Resting energy expenditure in the obese: a cross-validation and comparison of prediction equations

Resting energy expenditure in Korean type 2 diabetes patients: comparison between measured and predicted values

BACKGROUND/OBJECTIVES

Estimation of energy demand using resting energy expenditure (REE) is a reasonable approach for optimizing glycemic control and weight management in patients with type 2 diabetes mellitus (T2DM). This study aimed to compare REE predictions and objective measurements in patients with T2DM in Korea.

SUBJECTS/METHODS

This study enrolled 36 participants with T2DM (age range, 20-60 years). Anthropometric variables including height, weight, waist-hip ratio, blood pressure, body fat, body fat percentage, and total body weight were measured using bioimpedance. REE was evaluated using indirect calorimetry. The measured REE values were compared to values estimated using five predictive equations: the Harris-Benedict, Mifflin, Owen, Food and Agriculture Organization of the United Nations/World Health Organization (FAO/WHO), and Schofield equations. This study evaluated the associations between measured REE values and anthropometric/clinical data, including height, weight, and age, using multivariate linear regression.

RESULTS

The mean measured REE value was 1891.79 ± 288.03 kcal/day (male), 1,502.00 ± 202.96 kcal/day (female). REE estimates generated from the Mifflin equation showed the largest differences from measured REE values, whereas estimates derived from the FAO/WHO equation were the closest to the measured REE values. This study also identified associations between measured REE values and various anthropometric/clinical variables.

CONCLUSION

The accuracy of REE prediction equations is critically important in promoting the efficacy of dietary counseling and the effective treatment of diabetes. Our results indicate the need for additional studies informing more suitable methods for determining the energy requirements of Korean patients with T2DM.

Indirect Calorimetry: From Bench to Bedside

Accurate determination of energy expenditure (EE) is vitally important yet often neglected in clinical practice. Indirect calorimetry (IC) provides one of the most sensitive, accurate, and noninvasive measurements of EE in an individual. Over the last couple of decades, this technique has been applied to clinical circumstances such as acute illness and parenteral nutrition. Beyond assessing the nutritional needs, it has also shed light on various aspects of nutrient assimilation, thermogenesis, the energetics of physical exercise, and the pathogenesis of obesity and diabetes. However, because of little or no experience with IC provided during medical education, the benefits of IC are poorly appreciated. Newer technology, cost-effectiveness, and a better understanding of how to interpret measurements should lead to more frequent use of IC. This review focuses on the physicochemical background of IC, the various indications for use, techniques and instruments, potential pitfalls in measurement, and the recent advances in technology that has adapted the technique to long-term studies in humans.

Predicting Energy Expenditure in Extremely Obese Women

BACKGROUND

The most common clinical method for resting energy expenditure (REE) assessment is prediction equations. The purpose of this study was to elucidate which prediction equation is most accurate for REE assessment in extremely obese women.

METHODS

Fourteen extremely obese women (mean +/- SD body mass index: 49.8 +/- 6.2 kg/m(2); age: 49 +/- 10 years) were measured for height and weight and REE via indirect calorimetry (IC) by a metabolic cart system. Predicted REE was evaluated by several equations, including Harris-Benedict with actual body weight, Harris-Benedict with several adjustments to body weight, Cunningham, Mifflin-St Jeor, Owen, World Health Organization (WHO), and Bernstein equations. Accuracy was determined by mean difference data (IC REE - equation REE; Student's paired t-test), correlation coefficients, and agreement between methods by Bland-Altman plots. Accuracy was also evaluated on an individual basis, defined by the percentage of individuals within +/-10% of IC REE.

RESULTS

The Mifflin-St Jeor, Harris-Benedict with actual body weight, and the WHO equations were the most accurate in terms of mean predicted REE. The mean predicted REE values by all other equations were different from the IC REE values (p < .1). According to the individual data, the Mifflin-St Jeor was most accurate (14% outside +/-10% IC REE). The Harris-Benedict with actual body weight and WHO equations were less accurate on individual terms, with 29% and 42% of the predicted REE values, respectively, falling outside +/-10% of IC REE.

CONCLUSIONS

The Mifflin-St Jeor equation was most accurate method for REE assessment in extremely obese women.

Assessing resting energy expenditure in overweight and obese adolescents in a clinical setting: validity of a handheld indirect calorimeter

BACKGROUND

Accurately determining energy requirements is key for nutritional management of pediatric obesity. Recently, a portable handheld indirect calorimeter, MedGem (MG) has become available to measure resting energy expenditure (REE). Our work aims to determine the clinical validity and usefulness of MG to measure REE in overweight and obese adolescents.

METHODS

Thirty-nine overweight and obese adolescents (16 male (M): 23 female (F), 15.2 ± 1.9 y, BMI percentile: 98.6 ± 2.2%) and 15 normal weight adolescents (7M: 8F, age 15.2 ± 2.0 y, BMI percentile: 39.2 ± 20.9%) participated. REE was measured with both MG and standard indirect calorimeter (VMax) in random order.

RESULTS

MG REE (1,600 ± 372 kcal/d) was lower than VMax REE (1,727 ± 327 kcal/) in the overweight and obese adolescents. Bland Altman analysis (MG -VMax) showed a mean bias of -127 kcal/d (95% CI = -72 to -182 kcal/d, P < 0.001), and a proportional bias existed such that lower measured REE by VMax was underestimated by MG, and higher measured REE by VMax were overestimated by MG.

CONCLUSION

MG systematically underestimates REE in the overweight and adolescent population, thus the MG portable indirect calorimeter is not recommended for routine use. Considering that it is a systematic underestimation of REE, MG may be clinically acceptable, only if used with caution.

The importance of prediction model validation and assessment in obesity and nutrition research

Deriving statistical models to predict one variable from one or more other variables, or predictive modeling, is an important activity in obesity and nutrition research. To determine the quality of the model, it is necessary to quantify and report the predictive validity of the derived models. Conducting validation of the predictive measures provides essential information to the research community about the model. Unfortunately, many articles fail to account for the nearly inevitable reduction in predictive ability that occurs when a model derived on one data set is applied to a new data set. Under some circumstances, the predictive validity can be reduced to nearly zero. In this overview, we explain why reductions in predictive validity occur, define the metrics commonly used to estimate the predictive validity of a model (for example, coefficient of determination (R(2)), mean squared error, sensitivity, specificity, receiver operating characteristic and concordance index) and describe methods to estimate the predictive validity (for example, cross-validation, bootstrap, and adjusted and shrunken R(2)). We emphasize that methods for estimating the expected reduction in predictive ability of a model in new samples are available and this expected reduction should always be reported when new predictive models are introduced.

Accuracy of resting energy expenditure calculations in unselected overweight and obese patients

AIMS

Measurements of resting energy expenditure (REE) were compared with the data of 14 equations to determine their accuracy.

METHODS

REE measurements by indirect calorimetry in 1,032 unselected overweight and obese men (n = 306) and women (n = 726) were compared with calculations by 14 different formulas.

RESULTS

The mean (± SD) values calculated with the Owen, Robertson and Reid and WHO-I equations were not significantly different from our measurement of 1,682 ± 441.9 kcal/24 h. The values obtained with the Livingston, Mifflin, Müller and Bernstein equations were significantly different but still within a range of ±100 kcal/24 h. For females, the best comparison was observed with the Müller equation which, however, differed substantially in males. For men, the Cunningham equation was best, but it gave the worst comparison in women. A good individual match was only obtained with the equation of Robertson and Reid in 34% of the men and with the Owen equation in 38% of the women. All other formulas were less accurate. Drug treatment for 55% of the subjects had no effect on the mismatch between calculated and measured data.

CONCLUSION

Calculations of REE with most equations seem to be valid in a group analysis but they are not helpful for the estimation of an obese patient's individual energy expenditure.

A pocket-sized metabolic analyzer for assessment of resting energy expenditure

BACKGROUND & AIMS

The assessment of metabolic parameters related to energy expenditure has a proven value for weight management; however these measurements remain too difficult and costly for monitoring individuals at home. The objective of this study is to evaluate the accuracy of a new pocket-sized metabolic analyzer device for assessing energy expenditure at rest (REE) and during sedentary activities (EE). The new device performs indirect calorimetry by measuring an individual's oxygen consumption (VO2) and carbon dioxide production (VCO2) rates, which allows the determination of resting- and sedentary activity-related energy expenditure.

METHODS

VO2 and VCO2 values of 17 volunteer adult subjects were measured during resting and sedentary activities in order to compare the metabolic analyzer with the Douglas bag method. The Douglas bag method is considered the Gold Standard method for indirect calorimetry. Metabolic parameters of VO2, VCO2, and energy expenditure were compared using linear regression analysis, paired t-tests, and Bland-Altman plots.

RESULTS

Linear regression analysis of measured VO2 and VCO2 values, as well as calculated energy expenditure assessed with the new analyzer and Douglas bag method, had the following linear regression parameters (linear regression slope LRS0, and R-squared coefficient, r(2)) with p = 0: LRS0 (SD) = 1.00 (0.01), r(2) = 0.9933 for VO2; LRS0 (SD) = 1.00 (0.01), r(2) = 0.9929 for VCO2; and LRS0 (SD) = 1.00 (0.01), r(2) = 0.9942 for energy expenditure. In addition, results from paired t-tests did not show statistical significant difference between the methods with a significance level of α = 0.05 for VO2, VCO2, REE, and EE. Furthermore, the Bland-Altman plot for REE showed good agreement between methods with 100% of the results within ±2SD, which was equivalent to ≤10% error.

CONCLUSION

The findings demonstrate that the new pocket-sized metabolic analyzer device is accurate for determining VO2, VCO2, and energy expenditure.

Common Prediction Equations Overestimate Measured Resting Metabolic Rate in Young Hispanic Women

The accuracy of 6 resting metabolic rate (RMR) prediction equations to indirect calorimetry was compared in 38 Hispanic women (age = 30 ± 7 years; body mass index = 28.9 ± 7.2 kg/m2; body fat = 42% ± 8%). Paired t tests examined differences between predicted and measured RMR; significance defined as P < 0.05. Bias and agreement were displayed using Bland-Altman plots. Accuracy was defined when the predicted RMR was ± 10% of the measured RMR. Data were analyzed with SPSS (version 19). Only the equation of Owen et al was not significantly different from the measured RMR (1336 ± 142 and 1322 ± 203 kcal/d, respectively). The equation of Owen et al was accurate for 84.2% of women; RMR prediction equations had limited applicability for young Hispanic women.

Agreement between different methods and predictive equations for resting energy expenditure in overweight and obese Brazilian men

Predictive equations and methods tend to overestimate or underestimate resting energy expenditure (REE) compared with indirect calorimetry (IC). This cross-sectional study aimed to evaluate the agreement between methods and equations for REE estimation of overweight and obese Brazilian men. Data from 48 healthy volunteers, ages 20 to 43 years and with body mass index ranging from 26.4 to 35.2, were collected between October 2008 and October 2009. REE was measured by IC, using Deltatrac (IC1) and KORR-MetaCheck (IC2) devices. It was estimated by bioelectrical impedance analysis (BIA) using tetrapolar (BIA1) and bipolar (BIA2) devices, and by the equations of Mifflin, World Health Organization/Food and Agriculture Organization/United Nations University, Fleisch, Horie-Waitzberg and Gonzalez, and Ireton-Jones. The association and agreement among the methods and equations were assessed by the interclass correlation coefficient, Bland-Altman analysis, and by the percentage of the difference between values obtained from the standard method and alternative methods and equations. Most methods showed high agreement with IC1. The highest agreements were found for Mifflin (-2.14%), Fleisch (-3.05%), Horie-Waitzberg and Gonzalez (4.41%), and BIA2 (5.25%). Similar results were shown by the Bland-Altman analyses. BIA2, followed by BIA1, Ireton-Jones, Mifflin, and Fleisch, showed the highest association with IC1. Thus, the Mifflin, Fleisch, Horie-Waitzberg and Gonzalez equations, and BIA2, were the most accurate methods for REE estimation in this study. However, because those equations have shown considerable variability, they should be used cautiously. In addition, the IC2 was not found to be an accurate method for REE estimation in overweight and obese men included in this study.

Simplified Resting Metabolic Rate-Predicting Formulas for Normal-Sized and Obese Individuals

OBJECTIVE

Resting metabolic rate (RMR) is known to be proportional to body weight and to follow allometric scaling principles. We hypothesized that RMR can be predicted from an allometric formula with weight alone as an independent variable.

RESEARCH METHODS AND PROCEDURES

An allometric, power-law scaling model was fit to RMR measurements obtained from a cohort of patients being treated for weight loss. This, as well as many of the commonly used RMR-predicting formulas, was tested for RMR prediction ability against a large publicly available RMR database. Bland-Altman analysis was used to determine the efficacy of the various RMR-predicting formulas in obese and non-obese subjects.

RESULTS

Power law modeling of the RMR-body weight relationship yielded the following RMR-predicting equations: RMR(Women) = 248 x Weight(0.4356) - (5.09 x Age) and RMR(Men) = 293 x Weight(0.4330) - (5.92 x Age). Partial correlation analysis revealed that age significantly contributed to RMR variance and was necessary to include in RMR prediction formulas. The James, allometric, and Harris-Benedict formulas all yielded reasonable RMR predictions for normal sized and obese subjects.

DISCUSSION

A simple power formula relating RMR to body weight can be a reasonable RMR estimator for normal-sized and obese individuals but still requires an age term and separate formulas for men and women for the best possible RMR estimates. The apparent performance of RMR-predicting formulas is highly dependent on the methodology employed to compare the various formulas.

A comparison of predictive equations of energy expenditure and measured energy expenditure in critically ill patients

PURPOSE

Multiple equations exist for predicting resting energy expenditure (REE). The accuracy of these for estimating energy requirements of critically ill patients is not clear, especially for obese patients. We sought to compare REE, calculated with published formulas, with measured REE in a cohort of mechanically ventilated subjects.

MATERIALS AND METHODS

We retrospectively identified all mechanically ventilated patients with measured body mass index who underwent indirect calorimetry in the intensive care unit. Limits of agreement and Pitman's test of difference in variance were performed to compare REE by equations with REE measured by indirect calorimetry.

RESULTS

A total of 927 patients were identified, including 401 obese patients. There were bias and poor agreement between measured REE and REE predicted by the Harris-Benedict, Owen, American College of Chest Physicians, and Mifflin equations (P > .05). There was poor agreement between measured and predicted REE by the Ireton-Jones equation, stratifying by sex. Ireton-Jones was the only equation that was unbiased for men and those in weight categories 1 and 2. In all cases except Ireton-Jones, predictive equations underestimated measured REE.

CONCLUSION

None of these equations accurately estimated measured REE in this group of mechanically ventilated patients, most underestimating energy needs. Development of improved predictive equations for adequate assessment of energy needs is needed.

A weight-loss diet including coffee-derived mannooligosaccharides enhances adipose tissue loss in overweight men but not women

Mannooligosaccharides (MOS), extracted from coffee, have been shown to promote a decrease in body fat when consumed as part of free-living, weight-maintaining diets. Our objective was to determine if MOS consumption (4 g/day), in conjunction with a weight-loss diet, would lead to greater reductions in adipose tissue compartments than placebo. We conducted a double-blind, placebo-controlled weight-loss study in which 60 overweight men and women consumed study beverages and received weekly group counseling for 12 weeks. Weight and blood pressure were measured weekly, and adipose tissue distribution was assessed at baseline and at end point using magnetic resonance imaging. A total of 54 subjects completed the study. Men consuming the MOS beverage had greater loss of body weight than men consuming the Placebo beverage (-6.0 ± 0.6% vs. -2.3 ± 0.5%, respectively, P < 0.05). Men consuming the MOS beverage also had reductions in total body volume (P < 0.0001), total (P < 0.0001), subcutaneous (P < 0.0001), and visceral (P < 0.05) adipose tissue that were greater than changes observed in those consuming the Placebo beverage. In women, changes in body weight and adipose tissue compartments were not different between groups. Adding coffee-derived MOS to a weight-loss diet enhanced both weight and adipose tissue losses in men, suggesting a potential functional use of MOS for weight management and improvement in adipose tissue distribution. More studies are needed to investigate the apparent gender difference in response to MOS consumption.

Comparison of resting energy expenditure prediction methods with measured resting energy expenditure in obese, hospitalized adults

BACKGROUND

Several methods are available to estimate caloric needs in hospitalized, obese patients who require specialized nutrition support; however, it is unclear which of these strategies most accurately approximates the caloric needs of this patient population. The purpose of this study was to determine which strategy most accurately predicts resting energy expenditure in this subset of patients.

METHODS

Patients assessed at high nutrition risk who required specialized nutrition support and met inclusion and exclusion criteria were enrolled in this observational study. Adult patients were included if they were admitted to a medical or surgical service with a body mass index > or = 30 kg/m(2). Criteria excluding patient enrollment were pregnancy and intolerance or contraindication to indirect calorimetry procedures. Investigators calculated estimations of resting energy expenditure for each patient using variations on the following equations: Harris-Benedict, Mifflin-St. Jeor, Ireton-Jones, 21 kcal/kg body weight, and 25 kcal/kg body weight. For nonventilated patients, the MedGem handheld indirect calorimeter was used. For ventilated patients, the metabolic cart was used. The primary endpoint was to identify which estimation strategy calculated energy expenditures to within 10% of measured energy expenditures.

RESULTS

The Harris-Benedict equation, using adjusted body weight with a stress factor, most frequently estimated resting energy expenditure to within 10% measured resting energy expenditure at 50% of patients.

CONCLUSION

Measured energy expenditure with indirect calorimetry should be employed when developing nutrition support regimens in obese, hospitalized patients, as estimation strategies are inconsistent and lead to inaccurate predictions of energy expenditure in this patient population.

Grade of adiposity affects the impact of fat mass on resting energy expenditure in women

Body fat mass (FM) adds to the variance in resting energy expenditure (REE). However, the nature and extent of this relationship remains unclear. Using a database of 1306 women and a linear regression model, we systematically analysed the contribution of FM to the total variance in REE at different grades of adiposity (ranges of body %FM). After adjusting for age, the relative contribution of FM on REE variance increased from low ( ≤ 10 %FM) to normal (>10– ≤ 30 %FM) and moderately elevated (>30– ≤ 40 %FM) grades of adiposity but decreased sharply at high (>40– ≤ 50 %FM) and very high (>50 %FM) grades of adiposity according to the ratio between regression coefficients. These data suggest that the specific metabolic rate of fat tissue is reduced at high adiposity. This should be considered when REE is normalized for FM in obesity.

Effect of a moderately hypoenergetic Mediterranean diet and exercise program on body cell mass and cardiovascular risk factors in obese women

OBJECTIVE

To assess the effects of a moderately hypoenergetic Mediterranean diet (MHMD) and exercise program on body cell mass (BCM) and cardiovascular disease risk factors in obese women.

SUBJECTS/METHODS

Forty-seven obese women, 39.7+/-13.2 years of age, with a body mass index (BMI)=30.7+/-6.0 kg/m(2), completed the study. The following were measured at baseline, 2 and 4 months: BCM, BCM index (BCMI), body weight, BMI, fat-free mass (FFM), fat mass (FM), total body water (TBW), extracellular water (ECW) and intracellular water (ICW) using bioelectrical impedance analysis; fasting blood glucose (FBG), serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) concentrations; systolic (SBP) and diastolic (DBP) blood pressure.

RESULTS

Body weight, BMI, FM, TC and TG significantly decreased (P<0.001; P<0.002 (TG)) at 2 and 4 months. FFM, TBW, ECW, FBG and DBP significantly decreased at 2 months (P<0.05 (FFM); P<0.001). LDL-C significantly decreased (P<0.001), while HDL-C significantly increased (P<0.002) at 4 months. BCM, BCMI, ICW and SBP remained stable over time.

CONCLUSION

BCM was preserved and cardiovascular disease risk factors improved in obese women placed on a MHMD and exercise program for 4 months.

Accurate Determination of Energy Needs in Hospitalized Patients

OBJECTIVE

To evaluate the accuracy of seven predictive equations, including the Harris-Benedict and the Mifflin equations, against measured resting energy expenditure (REE) in hospitalized patients, including patients with obesity and critical illness.

DESIGN

A retrospective evaluation using the nutrition support service database of a patient cohort from a similar timeframe as those used to develop the Mifflin equations.

SUBJECTS/SETTING

All patients with an ordered nutrition assessment who underwent indirect calorimetry at our institution over a 1-year period were included.

INTERVENTION

Available data was applied to REE predictive equations, and results were compared to REE measurements.

MAIN OUTCOME MEASURES

Accuracy was defined as predictions within 90% to 110% of the measured REE. Differences >10% or 250 kcal from REE were considered clinically unacceptable.

STATISTICAL ANALYSES PERFORMED

Regression analysis was performed to identify variables that may predict accuracy. Limits-of-agreement analysis was carried out to describe the level of bias for each equation.

RESULTS

A total of 395 patients, mostly white (61%) and African American (36%), were included in this analysis. Mean age+/-standard deviation was 56+/-18 years (range 16 to 92 years) in this group, and mean body mass index was 24+/-5.6 (range 13 to 53). Measured REE was 1,617+/-355 kcal/day for the entire group, 1,790+/-397 kcal/day in the obese group (n=51), and 1,730+/-402 kcal/day in the critically ill group (n=141). The most accurate prediction was the Harris-Benedict equation when a factor of 1.1 was multiplied to the equation (Harris-Benedict 1.1), but only in 61% of all the patients, with significant under- and over-predictions. In the patients with obesity, the Harris-Benedict equation using actual weight was most accurate, but only in 62% of patients; and in the critically ill patients the Harris-Benedict 1.1 was most accurate, but only in 55% of patients. The bias was also lowest with Harris-Benedict 1.1 (mean error -9 kcal/day, range +403 to -421 kcal/day); but errors across all equations were clinically unacceptable.

CONCLUSIONS

No equation accurately predicted REE in most hospitalized patients. Without a reliable predictive equation, only indirect calorimetry will provide accurate assessment of energy needs. Although indirect calorimetry is considered the standard for assessing REE in hospitalized patients, several predictive equations are commonly used in practice. Their accuracy in hospitalized patients has been questioned. This study evaluated several of these equations, and found that even the most accurate equation (the Harris-Benedict 1.1) was inaccurate in 39% of patients and had an unacceptably high error. Without knowing which patient's REE is being accurately predicted, indirect calorimetry may still be necessary in difficult to manage hospitalized patients.

Comparison of Predictive Equations for Resting Metabolic Rate in Healthy Nonobese and Obese Adults: A Systematic Review

BACKGROUND

An assessment of energy needs is a necessary component in the development and evaluation of a nutrition care plan. The metabolic rate can be measured or estimated by equations, but estimation is by far the more common method. However, predictive equations might generate errors large enough to impact outcome. Therefore, a systematic review of the literature was undertaken to document the accuracy of predictive equations preliminary to deciding on the imperative to measure metabolic rate.

METHODS

As part of a larger project to determine the role of indirect calorimetry in clinical practice, an evidence team identified published articles that examined the validity of various predictive equations for resting metabolic rate (RMR) in nonobese and obese people and also in individuals of various ethnic and age groups. Articles were accepted based on defined criteria and abstracted using evidence analysis tools developed by the American Dietetic Association. Because these equations are applied by dietetics practitioners to individuals, a key inclusion criterion was research reports of individual data. The evidence was systematically evaluated, and a conclusion statement and grade were developed.

RESULTS

Four prediction equations were identified as the most commonly used in clinical practice (Harris-Benedict, Mifflin-St Jeor, Owen, and World Health Organization/Food and Agriculture Organization/United Nations University [WHO/FAO/UNU]). Of these equations, the Mifflin-St Jeor equation was the most reliable, predicting RMR within 10% of measured in more nonobese and obese individuals than any other equation, and it also had the narrowest error range. No validation work concentrating on individual errors was found for the WHO/FAO/UNU equation. Older adults and US-residing ethnic minorities were underrepresented both in the development of predictive equations and in validation studies.

CONCLUSIONS

The Mifflin-St Jeor equation is more likely than the other equations tested to estimate RMR to within 10% of that measured, but noteworthy errors and limitations exist when it is applied to individuals and possibly when it is generalized to certain age and ethnic groups. RMR estimation errors would be eliminated by valid measurement of RMR with indirect calorimetry, using an evidence-based protocol to minimize measurement error. The Expert Panel advises clinical judgment regarding when to accept estimated RMR using predictive equations in any given individual. Indirect calorimetry may be an important tool when, in the judgment of the clinician, the predictive methods fail an individual in a clinically relevant way. For members of groups that are greatly underrepresented by existing validation studies of predictive equations, a high level of suspicion regarding the accuracy of the equations is warranted.

Resting metabolic rate in severely obese diabetic and nondiabetic subjects

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 > or = 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 x age + 257.293 x gender (women = 0 and men = 1) + 9.996 x weight (in kilograms) + 4.132 x height (in centimeters) + 145.959 x 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.

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.

Physical activity and resting metabolic rate

The direct effects of physical activity interventions on energy expenditure are relatively small when placed in the context of total daily energy demands. Hence, the suggestion has been made that exercise produces energetic benefits in other components of the daily energy budget, thus generating a net effect on energy balance much greater than the direct energy cost of the exercise alone. Resting metabolic rate (RMR) is the largest component of the daily energy budget in most human societies and, therefore, any increases in RMR in response to exercise interventions are potentially of great importance. Animal studies have generally shown that single exercise events and longer-term training produce increases in RMR. This effect is observed in longer-term interventions despite parallel decreases in body mass and fat mass. Flight is an exception, as both single flights and long-term flight training induce reductions in RMR. Studies in animals that measure the effect of voluntary exercise regimens on RMR are less commonly performed and do not show the same response as that to forced exercise. In particular, they indicate that exercise does not induce elevations in RMR. Many studies of human subjects indicate a short-term elevation in RMR in response to single exercise events (generally termed the excess post-exercise O2 consumption; EPOC). This EPOC appears to have two phases, one lasting < 2 h and a smaller much more prolonged effect lasting up to 48 h. Many studies have shown that long-term training increases RMR, but many other studies have failed to find such effects. Data concerning long-term effects of training are potentially confounded by some studies not leaving sufficient time after the last exercise bout for the termination of the long-term EPOC. Long-term effects of training include increases in RMR due to increases in lean muscle mass. Extreme interventions, however, may induce reductions in RMR, in spite of the increased lean tissue mass, similar to the changes observed in animals in response to flight.

Development and cross-validation of a prediction equation for estimating resting energy expenditure in healthy African-American and European-American women

OBJECTIVE

To develop, validate, and cross-validate a formula for predicting resting energy expenditure (REE) in African-American and European-American women.

DESIGN

A cross-sectional study of REE in women. Participants were randomly assigned to one of two groups. One group served to develop and validate a new equation for predicting REE while the second was used to cross-validate the prediction equation. The accuracy of the equation was compared to several existing formulae.

SETTING

University metabolic laboratory, Memphis, TN, USA.

SUBJECTS

Healthy, premenopausal African-American and European-American women between 18 and 39 y of age. The validation sample included 239 women (age: 28.4 y, wt: 70.7 kg, body mass index (BMI): 25.2 kg/m(2), REE: 5840 kJ/day), while the cross-validation sample consisted of 232 women (age: 27.5 y, wt: 70.7 kg, BMI: 25.2 kg/m(2), REE: 5784 kJ/day).

RESULTS

The prediction equation derived from the current sample, which included adjustments for ethnicity, was the only formula that demonstrated a high level of accuracy for predicting REE in both African-American and European-American women. The mean difference between REE predicted from the new formula and measured REE was 28 kJ/day (s.d.=668) for European-American women and 142 kJ/day (s.d.=584) for African-American women.

CONCLUSIONS

Previous equations for predicting energy needs may not be appropriate for both African-American and European-American women due to ethnic differences in REE. A new equation that makes adjustments in predicted REE based on ethnicity is recommended for determining energy needs in these groups (Predicted REE (kJ/day)=616.93-14.9 (AGE (y))+35.12 (WT (kg))+19.83 (HT (cm))-271.88 (ETHNICITY: 1=African American; 0=European American)).

SPONSORSHIP

Support for this study was provided by Grant #HL53261 from the National Heart, Lung, and Blood Institute.

Estimation of energy requirements in a controlled feeding trial

BACKGROUND

Estimating energy requirements is a frequent task in clinical studies.

OBJECTIVE

We examined weight patterns of participants enrolled in a clinical trial and evaluated factors that may affect weight stabilization. The Harris-Benedict equation and the FAO/WHO equation, used in conjunction with physical activity levels estimated with the 7-d Physical Activity Recall, were compared for estimating energy expenditure.

DESIGN

This was a multicenter, randomized controlled feeding trial with participants of the Dietary Approaches to Stop Hypertension Trial. For 11 wk, the amount of food participants received was adjusted to maintain their body weights as close to their initial weights as possible. Change-point regression techniques were used to identify weight-stable periods. Factors related to achieving weight stabilization were examined with logistic regression.

RESULTS

A stable weight was achieved by 86% of the 448 participants during the run-in period and by 78% during the intervention period. Energy intake averaged 11 +/- 2.4 MJ/d (2628 +/- 578 kcal/d), with most participants (n = 270) requiring 9-13 MJ/d (2100-3100 kcal/d). The difference between predicted and observed intakes was highest at high estimated energy intakes, mainly because of high and probably incorrect estimates of the activity factor. Participants with lower energy intakes tended to need less adjustment of their energy intakes to maintain a stable weight than did participants with higher energy intakes.

CONCLUSIONS

Weight stabilization is not affected by diet composition, sex, race, age, or baseline weight. Either the Harris-Benedict equation or the FAO/WHO equation can be used to estimate energy needs. Activity factors > 1.7 often lead to overestimation of energy needs.

The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia

BACKGROUND

Predictive equations have been reported to overestimate resting energy expenditure (REE) for obese persons. The presence of hyperglycemia results in elevated REE in obese persons with type 2 diabetes, and its effect on the validity of these equations is unknown.

OBJECTIVE

We tested whether (1) indicators of diabetes control were independent associates of REE in type 2 diabetes and (2) their inclusion would improve predictive equations.

DESIGN

A cross-sectional study of 65 (25 men, 40 women) obese type 2 diabetic subjects. Variables measured were: REE by ventilated-hood indirect calorimetry, body composition by bioimpedance analysis, body circumferences, fasting plasma glucose (FPG) and hemoglobin A(1c). Data were analyzed using stepwise multiple linear regression.

RESULTS

REE, corrected for weight, fat-free mass, age and gender, was significantly greater with FPG>10 mmol/l (P=0.017) and correlated with FPG (P=0.013) and hemoglobin A(1c) as percentage upper limit of normal (P=0.02). Weight was the main determinant of REE. Together with hip circumference and FPG, it explained 81% of the variation. FPG improved the predictability of the equation by >3%. With poor glycemic control, it can represent an increase in REE of up to 8%.

CONCLUSION

Our data indicate that in a population of obese subjects with type 2 diabetes mellitus, REE is better predicted when fasting plasma glucose is included as a variable.

Binge size increases with body mass index in women with binge-eating disorder

OBJECTIVE

To determine whether meal size is related to body mass index (BMI) in obese subjects with binge-eating disorder (BED).

RESEARCH METHODS AND PROCEDURES

Five groups of subjects each consumed two laboratory-test meals on nonconsecutive days. Forty-two women, categorized by BMI and BED diagnosis, were instructed to "binge" during one meal and to eat "normally" during another. Eighteen women had BMI values >38 kg/m(2) (more-obese) and 17 had BMI values between 28 to 32 kg/m(2) (less-obese). Twelve of the more-obese and nine of the less-obese individuals met Diagnostic and Statistical Manual (DSM)-IV criteria for BED. Seven normal-weight women also participated as controls.

RESULTS

Subjects with BED ate significantly more in both meals than subjects without BED. Binge meals were significantly larger than normal meals only among subjects with BED. The more-obese subjects with BED ate significantly more than the less-obese subjects with BED, but only when they were asked to binge. Intake of the binge meal was significantly, positively correlated with BMI among subjects with BED. Subjects with BED reported significantly higher satiety ratings after the binge than after the normal meal, but subjects without BED reported similar ratings after both meals. Regardless of instructions and diagnosis, obese subjects consumed a significantly higher percentage of energy from fat (38.5%) than did normal-weight subjects (30.8%).

DISCUSSION

During binge meals, the energy intake of subjects with BED is greater than that of individuals of similar body weight without BED and is positively correlated with BMI.

Indirect calorimetry protocol development for measuring resting metabolic rate as a component of total energy expenditure in free-living postmenopausal women

An objective measure of energy intake is needed in epidemiologic studies to evaluate random and systematic error associated with dietary self-report tools. Total energy expenditure in weight-stable humans is accepted as a measure of energy intake, but doubly labeled water remains cost prohibitive for large studies. Our purpose was to develop a practical indirect calorimetry (IC) protocol for estimating resting metabolic rate (RMR) in free-living, postmenopausal women. We conducted duplicate IC measures 1 wk apart using a canopy system on 102 women ages 50-79 y from the Seattle area. We compared RMR for 0-5, 5-10, 5-15, 5-20, 5-25, 5-30, and 0- to 30-min IC segments and segments meeting stability criteria. The mean RMR for the first 5 min was significantly higher than other time segments (P = 0.001). Correlation coefficients between duplicate measures were high (r = 0.90). Use of defined stability criteria produced RMR measures that were 10-30 kcal (42-126 kJ) higher than the 5- to 10-min RMR measures and 40-60% of subjects did not achieve these stability criteria. For protocols including IC to assess RMR as a component of total energy expenditure in free-living, postmenopausal women, a single 10-min canopy study, excluding the first 5 min of data, produces reliable results with minimal subject burden.

Determination of energy density of freely selected diets: methodological issues and implications

BACKGROUND

There is increasing evidence that dietary energy density (ED, kJ/g) may be an important dietary characteristic, particularly in respect to control of energy intake; however, there are no agreed methods for deriving the ED of freely selected diets, and ED values may be markedly affected by the inclusion or exclusion of specific dietary items, particularly beverages.

OBJECTIVE

To highlight the consequences of using six different methods of ED calculation, and their implications for characterizing differences between weight status groups and identifying associations of ED with macronutrient intakes.

DESIGN

ED was calculated using six defined methods: (1) all food and beverages; (2) all food and energy beverages; (3) food, milk and alcohol; (4) food only; (5) all dry matter; (6) protein, carbohydrate and fat only, of varying exclusions of different beverages and water. For illustrative purposes, data from 41 lean (LE, body mass index (BMI) 20-25 kg/m2) and 34 obese (OB, BMI>/=30 kg/m2) adults who kept 4-day weighed dietary intake records are described.

RESULTS

ED values (and coefficient of variation, CV) differed substantially by methods of calculation. OB reported significantly greater mean ED compared with LE by one method (all food, milk and alcohol, excluding other non-alcoholic beverages); however, the opposite was found using another method (dry weight). For most calculation methods, ED was negatively associated with percentage energy from carbohydrate for LE, in contrast to OB. All methods found positive correlations for ED and fat (g) among LE, but only one method found such a correlation among OB. Similarly, three methods produced positive correlations between ED and percentage energy fat amongst LE; however, this was only observed amongst OB with one method.

CONCLUSIONS

Methods of calculating ED of freely selected diets must be carefully defined, and can markedly influence apparent relationships of ED with other dietary measures and subject characteristics. International Journal of Obesity (2000)24, 49-54

Characteristics of the low-energy reporters in a longitudinal national dietary survey

The aim of the present study was to establish whether the characteristics of members of a large national birth cohort study who submitted diet diaries with implausibly low-energy intake differed from those whose recorded energy intake was more plausible. Survey members (n 1898) recorded their diets in a 7 d diary in household measures. Those whose reported energy intake (EI) as a fraction of their estimated BMR was less than 1.10, here termed low-energy reporters (LER) but often called under-reporters, constituted 20.6% of the study population. None of the variables describing dietary, smoking or exercise behaviour bore a significant relationship with low EI/BMR (< 1.10), neither did those describing region of residence, subjective adequacy of income, current social class, social relations or the social environment of the subjects. Results of logistic regression analysis showed that the only independently significant characteristic for men was higher BMI. In women, in addition to higher BMI, having been overweight or obese as an adult independently, but less significantly, predicted low EI/BMR, while membership as a child of social class III (non-manual), having more children in the household and having a paid job marginally but independently decreased the probability of reporting low EI/BMR. Submission of a diary with EI/BMR < 1.10 7 years earlier in the same survey was an even more powerful predictor of current low EI/BMR than higher BMI in both sexes. The average reported diet-composition of LER was more micronutrient- and protein-rich than that of the others, indicating different dietary, or diet-recording, behaviour in this group of subjects. LER are not a random sample of the survey population, and their characteristics, definable to some extent, put them at risk for lower health status. Although EI/BMR cut-off points can be used to identify LER, the problem of how to use their data is still unresolved.

Unexplained disturbance in body weight regulation: diagnostic outcome assessed by doubly labeled water and body composition analyses in obese patients reporting low energy intakes

SUBJECTS

Ten patients who had long-term disturbances in body weight regulation, were referred over a 3-year period for obesity evaluation, and reported low energy intakes (< 1,200 kcal/day).

OBJECTIVE

To ascertain whether these patients had a low energy expenditure and thus reduced energy requirement, and/or whether they were misreporting their energy intake.

DESIGN

Comparison of outcome measures in referred patients and in obese control patients who did not report low energy intakes and disturbances in body weight regulation.

MAIN OUTCOME MEASURES

Low energy expenditure was evaluated with serum thyroid hormone levels, resting metabolic rate (RMR), thermic effect of food (TEF), and total energy expenditure (TEE) by doubly labeled water technique. Misreporting of energy intake was evaluated by comparing patients' self-reported energy intake with energy intake estimated by doubly labeled water and body composition analyses over a 14-day period.

STATISTICAL ANALYSES PERFORMED

Low energy expenditure was considered present in a patient if RMR or TEE was more than 15% below predicted values according to results from the control group. Patient group TEF was compared with TEF results observed in the control group.

RESULTS

All patients had normal serum thyroid hormone levels. Eight patients had RMR and TEE values within 15% of predicted values and were substantially underreporting their energy intake. One patient had low TEE (-19%) and a normal RMR, a finding that implies a low level of physical activity. This patient also underreported energy intake as estimated by the doubly labeled water technique during the study (-38%). The 10th patient had a low RMR (-23.2%) and TEE (-25.0%), the mechanism of which was uncertain. This patient's reported food intake over the 14-day period was accurate but was less than her long-term intake over months or years as suggested by doubly labeled water TEE estimates. The TEF response in patients was not significantly different from that observed in the control group.

CONCLUSIONS

Underreporting of energy intake from foods is a frequent finding in patients with disturbances in body weight regulation who are referred for obesity evaluation. Severe underreporting may be detectable by means of screening measures available to most dietitians. Low energy expenditure, due either to physical inactivity or to metabolic factors, is also observed. Modern evaluation methods provide new insights into patients with weight regulatory disturbances and at the same time stimulate important new research questions.

Accuracy of equations to predict basal metabolic rate in older women

OBJECTIVE

To assess the accuracy of several published equations for predicting basal metabolic rate (BMR) in older women.

DESIGN

BMR was assessed in 116 healthy, older white women, aged 60 to 82 years, on three successive mornings by indirect calorimetry. Body composition was determined by dual energy X-ray absorptiometry or hydrostatic weighing. The measured BMRs were compared with values obtained from eight published prediction equations that used solely, or in various combinations, measures of height, weight, fat-free mass, age, and menopausal status.

STATISTICAL ANALYSES PERFORMED

The root mean squared prediction error (RMSPE) was used to determine how accurately predicted BMR matched actual BMR for each subject. In addition, regression analysis was used to evaluate accuracy of predicted BMR vs directly measured BMR.

RESULTS

Predicted mean BMR determined using all eight equations was significantly correlated to measured BMR (P = .0001), accounting for 30% to 52% of the variance of measured BMR. When analyzed by RMSPE, however, the equations of Owen et al (1986), Fredrix et al (1990), and Harris-Benedict (1919) predicted actual BMR for each subject within an average of 116 kcal/day, and the equation of Cunningham (1980) resulted in the largest prediction error at 208 kcal/day.

APPLICATIONS/CONCLUSIONS

The regression equations of Owen et al (1986), which used body weight, Fredrix et al (1990), which used body weight and age, and Harris-Benedict (1919), which used age, weight, and height as variables, were most accurate in predicting BMR in our sample of healthy older women.