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United States Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
ACKNOWLEDGEMENTS
FOOTNOTES
REFERENCES
ABSTRACT 
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 African-American 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 ± 46 kcal/day (range,
15-176 kcal/day). This overestimation was found to be
significantly greater (P < 0.05) for
African-Americans (77 ± 17 kcal/day) than for Caucasians (25 ± 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.
whole body calorimetry; energy metabolism; Caucasians; African-Americans
INTRODUCTION THE BASAL METABOLIC RATE (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 organizations
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
CO2) and oxygen
consumption rate
(O2) using these
respiration calorimeters were 
0.34 ± 1.24 and 0.11 ± 0.98%, respectively. Response times of these calorimeters were
2-6 min for O2, 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 O2 and
CO2 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)
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(2) |
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2 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.
RESULTS
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2 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 ± 3.3 counts for the Caucasian girls and
2.5 ± 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
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).
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0.053). ANCOVA indicated that the overestimation remained
significantly greater (P < 0.05) for
the African-American girls (77 ± 17 kcal/day) than for the
Caucasian girls (25 ± 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 BMR (predicted BMR 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.).
)and African-American (
) 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.
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 ± 110 kcal/day for the Caucasians and 68 ± 105 kcal/day for the African-Americans) vs. the measured values. Mean differences using the other three equations were larger on average or more variable for individuals (Talbot, 41 ± 162 kcal/day for Caucasians and 66 ± 181 kcal/day for African-Americans; Robertson and Reid, 40 ± 90 kcal/day for Caucasians and 104 ± 109 kcal/day for African-Americans; FAO/WHO/UNU, 17 ± 104 kcal/day for Caucasians and 106 ± 123 kcal/day for African-Americans). 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 African-Americans. 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).
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 ± 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 hypothesized to be responsible for the lower energy expenditure in Pima Indians (14, 20) and in Gambian men (17). The exaggerated overestimation of RMR in obese children might be due to the reduced diet-induced thermogenesis and RMR in obese and African-American subjects (1, 24).
Based on the comparisons shown in Table 4, it is easy to misinterpret that most of the equations are appropriate for estimation of BMR, particularly in our Caucasian girls. However, the detailed comparisons shown in Fig. 1 indicated that only three equations [Robertson and Reid (22), FAO/WHO/UNU with weight (30), Schofield with weight and height (23)] yielded constant mean differences over the range of BMR values between 900 and 2,100 kcal/day. Among these three equations, the Schofield and the FAO/WHO/UNU equations yielded average BMR values that were in closest agreement with the mean values measured by whole body calorimetry. This is consistent with the most recent observation made by Kaplan et al. (13) on 102 diseased subjects between 0.2 and 10.5 yr of age. However, detailed comparisons as shown in Fig. 1 also indicated that by using these equations, individual BMR values between 900 and 2,100 kcal/day could be underestimated by 200 kcal/day (Schofield, Fig. 1H) or overestimated by 286 kcal/day (FAO/WHO/UNU, Fig. 1F).
Because the Du Bois body surface area equation (7), which was validated mainly for adults, was used in the BMR prediction equations of Boothby et al. (3) and Robertson and Reid (22), the use of the Du Bois body surface area equation might not be appropriate in children and adolescents. However, we found no significant improvement in agreement between the predicted and measured BMR values when body surface areas of our subjects were calculated with the use of the equation of Haycock et al. (10). The latter body surface area equation has been validated in infants, children, and adults of various body shapes, sizes, and ethnicity.
It is interesting to note that the RMR of 33 obese and 97 nonobese Italian children measured by Maffeis et al. (15) by indirect calorimetry were consistently lower than those estimated using the equations formulated by FAO/WHO/UNU (30), Robertson and Reid (22), Talbot (26), and Boothby et al. (3). Although RMR by definition is higher than BMR, the RMR values predicted using the equation formulated by Maffeis et al. (15) also were found to be lower than the measured BMR values of our volunteers (Table 4). Therefore, it is reasonable to suspect that there might be a systematic error in the RMR measurements reported by Maffeis et al. (15).
In the formulation of the prediction equations, body weight has been considered to be the major determinant of BMR. Addition of height to the equations has been shown to contribute insignificantly to the accuracy and precision of the predicted BMR values. However, as shown in Fig. 1 (C vs. D, F vs. G, H vs. I), the use or inclusion of height in the prediction equations significantly changed the comparisons between the predicted and the measured BMR values in our subjects. 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 African-American 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.
ACKNOWLEDGEMENTS
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 Kelsey-Seybold 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.
FOOTNOTES
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.
REFERENCES
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| 2. | Bland, J. M., and D. G. Altman. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1: 307-310, 1986. |
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| 4. | Daly, J. M., S. B. Heymsfield, C. A. Head, L. P. Harvey, D. W. Nixon, H. Katzeff, and G. D. Grossman. Human energy requirements: overestimation by widely used prediction equation. Am. J. Clin. Nutr. 42: 1170-1174, 1985. |
| 5. | De Boer, J. O., A. J. H. van Es, L. E. Voorrips, F. Blokstra, and J. E. Vogt. Energy metabolism and requirements in different ethnic groups. Eur. J. Clin. Nutr. 42: 983-997, 1988. |
| 6. | Dietz, W. H., L. G. Bandini, and D. A. Schoeller. Estimates of metabolic rate in obese and nonobese adolescents. Pediatrics 118: 146-149, 1991. |
| 7. | Du Bois, D., and E. F. Du Bois. A formula to estimate the approximate surface area if height and weight be known. Arch. Intern. Med. 17: 863-871, 1916. |
| 8. | Garn, S. M., and D. C. Clark. Nutrition, growth, development, and maturation: findings from the Ten-State Nutrition Survey of 1968-1970. Pediatrics 56: 306-319, 1975. |
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| 11. | Hayter, J. E., and C. J. K. Henry. A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subjects in sample selection. Eur. J. Clin. Nutr. 48: 702-707, 1994. |
| 12. | Henry, C. J. K., and D. G. Rees. New predictive equations for the estimation of basal metabolic rate in tropical peoples. Eur. J. Clin. Nutr. 45: 177-185, 1991. |
| 13. | Kaplan, A. S., B. S. Zemel, K. M. Neiswender, and V. A. Stallings. Resting energy expenditure in clinical pediatrics: measured versus prediction equations. Pediatrics 127: 200-205, 1995. |
| 14. | Knowler, W. C., D. J. Pettitt, P. H. Bennett, and R. C. Williams. Diabetes mellitus in the Pima Indians: genetic and evolutionary considerations. Am. J. Phys. Anthropol. 62: 107-114, 1983. |
| 15. | Maffeis, C., Y. Schutz, R. Micciolo, L. Zoccante, and L. Pinelli. Resting metabolic rate in six- to ten-year-old obese and nonobese children. Pediatrics 122: 556-562, 1993. |
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