Predictive Equations For Resting Energy Expenditure Research Articles

DEVELOPMENT OF NEW PREDICTIVE EQUATIONS FOR ESTIMATING RESTING ENERGY EXPENDITURE IN ADULTS WITH CROHN'S DISEASE

Increased resting energy expenditure (REE) has been hypothesized to be a potential cause of weight loss in individuals with Crohn's Disease (CD), mainly due to inflammatory response. This study aimed to develop and validate new predictive equations for REE in adults with CD. Adult patients aged between 18-65 years and with CD were recruited. Anthropometry, indirect calorimetry and bio-impedance analysis (BIA) were performed in all patients. New predictive equations were generated using two models: Model 1 with age, weight, height and CDAI as predictors, and Model 2 in which both bioimpedance-index (BI-index) and phase angle were added. Finally, accuracy prediction within ±10% was assessed and then compared with several published equations. A total of 270 CD patients (159 males, 111 females) were included and assigned to the calibration (n=180; age: 37.9±13years; BMI: 22.2±3.4kg/m2) and the validation groups (n=90; age: 38.5±13years; BMI: 22.7±3.7kg/m2). REE was directly correlated with weight (r=0.733, p=0.000) and BI-index (r=0.762, p=0.000). Models were both suitable for estimating REE at population level (bias: -0.2, bias: -0.3; respectively). Individual accuracy was high in both models (∼80%; 83%;respectively), unrelated to gender. Similarly, the Harris and Benedict, FAO and Schofield equations showed a good accuracy in males and females. Therefore, new equations specifically derived from CD patients provided a very good prediction of REE at population level. Interestingly, the formula based on raw-BIA variables was as accurate as those based on anthropometry, unrelated to gender. However, further studies are required to verify the application of those formulas and the role of raw-BIA variables in REE prediction.

Resting Energy Expenditure In Crossfit® Practitioners: Indirect Calorimetry Versus Predictive Equations

BACKGROUND: Indirect calorimetry (IC) is the gold standard method to assess individual resting energy expenditure (REE). However, due to its high cost and time demand, predictive equations are largely used to estimate energy requirements, which may vary according to different body compositions and health status. Crossfit® is a training program created by Greg Glessman in 1995 and consists of performing high intensity functional movements. Some studies have already reported increase of metabolic rates in this population. PURPOSE: To measure REE in Crossfit® practitioners, using IC, and verify the most appropriate predictive equation to estimate this variable. METHODS: 142 Crossfit® practitioners, 91 female (64.1%), aged between 16-59 years, underwent nutritional assessment, including weight, height, waist circumference (WC) and body mass index (BMI). Body composition was measured by a portable ultrasound. REE was measured by IC (mREE) and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980), Cunningham (1991), and Mifflin-St.Jeor. Statistical analysis were carried out by Kolmogorov-Smirnov Test, Student’s t test and Bland and Altman plots. RESULTS: The mean age was 33.0 ± 6.3 years of age, without difference between men and women. The mean BMI was 24.7 ± 3.5 kg/m2. The mean mREE was 1583.2 ± 404.4 kcal, and the pREE ranged from 1455.5 ± 230.9 kcal to 1711.3 ± 285.5. The best REE predictive equations for Crossfit® practitioners were the Cunningham (1991) (P=0.338), WHO equation (P=0.494) and Harris-Benedict (P=0.705). Harris-Benedict presented the smaller difference comparing with IC (12.9 ± 307.6 kcal), Cunningham (1991) showed better adequacy (102.5%) and WHO equation presented greater percentage of accuracy (59.9%). The same equations remained adequate when data were stratified by gender. CONCLUSIONS: This study showed that Cunningham (1991), WHO (1985) and Harris-Benedict (1919) equations were the most appropriated REE equations for Crossfit® practitioners. Further studies should investigate more suitable methods to determine the energy requirements in Crossfit® and should, perhaps, create and propose a specific equation for this population.

RESTING ENERGY EXPENDITURE IN PARKINSON'S DISEASE PATIENTS UNDER DOPAMINERGIC TREATMENT

Weight homeostasis is complex in Parkinson's disease (PD) and body weight changes substantially throughout the course of the disease. We designed a case-control study to i) investigate whether PD is associated with changes in resting energy expenditure (REE), ii) to assess the accuracy of REE predictive equations for healthy people in PD and iii) to eventually construct a new formula. Measured REE (mREE) was compared between 122 PD patients and 122 gender and body mass index (BMI)-matched controls. The accuracy of estimated REE by 5 common equations (Harris/Benedict-1919, Harris/Benedict-1984, Mifflin St. Jeor, WHO/FAO and aggregate formula) was investigated in PD using Bland-Altman analysis and reported as the frequency of accurate predictions (±10%). Concordance correlation coefficients (CCC) were also calculated. Then, we regressed a new REE equation - using gender, age, weight, height and Hoehn-Yahr stage - and validated it in an independent sample (N=100). No significant difference in mREE was recorded between the whole PD sample and healthy controls. However, mREE was increased in patients with BMI≥30 kg/m2 and Hoehn-Yahr stage≥3. Available REE equations showed limited accuracy (accurate prediction [±10%] frequency, <60% for all). For the new equation the proportion of accurate prediction was 67.0% (overestimation, 24.0%) and CCC was 0.77. PD patients are not commonly characterized by an increase in REE. This is limited to patients suffering from obesity and more severe disease. Common REE equations appear to be inaccurate. The new formula provided better REE estimates and its use can be proposed.

Predictive Equations Based on Body Composition for Resting Energy Expenditure Estimation in Adults with Obesity.

An accurate estimation of Resting Energy Expenditure (REE) in patients with obesity is crucial. Therefore, our aim was to assess the validity of REE predictive equations based on body composition variables in treatment-seeking Arab adults with obesity. Body composition and REE were measured by Tanita BC-418 bioimpedance and Vmax Encore 229 IC, respectively, and predictive equations based on fat mass and fat-free mass were used in REE estimations among 87 adults of both genders, in the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). The mean differences between the measured and estimated REE values were calculated to assess the accuracy, and the Bland-Altman method was used to assess the level of agreement. Ten predictive equations were included. In males, all the predictive equations gave significantly different estimates of REE when compared to that measured by IC. On the other hand, in females, the mean difference between the REE value estimated by Huang and Horie-Waitzberg equations and that measured using IC was not significant, and the agreement was confirmed using Bland-Altman plots. Huang and Horie-Waitzberg equations are suggested for accurate REE estimation in females; however, new validated REE estimation equations for males in this population are still needed.

Energy Expenditure in 21-Hydroxylase Congenital Adrenal Hyperplasia Patients and Comparison with Predictive Equations

Objective: To characterize resting energy expenditure (REE) in patients with classic 21-hydroxylase congenital adrenal hyperplasia (21-OH CAH) using indirect calorimetry and compare it to the most commonly used REE predictive equations. Methods: This case-control study comprised 29 post-pubertal 21-OH CAH patients regularly followed at the University of Campinas. Elevated serum 17-hydroxyprogesterone and CYP21 gene molecular analysis confirmed the diagnosis. A healthy control group paired by age, gender, and body mass index was examined. Dual-energy X-ray absorptiometry (DEXA) measured body compositions. A bioimpedance analyzer determined fat-free mass, and indirect calorimetry using a metabolic cart measured REE. Results: Unlike our initial hypothesis, REE was similar between the groups (18.7 ± 3.1 kcal/kg/day in CAH vs. 20.3 ± 3.5 kcal/kg/day in controls; P = .728). No predictive equations reached the stipulated accuracy criteria, thus lacking validity in REE assessment in adults with the characteristics of the group studied. DEXA analysis revealed higher body fat and diminished nonbone lean mass in 21-OH CAH. Anthropometric and bioelectrical impedance parameters were not significantly different. Conclusion: Classic 21-OH CAH is generally followed in reference centers, which may facilitate indirect calorimetry use for REE measurement. Alternatively, considering our REE findings in adult 21-OH CAH patients, nutrition management based on 25 kcal/body weight/day (measured REE × activity factor 1.2 to 1.3) may be reasonable for current body weight maintenance in these patients. Abbreviations: 17-OHP = 17-hydroxyprogesterone; 21-OH CAH = classic 21-hydroxylase deficiency congenital adrenal hyperplasia; BMI = body mass index; REE = resting energy expenditure; VO2 = volume of oxygen; VCO2 = volume of carbon dioxide.

A systematic review and quantitative analysis of resting energy expenditure prediction equations in healthy overweight and obese children and adolescents.

Resting energy expenditure (REE) estimates are often needed in young people and can be predicted using prediction equations based on body weight. However, these equations may perform poorly in those who are obese and overweight. The aim of this systematic review was to identify equations based on simple anthropometric and demographic variables that provide the most accurate and precise estimates of REE in healthy obese and overweight young people. Systematic searches for relevant studies in healthy obese and overweight young people aged ≤18years were undertaken using PubMed, Scopus, Cinahl, OpenGrey and Cochrane Library (completed January 2018). Search terms included metabolism, calorimetry, obesity and prediction equation. Data extraction, study appraisal and synthesis followed PRISMA guidelines. From 390 screened titles, 13 studies met inclusion criteria. The most accurate REE predictions (least biased) were provided by Schofield equations [+0.8% (3-18years); 0% (11-18years); +1.1% (3-10years)]. The most precise REE estimations (percentage of predictions±10% of measured) for 11-18years were provided by Mifflin equations (62%) and, for 7-18years, by the equations of Schmelzle (57%), Henry (56%) and Harris Benedict (54%). Precision of Schofield predictions was 43% in both age groups. No accuracy data were available for those <3years or for precision for those <7years. No single equation provided accurate and precise REE estimations in this population. Schofield equations provided the most accurate REE predictions so are useful for groups. Mifflin equations provided the most precise estimates for individuals aged 11-18years but tended to underestimate REE.

Accuracy of Resting Energy Expenditure Predictive Equations in Patients With Cancer.

Our purpose was to assess the accuracy of resting energy expenditure (REE) equations in patients with newly diagnosed stage I-IV non-small cell lung, rectal, colon, renal, or pancreatic cancer. In this cross-sectional study, REE was measured using indirect calorimetry and compared with 23 equations. Agreement between measured and predicted REE was assessed via paired t-tests, Bland-Altman analysis, and percent of estimations ≤ 10% of measured values. Accuracy was measured among subgroups of body mass index (BMI), stage (I-III vs IV), and cancer type (lung, rectal, and colon) categories. Fat mass (FM) and fat-free mass (FFM) were assessed using dual x-ray absorptiometry. Among 125 patients, most had lung, colon, or rectal cancer (92%, BMI: 27.5±5.6kg/m2 , age: 61±11years, REE: 1629±321kcal/d). Thirteen (56.5%) equations yielded REE values different than measured (P<0.05). Limits of agreement were wide for all equations, with Mifflin-St. Jeor equation having the smallest limits of agreement, -21.7% to 11.3% (-394 to 203 kcal/d). Equations with FFM were not more accurate except for one equation (Huang with body composition; bias, limits of agreement: -0.3±11.3% vs without body composition: 2.3±10.1%, P<0.001). Bias in body composition equations was consistently positively correlated with age and frequently negatively correlated with FM. Bias and limits of agreement were similar among subgroups of patients. REE cannot be accurately predicted on an individual level, and bias relates to age and FM.

The validity of resting energy expenditure predictive equations in adults with central obesity: A sub-sample of the RaNCD cohort study.

There are different equations for estimating Resting Energy Expenditure (REE). However, these equations were mainly developed based on populations of western countries. The present study was conducted to determine the validity of REE predictive equations in adults with central obesity. This study was conducted with 129 adults with central obesity aged 35-65 years, a sub-sample from a large cohort study (Western Iran), Kurdish population. REE was measured by indirect calorimetry (IC) and REE predictive equations. Data were analysed using Pearson correlation, paired t-test, concordance correlation coefficient (CCC), mean squared deviation (MSD), level of agreement (LOA) and Bland-Altman plot. All REE predictive equations had low CCC and high LOA. Although there was no statistically significant difference in the REE measured with IC and the REE predicted with the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), FAO/WHO/UNU (Height), Muller and revised Harris-Benedict equations (P = 0.874, 0.113, 0.619, 0.143 and P = 0.121), other equations had statistically significant differences with IC (P<0.001). In addition, the highest correlation was found between the IC (r = 0.682). The least difference was related to the FAO/WHO/UNU equation, with an agreement limit of -507.96 to 500.79 Kcal/day, with a 95% confidence interval. The results of this study showed that the FAO/WHO/UNU, Muller, revised Harris-Benedict equations and Mifflin St Jeor equations are relatively acceptable for estimating REE. However, these prediction equations are not good at predicting REE; more precise equations are needed to apply for different ethnic groups.

The use of whole body calorimetry to compare measured versus predicted energy expenditure in postpartum women

Accurate assessment of energy expenditure may support weight-management recommendations. Measuring energy expenditure for each postpartum woman is unfeasible; therefore, accurate predictive equations are needed. This study compared measured with predicted resting energy expenditure (REE) and total energy expenditure (TEE) in postpartum women. This was a longitudinal observational study. REE was measured at 3 mo postpartum (n=52) and 9 mo postpartum (n=49), whereas TEE was measured once at 9 mo postpartum (n=43) by whole body calorimetry (WBC). Measured REE (REEWBC) was compared with 17 predictive equations; measured TEE plus breast milk energy output (ERWBC) was compared with the estimated energy requirements/Dietary Reference Intakes equation (EERDRI). Fat and fat-free mass were measured by dual-energy X-ray absorptiometry. Group-level agreement was assessed by the Pearson correlation, paired t test, and Bland-Altman (bias) analyses. Individual-level accuracy was assessed with the use of Bland-Altman limits of agreement, and by the percentage of women with predicted energy expenditure within 10% of measured values ("accuracy"). The cohort was primarily Caucasian (90%). At a group level, the best equation predicting REEWBC was the DRI at 3 mo postpartum (-7 kcal, -0.1%; absolute and percentage bias, respectively), and the Harris-Benedict at 9 mo postpartum (-17 kcal, -0.5%). At an individual level, the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) height and weight equation was the most accurate at 3 mo postpartum (100% accuracy) and 9 mo postpartum (98% accuracy), with the smallest limits of agreement. Equations including body composition variables were not more accurate. Compared with ERWBC, EERDRI bias was -36 kcal, with inaccurate predictions in 33% of women. Many REE predictive equations were accurate for group assessment, with the FAO/WHO/UNU height and weight equation having the highest accuracy for individuals. EERDRI performed well at a group level, but inaccurately for 33% of women. A greater understanding of the physiology driving energy expenditure in the postpartum period is needed to better predict TEE and ultimately guide effective weight-management recommendations.

Congruent Validity of Resting Energy Expenditure Predictive Equations in Young Adults.

Having valid and reliable resting energy expenditure (REE) estimations is crucial to establish reachable goals for dietary and exercise interventions. However, most of the REE predictive equations were developed some time ago and, as the body composition of the current population has changed, it is highly relevant to assess the validity of REE predictive equations in contemporary young adults. In addition, little is known about the role of sex and weight status on the validity of these predictive equations. Therefore, this study aimed to investigate the role of sex and weight status in congruent validity of REE predictive equations in young adults. A total of 132 young healthy adults (67.4% women, 18–26 years old) participated in the study. We measured REE by indirect calorimetry strictly following the standard procedures, and we compared it to 45 predictive equations. The most accurate equations were the following: (i) the Schofield and the “Food and Agriculture Organization of the United Nations/World Health Organization/United Nations” (FAO/WHO/UNU) equations in normal weight men; (ii) the Mifflin and FAO/WHO/UNU equations in normal weight women; (iii) the Livingston and Korth equations in overweight men; (iv) the Johnstone and Frankenfield equations in overweight women; (v) the Owen and Bernstein equations in obese men; and (vi) the Owen equation in obese women. In conclusion, the results of this study show that the best equation to estimate REE depends on sex and weight status in young healthy adults.

Accuracy and Validity of Resting Energy Expenditure Predictive Equations in Middle-Aged Adults.

Indirect calorimetry (IC) is considered the reference method to determine the resting energy expenditure (REE), but its use in a clinical context is limited. Alternatively, there is a number of REE predictive equations to estimate the REE. However, it has been shown that the available REE predictive equations could either overestimate or underestimate the REE as measured by IC. Moreover, the role of the weight status in the accuracy and validity of the REE predictive equations requires further attention. Therefore, this study aimed to determine the accuracy and validity of REE predictive equations in normal-weight, overweight, and obese sedentary middle-aged adults. A total of 73 sedentary middle-aged adults (53% women, 40–65 years old) participated in the study. We measured REE by indirect calorimetry, strictly following the standard procedures, and we compared it with the values obtained from 33 predictive equations. The most accurate predictive equations in middle-aged sedentary adults were: (i) the equation of FAO/WHO/UNU in normal-weight individuals (50.0% of prediction accuracy), (ii) the equation of Livingston in overweight individuals (46.9% of prediction accuracy), and (iii) the equation of Owen in individuals with obesity (52.9% of prediction accuracy). Our study shows that the weight status plays an important role in the accuracy and validity of different REE predictive equations in middle-aged adults.

Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients.

The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m2) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of −89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/−19.17 and 66%/−21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.

Accuracy of total energy expenditure predictive equations after a massive weight loss induced by bariatric surgery

Predictive equations remain the clinical tool of choice to estimate the energy expenditure, however, poor accuracy has been found when applied in patients with severe obesity. The aim of this study was to test the accuracy of the total energy expenditure (TEE) those obtained by predictive equations of resting energy expenditure (REE) times individual estimates of metabolic equivalents (MET), taking as reference the TEE measured by doubly labeled water (DLW), before, six and twelve months after bariatric surgery. Twenty class III obese women (age: 29.4±5.1 years; BMI: 44.9±2.5kg/m2), approved for Roux-en-Y gastric bypass participated in this study. TEE and body composition was measured after administration of a fixed dose of DLW. Predictive equations of REE were selected: Dietary Reference Intake (DRI), World Health Organization, Oxford, Harris-Benedict, Mifflin, De Lorenzo, Lazzer and Muller and their collaborators. The MET values were obtained individually by triaxial accelerometer. The patients showed 65% of excess weight loss. The body fat mass decreased 17% after 1 year of surgery. TEE (2930±525kcal.day-1) decreased by 20% (p<0.05) by the sixth postoperative month (2319±430kcal.day-1), increasing 10% by the twelfth month (2538±336kcal.day-1). The Harris and Benedict (accuracy: 65%) and DRI equations (accuracy: 60%) yielded better results in the prediction of TEE values at pre-surgery. In the sixth month after surgery, only the equation of Harris and Benedict kept accuracy above 50%. At twelve months post-surgery, only the equation of Lazzer etal. considering body composition showed better prediction (accuracy: 50%) in this period. None of the prediction equations tested was accurate for estimating TEE for the 3 periods evaluated; however, while there are no reports of specific equations for class III obese women, the Harris and Benedict x MET and DRI equations, can describe the TEE with acceptable accuracy. After surgery, the best equation to be used will depend if the patient has been treated for weight loss and in which post-treatment period it is used. Registration of clinical trial as an observational study in Brazilian Clinical Trials Registry: RBR-8k5jsj. Universal Trial Number: U1111-1206-0858.

Modifications of Resting Energy Expenditure After Sleeve Gastrectomy.

Resting energy expenditure (REE) declines more than what is expected according to body composition changes after caloric restriction. This metabolic adaptation is considered one of the factors favoring weight regain. The aim of this study is to evaluate the changes of REE and calculate the degree of metabolic adaptation occurring after laparoscopic sleeve gastrectomy (LSG). REE (by indirect calorimetry) and body composition (fat-free mass or FFM, fat mass or FM by bioelectrical impedance analysis) were determined before and after 12months in 154 patients with obesity treated with laparoscopic sleeve gastrectomy (LSG). Weight loss was 29.8 ± 10.6%, with corresponding relative reductions in FM (44.5 ± 22.8%), FFM (13.7 ± 9.9%), and REE (27.3 ± 12.9%). A predictive equation for REE was computed by using the baseline FFM and FM values to account for body composition changes. A predicted post-weight loss REE was calculated by using this equation and entering post-weight loss body composition values. Observed post-surgery REE was significantly lower than predicted one (1410 ± 312 vs 1611 ± 340kcal/day, P < 0.001) and metabolic adaptation, calculated as the difference between observed and predicted post-weight loss REE, was - 199 ± 238kcal/day. The post-surgery level of metabolic adaptation was inversely related to postoperative percent weight loss (r = - 0.170; P < 0.05) and FM loss (r = - 0.245; P < 0.01). A significant reduction of resting energy expenditure and a significant degree of metabolic adaptation both occur after sleeve gastrectomy. A greater metabolic adaptation could be partly responsible for a lower weight loss after surgery.

Energy Requirements in Critically Ill Patients.

During the management of critical illness, optimal nutritional support is an important key for achieving positive clinical outcomes. Compared to healthy people, critically ill patients have higher energy expenditure, thereby their energy requirements and risk of malnutrition being increased. Assessing individual nutritional requirement is essential for a successful nutritional support, including the adequate energy supply. Methods to assess energy requirements include indirect calorimetry (IC) which is considered as a reference method, and the predictive equations which are commonly used due to the difficulty of using IC in certain conditions. In this study, a literature review was conducted on the energy metabolic changes in critically ill patients, and the implications for the estimation of energy requirements in this population. In addition, the issue of optimal caloric goal during nutrition support is discussed, as well as the accuracy of selected resting energy expenditure predictive equations, commonly used in critically ill patients.

New equations to estimate resting energy expenditure in obese adults from body composition

The aims of this study were: to develop new equations for predicting resting energy expenditure (REE) in obese Italian subjects according to body composition parameters; to compare them with predicted values estimated by other REE prediction equations; and to cross-validate our equations using a validation set cohort. Four hundred patients were enrolled and divided into three groups. Besides anthropometry and REE (indirect calorimetry), total body fat and lean were evaluated by dual X-ray absorptiometry, and fat mass and fat-free mass by bioelectrical impedance analysis. The subjects eligible to participate were 330. Group 1 (n=174) was used to develop (R 2=0.79) and (R 2=0.77). Group 2 (n=115) was used to generate (R 2=0.85) and (R 2=0.81). Group 3 (n=41) was used to cross-validate the equations. Equations1 and 3 are reliable to measure REE from calorimetry and better than other equations that use anthropometric variables as predictors of REE. Further analysis in different populations is required before it can be applied in clinical practice.

OR25: Validity of Predictive Equations for Resting Energy Expenditure for Overweight Older Adults with and without Diabetes

Rationale: Predictive equations for resting energy expenditure (REE) are used in the treatment of overweight and obesity, but the validity of these equations in overweight older adults is unknown. This study evaluates which predictive REE equation is the best alternative to indirect calorimetry in overweight older adults with and without diabetes. Methods: In total 273 adults aged ≥55 years with a BMI of ≥25 kg/m2 were included. REE (by indirect calorimetry), body weight, body height, age, gender, and fat-free and fat mass (from air-displacement plethysmography) were measured. The measured REE was used as a reference and compared with 28 existing REE equations. The accuracy of the equations was evaluated by the percentage accurate predictions (within 10% of REE measured), the root mean squared error (RMSE), and the mean percentage difference (bias) between predicted and measured REE. Subgroup analyses were performed for type 2 diabetics (T2D) and non-T2D. Results: Mean age was 64 ± (SD 6) years, 42% had T2D (n = 116), and mean BMI was 32.8 ± (SD 4.5) with range 25–54 kg/m2. The adjusted Harris & Benedict (1984) provided the highest percentage accurate predictions in all adults (70%) and in T2D (74%), and second best in non-T2D (67%). RMSE was 184, 175 and 191 kcal/day, and bias −1.2%, −1.5% and −1.0% for all adults, T2D and non-T2D, respectively. Conclusion: For Dutch overweight older adults with and without diabetes the adjusted Harris–Benedict (1984) predictive equation for REE seems to be the best alternative to indirect calorimetry.

An artificial neural network to predict resting energy expenditure in obesity

The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50kgm-2) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision=68.1%, MAPE=8.6% and RMSPE=210kcal/d), independently from BMI subgroups within 30-50kgm-2. External validation confirmed the better predictive performances of ANN model (precision=73% and 65%, MAPE=7.7% and 8.6%, RMSPE=187kcal/d and 200kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive equations independent from BMI subgroups. For convenient use in clinical settings, we provide a simple ANN-REE calculator available at: https://www.crnh-rhone-alpes.fr/fr/ANN-REE-Calculator.

Validation of energy intake from a web-based food recall for children and adolescents.

The purpose of this study was to validate estimated energy intake from a web-based food recall, designed for children and adolescents. We directly compared energy intake to estimates of total energy expenditure, calculated from accelerometer outputs, combined with data on weight and sex or resting energy expenditure prediction equations. Children (8–9 years) and adolescents (12–14 years) were recruited through schools in Norway in 2013 (N = 253). Results showed that more than one third (36–37%) were identified as under-reporters of energy. In contrast, only 2–4% were defined as over-reporters of energy. The mean energy intake was under-reported with -1.83 MJ/day for the entire study sample. Increased underestimation was observed for overweight and obese participants, the oldest age group (12–14 years), boys, those with parents/legal guardians with low educational level and those living in non-traditional families. In conclusion, energy intake from the web-based food recall is significantly underestimated compared with total energy expenditure, and should be used with caution in young people.

Resting energy expenditure in male athletes with a spinal cord injury

Objective: To investigate whether there are differences in the resting energy expenditure (REE) and body composition of athletes with a spinal cord injury (SCI) compared to active able-bodied controls.Design: In this cross sectional study, male athletes with a SCI were compared to active able-bodied controls matched for age, stretch stature and body mass. In addition, the accuracy of standard REE prediction equations in estimating REE was assessed.Participants: Seven male wheelchair athletes with a SCI and six matched active able-bodied controls volunteered to participate.Outcome measures: REE was measured using indirect calorimetry and estimated using population-specific prediction equations. Body composition (lean tissue mass, fat mass and bone mineral content) was measured by dual energy X-ray absorptiometry (DXA).Results: While absolute and adjusted REE in the athletes with SCI was lower than controls, this difference was not significant (P = 0.259). When adjusted for lean tissue mass (LTM), REE was significantly higher (P = 0.038) in the athletes with SCI compared to the controls (146 ± 29kJ/kg LTM vs. 125 ± 8kJ/kg LTM). LTM was significantly lower in the athletes with SCI (44.35 ± 6.98 kg) compared to the able-bodied controls (56.02 ± 4.93 kg; P < 0.01). The differences between predicted and measured REE in the athletes with SCI were not statistically significant (except for the Owen equation), however there was no significant correlation between the measures.Conclusion: This suggests that existing prediction equations used to estimate energy requirements may require modification for athletes with SCI.