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OBJECTIVE: To map the international methods used to measure energy expenditure of adults living with motor neuron disease (MND) and to highlight discrepancies when indicating hypermetabolism in the MND literature. BACKGROUND: A decline in the nutritional status of patients is associated with exacerbated weight loss and shortened survival. Assessments of energy expenditure, using a variety of methods, are important to ensure an adequate energy intake to prevent malnutrition-associated weight loss. Assessments of energy expenditure are also commonly used to indicate hypermetabolism in MND, although these approaches may not be optimal. METHODS: A protocol based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews Guidelines was developed. Three electronic databases (Medline [Ovid], CINAHL [EBSCO], and Web of Science) were exhaustively searched. Identified publications were systematically screened according to predefined PICOS eligibility criteria. The primary outcome was the identification of methods used to measure energy expenditure in MND. The secondary outcome was the identification of applications of energy expenditure assessments to indicate hypermetabolism in MND. RESULTS: Thirty-two observational primary research publications were identified. Thirteen (40.6%) were longitudinal in design, with data on repeated measurements of energy expenditure presented in 3 (9.4%). Thirteen (40.6%) were case-control studies, of which 11 use a matched control group. Pulmonary function was used to assess eligibility in 10 publications. Energy expenditure was measured using indirect calorimetry (IC) in 31 studies. Discrepancies in the durations of fasted, measurement, and washout periods were observed. Of all included publications, 50% used assessments of resting energy expenditure to identify hypermetabolism. Bioelectrical impedance analysis was used to assess body composition alongside energy expenditure in 93.8% of publications. CONCLUSIONS: Resting energy expenditure is most frequently measured using an open-circuit IC system. However, there is a lack of a standardized, validated protocol for the conduct and reporting of IC and metabolic status in patients with MND.
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INTRODUCTION: Suboptimal nutrition promotes unfavorable outcomes in trauma patients, particularly among those aged 60 and over. While many institutions employ predictive energy equations to determine patients' energy requirements, mounting evidence shows these equations inaccurately estimate caloric needs. In this pilot randomized controlled trial, we sought to quantify the discrepancy between predictive equations and indirect calorimetry (IC)-the gold standard for determining energy requirements-in the older adult trauma population. METHODS: This is a nested cohort study within a pilot randomized control trial in which 32 older adult trauma patients were randomized 3:1 to receive IC-guided nutrition delivery versus standard of care. IC requirements of patients in the intervention arm were compared to Mifflin St. Jeor (MSJ), Harris-Benedict (HB), and the American Society for Parenteral and Enteral Nutrition-Society of Critical Care Medicine (ASPEN-SCCM) predictive energy equations. RESULTS: Twenty patients underwent IC to assess measured resting energy expenditure (mREE), yielding a mean (standard deviation) mREE of 23.1 ± 4.8 kcal/kg/d. MSJ and HB gave mean predictive resting energy expenditures of 17.5 ± 2.0 and 18.5 ± 2.0 kcal/kg/d in these patients, demonstrating that IC-derived values were 32.1% and 25.0% higher, respectively. When patients were stratified by body mass index (BMI), MSJ, and HB more severely underestimated caloric requirements in individuals with BMI <30 versus BMI 30-50. While the mean mREE fell within the mean predictive resting energy expenditure range prescribed by ASPEN-SCCM equations (21.4 ± 4.1 to 26.2 ± 4.3 kcal/kg/d), individuals' IC-derived values fell within their personal range in 8 of 20 cases. CONCLUSIONS: The MSJ and HB predictive energy equations consistently and significantly underpredict metabolic demands of older adult trauma patients compared to IC and perform worse in lower BMI individuals. ASPEN-SCCM equations frequently overpredict or underpredict resting energy expenditure. While these findings should be confirmed in a larger randomized control trial, this study suggests that institutions should prioritize IC to accurately identify the metabolic demands of older trauma patients.
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BACKGROUND AND AIMS: People living with motor neuron disease (MND) frequently struggle to consume an optimal caloric intake. Often compounded by hypermetabolism, this can lead to dysregulated energy homeostasis, prompting the onset of malnutrition and associated weight loss. This is associated with a poorer prognosis and reduced survival. It is therefore important to establish appropriate nutritional goals to ensure adequate energy intake. This is best done by measuring resting energy expenditure (mREE) using indirect calorimetry. However, indirect calorimetry is not widely available in clinical practice, thus dietitians caring for people living with MND frequently use energy equations to predict resting energy expenditure (pREE) and estimate caloric requirements. Energy prediction equations have previously been shown to underestimate resting energy expenditure in over two-thirds of people living with MND. Hypermetabolism has previously been identified using the metabolic index. The metabolic index is a ratio of mREE to pREE, whereby an increase of mREE by ≥110% indicates hypermetabolism. We aim to critically reflect on the use of the Harris-Benedict (1919) and Henry (2005) energy prediction equations to inform a metabolic index to indicate hypermetabolism in people living with MND. METHODS: mREE was derived using VO2 and VCO2 measurements from a GEMNutrition indirect calorimeter. pREE was estimated by Harris-Benedict (HB) (1919), Henry (2005) and kcal/kg/day predictive energy equations. The REE variation, described as the percentage difference between mREE and pREE, determined the accuracy of pREE ([pREE-mREE]/mREE) x 100), with accuracy defined as ≤ ± 10%. A metabolic index threshold of ≥110% was used to classify hypermetabolism. All resting energy expenditure data are presented as kcal/24hr. RESULTS: Sixteen people living with MND were included in the analysis. The mean mREE was 1642 kcal/24hr ranging between 1110 and 2015 kcal/24hr. When REE variation was analysed for the entire cohort, the HB, Henry and kcal/kg/day equations all overestimated REE, but remained within the accuracy threshold (mean values were 2.81% for HB, 4.51% for Henry and 8.00% for kcal/kg/day). Conversely, inter-individual REE variation within the cohort revealed HB and Henry equations both inaccurately reflected mREE for 68.7% of participants, with kcal/kg/day inaccurately reflecting 41.7% of participants. Whilst the overall cohort was not classified as hypermetabolic (mean values were 101.04% for HB, 98.62% for Henry and 95.64% for kcal/kg/day), the metabolic index ranges within the cohort were 70.75%-141.58% for HB, 72.82%-127.69% for Henry and 66.09%-131.58% for kcal/kg/day, indicating both over- and under-estimation of REE by these equations. We have shown that pREE correlates with body weight (kg), whereby the lighter the individual, the greater the underprediction of REE. When applied to the metabolic index, this underprediction biases towards the classification of hypermetabolism in lighter individuals. CONCLUSION: Whilst predicting resting energy expenditure using the HB, Henry or kcal/kg/day equations accurately reflects derived mREE at group level, these equations are not suitable for informing resting energy expenditure and classification of hypermetabolism when applied to individuals in clinical practice.