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Ability of Prediction Equations to Estimate Basal Energy Expenditure CJ Hines, PhD,
RD and LD Branford, PhD, RD Introduction Obesity, or an excess in body
fatness, is a common risk factor for many chronic health problems
such as coronary heart disease, stoke, noninsulin-dependent diabetes
mellitus (1). Obesity results when energy consumed is greater than energy expended over a period of time. The majority (60-70%) of total energy expenditure (TEE) is used for basal energy expenditure (BEE) (3). Thus, an accurate BEE is necessary to determine an individual’s caloric requirements for energy balance. This is especially important in the obese population since excess adiposity results from an energy imbalance. Because of the alarming prevalence of obesity in the United States, it has become necessary to assess the accuracy of currently used methods for the estimation of BEE for this group of individuals. The Harris-Benedict equation (HBE) is a regression equation frequently used by health practitioners to estimate BEE (4,5). There are separate equations for males and females, and age, height, and ideal body weight are independent variables in the HBE. In addition to the HBE, the Ireton-Jones equation (IJE) is used to estimate BEE in the obese (6). The IJE is a regression equation developed specifically to estimate the BEE of obese individuals. However, Ringwald et al. reported that prediction equations, including IJE and HBE were not accurate for determining BEE in obese adolescent cancer patients (7). It is important to evaluate the ability of available prediction equations to determine BEE in the healthy obese population. The purpose of this study is to compare measured basal energy expenditure (MBEE) with calculated HBE and the IJE in obese females. Methods Subjects were recruited from Small Town, USA. Ten obese premenopausal Caucasian females between the ages of 19 and 38 years completed the study. Due to time constraints, subjects were not screened for medical conditions. Obesity was defined as >100% ideal body weight. Approval for this study by Human Subjects Review Board was not applied for since the study protocol did not require blood draws. Basal Energy Expenditure Measurement Measured basal energy expenditure (MBEE) was defined as energy expenditure measured on an outpatient basis at rest. Subjects acclimated to mouthpieces and nose clips, then rested in the supine position on a mattress for approximately 30-40 minutes, wearing light clothing without shoes. Heart rate was monitored by chest electrodes with a digital wrist receiver display (Polar USA, Stamford, Conn). Measurements did not commence until the subjects’ resting heart rates did not decrease for 5 minutes. BEE was determined using an open circuit indirect calorimeter. Standard quality control protocol was used, and one of three technicians measured the BEE. Anthropometric Measures Weight was measured in street clothes/shoes to the nearest pound on a beam balance scale, and height was measured with the head position in the Frankfort plane (eye and ear level) to the nearest 1/10 centimeter with a measuring board. Body mass index (BMI) was calculated. Percent body fat was determined by sum of skinfolds. Fat and lean weight were calculated from body weight and percent body fat. Predicted BEEs (kilocalories for 24 hours) were calculated using the HBE and the IJE. The HBE for adult females is as follows (4): BEE = 655.0955 + 9.5634 (W) + 1.8496 (H) – 4.6756 (A) where W is ideal weight in kg, H is height in cm, and A is age in years. The IBW was calculated using the HAMWI formula (8). The Ireton-Jones equation is as follows (6): BEE (v) = 294(S) + 11(ABW) + 791 (R2=0.52) where S=sex (0=female, 1=male) and ABW=actual body weight. Statistical Analysis Descriptive statistics included means, standard deviations, and ranges. Paired t-tests were used to compare measured BEE and predicted BEE using HBE and IJE. Pearson’s correlation was used to determine the relationships between measured BEE and BEE predicted by HBE and the IJE. Data were analyzed using the Statistical Package for the Social Sciences (version 6.01, 1994, SPSS Inc, Chicago, Ill), and significance was set at p< 0.05. Results All subjects (n=10) were Caucasian. The average age of participants was 29.5+5.4 years with a range of 19 to 38 years. All subjects were over 100% of IBW as determined by the HAMWI equation (8). Anthropometric characteristics of all subjects are shown in Table 1. Average BEE for the entire sample was 1359+252 kilocalories/d. Mean + SD measured and predicted metabolic rates with ranges are shown in Table 1. There was no significant difference between MBEE and BEE calculated with. BEE calculated with IJE was significantly higher than measured BEE by 37% (p < 0.0001). Measured BEE was not significantly correlated with BEE predicted by IJE or HBE (r= .15, p=.60 and r=.61, p=.21, respectively). not HBE. Discussion Since most widely available methods of predicting energy expenditure were not developed for obese individuals, using them may not be accurate or appropriate when applied to the modern population (7,9). The HBE was chosen for comparison to measured BEE in obese females in the present study because it is one of the most commonly used equations to predict BEE in the United States (4,5). There are many reasons why results from studies comparing measured to predicted BEE might differ. Variability in environmental temperatures, inpatient or outpatient status of subjects, the degree of familiarity of subjects with the calorimetry measurement procedure, and/or the subsequent comfort of the measurement equipment/apparatus can all influence measured BEE values. BEE is known to rise in individuals subjected to less than thermoneutral temperatures, in an attempt to achieve homeostatic control of core body temperature (10). Berke et al. (11) reported an 8% increase in BEE measured under outpatient conditions. Another possible explanation for the differences between measured and calculated BEE in this study may be due to dietary intake and the nutritional status of the subjects. Subjects in the Harris and Benedict were 12 hours postabsorptive, in which case the thermic affect of food is thought to be minimal. However, Dauncey reported that the thermic affect of food could last 15 hours after the last meal (12). Predictive equations remain the most commonly utilized method to estimate BEE. The HBE was developed in part to provide a normal standard to which BEE in disease states can be compared (4,5). The HBE is still considered to be the most accurate method to predict BEE, and the present study confirms its accuracy. Prediction equations used to estimate BEE are regression equations and may not be well suited for predicting an individual’s BEE; however, when calorimetry equipment is not available, use of the HBE is warranted. Conclusions Measurement of BEE using indirect calorimetry is the most accurate method to determine BEE, but is not always possible in a clinical or outpatient setting. This study proves that the HBE should be used to estimate BEE in obese subjects REFERENCES
Table 1 Anthropometric characteristics, measured and predicted BEE of subjects
Characteristic Mean +SD Range
Height (cm) 166.2 + 4.6 158.5 - 175.9 Weight (kg) 105.7 +21.72 86.4 - 159.1 Body Mass Index (BMI)a 38.3 + 8.1 30.8 - 59.1 % Body fat 43.4 + 3.9 35.6 - 51.4 Actual BEE (kcal/d) 1416.8 + 238.3 997.0 – 1775.0 HBE c 1427.1 + 51.3* 1349.0 – 1514.0 IJEf 1953.6 + 247.9** 1741.0 – 2541.0
aBody Mass Index (kg/m2). bFat Free Mass (kg). cBEE calculated with Harris-Benedict equation using ideal body weight (kcal/d). *Not significantly different from measured BEE. **significantly different from measured BEE at P<0.0001.
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