BMR 2 measured BMR) and the
average BMR values. As shown in Fig. 1, A-C, G, H, and
J, the average differences in BMR using these equations were not constant but rather varied depending on
the BMR values. For some individuals, a BMR value of
2,100 kcal/day could be underestimated by as much as
378 kcal/day or 18% (Fig. 1J, Maffeis et al.) or overestimated by as much as 503 kcal/day or 24% (Fig. 1B,
Boothby et al.). At a BMR value of 900 kcal/day, these
equations could underestimate an individual BMR
value by 278 kcal/day or 31% (Fig. 1H, Schofield, based
on weight) or overestimate it by 304 kcal/day or 34%
(Fig. 1B, Boothby et al.).
Among the four equations (Fig. 1D, Talbot, based on
height; E, Robertson and Reid; F, FAO/WHO/UNU,
based on weight; I, Schofield, based on weight and
height) that yielded constant mean differences over the
range of BMR values between 900 and 2,100 kcal/day,
the Schofield equation yielded the smallest mean differences for both ethnic groups (1 6 110 kcal/day for the
Caucasians and 68 6 105 kcal/day for the AfricanAmericans) vs. the measured values. Mean differences
using the other three equations were larger on average
or more variable for individuals (Talbot, 41 6 162
kcal/day for Caucasians and 66 6 181 kcal/day for
African-Americans; Robertson and Reid, 40 6 90 kcal/
day for Caucasians and 104 6 109 kcal/day for AfricanAmericans; FAO/WHO/UNU, 17 6 104 kcal/day for
Caucasians and 106 6 123 kcal/day for AfricanAmericans). As shown in Fig. 1D, Talbot’s BMR table
based on height could underestimate or overestimate
individual BMR values by as much as 287 or 387
kcal/day, respectively. Therefore, further statistical
analyses were performed only on the BMR predicted,
using the equation formulated by Schofield based on
weight and height (23) against the BMR measured by
whole body calorimetry.
With the Schofield equation, the difference between
predicted and measured BMR values by univariate
analysis differed by ethnicity (P , 0.01) and by sexual
maturation (P , 0.01). The differences were smaller
among the Caucasians than among the AfricanAmericans. The differences also were smaller at the
early stages of sexual maturity in both ethnic groups.
Because the African-American subjects were older,
more mature, and had more body mass than the
Caucasian subjects, ANCOVA was applied. This analysis indicated that the mean difference between predicted and measured BMR remained significantly affected by ethnicity (P , 0.04) after controlling for
differences in age, body weight, and sexual maturation
between the two ethnic groups (Table 2).
BMR EQUATIONS VS. INDIRECT CALORIMETRY
jects. The different responses to the inclusion of height
in the prediction of BMR between the FAO/WHO/UNU
and the Schofield equations could be due to the elimination of 220 outliers in the FAO/WHO/UNU database
before the formulation of the Schofield equation. Therefore, height should not be completely disregarded in the
BMR prediction equations.
It is well documented that African-Americans grow
faster and have more lean body mass than Caucasians
from 2 yr of age (8). Lean body mass, estimated by total
body electrical conductivity (28), of our AfricanAmerican girls was significantly higher than that of the
Caucasian girls. However, after controlling for differences in age, sexual maturation, and lean body mass,
the overestimation of BMR using the Schofield equation with weight and height remained significantly
higher (P , 0.03) among the African-American girls
than among the Caucasian girls.
In conclusion, we demonstrated in this study that the
magnitude of the differences between predicted and the
measured BMR values is significantly associated with
ethnicity. The prediction equations were found to overestimate BMR more in African-American girls than in
Caucasian girls. Although several of the prediction
equations yielded average BMR values similar to the
mean values measured by whole body calorimetry,
detailed comparisons as shown in Fig. 1 indicated that
significant underestimation (22%) and overestimation
(31%) can occur on an individual basis. Therefore, we
conclude that although some prediction equations might
be appropriate for estimation of mean BMR on a
population basis, they are not appropriate for estimating BMR of individual female children and adolescents.
Because the magnitude of the differences between
predicted and measured BMR values is significantly
associated with ethnicity after controlling for differences in age, weight, sexual maturation, and lean body
mass, we recommend that ethnicity should be included
in future refinement of these prediction equations.
The authors are indebted to the volunteers; to the staff of the
Metabolic Research Unit for meeting the needs of the subjects during
the study; to Dr. J. Hoyles at the Pediatric Department of KelseySeybold West Clinic, Dr. M. desVignes-Kendrick at the City of
Houston Health and Human Services Department, X. Earlie of the
Aldine Independent School District, S. Wooten at the Teague Middle
School, Dr. B. Shargey and C.C. Collins at the High School for Health
Professions, Mt. Carmel High School, and K. Wallace for subject
recruitment; to Dr. J. Moon, M. Puyau, and F. A. Vohra for the
calorimetric measurements; and to L. Loddeke for editorial assistance.
This work is funded in part with federal funds from the US
Department of Agriculture (USDA), Agricultural Research Service,
under Cooperative Agreement 58–7MNI-6–100. The contents of this
publication do not necessarily reflect the views or policies of the
USDA, nor does mention of trade names, commercial products, or
organization imply endorsement by the US Government.
Address for reprint requests: W. W. Wong, USDA/ARS Children’s
Nutrition Research Center, 1100 Bates St., Houston, TX 77030.
Received 23 February 1996; accepted in final form 25 July 1996.
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