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1 From the Department of Pediatrics, University of Texas Medical Branch (CLK and FU) and the Shriners Hospital for Children (CLK), Galveston, TX.
2 Supported by the Shriners Hospital for Children (grant no. 8760) and the NIH (grant R01 DK55384). The studies were conducted at the General Clinical Research Centers at the University of Texas Medical Branch (Galveston) and The Ohio State University, which were funded by grants M01 RR 00073 and M01 RR 00034 from the National Center for Research Resources, NIH, US Public Health Service. 3 Reprints not available. Address correspondence to CL Kien, E203 Given Medical Building, Department of Pediatrics, University of Vermont, 89 Beaumont Avenue, Burlington, VT 05405. E-mail: clkien{at}utmb.edu.
ABSTRACT
Background:During feeding trials, it is useful to predict daily energy expenditure (DEE) to estimate energy requirements and to assess subject compliance.
Objective:We examined predictors of DEE during a feeding trial conducted in a clinical research center.
Design:During a 28-d period, all food consumed by 26 healthy, nonobese, young adults was provided by the investigators. Energy intake was adjusted to maintain constant body weight. Before and after this period, fat-free mass (FFM) and fat mass were assessed by using dual-energy X-ray absorptiometry, and DEE was estimated from the change (after – before) in body energy (BE) and in observed energy intake (EI): DEE = EI – BE. We examined the relation of DEE to pretrial resting energy expenditure (REE), FFM, REE derived from the average of REE and calculated from FFM [REE = (21.2 x FFM) + 415], and an estimate of DEE based on the Harris-Benedict equation (HB estimate) (DEE = 1.6 REE).
Results:DEE correlated (P < 0.001) with FFM (r = 0.78), REE (r = 0.73), average REE (r = 0.82), and the HB estimate (r = 0.81). In a multiple regression model containing all these variables, R2 was 0.70. The mean (±SEM) ratios of DEE to REE, to average REE, and to the HB estimate were 1.86 ± 0.06, 1.79 ± 0.04, and 1.02 ± 0.02, respectively.
Conclusions:Although a slightly improved prediction of DEE is possible with multiple measurements, each of these measurements suggests that DEE equals 1.60–1.86 x REE. The findings are similar to those of previous studies that describe the relation of REE to DEE measured directly.
Key Words: Daily energy expenditure • resting energy expenditure • energy balance • dual-energy X-ray absorptiometry • feeding trials
INTRODUCTION
During feeding trials in which energy balance is a goal, it is of great value to be able to predict the daily energy expenditure (DEE) of the subjects to estimate goals for initial energy intake for weight maintenance and nitrogen balance and perhaps to detect outliers because of noncompliance or exceptional degrees of physical activity. Because resting energy expenditure (REE) is an important determinant of DEE (1), the measurement or prediction of REE is generally the first step in trying to then predict DEE. Previous studies have examined the relation between REE and DEE estimated with the use of the Harris-Benedict equation (HB estimate), in which weight, height, sex, and age are used to estimate REE. In general, the prediction of group means is fairly good with this approach, but individual estimates are quite variable (2, 3). Others have shown that fat-free mass (FFM) correlates with REE (4, 5). There also are published data correlating REE with total DEE measured by using the doubly labeled water (DLW) technique (6, 7). The accuracy and precision of the DLW technique makes this the "gold standard" for estimating the DEE of persons who are free-living and not confined, eg, to a room calorimeter (1). The DLW technique is an expensive method with relatively complicated sample preparation and analysis (8–13). This technique can provide very important insights into how diets or pharmacologic treatments may affect DEE, but the analytic procedures are generally too time consuming to be used at the inception of a study to determine energy requirements (as opposed to providing a post hoc interpretation of subject compliance with reports of energy intake).
We are conducting a series of dietary trials in a General Clinical Research Center (GCRC) to determine the effect of dietary fatty acid composition on fat oxidation. To limit the effects of fat or FFM gain or loss on the outcome variables, our aim is to maintain our subjects in zero energy balance on the basis of post hoc measurements of body composition and daily proxy measurements of body energy, namely body weight. However, although our subjects are prohibited from engaging in actual exercise training during the studies, considerable variation in physical activity exists among subjects. Despite this factor, we have found that body weight, fat mass, and FFM remain remarkably constant during the diet periods, with relatively little manipulation of energy intake once each diet period has begun. This finding suggests that the technique used by dietitians to predict energy requirements, namely a variation of the Harris-Benedict equation, has practical utility. The objective of this study was to evaluate this hypothesis.
SUBJECTS AND METHODS
Experimental design
This study was conducted primarily at the GCRCs of The Ohio State University (n = 3) and The University of Texas Medical Branch (n = 23). The study protocol was approved by the Institutional Review Board for human subjects at our institution, and all subjects gave written informed consent. The 26 subjects of this study participated in the control, 28-d run-in phase of an ongoing study examining the effects of substituting oleic acid for palmitic acid on fat oxidation and energy expenditure, which is based on previous data from our laboratory and suggests that oleic acid was preferentially oxidized compared with palmitic acid (14). As part of this trial, subjects are given all the food and drink (except water) that they need to maintain body weight and (in retrospect) body composition for 28-d periods of each diet. The subjects all ate breakfast every day under supervision, and some subjects ate additional meals in the GCRC, especially during the week.
Using the techniques described below, FFM was measured before beginning and after the run-in diet ended, and REE was measured at the beginning of the trial. DEE was also estimated at the onset of the trial with the use of the Harris-Benedict equation to predict REE. Energy intake and body weight were monitored daily in the GCRC. Changes in body weight exceeding 0.3 kg after the first 3 d of the study were considered a reason to adjust energy intake by 100–200 kcal/d, after consideration of extraneous factors such as exercise habits, hydration status, ambient temperature (and resultant changes in clothing), bowel habits, and menstrual cycle. During the outpatient phase of our trial, body weight was measured daily. The subjects removed their shoes, all overclothing (such as sweaters or jackets), and heavy objects such as keys and beepers, but they did not wear gowns. The estimates of REE detailed below were compared with each other in helping to determine reasons why a subject’s apparent energy intake did not match that predicted from the dietitian’s estimate (described below). The subjects were interviewed by research staff (including GCRC nurses and diet staff and clinical study coordinators) daily, and they signed an attestation statement that they consumed all food provided to them and did not eat food (or drink) not provided by the GCRC. The average daily energy intake was then estimated from the GCRC’s dietary records of what the subject was fed.
Measurement techniques and calculations
Measurements of REE were made in the early morning while the subjects were in a fasting state and after they had rested in bed for 30 min. Specifically, oxygen consumption, and carbon dioxide production, were measured via indirect calorimetry with the use of a metabolic cart with a canopy (Vmax 29; Sensor Medics Corp, Yorba Linda, CA). This measurement was only used before the study to estimate energy intakes; the subjects did not sleep overnight in the GCRC but were asked not to exercise vigorously before arriving at the GCRC, usually at 0630. For this reason, energy expenditure was estimated by using the software that assumes a rate of protein oxidation.
Body composition was assessed by using dual-energy X-ray absorptiometry (DXA) (Hologic Delphi QDR 4500A Bone Densitometer Model DPX- IQ, software version 4.6b; Lunar Corp, Madison, WI) (15). Body energy was estimated by using Atwater conversion values for body fat (9.3 kcal/g) and body protein (assuming 0.2 g protein/g FFM and 4.1 kcal/g protein). DEE was determined from the average energy intake (EI), which was based on what the subjects were given each day to eat and the change in body energy (BE) estimated from the DXA measurements:
RESULTS
Group means (±SEM) for DEE, the HB estimate, measured REE, and REE based on FFM and average REE. DEE correlated (P < 0.001) with FFM (r = 0.78), measured REE (r = 0.73), average REE (r = 0.82), and the HB estimate (r = 0.81) (Table 1). In a multiple regression model containing the first 3 of these variables, R2 was 0.70:
DISCUSSION
Numerous studies have examined ways to estimate REE in both healthy and sick people (2–5, 18, 19). Also, many studies have examined the relation of REE and DEE, the latter measured with the DLW technique (summarized in references 6, 7). However, these studies have not examined how measurements that can be rapidly completed in human subjects, such as measurements or estimates of REE or FFM, predict the energy cost of weight maintenance or DEE as defined in this study. Our results suggest that any of the estimates examined in this study provide a good indication of the behavior of a group of subjects, but a relatively poor estimate, on average, for individuals. In this way, our study is similar to what other investigators have found in trying to predict REE (2–5, 18, 19). However, as noted in Results, for 50% of the subjects, any of 3 estimates of DEE were within 200 kcal/d of our estimate of DEE based on energy balance and within the range of daily variation in DEE (1).
This study was conducted as part of an NIH-sponsored trial, which provided the resources along with the GCRC for the study team to devote special care to screening and monitoring of the subjects. Clearly, the utility of our results must be considered in this context. Nevertheless, there are limits to our estimate of DEE, which was based on the observed energy intake, the observed change in body composition, and the so-called Atwater estimates of the energy density of FFM and fat mass. If our subjects are not truthful about what they ate and did not eat all of the food given to them when out of our direct supervision, then the actual DEE for such subjects is not accurate. We were heartened to find that the average ratios of DEE to our estimates of REE were very similar to those reported in the literature (Figure 1) (6, 7). This finding implies that for the group of subjects studied, compliance with the diet was probably reasonably good because the energy intake for the subjects used in our estimate of DEE was derived from what the subjects were given to eat by the GCRC. Because most trials are ultimately based on group means and variance, this is important and relevant information. Moreover, we suggest that if one cannot perform DLW studies to check the accuracy of energy intake (20–22), assessment of DEE as we have done in our study with a comparison to REE may be useful to those conducting dietary trials with or without the provision of all the food that the subjects eat.
ACKNOWLEDGMENTS
We thank our subjects for their thoughtful participation in this study. We are especially grateful to Steven Heymsfield, a Consultant on our grant, for his overall guidance and advice and specifically for providing us with the equation used to estimate REE from FFM. We thank Diane Habash (The Ohio State University GCRC) and J Ann Livengood (University of Texas Medical Branch GCRC) and their respective staffs for their assistance with the dietary aspects of the study. We are thankful to the nursing staffs at both institutions for their assistance with the clinical aspects of the study, including the performance of the indirect calorimetry, and to Travis Solley, Mary Schmitz-Brown, and Regina Minton for assistance with data management in general and with the indirect calorimetry. Finally, we are grateful to the body composition staff at the University of Texas Medical Branch GCRC and Shriners Hospital for Children.
REFERENCES