Measurement Of Resting Energy Expenditure Research Articles

Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry.

Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.

Resting energy expenditure in women with polycystic ovary syndrome.

Is resting energy expenditure (REE) altered in women with polycystic ovary syndrome (PCOS)? Women with PCOS have a reduction in REE, when corrected for fat-free mass, independent of PCOS clinical phenotypes and BMI categories. Obesity is an important issue in women with PCOS, in terms of frequency and pathophysiological implications. It has been hypothesized that obesity may be favoured by alterations in REE, but the studies have been limited and conflicting. This case-control study was a comparison of 266 women with PCOS and 51 healthy controls, recruited in the Verona 3P study from 2010 to 2021. Women with PCOS diagnosed by the Rotterdam criteria, with normal thyroid function and no interfering medications, were referred to the outpatient clinic of a tertiary care centre of endocrinology and metabolism for a measurement of REE. Healthy controls were recruited in the same period and submitted to the same procedure. In all subjects, REE was measured by indirect calorimetry and serum androgens were measured by LC-MS/MS. In women with PCOS, insulin sensitivity was assessed using the hyperinsulinemic-euglycemic clamp. REE was similar in women with PCOS and controls. However, REE corrected for fat-free mass (REE/FFM) was significantly lower in women with PCOS than in controls (31.8 ± 4.0 vs 35.4 ± 3.9kcal/kgFFM·day, P < 0.001). REE/FFM did not differ between normal-weight, overweight, or obese women with PCOS, and each of these subgroups showed lower REE/FFM values than controls. Reduced REE/FFM values were found in each phenotype of the syndrome. In multiple regression analysis, REE/FFM was independently associated with age and PCOS status, but not with fat mass. In PCOS women, REE/FFM was independently and directly associated with ovarian follicle number. Limitations of the study are the cross-sectional design, which limits the causal inference of the results, and the unavailability of precise information about lifestyle factors, which may be potential confounders. Further prospective studies are needed to establish the importance of this phenomenon in contributing to the weight excess of PCOS. A reduction of REE could potentially favour weight gain in women with PCOS and possibly contribute to the altered metabolic profile typical of this condition, even counteracting the therapeutic strategies aimed to reduce excess body fat in these women. Nevertheless, the presence of this abnormality in both obese/overweight and normal-weight patients suggests that other factors must play a role in this phenomenon. This work was supported by academic grants to PM from the University of Verona (FUR 2010-2022). All authors declare no conflict of interest. N/A.

Energy expenditure estimation during activities of daily living in middle-aged and older adults using an accelerometer integrated into a hearing aid.

Accelerometers were traditionally worn on the hip to estimate energy expenditure (EE) during physical activity but are increasingly replaced by products worn on the wrist to enhance wear compliance, despite potential compromises in EE estimation accuracy. In the older population, where the prevalence of hearing loss is higher, a new, integrated option may arise. Thus, this study aimed to investigate the accuracy and precision of EE estimates using an accelerometer integrated into a hearing aid and compare its performance with sensors simultaneously worn on the wrist and hip. Sixty middle-aged to older adults (average age 64.0 ± 8.0 years, 48% female) participated. They performed a 20-min resting energy expenditure measurement (after overnight fast) followed by a standardized breakfast and 13 different activities of daily living, 12 of them were individually selected from a set of 35 activities, ranging from sedentary and low intensity to more dynamic and physically demanding activities. Using indirect calorimetry as a reference for the metabolic equivalent of task (MET), we compared the EE estimations made using a hearing aid integrated device (Audéo) against those of a research device worn on the hip (ZurichMove) and consumer devices positioned on the wrist (Garmin and Fitbit). Class-estimated and class-known models were used to evaluate the accuracy and precision of EE estimates via Bland-Altman analyses. The findings reveal a mean bias and 95% limit of agreement for Audéo (class-estimated model) of -0.23 ± 3.33 METs, indicating a slight advantage over wrist-worn consumer devices (Garmin: -0.64 ± 3.53 METs and Fitbit: -0.67 ± 3.40 METs). Class-know models reveal a comparable performance between Audéo (-0.21 ± 2.51 METs) and ZurichMove (-0.13 ± 2.49 METs). Sub-analyses show substantial variability in accuracy for different activities and good accuracy when activities are averaged over a typical day's usage of 10 h (+61 ± 302 kcal). This study shows the potential of hearing aid-integrated accelerometers in accurately estimating EE across a wide range of activities in the target demographic, while also highlighting the necessity for ongoing optimization efforts considering precision limitations observed across both consumer and research devices.

(850) - Measured Resting Energy Expenditure and Predicted Basal Metabolic Rate Early After Left Ventricular Assist Device Implantation

(850) - Measured Resting Energy Expenditure and Predicted Basal Metabolic Rate Early After Left Ventricular Assist Device Implantation

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.

Measured Resting Energy Expenditure by Indirect Calorimetry and Energy Intake in Long-Term Growth Hormone-Treated Children with PWS

Introduction: Severe obesity can develop in children with PWS when food intake is not controlled. Maintenance of body weight requires an energy balance, of which energy intake and energy expenditure are important components. Previous studies described a decreased resting energy expenditure (REE) in growth hormone (GH)-untreated children with PWS. In short-term studies, no difference in REE was found between GH-treated and untreated children with PWS. However, there are limited data on REE in children with PWS who were GH-treated for a long period. Methods: This study describes measured REE (mREE), energy intake, and body composition during long-term GH treatment in children with PWS. Patients were treated with 1.0 mg GH/m2/day (∼0.035 mg/kg/day). REE was determined by indirect calorimetry; dietary energy intake was calculated using a 3-day dietary record. Body composition by dual-energy X-ray absorptiometry (DXA) scans. Results: We included 52 GH-treated children with PWS with a mean (SD) age of 8.53 (4.35) years and a median (IQR) GH-treatment duration of 7 (4–11) years. mREE increased with age, but was not associated with GH-treatment duration. A higher LBM was associated with higher mREE. Mean energy intake was significantly lower compared to daily energy requirements (DER) for age- and sex-matched healthy children (p Conclusion: In children with PWS, mREE increases with age. GH-treatment duration is not associated, whereas LBM is an important determinant of mREE. Children with PWS have a low to very low energy intake compared to DER for age- and sex-matched children, with a declining intake when becoming older.

Trajectories of resting energy expenditure and performance of predictive equations in children hospitalized with an acute illness and malnutrition: a longitudinal study

There is scarce data on energy expenditure in ill children with different degrees of malnutrition. This study aimed to determine resting energy expenditure (REE) trajectories in hospitalized malnourished children during and after hospitalization. We followed a cohort of children in Bangladesh and Malawi (2–23 months) with: no wasting (NW); moderate wasting (MW), severe wasting (SW), or edematous malnutrition (EM). REE was measured by indirect calorimetry at admission, discharge, 14-and-45-days post-discharge. 125 children (NW, n = 23; MW, n = 29; SW, n = 51; EM, n = 22), median age 9 (IQR 6, 14) months, provided 401 REE measurements. At admission, the REE of children with NW and MW was 67 (95% CI [58, 75]) and 70 (95% CI [63, 76]) kcal/kg/day, respectively, while REE in children with SW was higher, 79 kcal/kg/day (95% CI [74, 84], p = 0.018), than NW. REE in these groups was stable over time. In children with EM, REE increased from admission to discharge (65 kcal/kg/day, 95% CI [56, 73]) to 79 (95% CI [72, 86], p = 0.0014) and was stable hereafter. Predictive equations underestimated REE in 92% of participants at all time points. Recommended feeding targets during the acute phase of illness in severely malnourished children exceeded REE. Acutely ill malnourished children are at risk of being overfed when implementing current international guidelines.

Validation of predictive equations for resting energy expenditure in children and adolescents with different body mass indexes

BackgroundAccurate estimation of resting energy expenditure (REE) in children and adolescents is important to establish estimated energy requirements. The objective of this study was to assess the validity of existing equations in literature and a newly developed equation in estimating REE in children and adolescents.Methods275 participants (148 boys, 127 girls) aged 6–18 years included in the study were classified as normal-weighted, overweight, obese based on BMI z-scores for age according to WHO-2007 growth curves for 5–19 years of age. REEs were measured using an indirect calorimeter, with various equations, and a newly established equation [REE = 505.412+(24.383*FFM);Adjusted R2 = 0.649] were compared with REE measured using Bland-Altman and further validation parameters.ResultsWhen the predicted REEs were compared with the measured REEs, the highest prediction accuracy was achieved using the new Eq. (64.8%) and IOM (63.8%) for normal-weight participants, Müller FFM and new Eq. (59.6%) for overweight participants and Lazzer (44.9%) for obese participants. In normal and overweight participants, lowest root mean squared error (RMSE) values were acquired from Schmelzle’s equation (respectively 136.2;159.9 kcal/d), and the highest values were found in Kim’s Eq. (315.2; 295.2 kcal/d respectively). RMSE value of the new equation was 174.7 kcal/d for normal-weight children and adolescents, and 201.9 kcal/d for overweight ones. In obese participants, the lowest RMSE value was obtained from Schmelzle’s Eq. (305.4 kcal/d) and the new Eq. (317.4 kcal/d), while the highest value was obtained from IOM Eq. (439.9 kcal/d). RMSE was higher in obese groups compared to the other BMI groups.ConclusionIndirect-calorimeter is the most suitable method for REE measurement in especially obese children and adolescents. The new equation and Schmelzle’s equation appear to be most accurate equations for normal and overweight children and adolescents.

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.

Measuring Energy Requirements of Traumatic Brain Injury Patients in Pediatric Intensive Care With Indirect Calorimetry: A Comparison With Empiric Methods.

Energy requirements following moderate or severe pediatric traumatic brain injury (TBI) have not been fully elucidated. Indirect calorimetry (IC) is the gold standard for measuring resting energy expenditure (MREE) in PICU. However, technical complexity limits its use. We aimed to determine whether MREE differs from standard of care energy estimation and delivery in a cohort of pediatric patients following moderate to severe TBI during PICU admission. Retrospective case series study. Single-center, 16-bed general PICU in Canada between May 2011 and January 2019. Children (0-18 yr) admitted to a PICU for moderate (Glasgow Coma Scale [GCS] 9-12) to severe TBI (GCS < 9) and had an IC study performed while mechanically ventilated. None. During the study period, 245 patients were admitted with the diagnosis of trauma with TBI. The study includes a convenience sample of 26 patients with severe ( n = 23) and moderate ( n = 3) TBI who underwent a total of 34 IC measurements. MREE varied considerably from 29% to 144% of predicted energy expenditure. Using Bland-Altman comparative analysis, neither Schofield nor World Health Organization predictive equations were in agreement with MREE. Only one measurement revealed that the patient was appropriately fed (energy provided in nutrition support was within 10% of MREE); 10 (38%) measurements revealed overfeeding and 15 (58%) underfeeding at the time of testing. The present study adds to the small body of literature highlighting the limitations of predictive equations to evaluate energy requirements following moderate to severe pediatric TBI. IC, when feasible, should be used as the preferred method to orient PICU teams to feed such vulnerable patients.

The Berlin-Buch respiration chamber for energy expenditure measurements

PurposeWe present a methodological overview of a respiration chamber at the Experimental and Clinical Research Center in Berlin, Germany. Since 2010, we investigated 750 healthy subjects and patients with various diseases. We routinely measure resting energy expenditure (REE), dietary-induced thermogenesis, and activity energy expenditure.MethodsThe chamber is a pull calorimeter with a total volume of 11,000 L. The majority of measurements is done with a flow rate of 120 L/min, yielding a favorable time constant of 1.53 h. The gas analysis system consists of two paramagnetic O2 sensors and two infrared CO2 sensors, one for incoming and one for outgoing air samples. O2 and CO2 sensors are calibrated simultaneously before each measurement with a 6 min calibration routine. To verify the accuracy of the whole the calorimetric system, it is validated every 2 weeks by 2 h acetone burning tests.ResultsValidation factors (calculated/measured) of 20 representative 2 h acetone burning tests were 1.03 ± 0.03 for dot{V}{text{O}}_{2}, 1.02 ± 0.02 for dot{V}{text{CO}}_{{2}}, 0.99 ± 0.02 for RER, and 1.03 ± 0.03 for EE. Four repeated 60 min REE measurements of a healthy woman showed variabilities of 231.9 ± 4.8 ml/min for dot{V}{text{O}}_{2} (CV 2.1%), 166.0 ± 6.3 ml/min for dot{V}{text{CO}}_{{2}} (CV 3.8%), 0.73 ± 0.03 for RER (CV 4.6%), and 4.55 ± 0.07 kJ/min for EE (CV 1.6%).ConclusionsThe data presented show that our respiration chamber produces precise and valid EE measurements with an exceptionally fast responsiveness.Graphical

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.

Triglycerides and systolic blood pressure negatively mediate the direct relationship of vitamin D status to resting energy expenditure: A cross sectional analysis

Background and aimsWe determined whether individual components of metabolic syndrome (MetS) mediated the direct association of vitamin D status (25OHD) on resting energy expenditure (REE). MethodsMultiple linear regression determined predictors of REE from data on 180 men and women from two ethnic groups. We then modelled a mediation pathway through components of MetS on the direct association between 25OHD and REE. The mediation modelling used the PROCESS SPSS Macro (version 4.0) based on 5000 bootstrapped samples, with the adjustment for different sets of covariates. ResultsREE was significantly predicted by age, fat mass (FM), fat free mass (FFM), ethnicity, inverse ln insulin, 25OHD, triglycerides (TG), systolic blood pressure (SBP) and, to some extent, by time of REE measurements (p < 0.094). Adjustment for all these covariates, resulted in a negative indirect mediation effect of TG [β coefficient (bootstrapped SE): 0.95 (0.519); bootstrapped 95% CI: 2.172, -0.165; p < 0.05] and a concurrent negative mediation of SBP [β coefficient (bootstrapped SE): 0.72(0.484); bootstrapped 95% CI: 1.851, -0.011; p < 0.05]. There remained a positive direct pathway from 25OHD to REE [β coefficient (S.E): 4.715 (2.129); p = 0.028], however the total effect of 25OHD was dampened [β coefficient (S.E): 3.04 (2.126); p = 0.154]. ConclusionsIndependent of insulin sensitivity, a negative mediation by TG and SBP dampened the overall effect of 25OHD on REE.

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.

Association of Energy Expenditure and Efficacy in Metastatic Renal Cell Carcinoma Patients Treated with Nivolumab.

Simple SummaryImmune checkpoint inhibitors have improved the tumor response and survival of patients with metastatic renal cell carcinoma. However, no predictive factor of response to immunotherapy is currently available. The aim of our study was to evaluate metabolism, assessed with resting energy expenditure as a biomarker of response, in patients with metastatic renal cell carcinoma treated with nivolumab. We found that the 15 patients (29% of the cohort) that had hypermetabolism also had decreased tumor control and tended to have a worse overall survival compared to patients with normal or low metabolism. We conclude that the measurement of resting energy expenditure could help identify patients who will benefit most from immunotherapy in metastatic renal cell carcinoma.Background: Nivolumab improved patients’ survival in metastatic renal cell carcinoma (mRCC). We aimed to evaluate resting energy expenditure (REE) (i.e., patients’ basal metabolism) to predict efficacy. Methods: We conducted a monocentric, observational study of mRCC patients receiving nivolumab between October 2015 and May 2020. REE was measured prior to initiating immunotherapy using indirect calorimetry to determine hypo, normo and hypermetabolism. Primary endpoint was 6-month, progression-free survival (PFS), and secondary endpoints were response rate, PFS and overall survival (OS). Results: Of the 51 consecutive patients, 15 (29%) were hypermetabolic, 24 (47%) normometabolic, and 12 (24%) hypometabolic. The 6-month PFS was 15% for hypermetabolic patients and 65% for non-hypermetabolic patients (p < 0.01). In the multivariate analysis, hypermetabolism was the only baseline factor predicting 6-month PFS (OR 9.91, 95%CI [1.62–60.55], p = 0.01). Disease progression was noted as the best response in 73% of hypermetabolic patients and 26% of non-hypermetabolic patients (p = 0.02). Median PFS was 2.8 and 8.7 months (p < 0.01), and median OS was 20.2 and 35.1 months (p = 0.13) in the hypermetabolic and non-hypermetabolic groups, respectively. Conclusions: Our study identifies an association between mRCC patients’ energy expenditure and nivolumab efficacy. The measurement of REE by indirect calorimetry in routine practice could help identify patients at risk of nivolumab failure.

Resting energy expenditure in children at risk of hypothalamic dysfunction.

ObjectiveChildren with suprasellar brain damage are at risk of hypothalamic dysfunction (HD). HD may lead to decreased resting energy expenditure (REE). Decreased REE, however, is not present in all children with HD. Our aim was to assess which children suspect for HD have low REE, and its association with clinical severity of HD or radiological hypothalamic damage.Patients and methodsA retrospective cohort study was performed. Measured REE (mREE) of children at risk of HD was compared to predicted REE (pREE). Low REE was defined as mREE <90% of predicted. The mREE/pREE quotient was associated to a clinical score for HD symptoms and to radiological hypothalamic damage.ResultsIn total, 67 children at risk of HD (96% brain tumor diagnosis) with a mean BMI SDS of +2.3 ± 1.0 were included. Of these, 45 (67.2%) had low mREE. Children with severe HD had a significant lower mean mREE/pREE quotient compared to children with no, mild, or moderate HD. Mean mREE/pREE quotient of children with posterior hypothalamic damage was significantly lower compared to children with no or anterior damage. Tumor progression or tumor recurrence, severe clinical HD, and panhypopituitarism with diabetes insipidus (DI) were significant risk factors for reduced REE.ConclusionREE may be lowered in children with hypothalamic damage and is associated to the degree of clinical HD. REE is, however, not lowered in all children suspect for HD. For children with mild or moderate clinical HD symptoms, REE measurements may be useful to distinguish between those who may benefit from obesity treatment that increases REE from those who would be better helped using other obesity interventions.

RARE-07. Low resting energy expenditure is associated with clinical and radiological hypothalamic damage in children surviving a suprasellar brain tumor

INTRODUCTION: Children with suprasellar brain damage are at risk for hypothalamic dysfunction (HD). HD may lead to decreased resting energy expenditure (REE) contributing to the development of hypothalamic obesity. Decreased REE, however, is not present in all children with hypothalamic damage. Our aim was to assess which children suspect for HD have low REE, and if REE outcome can be associated with clinical severity of HD or with radiological posterior hypothalamic damage. METHODS: A retrospective cohort study was performed evaluating all children diagnosed with brain injury at risk for HD in whom REE measurement was performed. Measured REE (mREE) was compared to predicted REE (pREE) using amongst others the Schofield equation. Low REE was defined as mREE<90% compared to pREE. Radiologic hypothalamic damage was scored using Muller grading score. The mREE/pREE quotient was associated to a clinical score for HD symptoms and to radiological hypothalamic damage. RESULTS: Sixty-seven children suspected for HD (94% brain tumor diagnosis) with a mean BMI SDS of +2.3 ± 1.0 were included. Of these, 45 (67.2%) had mREE <90% compared to the pREE. Children with severe HD symptoms had a significant lower mean mREE/pREE quotient compared to children with no, mild, or moderate HD symptoms. Mean mREE/pREE quotient of children with posterior hypothalamic damage was significantly lower compared to children with no damage or with anterior damage. Tumor progression or tumor recurrence, severe clinical HD, and panhypopituitarism with DI were significant risk factors for reduced REE. CONCLUSION: Not all children suspect for HD have a low REE. Low REE is associated with clinical and radiological scores for hypothalamic damage. REE measurements in childhood brain tumor survivors may be useful to distinguish between those who may benefit from obesity treatment that increases REE from those who would be better helped using other obesity interventions.

Clinical evaluation of the new indirect calorimeter in canopy and face mask mode for energy expenditure measurement in spontaneously breathing patients

The new indirect calorimeter developed in the framework of the ICALIC project was first evaluated in ventilation mode. This second phase aimed to compare its ease of use and precision with another commonly used device in spontaneously breathing adult patients using a canopy hood or a face mask. The time required to measure resting energy expenditure (REE) with Q-NRG® in canopy and face mask mode was compared with Quark RMR® in canopy mode by sequential measurements in 45 and 40 spontaneously breathing adult patients, respectively. Their precision was assessed at different time intervals, using coefficients of variation (CV%) and repeated measures one-way ANOVA. Agreement between the two devices was evaluated by correlation coefficients, Bland-Altman plots, and paired t-test. Patients' characteristics potentially affecting the measurement were assessed using linear regression analysis. REE measurement with Q-NRG® was faster than Quark RMR® (19.7±2.9min vs 24.5±4.3min, P<0.001). In canopy mode, Q-NRG® gave values similar to Quark RMR®, with 73% of patients achieving a steady state (CV% <10%) within the 5-15min interval. In face mask mode, Q-NRG® was less stable than Quark RMR® in canopy mode, and steady state was achieved in only 40% of the patients within the 5-15min interval. Correlation between the two devices was stronger when Q-NRG® was used in canopy than in face mask mode, with Pearson coefficients of 0.96 and 0.86, respectively. Compared to Quark RMR® in canopy mode, systematic bias±1.96∗SD with Q-NRG® was-14±236kcal/day in canopy and 73±484kcal/day in face mask mode. Q-NRG® in face mask mode overestimated REE by 150±51kcal/day in men compared to Quark RMR® in canopy mode. Q-NRG® in canopy mode made it possible to save at least 5min compared to Quark RMR® while maintaining the same measurement precision. However, its use in face mask mode could lead to REE overestimation in men and, therefore, should not be recommended in the clinical setting. ClinicalTrials.gov no. NCT03947294.

Measuring the Resting Energy Expenditure in Children on Extracorporeal Membrane Oxygenation: A Prospective Pilot Study.

Both overfeeding and underfeeding critically ill children are problematic. This prospective pilot study evaluated the resting energy expenditure in infants and children requiring extracorporeal membrane oxygenation (ECMO) support. An indirect calorimeter was used to measure oxygen consumption (VO 2 ) and carbon dioxide production (VCO 2 ) from the mechanical ventilator. Blood gases were used to determine VO 2 and VCO 2 from the ECMO circuit. Values from the mechanical ventilator and ECMO circuit were added, and the resting energy expenditure (REE) (Kcal/kg/day) was calculated. Measurements were obtained > 24 hours after ECMO support was initiated (day 2 of ECMO), 1 day before ECMO discontinuation or transfer, and 1 day after decannulation. Data were compared with the predicted energy expenditure. Seven patients aged 3 months to 13 years were included. The REE varied greatly both above and below predicted values, from 26 to 154 KCal/kg/day on day 2 of ECMO support. In patients with septic shock, the REE was > 300% above the predicted value on day 2 of ECMO. Before ECMO discontinuation, two of six (33%) children continued to have a REE > 110% of predicted. Three patients had measurements after decannulation, all with a REE < 90% of predicted. REE measurements can be obtained by indirect calorimetry in children receiving ECMO support. ECMO may not provide metabolic rest for all children as a wide variation in REE was observed. For optimal care, individual testing should be considered to match calories provided with the metabolic demand.

Energy expenditure profiles and the risk of early limiting toxicity in older patients with cancer: The ELCAPA-25 prospective cohort survey

Predicting the risk of early limiting toxicity (ELT) is major challenge for the clinician seeking an effective, safe treatment for older patients with cancer. The Cancer and Aging Research Group (CARG) and CRASH (Chemotherapy Risk Assessment Scale for High-Age Patients) toxicity scores were designed to predict chemotherapy-related toxicity. Elevated resting energy expenditure (REE) may predispose to cachexia and increase ELT and mortality in older patients with cancer. The primary objective was to assess the association between elevated REE and ELT in older patients with cancer. The secondary objectives were to assess the discriminant ability of a predictive model including REE (relative to the CARG and CRASH scores) and the prognostic value of elevated REE. We assessed patients aged 70 or over included in the prospective ELCAPA cohort between 2014 and 2018. The inclusion criteria were a solid tumour, a measurement of REE at baseline (mREE, by indirect calorimetry), and a geriatric assessment prior to cancer treatment in a teaching hospital (Paris, France). The mREE was compared with the predicted REE (pREE), as defined by the Harris-Benedict equation. Depending on the mREE/pREE ratio, study participants were classified as hypermetabolic, hypometabolic or normometabolic. The primary endpoint was 3-month ELT, defined as any unplanned hospitalization or any event leading to dose reduction, a treatment delay of more than 7 days, or treatment discontinuation within 3 months of initiation. The secondary endpoint was the 3-month mortality rate. A total of 179 patients were included. The median age was 80 [interquartile range: 76-84] years, 37% of the patients were female, 81.8% had metastatic disease, 67.6% received chemotherapy, 20.7% received hormone therapy, and 11.7% received targeted therapies. According to the mREE/pREE ratio, 85 patients (47%) were hypermetabolic, 63 (35%) were normometabolic, and 31 (18%) were hypometabolic. Sixty patients (33.5%; 95% confidence interval (CI): 26.7-40.9) experienced ELT. The discriminant ability (as assessed by the C-index) of a multivariate model including REE and adjustment factors was 0.82 [95%CI: 0.73-0.91]. In comparison, the discriminant ability of the CARG and CRASH models was 0.57 [0.45-0.68] and 0.51 [0.40-0.62], respectively. In our model, hypermetabolism was an independent risk factor for ELT (adjusted odds ratio=2.44; 95%CI: 1.02-5.80). Other risk factors were the cancer type and stage, the treatment protocol, a clinical diagnosis of depression, the presence of grade 3 or 4 comorbidities, and the serum lactate dehydrogenase level. Hypermetabolism status is an independent predictor of ELT in older patients with cancer, relative to normometabolic status. Baseline REE measurement might improve the ELT risk assessment and decision-making process.