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Resting Metabolic Rate in Severely ObeseDiabetic and Nondiabetic SubjectsKuo-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:

840845.

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 35 kg/m2

(mean BMI 46.4 8.4 kg/m2) were recruited. One

hundred forty-two subjects (61 men, 81 women) were di-

agnosed 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 re-

gression 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 consis-

tently 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 kilo-

grams) 4.132 height (in centimeters) 145.959DM

(nondiabetic 0 and diabetic 1). The age, weight, and

height-adjusted least square means of RMR between dia-

betic and nondiabetic groups were significantly different in

both genders.

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

IntroductionThe prevalence of obesity is increasing in many coun-

tries, 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 dis-

orders and mortality. Among these disorders, type 2 diabe-

tes 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 devel-

opment 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 demon-strates 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

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

1 Nonstandard abbreviations: RMR, resting metabolic rate; DM, type 2 diabetes; FM, fat

mass; FM%, fat mass percentage; FFM, fat-free mass.

840 OBESITY RESEARCH Vol. 12 No. 5 May 2004


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change in RMR could lead to a significant energy imbalance

and huge change of body weight over a long period (1316).

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 over-

estimation of RMR by these prediction equations (1719).

In addition, obese subjects with diabetes are generally ex-

cluded 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 35 kg/m2 who attended the Metabo-

lism 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 ob-

tained 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 anthropomet-

ric measurements, pathology tests, and RMR were per-

formed 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 circumfer-

ence 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 Auto-

mated Chemistry analyzer; Roche Diagnostics, Indianapo-

lis, IN), and type 2 diabetes was defined as fasting plasma

glucose 7.0 mM. Body composition was measured using

the bioelectrical impedance analysis method with a four-

terminal bioimpedance analyzer (Bodystat 1500; BodystatLtd., Tampa, FL). RMR was measured for 40 minutes under

standardized conditions, using ventilated hood indirect cal-

orimetry (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 Students t test, correlation analyses,

and multivariate linear regression analyses were performed

by the SPSS/PC statistical program (version 10.0 for Win-

dows; 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 inde-

pendent variables. The least square (LS) means of RMR

between the diabetic and nondiabetic obese with the adjust-

ment for age, weight, and height among these subjects in

each gender were tested by ANOVA.

ResultsThe 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 nondi-abetic 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 se-

verely obese after adjustment for other variables in differentmodels (Table 3). The coefficient of the FM variable was

found to be negative when the FM variable was incorpo-

rated in model 1 in Table 3 (data not shown). Among these

models, the best predictive equation of RMR in these sub-

jects (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

Resting Metabolic Rate in Severe Obese, Huang et al.

OBESITY RESEARCH Vol. 12 No. 5 May 2004 841


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independent population, a predictive equation of RMR us-

ing age, gender, weight, height, and DM as predictors was

derived from 710 subjects (two-thirds of our patients), and

then the predicted RMR from this equation and the mea-

sured RMR in the other 328 subjects (one-third of our

patients) were compared by paired Students 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).

DiscussionIn 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 othervariables. This finding that RMR was greater in obese type

2 diabetic subjects compared with nondiabetic obese sub-

jects is similar to previous studies (2224). 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 knowl-

edge, that the best predictive equation for RMR in the

severely obese subjects is RMR 71.767 2.337 age

Table 1.Comparison of general characteristics categorized by gender between the diabetic and nondiabetic obesepatients

Variables

Men (n 279) Women (n 759)

Diabetic

(n 61)

Nondiabetic

(n 218) p values

Diabetic

(n 81)

Nondiabetic

(n 678) p values

Age (years) 51.9 11.7 43.9 12.9 0.001 51.6 11.9 43.7 12.4 0.001

BW* (kg) 148.6 26.9 146.4 32.3 NS* 123.1 25.7 121.2 24.1 NS

BH* (cm) 175.7 6.0 176.1 8.6 NS 161.0 8.0 162.3 7.7 NS

BMI* (kg/m2) 48.0 7.9 47.1 9.2 NS 47.4 8.8 46.0 8.2 NS

WC* (cm) 142.0 13.6 136.9 21.8 0.035 121.4 26.4 116.6 18.4 NS

FM%* (%) 44.1 4.9 42.5 7.4 NS 53.2 6.7 52.0 6.8 NS

FM* (kg) 66.0 18.1 62.5 21.9 NS 67.0 20.4 63.8 18.8 NS

FFM* (kg) 81.5 9.6 81.0 12.0 NS 57.2 10.1 57.3 9.5 NS

Glucose (mmol/l) 9.53 3.13 5.34 0.72 0.001 10.02 3.50 5.17 0.65 0.001

RMRm* (Kcal/day) 2538.0 439.7 2426.2 424.9 NS 2006.8 436.5 1849.4 326.7 0.005

RMRp* (Kcal/day) 2593.8

408.0 2664.7

524.0 NS 1907.4

292.3 1916.3

259.8 NSRMRd* (%) 3.3 14.7 10.2 12.4 0.002 3.1 11.6 4.9 11.4 0.001

Data are means SD; statistics were computed by Students ttests.

* 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.

Table 2.Correlation coefficients of RMRm* and dif-ferent variables* among diabetic and non-diabeticobese patients after adjustment for age and gender

Variables Diabetic (n 142) Nondiabetic (n 896)

BW 0.694 0.759

BH 0.330 0.364

BMI 0.590 0.644

WC 0.427 0.617

FM% 0.462 0.361

FM 0.666 0.663

FFM 0.475 0.592Glucose 0.052 0.150

RMRp 0.623 0.734

RMRd 0.701 0.545

* Abbreviations are as defined in Table 1.

p 0.05.

p 0.01.

p 0.001. Statistics were tested by Pearsons correlations.




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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 suchprediction 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 Harris-

Benedict equation is the most common method for calcu-

lating 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,

Table 3. Multivariate linear regression models showing regression coefficients (SE) with measured RMR* asdependent variable, and listed variables* as independent variables

Independent

variables

Model 1

(R2 0.750)

Model 2

(R2 0.737)

Model 3

(R2 0.723)

Model 4

(R2 0.657)

Model 5

(R2 0.647)

Model 6

(R2 0.588)

Constant 71.767 60.655 521.995 1384.640 886.220 788.810(183.803) (187.416) (68.231) (47.999) (63.293) (82.774)

Age 2.337 1.440 1.515 6.184 6078 2.870

(0.657) (0.658) (0.696) (0.744) (0.742) (0.869)

Gender 257.293 273.821 220.863 541.048 550.221 37.156

(23.415) (23.415) (29.301) (21.169) (20.903) (34.325)

BW (kg) 9.996 10.158

(0.323) (0.330)

BH (cm) 4.132 3.933

(1.139) (1.161)

DM 145.959 171.074 149.951 190.961

(23.295) (28.184) (27.627) (30.862)

FFM (kg) 14.118 20.545

(0.916) (1.046)

FM (kg) 9.367 11.537

(0.443) (0.462)

BMI (kg/m2) 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 (#).




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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 im-

proved 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 deter-

minant 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 con-

tribute to predicting RMR in severely obese subjects. Insu-lin and sulfonylurea therapies in diabetic patients, for ex-

ample, 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 dem-

onstrated 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 othervariables. The RMR of severely obese subjects could be

predicted by age, gender, weight, height, and DM vari-

ables. It is possible that the addition of other variables in

this predictive equation might further improve its accu-

racy in the severely obese and allow a better individual

prescription of diet and exercise in a weight management

program.

AcknowledgmentThere was no outside funding/support for this study. We

thank Julie Hetherington in the Metabolism and Endocrine

Unit for technical assistance.

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