Resting Metabolic Rate in Severely Obese ... - Wiley Online Library

Resting Metabolic Rate in Severely Obese Diabetic and Nondiabetic Subjects Kuo-Chin Huang,*†‡ Nic Kormas,* Katharine Steinbeck,*§ Georgina Loughnan,* and Ian D. Caterson*‡

Abstract HUANG, KUO-CHIN, NIC KORMAS, KATHARINE STEINBECK, GEORGINA LOUGHNAN, AND IAN D. CATERSON. Resting metabolic rate in severely obese diabetic and nondiabetic subjects. Obes Res. 2004;12: 840 – 845. Objectives: To compare the resting metabolic rate (RMR) between diabetic and nondiabetic obese subjects and to develop a predictive equation of RMR for these subjects. Research Methods and Procedures: Obese adults (1088; mean age ⫽ 44.9 ⫾ 12.7 years) with BMI ⱖ 35 kg/m2 (mean BMI ⫽ 46.4 ⫾ 8.4 kg/m2) were recruited. One hundred forty-two subjects (61 men, 81 women) were diagnosed with type 2 diabetes (DM), giving the prevalence of DM in this clinic population as 13.7%. RMR was measured by indirect calorimetry, and several multivariate linear regression models were performed using age, gender, weight, height, BMI, fat mass, fat mass percentage, and fat-free mass as independent variables. Results: The severely obese patients with DM had consistently higher RMR after adjustment for all other variables. The best predictive equation for the severely obese was RMR ⫽ 71.767 ⫺ 2.337 ⫻ age ⫹ 257.293 ⫻ gender (women ⫽ 0 and men ⫽ 1) ⫹ 9.996 ⫻ weight (in kilograms) ⫹ 4.132 ⫻ height (in centimeters) ⫹ 145.959 ⫻ DM (nondiabetic ⫽ 0 and diabetic ⫽ 1). The age, weight, and height-adjusted least square means of RMR between diabetic and nondiabetic groups were significantly different in both genders.

Received for review July 7, 2003. Accepted in final form March 16, 2004. The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. *Metabolism and Obesity Services, Department of Endocrinology, Royal Prince Alfred Hospital, Missenden Road, New South Wales, Australia; †Departments of Family Medicine, National Taiwan University Hospital, Taipei, Taiwan; and ‡Human Nutrition Unit, School of Molecular and Microbial Biosciences and §Department of Medicine, University of Sydney, New South Wales, Australia. Address correspondence to Ian D. Caterson, Human Nutrition Unit, School of Molecular and Microbial Biosciences, University of Sydney, NSW 2006, Australia. E-mail: [email protected] Copyright © 2004 NAASO

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Discussion: Severely obese patients with type 2 diabetes had higher RMR than those without diabetes. The RMR of severely obese subjects was best predicted by an equation using age, gender, weight, height, and DM as variables. Key words: resting metabolic rate, prediction, diabetes, severely obese

Introduction The prevalence of obesity is increasing in many countries, and obesity is a major global health problem (1,2). In Australia, 19.3% of men and 22.2% of women are obese, and the prevalence of obesity is 2.5 times higher than in 1980 (3). Obesity increases the risk of many medical disorders and mortality. Among these disorders, type 2 diabetes has a close relationship with severity of obesity (4,5). The rationale for treating type 2 diabetic patients with pharmacotherapy and diet control is to improve glycemia and, thereby, reduce the risk of diabetic complications (6,7). However, for obese subjects, weight reduction remains the optimal method for the prevention and management of type 2 diabetes. In two prospectively randomized and controlled studies, intensive lifestyle modification with mild weight loss has been shown to reduce the incidence of diabetes by 58% in obese subjects with impaired glucose tolerance compared with a similar control group (8,9). Orlistat, an oral antiobesity agent, has been found to improve oral glucose tolerance and diminish the rate of progression to the development of impaired glucose tolerance and type 2 diabetes in obese subjects (10). Furthermore, the use of antiobesity agents in obese subjects with type 2 diabetes also demonstrates that weight reduction is associated with improved control of blood glucose and amelioration of cardiovascular risk factors (11,12). Resting metabolic rate (RMR)1 is the main component of daily energy expenditure, accounting for 60% to 70% of total energy expenditure in most individuals, and a minor

1 Nonstandard abbreviations: RMR, resting metabolic rate; DM, type 2 diabetes; FM, fat mass; FM%, fat mass percentage; FFM, fat-free mass.

Resting Metabolic Rate in Severe Obese, Huang et al.

change in RMR could lead to a significant energy imbalance and huge change of body weight over a long period (13–16). Measuring RMR is time consuming and costly; therefore, a number of recognized prediction equations to calculate RMR have been developed in clinical practice. These can provide the basis for prescribing an individualized energy intake to attain a desired level of energy deficit. However, RMR seems to be inaccurate in obese subjects, with overestimation of RMR by these prediction equations (17–19). In addition, obese subjects with diabetes are generally excluded or not specified in these equations. In the present study, we aimed to compare the difference in measured RMR between diabetic and nondiabetic subjects in a large population of severely obese patients. The second aim was to generate a predictive equation of RMR in these subjects using variables inclusive of diabetic status. Research Methods and Procedures Subjects and Characterization The study was a retrospective analysis of adult obese patients with BMI ⱖ 35 kg/m2 who attended the Metabolism and Obesity Service in Royal Prince Alfred Hospital from January 1999 to May 2003. Approval for analyzing the demographic and biological data in these subjects was obtained from the Central Sydney Area Health Service Ethics Review Committee. The subjects were all referred from primary care physicians for weight management. At the initial visit, a series of assessments including anthropometric measurements, pathology tests, and RMR were performed for every patient. Trained staff measured height, waist circumference (measured to the nearest 0.1 cm), and weight (measured to the nearest 0.1 kg). Waist circumference was taken midway between the inferior margin of the last rib and the crest of the ilium in a horizontal plane. BMI was calculated as weight (kilograms) divided by height squared (meters squared). A venous blood sample was taken after a 12-hour fast for measuring plasma glucose by an automated spectrophotometer (Roche/Hitachi 917 Automated Chemistry analyzer; Roche Diagnostics, Indianapolis, IN), and type 2 diabetes was defined as fasting plasma glucose ⱖ 7.0 mM. Body composition was measured using the bioelectrical impedance analysis method with a fourterminal bioimpedance analyzer (Bodystat 1500; Bodystat Ltd., Tampa, FL). RMR was measured for 40 minutes under standardized conditions, using ventilated hood indirect calorimetry (Deltatrac, Datex Division Instrumentarium Corp., Helsinki, Finland), and calibrated using a precision gas mixture before each measurement. The mean value of the data used in the calculation of RMR was obtained from a measurement taken within the mid-20-minute period. RMR was derived without taking urinary nitrogen excretion into account due to its minimal effect on RMR (20). Eighteen

participants had RMR performed on 2 consecutive days, and the test-retest coefficient of variation was 1.0 ⫾ 0.8%. Statistical Analysis Data were presented as mean and SD. Statistical analyses including two-sample Student’s t test, correlation analyses, and multivariate linear regression analyses were performed by the SPSS/PC statistical program (version 10.0 for Windows; SPSS, Inc., Chicago, IL). Partial correlation of the clinical characteristics and RMR with adjustment for age and gender was performed in the diabetic and nondiabetic obese. Several multivariate linear regression models were performed using plasma RMR as the dependent variable and using age, gender, weight, height, BMI, fat mass (FM), fat mass percentage (FM%), and fat-free mass (FFM) as independent variables. The least square (LS) means of RMR between the diabetic and nondiabetic obese with the adjustment for age, weight, and height among these subjects in each gender were tested by ANOVA.

Results The baseline characteristics of the subjects are shown in Table 1. The mean age and mean plasma glucose of the diabetic obese subjects were significantly greater than in the nondiabetic obese (p ⬍ 0.001) for both genders. There were no statistically significant differences in weight, height, FM, FM%, lean mass, and predictive RMR between diabetic and nondiabetic groups. RMR was higher in the diabetic obese than the nondiabetic obese in women (p ⫽ 0.005) and men (p ⫽ 0.013). The predicted RMR using the Harris-Benedict equation (21) was found to be overestimated in the nondiabetic and diabetic men and underestimated in the diabetic women. The percentage difference between measured RMR and predicted RMR was higher in the nondiabetic obese than in the diabetic obese (p ⬍ 0.001). After adjustment for age and gender, measured RMR was positively correlated with weight, height, BMI, WC, FM, FM%, FFM, plasma glucose levels, and predicted RMR in the diabetic and nondiabetic groups (Table 2). The percentage difference from measured RMR was negatively correlated with RMR (p ⬍ 0.001). Using multivariate linear regression analysis, the diabetic severely obese had higher RMR than the nondiabetic severely obese after adjustment for other variables in different models (Table 3). The coefficient of the FM variable was found to be negative when the FM variable was incorporated in model 1 in Table 3 (data not shown). Among these models, the best predictive equation of RMR in these subjects (R2 ⫽ 0.750) was RMR ⫽ 71.767 ⫺ 2.337 ⫻ age ⫹ 257.293 ⫻ gender (women ⫽ 0 and male ⫽ 1) ⫹ 9.996 ⫻ weight (in kilograms) ⫹ 4.132 ⫻ height (in centimeters) ⫹ 145.959 ⫻ DM (nondiabetic ⫽ 0 and diabetic ⫽ 1). To examine whether the equation was accurate or not in an OBESITY RESEARCH Vol. 12 No. 5 May 2004

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Table 1. Comparison of general characteristics categorized by gender between the diabetic and nondiabetic obese patients Men (n ⴝ 279) Variables

Diabetic (n ⴝ 61)

Nondiabetic (n ⴝ 218)

Age (years) BW* (kg) BH* (cm) BMI* (kg/m2) WC* (cm) FM%* (%) FM* (kg) FFM* (kg) Glucose (mmol/l) RMRm* (Kcal/day) RMRp* (Kcal/day) RMRd* (%)

51.9 ⫾ 11.7 148.6 ⫾ 26.9 175.7 ⫾ 6.0 48.0 ⫾ 7.9 142.0 ⫾ 13.6 44.1 ⫾ 4.9 66.0 ⫾ 18.1 81.5 ⫾ 9.6 9.53 ⫾ 3.13 2538.0 ⫾ 439.7 2593.8 ⫾ 408.0 3.3 ⫾ 14.7

43.9 ⫾ 12.9 146.4 ⫾ 32.3 176.1 ⫾ 8.6 47.1 ⫾ 9.2 136.9 ⫾ 21.8 42.5 ⫾ 7.4 62.5 ⫾ 21.9 81.0 ⫾ 12.0 5.34 ⫾ 0.72 2426.2 ⫾ 424.9 2664.7 ⫾ 524.0 10.2 ⫾ 12.4

Women (n ⴝ 759) p values

Diabetic (n ⴝ 81)

Nondiabetic (n ⴝ 678)

p values

⬍0.001 NS* NS NS 0.035 NS NS NS ⬍0.001 NS NS 0.002

51.6 ⫾ 11.9 123.1 ⫾ 25.7 161.0 ⫾ 8.0 47.4 ⫾ 8.8 121.4 ⫾ 26.4 53.2 ⫾ 6.7 67.0 ⫾ 20.4 57.2 ⫾ 10.1 10.02 ⫾ 3.50 2006.8 ⫾ 436.5 1907.4 ⫾ 292.3 ⫺3.1 ⫾ 11.6

43.7 ⫾ 12.4 121.2 ⫾ 24.1 162.3 ⫾ 7.7 46.0 ⫾ 8.2 116.6 ⫾ 18.4 52.0 ⫾ 6.8 63.8 ⫾ 18.8 57.3 ⫾ 9.5 5.17 ⫾ 0.65 1849.4 ⫾ 326.7 1916.3 ⫾ 259.8 4.9 ⫾ 11.4

⬍0.001 NS NS NS NS NS NS NS ⬍0.001 0.005 NS ⬍0.001

Data are means ⫾ SD; statistics were computed by Student’s t tests. * BW, body weight; BH, body height; WC, waist circumference; RMRm, measured RMR; RMRp, predictive RMR by Harris-Benedict equation; RMRd, {(RMRp ⫺ RMRm)/RMRm} ⫻ 100%; NS, not significant.

independent population, a predictive equation of RMR using age, gender, weight, height, and DM as predictors was derived from 710 subjects (two-thirds of our patients), and

Table 2. Correlation coefficients of RMRm* and different variables* among diabetic and non-diabetic obese patients after adjustment for age and gender Variables Diabetic (n ⴝ 142) Nondiabetic (n ⴝ 896) BW BH BMI WC FM% FM FFM Glucose RMRp RMRd

0.694§ 0.330†§ 0.590§ 0.427§ 0.462§ 0.666§ 0.475§ 0.052 0.623§ ⫺0.701§

0.759§ 0.364§ 0.644§ 0.617§ 0.361§ 0.663§ 0.592§ 0.150§ 0.734§ ⫺0.545§

* Abbreviations are as defined in Table 1. † p ⬍ 0.05. ‡ p ⬍ 0.01. § p ⬍ 0.001. Statistics were tested by Pearson’s correlations.

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then the predicted RMR from this equation and the measured RMR in the other 328 subjects (one-third of our patients) were compared by paired Student’s t test. We found that there was no statistically significant difference between the predicted and measured RMR. The age, weight, and height-adjusted LS means of the RMR by gender are shown in Figure 1. The LS means of RMR between diabetic (2546.8 ⫾ 40.9 kcal/d in men and 2007.6 ⫾ 25.6 kcal/d in women) and nondiabetic groups (2423.9 ⫾ 20.3 kcal/d in men and 1849.3 ⫾ 8.6 kcal/d in women) were significantly different in both genders (p ⬍ 0.01 in men and p ⬍ 0.001 in women, respectively).

Discussion In the present study, we demonstrated that severely obese subjects with type 2 diabetes had a higher measured RMR than obese people without DM after adjustment for other variables. This finding that RMR was greater in obese type 2 diabetic subjects compared with nondiabetic obese subjects is similar to previous studies (22–24). The etiology of a greater RMR in diabetics has been suggested to be the result of abnormal protein metabolism (24) and high insulin resistance (25). However, the exact mechanism still remains unclear. We also propose, for the first time to our knowledge, that the best predictive equation for RMR in the severely obese subjects is RMR ⫽ 71.767 ⫺ 2.337 ⫻ age ⫹


Table 3. Multivariate linear regression models showing regression coefficients (⫾SE) with measured RMR* as dependent variable, and listed variables* as independent variables Independent variables Constant Age Gender BW (kg) BH (cm) DM† FFM (kg) FM (kg)

Model 1 (R2 ⴝ 0.750)

Model 2 (R2 ⴝ 0.737)

Model 3 (R2 ⴝ 0.723)

Model 4 (R2 ⴝ 0.657)

Model 5 (R2 ⴝ 0.647)

Model 6 (R2 ⴝ 0.588)

71.767 (183.803) ⫺2.337¶ (0.657) 257.293¶ (23.415) 9.996¶ (0.323) 4.132¶ (1.139) 145.959¶ (23.295)

60.655 (187.416) ⫺1.440‡ (0.658) 273.821¶ (23.415) 10.158¶ (0.330) 3.933‡ (1.161)

521.995¶ (68.231) ⫺1.515¶ (0.696) 220.863¶ (29.301)

1384.640¶ (47.999) ⫺6.184¶ (0.744) 541.048¶ (21.169)

886.220¶ (63.293) ⫺6078¶ (0.742) 550.221¶ (20.903)

788.810¶ (82.774) ⫺2.870‡ (0.869) 37.156 (34.325)

171.074¶ (28.184)

149.951¶ (27.627)

190.961¶ (30.862) 20.545¶ (1.046)

14.118¶ (0.916) 9.367¶ (0.443)

BMI (kg/m2)

11.537¶ (0.462) 26.807¶ (1.071)

* Abbreviations are as defined in Table 1. Gender: female, 0; male, 1. † DM, diabetic ⫽ 1, nondiabetic ⫽ 0. ‡ p ⬍ 0.05. § p ⬍ 0.01. ¶ p ⬍ 0.001.

Figure 1: The measured RMR values (LS mean ⫾ SE) with the adjustment for the age, body weight, and height among diabetic (2546.8 ⫾ 40.9 kcal/d in men and 2007.6 ⫾ 25.6 kcal/d in women) and nondiabetic (2423.9 ⫾ 20.3 kcal/d in men and 1849.3 ⫾ 8.6 kcal/d in women) groups categorized by gender in an ANOVA model. p ⬍ 0.05 (*), p ⬍ 0.01 (**), p ⬍ 0.001 (#).

257.293 ⫻ gender (women ⫽ 0 and male ⫽ 1) ⫹ 9.996 ⫻ weight (in kilograms) ⫹ 4.132 ⫻ height (in centimeters) ⫹ 145.959 ⫻ DM (nondiabetic ⫽ 0 and diabetic ⫽ 1). A measurement of RMR in the obese is important but is not generally available in clinical practice, and there is a reliance on predictive equations. A predictive equation of RMR for the severely obese is important to provide the basis for an individualized treatment plan for weight loss. Data from previous studies of several predictive equations in different populations show substantial differences. RMR seems to be inaccurate and mostly overestimated by such prediction equations in obese subjects (16 –19). In addition, RMR is generally estimated by predictive formulae based on weight, height, age, and gender. For instance, the HarrisBenedict equation is the most common method for calculating RMR (21). In our results, the predicted RMR by Harris-Benedict equation overestimated RMR in the both nondiabetic obese and diabetic men and underestimated it in diabetic women (Table 1). In multiple linear regression models with four independent variables of age, gender, OBESITY RESEARCH Vol. 12 No. 5 May 2004

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weight, and height, RMR could be estimated from model 2 in Table 3. In model 1, addition of the DM variable improved the prediction accuracy, and the equation with all five variables was the best model for estimating RMR in severely obese subjects. FFM, reflecting the amount of metabolically active tissue, has been found to be closely correlated with RMR (26). In the present study, body weight had a stronger correlation with RMR than FFM and was a better determinant in the predictive model for RMR than was FFM, a finding similar to that of Karhunen et al. (27) and Mifflin et al. (28) in less obese subjects. The positive correlation between FM and RMR in Tables 2 and 3 could be explained by close relationship between weight and FM. In fact, the coefficient of the FM variable was found to be negative when the FM variable was put into model 1 in Table 3 (data not shown). It is possible that there are other factors that may contribute to predicting RMR in severely obese subjects. Insulin and sulfonylurea therapies in diabetic patients, for example, have been noted to be associated with reduction of RMR (23,29,30). Improvement in the prediction of RMR among obese subjects with type 2 diabetes has been demonstrated by factoring glycemia, not hemoglobin A1c, into the equation (31). Furthermore, RMR has been found to be lower in African Americans than whites (32). Whether the addition of these variables can improve the accuracy of predicting RMR in the severely obese deserves further study. In conclusion, this study demonstrated that severely obese subjects with type 2 diabetes had a higher RMR than the obese without DM after adjustment for other variables. The RMR of severely obese subjects could be predicted by age, gender, weight, height, and DM variables. It is possible that the addition of other variables in this predictive equation might further improve its accuracy in the severely obese and allow a better individual prescription of diet and exercise in a weight management program.

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