1methods in nutrition

A reanalysis of the factors influencing basalmetabolic rate in normal adults1John J. Cunningham? Ph.D.

ABSTRACT A multiple regression analysis of several factors influencing basal metabolic raic(BMR) was performed using dala for 223 subjects from the classic metabolism studies published byHarris and Benedict in 1919. These data had previously been analyzed by Kleiber using metabolicbody size, the three-fourths power of body mass, as a predictor of BMR. His prediction equationswere separated by sex and each contained components for age and height. Factors in the presentanalysis included sex, age. height, body mass, and estimated lean body mass (LBM). Lean bodvmass was found to be the single predictor of BMR. A best estimate prediction equation: BMR(cal/day) = 500 + 22 (LBM) is proposed. The previously presumed influences of sex and age are shownto add little to this estimation. Am. J. Clin. Nutr. 33: 2372-2374, 1980.

Standard physiology texts list sex, age, andbody composition as factors related to basalmetabolic rate (BMR) in man. Indeed, differences due to age and sex exist when BMRvalues are expressed as calories per unit ofbody surface area. The physiological basis fora body surface relationship has been questioned by Kleiber (l). His expression of BMRon the basis of metabolic size retains age andsex differences. Benedict (2) reported thatbody mass and body surface area were bothinadequate indices for the expression of BMRand suggested that the mass of active protoplasmic tissue might be useful. In this regard,Miller and Blyth (3) proposed lean body mass(LBM) as a metabolic standard and demonstrated that LBM was a better predictor ofoxygen consumption than surface area in 47young men. The inverse relationship betweenage and basal oxygen consumption recentlyhas been shown to be wholly attributable tochanges in LBM (4, 5). Sex differences havenot been examined using LBM as a predictorof BMR. The present study estimates LBMas a function of sex, age, and body massbefore a regression analysis for the influenceof several variables on BMR.

MethodsData for 223 healthy adult subjects from ihe classic

metabolism studies of Harris and Benedict (6) were usedin the present analysis. Sixteen other male subjects wereomitted as they were identified as trained athletes, Age,height, and body mass values for 120 males and 103females were taken directly from the published tablesValues for BMR in calories per day were recalculatedand rounded to the nearest 10 cal, representing themaximum possible statistical significance assuming thatoxygen consumption per minute was accurate to 10 ml.

Since the body composition of the subjects are notreported, estimates of LBM were calculated from theequations below, where M is body mass in kg and A isage in years, assuming all subjects to be normal. Thesewere derived by substitution and rearrangement of theprediction equations for total body water published h\Moore et al. (7).

male LBM = (79.5 - 0.24 M - 0.15 A) x M -=- 73.2

female LBM = (69.8 - 0.26 M - 0.12 A) x M + 73.2

A multiple regression analysis for the predictors ofBMR was performed using the SPSS program (8). AsKleiber (9) previously reported an analysis of this samedata for a body surface area correlation with BMR. thisvariable was not included.

1 From the Section of Physiology and Biophysics.Division of Biology and Medicine, Brown University,Providence. Rhode Island 02912." Assistant Professor of Medical Science.

The American Journal of Clinical Nutrition 33: NOVEMBER 1980, pp. 2372-2374. Printed in U.S.A.

BASAL METABOLIC RATE IN NORMAL ADULTS

Results and Discussion edly from normal. It is illustrative that if these, c ■ , , data are separated by sex prior to analysis.The stepwise inclusion of variables into a . ,. ..r ... J c T JL . r DX/rn1 . . v t- c DX.D • • , the predictive utility of LBM for BMR re-

LBM, accounting for 70% of the variabilityof BMR- The strength of this predictor isperhaps surprising considering the assumption of normality and inherent variabilities inthe estimation of LBM. The prediction equation for BMR from LBM by regression is:

BMR (cal/day) = 501.6 + 21.6 (LBM)The 95% confidence limit for the slope of thisregression is ± 1.9 so that a prediction errorof 10% is expected for an individual BMR.

Addtional variables add little to the estimation. Whereas age is the second variableadded, this negative component is compensated for by an increased constant term, i.e.,BMR (cal/day) = 601.2 + 21 (LBM) - 2.6

(age)At the mean values of 29 years and 46 kgLBM, this second equation changes the predicted BMR by only 1.4 cal/day. This agreeswell with the previously cited studies (4, 5) inwhich the age contribution was shown to bedue simply to changes in body composition.

Kleiber (9) analyzed these same data in1932 and proposed the computation of thethree-fourths power of body mass as an estimate of metabolic size. As this term does notreflect the contributions of sex and age tobody composition, his prediction equationsfor BMR based on metabolic size (1) areseparated by sex and each contains an agecomponent. The present analysis shows sexto contribute little beyond the estimation ofLBM. In this respect, sex may even be lessimportant for individuals who deviate mark-

TABLE IRegression analysis for the predictors of BMR

StepVariableentered"

MultipleR

AdjustedR-' F ratio

1 LBM 0.84 0.70 517.52 Age 0.85 0.72 284.33 Height 0.85 0.73 193.64 Sex 0.86 0.73 150.25 Body mass 0.86 0.73 120.5

in the male group, and for 95% of the LBMvalues, estimates of BMR agree within 20cal/day.

The use of sex and age in the computationof LBM does not bias the regression analysis.If sex or age had an independent correlationto BMR, it would not be altered by this addedfactor in the analysis. It would be valuable tocompare the prediction equation presentedhere to a set of data containing measuredLBM and oxygen consumption from a statistical sample of mixed sex.

Miller and Blyth (10) reported that oxygenconsumption correlated well with LBM (r =0.92) in 79 college students of whom 78 weremale. However, their regression equation forcomputing LBM from oxygen consumptionconsistently overestimates LBM for malesrelative to the present formula by an averageof 7.8 kg. In fact, this regression yields LBMvalues greater than the body weight for 34%of the males in the Harris Benedict data set.

A comparison of BMR calculated on thebasis of body surface area or by the presentregression equation is of interest. In the former, one must use separate conversion valuesfor each sex. At the mean values for heightand weight in Table 2, body surface area (foreither sex) is 1.68 cm2. The adult male BMRis estimated using 36.8 cal/m2/hr while theadult female BMR is estimated at 35.1 cal/nr/hr (II). The respective BMR estimatesare thus 1480 and 1420 cal/day. The meanmeasured values is 1490 cal/day which isidentical to the estimate from the presentregression at the mean LBM regardless ofsex. A comparison of other representative

TABLE 2Summary of regression data

" Each step of the regression adds a variable to the

previous predictors.

Variable Mean" SD Units

BMRAgeHeightBody massLBM

149029

16859.845.8

210118

118

Cal/dayYrCmKgKg

" n = 120 males + 103 females from Reference (6).

Z J / H C U N N I N G H A M

TABLE 3A comparison of BMR estimates calculated from body surface area (BSA) or LBM

Lean maleMedium maleHeavy maleLean femaleMedium femaleHeavy female

Body surface''

data are presented in Table 3. Notice that theBMR estimates calculated from body surfacefor the lean male and medium female aresimilar, as are their LBM estimates from theformulae. This suggests that perhaps estimations of BMR based on body surface areaowe their utility to a hidden correlation toLBM within each sex. Miller and Blyth (3)reported this for their male subjects.

ConclusionThe suggestion of Benedict (2), made over

65 years ago, that active body mass determines BMR is supported. Sex and age arefactors influencing the body composition ofan individual, but body composition is theprinciple determinant of BMR. An equationusing LBM as the single predictor of BMR innormal adults can replace the present cumbersome tables for separate sexes and ages.Basal metabolism in normal adults can beestimated from a simple linear equationwhere:

BMR (cal/day) = 500 + 22 (LBM)

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'' Regression equation in text independent of sex"""* *"

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