Resting Energy Expenditure Prediction Research Articles

Prediction of resting energy expenditure for adolescents with severe obesity: A multi-centre analysis.

Resting energy expenditure (REE) assessments can help inform clinical treatment decisions in adolescents with elevated body mass index (BMI), but current equations are suboptimal for severe obesity. We developed a predictive REE equation for youth with severe obesity and obesity-related comorbidities and compared results to previously published predictive equations. Data from indirect calorimetry, clinical measures, and body composition per Dual x-ray absorptiometry (DXA) were collected from five sites. Data were randomly divided into development (N = 438) and validation (N = 118) cohorts. A predictive equation was developed using Elastic Net regression, using sex, race, ethnicity, weight, height, BMI percent of the 95th%ile (BMIp95), waist circumference, hip circumference, waist/hip ratio, age, Tanner stage, fat and fat-free mass. This equation was verified in the validation cohort and compared with 11 prior equations. Data from the total cohort (n = 556, age 15 ± 1.7 years, 77% female, BMIp95 3.3 ± 0.94) were utilized. The best fit equation was REE = -2048 + 18.17 × (Height in cm) - 2.57 × (Weight in kg) + 7.88 × (BMIp95) + 189 × (1 = male, 0 = female), R2 = 0.466, and mean bias of 23 kcal/day. This new equation provides an updated REE prediction that accounts for severe obesity and metabolic complications frequently observed in contemporary youth.

Estimates of resting energy expenditure using predictive equations in adults with severe burns: A systematic review and meta-analysis.

Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns. A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively. Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%). For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

Prediction of resting energy expenditure in Italian older adults with severe obesity.

In the last decade a large number of studies proposed and/or validated equations to estimate the Resting Energy Expenditure (REE) in adults and/or older adults, however, no equation currently available showed good accuracy for older adults with severe obesity. Thus, this study aimed to develop and validate new predictive equations for REE, based on data from the indirect calorimetry, in Italian older adults with severe obesity. A retrospective study was as conducted with 764 Caucasian older adults with severe obesity (age range: 60-74 years and BMI ≥ 35 kg/m/²). Four models were used to test the accuracy of anthropometry and body composition variables in multivariable prediction of REE. All models were derived by stepwise multiple regression analysis using a calibration group of 382 subjects [295 females and 87 males] and the equations were cross-validated in the remaining 382 subjects [295 females and 87 males] as validation group. The new prediction equations and the other published equations were tested using the Bland-Altman method. Prediction accuracy was defined as the percentage of subjects whose REE was predicted within ± 10% of measured REE. All the equations analyzed predicted higher energy requirements for males than females, and most of them underestimated the energy requirement values of our sample. The highest accuracy values were observed in the new equations, with 62% in the anthropometric model and 63% in the body composition model. Although the accuracy of our equations was slightly higher in comparison with the other taken into consideration, they cannot be considered completely satisfactory for predicting REE in Italians older adults with severe obesity. When predicting equations cannot guarantee precise or acceptable values of REE, the use of indirect calorimetry (if available) should be always recommended, especially in clinical practice.

Determining the appropriate resting energy expenditure requirement for severe trauma patients using indirect calorimetry in Korea: a retrospective observational study

Purpose: This study aimed to compare the resting energy expenditure (REE) measured using indirect calorimetry with that estimated using predictive equations in severe trauma patients to determine the appropriate caloric requirements.Methods: Patients admitted to the surgical intensive care unit between January 2020 and March 2023 were included in this study. Indirect calorimetry was used to measure the patients’ REE values. These values were subsequently compared with those estimated using predictive equations: the weight-based equation (rule of thumb, 25 kcal/kg/day), Harris-Benedict, Ireton-Jones, and the 2003 Penn State equations.Results: A total of 27 severe trauma patients were included in this study, and 47 indirect calorimetric measurements were conducted. The weight-based equation (mean difference [MD], –28.96±303.58 kcal) and the 2003 Penn State equation (MD, –3.56±270.39 kcal) showed the closest results to REE measured by indirect calorimetry. However, the REE values estimated using the Harris-Benedict equation (MD, 156.64±276.54 kcal) and Ireton-Jones equation (MD, 250.87±332.54 kcal) displayed significant differences from those measured using indirect calorimetry. The concordance rate, which the predictive REE differs from the measured REE value within 10%, was up to 36.2%.Conclusions: The REE values estimated using predictive equations exhibited substantial differences from those measured via indirect calorimetry. Therefore, it is necessary to measure the REE value through indirect calorimetry in severe trauma patients.

Resting energy expenditure in children and adolescents with cerebral palsy: accuracy of available prediction formulas and development of population-specific methods.

Energy requirements are difficult to estimate in children with cerebral palsy (CP). Resting energy expenditure (REE), necessary to implement personalized nutritional interventions, is most commonly estimated using prediction formulae since indirect calorimetry, the reference method, is not available in all nutrition units. The aims of the present study were: (1) to evaluate the accuracy of the most commonly used REE prediction formulae developed for healthy children, in children with CP; (2) to assess the accuracy of the REE population-specific formula for CP children proposed in our preliminary report; (3) to develop new population-specific methods. REE was measured by indirect calorimetry in 100 children and adolescents with spastic quadriplegic cerebral palsy (SQCP) and estimated on the basis of predictive formulas selected by the clinicians [World Health Organization (WHO), Harris-Benedict, Schofield weight, Schofield weight & height, Oxford, Mifflin formulae and a population-specific formula for CP children developed in our preliminary report]. 100 children with SQCP (35 girls, 35%) classified as level V according to gross motor function classification system (GMFCS-V); 64% with oral nutrition, 29% total enteral nutrition (nasogastric tube feeding, percutaneous endoscopic gastrostomy, percutaneous endoscopic transgastric jejunostomy) and 7% mixed nutrition. The median (IQR) REE was 41.96 (17.5) kcal/kg/day.Statistical analysis highlighted a proportional bias between the indirect calorimetry and all considered predictive formulae for REE determination. By studying the relationship between the bias and the mean values of REE, specific conversion equations were obtained. With a pre-specified model having as predictors the variable weight and the variable Triceps Skinfold (TSF) and, as response the variable REE measured by indirect calorimetry, a predictive nomogram was developed to estimate the REE in this population of children. We suggest using predictive formulae for healthy children with caution, and where possible carrying out indirect calorimetry to assess REE in children with CP. However, we propose a new tool which could be developed to become an additional help for assessment of REE in the clinical practice.Future objectives will be to obtain a larger sample size, in a multicenter perspective study, to build a specific predictive model for the REE of the studied population.

New prediction equations for resting energy expenditure in older hospitalized patients: Development and validation

ObjectivesAccurate resting energy expenditure (REE) prediction is needed to prevent over- or underfeeding in older hospitalized patients. However, few validated REE prediction Equations are known for such patients. Therefore, this study aimed to develop new REE prediction Equations and evaluate their validity. MethodsThis single-center, cross-sectional study enrolled 134 patients ages ≥70 y. For holdout validation, patients were randomized in a 3:1 ratio; for the development data set, a new Equation was developed according to the measured REE using indirect calorimetry. The new and existing Equations were compared using the validation data set. ResultsMean patient age was 87.4 ± 6.9 y, and 34.3% were male. Two Equations were developed in multivariable regression models: Equation 1: REE (kcal/day) = 313.582 + Height (cm) × 3.973 + Body weight (kg) × 5.332 - Age (y) × 5.474 - (0 if male; 1 if female) × 20.012 + Calf circumference (cm) × 12.174; and Equation 2: REE (kcal/day) = 594.819 + Height (cm) × 3.760 + Body weight (kg) × 8.888 − Age (y) × 6.298 − (0 if male; 1 if female) × 16.396. The mean relative bias (95% CI) with measured REE as a reference had a small bias for Equations 1 and 2 (−0.1 [−4.1 to 3.9]% and −0.2 [−4.4 to 4.1]%, respectively); however, the Harris-Benedict, Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University, Ganpule, and body weight × 20 Equations had larger biases (−6.2 [−10.3 to −2.0]%; 5.3 [1.3 to 9.3]%; −13.9 [−18.6 to −9.3]%; and −11.6 [−16.1 to −7.1]%, respectively). ConclusionsNew prediction Equations using height, body weight, age, sex, and calf circumference improve REE prediction accuracy in older hospitalized patients.

Predicting resting energy expenditure: a critical appraisal.

The most commonly used prediction models for resting energy expenditure (REE) are Harris-Benedict (1919), Schofield (1985), Owen (1986), and Mifflin-St Jeor (1990), based on height, weight, age and gender, and Cunningham (1991), based on body composition. Here, the five models are compared with reference data, consisting of individual REE measurements (n = 353) from 14 studies, covering a large range of participant characteristics. For white adults, prediction of REE with the Harris-Benedict model approached measured REE most closely, with estimates within 10% for more than 70% of the reference population. Sources of differences between measured and predicted REE include measurement validity and measurement conditions. Importantly, a 12- to 14-h overnight fast may not be sufficient to reach post-absorptive conditions and may explain differences between predicted REE and measured REE. In both cases complete fasting REE may not have been achieved, especially in participants with high energy intake. In white adults, measured resting energy expenditure was closest to predicted values with the classic Harris-Benedict model. Suggestions for improving resting energy expenditure measurements, as well as prediction models, include the definition of post-absorptive conditions, representing complete fasting conditions with respiratory exchange ratio as indicator.

Accuracy of predictive equations for resting energy expenditure estimation in mechanically ventilated Thai patients.

Indirect calorimetry (IC) is the most precise approach for estimating calorie demand in critically ill patients. Despite this, owing to unaffordable devices, it is rarely used in practice. Predictive equations are the alternatives. To assess the accuracy of 14 predictive resting energy expenditure(REE) equations in ventilated Thai patients. We compared the accuracy and agreement of 14 equations. The equations included the American College of Chest Physicians(ACCP) equation, Harris-Benedict equation(HBE), 1.2×HBE, 1.5×HBE, Mifflin-St. Jeor(MSJ), Ireton-Jones 1992 and 2002, Penn State 2003(HBE and MSJ) and 2010, Swinamer 1990, Faisy, Brandi 1999, and 25 kcal/kg equation. An equation was ascertained as accurate if the calculated values fell within ±10% of the measured REEs. Spearman correlation coefficient, Bland-Altman method, and intraclass correlation coefficient were used to analysis. We obtained data from 24 ventilated patients undergoing REE measurement by IC. Fifty percent of them were male with a median age of 64.5 years, a median height of 160 cm, and a median body mass index of 22.95 kg/m2. The predictive precision of all equations was poor, with largely different accuracies from 6.7% to 48.1%. The most reliable equation was Penn State 2010. The ACCP, HBE, MSJ, and Penn State 2003(HBE) tended to underestimate calorie need. Contrastingly, the other equations tended to overestimate REEs. Despite a moderate degree of correlations, the Bland-Altman plots demonstrated clinically unacceptable discrepancies between measured REE and REE calculated by each equation. In ventilated Thai patients, there were no precise equations for determining REE.

Comparison of three different measurement methods to determine resting energy expenditure in patients with decompensated hepatitis B cirrhosis

Objective: To compare the differences to determine resting energy expenditure (REE) measured with indirect calorimetry and REE predicted by formula method and body composition analyzer in patients with decompensated hepatitis B cirrhosis, so as to provide theoretical guidance for the implementation of precision nutrition intervention. Methods: Patients with decompensated hepatitis B cirrhosis who were admitted to Henan Provincial People's Hospital from April 2020 to December 2020 were collected. REE was determined by the body composition analyzer and the H-B formula method. Results: were analyzed and compared to REE measured by the metabolic cart. Results A total of 57 cases with liver cirrhosis were included in this study. Among them, 42 were male, aged (47.93 ± 8.62) years, and 15 were female aged (57.20 ± 11.34) years. REE measured value in males was (1 808.14 ± 201.47) kcal/d, compared with the results calculated by the H-B formula method and the measured result of body composition, and the difference was statistically significant (P = 0.002 and 0.003, respectively). REE measured value in females was (1 496.60 ± 131.28) kcal/d, compared with the results calculated by the H-B formula method and the measured result of body composition, and the difference was statistically significant (P = 0.016 and 0.004, respectively). REE measured with the metabolic cart had correlation with age and area of visceral fat in men (P = 0.021) and women (P = 0.037). Conclusion: Metabolic cart use will be more accurate to obtain resting energy expenditure in patients with decompensated hepatitis B cirrhosis. Body composition analyzer and formula method may underestimate REE predictions. Simultaneously, it is suggested that the effect of age on REE in H-B formula should be fully considered for male patients, while the area of visceral fat may have a certain impact on the interpretation of REE in female patients.

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

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. 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. 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. 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.

A longitudinal analysis of resting energy expenditure and body composition in people with spinal cord injury undergoing surgical repair of pressure injuries: a pilot study.

Data informing energy needs of people with spinal cord injury (SCI) and pressure injuries are scarce, the impact of surgical repair unknown, and the role of body composition in healing unexplored. The study aims were to investigate resting energy expenditure (REE) over the course of pressure injury surgical repair, compare with available energy prediction equations, and explore associations between body composition and wound healing. Indirect calorimetry measured REE pre-surgery, post-surgery, at suture removal and hospital discharge. A clinically significant change was defined as +/-10% difference from pre-surgery. Eight SCI-specific energy prediction equations were compared to pre-surgery REE. Wound breakdown (Yes/No), weight, waist circumference (WC), and body composition (fat mass [FM], fat-free mass [FFM], bioimpedance spectroscopy) were measured. Twenty people underwent pressure injury surgical repair (95% male, mean age 56 ± 12 years, 70% paraplegia). Between pre-surgery and discharge, mean REE increased (+118 kcal/d, p = 0.005), but with <10% change at any timepoint. An energy prediction equation incorporating FFM showed greatest agreement (rc = 0.779, 95% CI: 0.437, 0.924). Those with wound breakdown (65%) had a higher weight (12.7 kg, 95% CI: -4.0, 29.3), WC (17.8 cm, 95% CI: -5.1, 40.7), and FM % (36.0% [IQR 31.8, 40.2] vs 26.0% [IQR 15.6, 41.3]) than those without wound breakdown, although statistical significance was not reached. The presence of pressure injuries and subsequent surgical repair did not impact REE and energy prediction equations incorporating FFM performed best. While not statistically significant, clinically important differences in body composition were observed in those with wound breakdown.

Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements.

Determining energy requirements are an important component of nutritional support for patients with malnutrition; however, the validity of prediction equations for resting energy expenditure (REE) is disputed in older hospitalized patients. We aimed to assess the validity of these equations in older hospitalized patients in Japan. This was a single-center, cross-sectional study of 100 patients aged ≥70 years, hospitalized between January 2020 and December 2021. REE was measured using an indirect calorimeter and was compared to the predicted values calculated from five REE prediction equations. The mean (95% confidence interval) measured REE was 968.1 (931.0, 1005.3) kcal/day, and the mean predicted REE was higher for the FAO/WHO/UNU (1014.3 [987.1, 1041.6] kcal/day, p = 0.164) and Schofield (1066.0 [1045.8, 1086.2] kcal/day, p < 0.001) equations and lower for the Harris-Benedict (898.6 [873.1, 924.1] kcal/day, p = 0.011), Ganpule (830.1 [790.3, 869.9] kcal/day, p < 0.001), and body weight (kg) × 20 (857.7 [821.9, 893.5] kcal/day, p < 0.001) equations. In the age group analysis, none of the predicted values were within a 10% error for more than 80% of patients aged 70−89 years and ≥90 years. The five REE prediction equations did not provide accurate estimates. Validated REE prediction equations need to be developed for older hospitalized patients.

TG/HDL Ratio Is an Independent Predictor for Estimating Resting Energy Expenditure in Adults with Normal Weight, Overweight, and Obesity

Factors that determine resting energy expenditure (REE) remain under investigation, particularly in persons with a high body mass index (BMI). The accurate estimation of energy expenditure is essential for conducting comprehensive nutrition assessments, planning menus and meals, prescribing weight and chronic disease interventions, and the prevention of malnutrition. This study aimed to: (a) determine the contribution of cardiometabolic biomarkers to the inter-individual variation in REE in persons categorized by BMI; and (b) assess the contribution of these biomarkers in the prediction of REE when persons of varying BMI status were categorized by their glycemic and metabolic syndrome status. Baseline data from 645 adults enrolled in diet intervention trials included REE measured by indirect calorimetry, body composition by dual energy X-ray absorptiometry, anthropometrics, and cardiometabolic biomarkers. Multivariate linear regression modeling was conducted to determine the most parsimonious model that significantly predicted REE by BMI category, metabolic syndrome status, and glycemic status. Modeling with the traditional predictors (age, sex, height, weight) accounted for 58-63% of the inter-individual variance in REE. When including age, sex, height, weight and fat-free mass as covariates, adding TG/HDL to regression modeling accounted for 71-87% of the variance in REE. The finding that TG/HDL is an independent predictor in estimating REE was further confirmed when participants were categorized by metabolic syndrome status and by glycemic status. The clinical utility of calculating the TG/HDL ratio not only aids health care providers in identifying patients with impaired lipid metabolism but can optimize the estimation of REE to better meet therapeutic goals for weight and disease management.

Resting energy expenditure in adolescents with Down syndrome: a comparison of commonly used predictive equations.

Adolescents with Down syndrome (DS) are two to three times more likely to be obese than their typically developing peers. When preventing or treating obesity, it is useful for clinicians to understand an individual's energy intake needs. Predictive resting energy expenditure (REE) equations are often recommended for general use in energy intake recommendations; however, these predictive equations have not been validated in youth with DS. The aim of this study was to compare the accuracy of seven commonly used predictive equations for estimating REE in adolescents who are typically developing to REE measured by indirect calorimetry in adolescents with DS. Adolescents with DS participated in a 90-min laboratory visit before 10:00a.m. after a 12-h overnight fast and a 48-h abstention from aerobic exercise. REE was measured via indirect calorimetry, and estimated REE was derived using the Institute of Medicine, Molnar, Muller and World Health Organization equations. Mean differences between the measured and predicted REE for each equation were evaluated with equivalency testing, and P-values were adjusted for multiple comparisons using the Holm method. Forty-six adolescents with DS (age: 15.5±1.7years, 47.8% female, 73.9% non-Hispanic White) completed the REE assessment. Average measured REE was 1459.5±267.8kcal/day, and the Institute of Medicine equations provided the most accurate prediction of REE with a 1.7±11.2% (13.9±170.3kcal/day) overestimation. This prediction was not statistically different from the measured REE [P-value=0.582; 95% confidence interval (CI): -64.5, 36.7], and the difference between the measured and predicted REE was statistically equivalent to zero (P-value=0.024; 90% CI: -56.1, 28.3). The results suggest that the Institute of Medicine equation may be useful in predicting REE in adolescents with DS. Future research should confirm these results in a larger sample and determine the utility of the Institute of Medicine equation for energy intake recommendations during a weight management intervention.

Comparison between predicted and measured resting energy expenditures in Korean male collegiate soccer players

This study aimed to evaluate the differences between predicted resting energy expenditure (REE), using fat-free mass (FFM)-based prediction equations, and measured REE in Korean male collegiate soccer players. Fifteen male collegiate soccer players (18-21 years) participated in this study. The REE measurements were conducted using the Douglas bag method. Body composition was measured by dual-energy X-ray absorptiometry (DXA). The differences between the measured REE and predicted REE, using the five FFM-based REE equations, were analyzed using the t-test, calculation of errors, regression analysis, and the Bland-Altman method. The Cunningham (1980) and ten Haaf and Weijs (2014) equations showed significantly overestimated REE (1,808 ± 99 kcal/d, p <0.01; 1,838 ± 103 kcal/d, p <0.01; respectively), but the Owen (1988), Taguchi (2011), and Kim (2015) equations' estimations were not significantly different from the measured REE (1,589 ± 106 kcal/d, 1,640 ± 124 kcal/d, and 1,622 ± 68 kcal/d, respectively). The Taguchi equation gave the best prediction of REE with the lowest constant error (-6 ± 125) and effect size (-0.05), and a non-significant proportional bias (p = 0.95). The Taguchi equation is recommended for predicting REE in Korean collegiate soccer players. The selection process of a REE-prediction equation must take into consideration the target population's characteristics. Future studies are recommended to evaluate the validity of the different FFM-based REE-prediction equations in various Korean athletes.

Clinical Correlates of Measured and Predicted Resting Energy Expenditure in Patients with Anorexia Nervosa: A Retrospective Cohort Study.

Resting energy expenditure (REE; i.e., the calorie amount required for 24 h during a non-active period) is an important parameter in nutritional rehabilitation of patients with anorexia nervosa (AN). This study determined whether age, body mass index, AN duration/subtype/specific symptoms/clinical severity, cognitive function alterations, and psychiatric comorbidities influenced REE or the difference between the calculated and estimated REE. Patients with AN who were followed at a daycare treatment facility between May 2017 and January 2020 (n = 138) underwent a complete assessment that included the MINI, Eating Disorder Examination Questionnaire, d2 test of attention, body fat composition by bioelectrical impedance analysis (BIA) and REE measurement by indirect calorimetry (REEIC). AN subtype (N = 66 for restrictive subtype and N = 69 for non-restrictive subtype; p = 0.005), free-fat mass (<0.001), and fat mass (<0.001) were associated with REEIC. Age (p < 0.001), height (p = 0.003), and AN duration (N = 46 for <3 years and N = 82 for ≥3 years; p = 0.012) were associated with the difference between estimated REE (using the Schebendach equation) and measured REEIC. Therefore, the Schebendach equation was adjusted differently in the two patients’ subgroups (AN duration ≤ or >3 years). Overall, REE was higher in patients with restrictive than non-restrictive AN. In the absence of BIA measures, REE-estimating equations should take into account AN duration.

A comparative study of indirect calorimetry and prediction equations in overweight and obese Chinese adults

ObjectivesTo determine the resting energy expenditure (REE) in Chinese adults using indirect calorimetry (IC), and evaluate the validity of published predictive equations for estimating REE in overweight and obese subjects comparing to IC. Material and methods181 subjects with different body mass index (BMI) were recruited. Body composition was determined by bio-electrical impedance analysis (BIA). REE was measured by IC and estimated by 29 REE prediction equations to analyze the accuracy of REE prediction equations. ResultsFor all participants combined, the Bernstein et al. and Bernestin et al. (BC) equations similarly predicted REE values between -4% and 8% (P>0.05) of measured REE. When participant data were stratified by sex, BMI, the accuracy of each regression equation is different. In male overweight and obese group, the highest accuracy of the Bernestin et al. (BC) was 65.5% and 70.4%. In female overweight and obese group, the highest accuracy of the Bernestin et al. (BC) and Bernestin et al. was 79.6% and 69.4%. ConclusionThe Bernestin et al. (BC) was closest to IC in male overweight/obese and female overweight, and the Bernestin et al was closest to IC in female obese. However, because those equations have shown considerable variability, they should be used cautiously when estimating the individual REE. When the IC method cannot be used, the Bernestin et al. (BC) and Bernestin et al are more accurate.

Comparison of Measured Versus Predicted Resting Energy Expenditure in Individuals With Excess Body Weight

ObjectivesAssessing resting energy expenditure (REE) is important for determining energy requirements. Indirect calorimetry is usually not available in clinical settings and for this reason, predictive equations for estimating REE have been developed. Body composition influences REE; therefore, body compartments such fat-free mass (FFM) and fat mass (FM) should be considered in predictive equations. The aim of this study was to compare REE measured by indirect calorimetry with REE estimated from seven predictive equations that consider body composition in individuals with excess body weight. MethodsThis was a preliminary baseline data from a randomized controlled trial. REE was measured in adults with overweight and obesity using a whole-body calorimetry unit. Dual-energy X-ray absorptiometry was used to assess FM and FFM. Measured REE (mREE) was compared to seven predictive equations that incorporated FFM and FM. Age and body composition were compared between sexes using independent-samples t-test. A paired-samples t-test was used to compare mREE versus predicted REE (pREE). Bias and limits of agreement (LOA) were accessed by Bland-Altman analysis. pREE values between 95% and 105% of mREE were considered accurate. ResultsTwenty-one adults (n = 15 females, age: 27 ± 7 years, BMI: 29.0 ± 2.9 kg/m2, mREE: 1769 ± 342 kcal/day) were assessed. Age, FM, and FFM were not different between sexes (P > 0.05). The Muller et al., 2001 (Praxis 90: 1955–1963) and Horie et al., 2007 (Obesity 19: 1090–1094) equations were considered accurate and showed small bias, but high standard deviation (33.5 ± 178.2 and –51.4 ± 208.6 kcal/day) and wide LOAs (-316 to 383 and -460 to 357 kcal/day, respectively). However, when separated by sex, only the equation by Muller et al., 2001 was accurate for males (bias: –85.3 ± 161.4; LOA: -402 to 231 kcal/day) and only the equation by Horie et al., 2007 was accurate for females (bias: 28.0 ± 162.4; LOA: -290 to 346 kcal/day). All other equations (5) underestimated mREE. ConclusionsThe equations by Muller et al., 2001 and Horie et al., 2007 can be used for estimating REE in adults with excess body weight. The use of body composition per se in not enough to ensure accuracy of predictive equations. Funding SourcesAlmased® (Almased USA, Inc., Wellington, FL, USA) and Alberta Diabetes Institute (University of Alberta, Edmonton, AB, Canada).

The Association of Predicted Resting Energy Expenditure with Risk of Breast Cancer among Postmenopausal Women in the Women's Health Initiative Cohort.

This study showed that higher resting energy expenditure (REE) was associated with higher postmenopausal breast cancer risk. REE provides energy to support cancer-associated disorders such as obesity and inflammation. Thus, studies on its association with breast cancer can help to improve our understanding of the pathophysiology of breast cancer.