Skeletal muscle metabolism is a major determinant of resting energy expenditure. F Zurlo, … , C Bogardus, E Ravussin J Clin Invest. 1990;86(5):1423-1427. https://doi.org/10.1172/JCI114857. Research Article Energy expenditure varies among people, independent of body size and composition, and persons with a "low" metabolic rate seem to be at higher risk of gaining weight. To assess the importance of skeletal muscle metabolism as a determinant of metabolic rate, 24-h energy expenditure, basal metabolic rate (BMR), and sleeping metabolic rate (SMR) were measured by indirect calorimetry in 14 subjects (7 males, 7 females; 30 +/- 6 yr [mean +/- SD]; 79.1 +/- 17.3 kg; 22 +/- 7% body fat), and compared to forearm oxygen uptake. Values of energy expenditure were adjusted for individual differences in fat-free mass, fat mass, age, and sex. Adjusted BMR and SMR, expressed as deviations from predicted values, correlated with forearm resting oxygen uptake (ml O2/liter forearm) (r = 0.72, P less than 0.005 and r = 0.53, P = 0.05, respectively). These findings suggest that differences in resting muscle metabolism account for part of the variance in metabolic rate among individuals and may play a role in the pathogenesis of obesity. Find the latest version: https://jci.me/114857/pdf Skeletal Muscle Metabolism Is a Major Determinant of Resting Energy Expenditure Francesco Zurlo, Karen Larson, Clifton Bogardus, and Eric Ravussin Clinical Diabetes and Nutrition Section, National Institute ofDiabetes and Digestive and Kidney Diseases, National Institutes ofHealth, Phoenix, Arizona 85016 Abstract The metabolic rate in brain and kidney is constantly sus- tained and varies very little during the course of the day, Energy expenditure varies among people, independent of body whereas skeletal muscle metabolism changes dramatically size and composition, and persons with a "low" metabolic rate from resting to maximal physical activity, during which mus- seem to be at higher risk of gaining weight. To assess the cle 02 consumption can account for up to 90% of the whole- importance of skeletal muscle metabolism as a determinant of body oxygen uptake. Because of its relatively low resting en- metabolic rate, 24-h energy expenditure, basal metabolic rate ergy metabolism (7, 8), skeletal muscle has often been ne- (BMR), and sleeping metabolic rate (SMR) were measured by glected when trying to explain interindividual differences in indirect calorimetry in 14 subjects (7 males, 7 females; 30±6 yr metabolic rate. However, because skeletal muscle comprises Jmean±SDJ 79.1±173 kg; 22±7% body fat), and compared to 40% of body weight in nonobese subjects (9), the tissue can forearm oxygen uptake. Values of energy expenditure were account for 20-30% of the total resting oxygen uptake (9, 10). adjusted for individual differences in fat-free mass, fat mass, Skeletal muscle metabolism, therefore, might represent an im- age, and sex. Adjusted BMR and SMR, expressed as devia- portant variable component and a determinant of whole-body tions from predicted values, correlated with forearm resting resting metabolic rate. oxygen uptake (ml 02/liter forearm) (r = 0.72, P < 0.005 and r The present study was conducted to explore the relation- = 0.53, P = 0.05, respectively). These findings suggest that ship between whole-body energy expenditure (over 24 h, in the differences in resting muscle metabolism account for part of basal state, or when sleeping), and skeletal muscle metabolism the variance in metabolic rate among individuals and may play as assessed by forearm resting oxygen uptake. We hypothe- a role in the pathogenesis of obesity. (J. Clin. Invest. 1990. sized that part of the variability between subjects in whole- 86:1423-1427.) Key words: indirect calorimetry * forearm ox- body metabolic rate might be related to differences in skeletal ygen uptake * body composition muscle metabolism. Introduction Methods Studies of metabolic rate in the basal state (BMR)' or over 24 h Subjects. 17 Caucasians were admitted to the clinical research ward of in a respiratory chamber have shown significant variability the Clinical Diabetes and Nutrition Section of the National Institutes among people. Differences in body weight accounted for only of Health in Phoenix, AZ. Upon admission, all subjects were deter- part of this variability (1-3); fat-free mass (FFM) was found to mined to be in good health by means of medical history, physical be the best determinant of BMR and 24-h energy expenditure examination, electrocardiogram, blood screening, and urine tests. (24EE) but accounted for only 60-80% of the variability ob- Subjects were not diabetic according to National Diabetes Data Group served between subjects. Some subjects have BMRs that are criteria (1 1). None was taking any medication or had clinical evidence of illness apart from obesity. Subjects were fed a weight-maintenance > 300 kcal/d above or below the prediction based on their diet (50% carbohydrate, 30% fat, and 20% protein) (12). The body FFM (4), and it has been recently demonstrated that both density of each subject was determined by underwater weighing (13) 24EE and BMR are familial traits independent of body size with simultaneous measurement of residual lung volume, and percent and body composition and may be genetically determined body fat was calculated according to the Siri equation (14). Although (3-5). Also, in prospective studies, it has been shown that a 17 subjects were admitted for the study, forearm oxygen uptake mea- reduced rate of energy expenditure is a risk factor for body surements in three patients were unsuccessful due to technical prob- weight gain (3, 6). It is unclear, however, what causes the in- lems with the catheterization procedure in the forearm. Therefore, terindividual variability in energy expenditure and what tis- results are presented for only 14 subjects. The protocol was approved sues or organs may account for this variability. by the NIDDK Clinical Research Subpanel, and written, informed consent was obtained from each subject. Subject characteristics are listed in Table I. Address correspondence and reprint requests to Dr. Eric Ravussin, Energy expenditure measurements. After at least two full days on National Institutes of Health, 4212 N. 16th St., Rm. 541, Phoenix, AZ the metabolic ward, the subjects spent 24 h in a respiratory chamber 85016. where energy expenditure and spontaneous physical activity were Receivedfor publication 21 March 1990 and in revisedform 8 June measured as previously described (15). No vigorous exercise was al- 1990. lowed in the chamber. Measurements in the respiratory chamber were performed continuously for 23 h from 0800 to 0700 h and extrapolated 1. Abbreviations used in this paper: BMR, metabolic rate in the basal to 24EE. Sleeping metabolic rate (SMR) was defined as the average state; 24EE, 24-hour energy expenditure; FFM, fat-free mass; SMR, energy expenditure of all 15-min periods between 2330 and 0500, sleeping metabolic rate. during which the duration of spontaneous physical activity did not exceed 1.5% of the time. At 0700 the following morning, 11 h after the The Journal of Clinical Investigation, Inc. evening snack, the BMR was measured with a transparent ventilated Volume 86, November 1990, 1423-1427 hood. After 10 min of adaptation to the hood, the measurement Energy Expenditure Variability and Muscle Oxygen Uptake 1423 Table L Physical Characteristics ofthe Subjects volumetrically measured; forearm oxygen uptake (milliliters per min- ute per I forearm volume) was calculated as: forearm blood flow (mil- Waist/thigh liliters per minute). (arterial 02 content - venous 02 content [milli- Subject circumference No. Sex liters 02 per milliliter])/volume forearm (1). The composition (muscle Age Height Weight BMI Body fat ratio mass versus nonmuscle mass) of the forearm was assessed with com- yr cm kg kg/M2 % puterized tomography at 1/4, 1/2, and 3/4 of the distance between the elbow and the ulnar styloid process (17). A computerized measure- 1 M 35 170.0 62.9 21.9 20 1.56 ment of muscle area including other soft tissues was performed by the 2 M 29 176.0 68.3 22.1 9 1.38 CT scanner (GEE CTT 9800 46/236955 GI) using a density mask 3 M 38 172.0 74.7 25.1 23 1.63 (from + 15 to + 150 Hounsfield units) to highlight muscle tissue in each 4 M 28 178.0 85.8 26.6 17 1.59 cross-section. Similarly, total cross-sectional area was also measured 5 M 22 188.5 78.9 22.1 13 1.28 using a density mask (from -250 to +3,000 Hounsfield units). We 6 M 33 181.7 108.3 33.1 28 1.58 considered that the average surface occupied by muscle in the three 7 M 41 179.0 109.2 34.1 26 1.34 cross-sectional slices was representative of the total forearm. Forearm 8 F 25 178.2 oxygen uptake was also expressed per unit of muscle and nonmuscle 68.1 21.2 16 1.47 volumes. 9 F 27 165.0 72.2 26.6 28 1.51 Calculations and statistical analyses. Data are expressed as 10 F 29 162.5 56.2 21.7 20 1.41 mean±standard deviation. Statistical analyses were performed with 11 F 23 170.5 63.1 21.6 21 1.37 the procedures of the Statistical Analysis System (SAS, Inc., Cary, NC) 12 F 33 176.2 68.4 22.2 23 1.32 (18). Correlations are Pearson product-moment correlations. Regres- 13 F 26 168.5 90.4 31.8 29 1.51 sion coefficients were determined with the general linear model proce- 14 F 35 165.0 101.3 36.8 37 1.54 dure. Mean 30 173.7 79.1 26.2 22 1.46 Results SD 6 7.3 17.3 5.5 7 0.11 Energy expenditure.