Variation In Resting Energy Expenditure Research Articles

Resting Energy Expenditure Equations Have Lower Accuracy for Adolescents with Overweight/Obesity Versus Healthy-Weight Adolescents.

Objective: The objectives of the study were (1) to assess whether resting energy expenditure (REE) equations have comparable validity for adolescents with overweight/obesity vs. adolescents with healthy weight and (2) to examine determinants of measured REE in adolescents with overweight/obesity vs. adolescents with healthy weight. Methods: Ten equations were used to predict REE for 109 adolescents (70% males; 36.7% with overweight/obesity); 95% equivalence testing was used to assess how well each equation agreed with the criterion measure of indirect calorimetry. Linear regression models were fitted to examine how much REE variance was accounted for by age, sex, race, fat-free mass (FFM), and fat mass. Results: For adolescents with healthy weight, all ten equations were significantly equivalent to the criterion measure within ±8.4% (p < 0.05), whereas for participants with overweight/obesity, only three equations were equivalent within the same range (p < 0.05). Controlling for age, sex, race, fat mass, and FFM accounted for 74% of REE variance. FFM explained the greatest amount (26%) of variance in REE, while weight status itself explained an additional 22%. Conclusions: Prediction equations tend to be more accurate for adolescents with healthy weight than adolescents with overweight/obesity unless the original sample specifically included participants with overweight/obesity. Determinants of REE are similar regardless of weight status.

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.

Challenging obesity and sex based differences in resting energy expenditure using allometric modeling, a sub-study of the DIETFITS clinical trial

Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention. Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340at 6 months and 323at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention. A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM0.66×FM0.066] was body size and sex independent (p=0.91 and p=0.73, respectively) in contrast to BW based indexing which showed a significant inverse relationship to BW (r=-0.47 for female and r=-0.44 for male, both p<0.001). When indexed to BW, significant baseline differences in REE were observed between male and female (p<0.001) and between individuals who are overweight and obese (p<0.001) while no significant differences were observed when indexed to REE/[LBM0.66×FM0.066], p>0.05). Percentage predicted REE adjusted for LBM, FM and DP remained stable following weight loss intervention (p=0.614). Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

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.

Effects of adiposity and body composition on adjusted resting energy expenditure in women.

Fat-free mass (FFM) accounts for ~80% of the variance in resting energy expenditure (REE), and this relationship is complicated by adiposity. The objective was to compare adjusted REE and contributions of skeletal lean mass and fat mass (FM) to adjusted REE in women with varying adiposity levels using a novel approach. Women were divided into tertiles by body fat percent (%fat): Tertile 1 (T1): %fat=18.5%-28.4%; Tertile 2 (T2): %fat=28.5%-33.8%; Tertile 3 (T3): %fat=34.0%-61.0%. Outcome measures were measured and adjusted REE, body composition (skeletal lean mass, FM, %fat) from dual-energy X-ray absorptiometry, and percent contribution of skeletal lean mass and FM to adjusted REE. The main effect for tertiles (T1 vs. T2 vs. T3) was significant (p=.001); REE was significantly higher in T3 versus both T1 by 281 kcal/day (p=.001) and T2 by 215 kcal/day (p=.001). Expenditure from skeletal lean mass in T1 was significantly higher than T3 by 3.2% (p=.001). T3 had a significantly higher FM contribution than T1 by 5.1% (p=.001) and T2 by 3.9% (p=.001). Women with elevated %fat experienced lower skeletal lean mass contribution and higher FM contribution to adjusted REE. FM may explain more of the variance in REE between women of different levels of adiposity.

Study of the relevant threshold of resting energy expenditure variation to screen at diagnosis patients with amyotrophic lateral sclerosis with the higher evolving risk

Rationale: About 50-60% of Amyotrophic Lateral Sclerosis (ALS) is characterized by an increase of metabolic rate. The increase of metabolic rate in ALS is defined by more than +10% of resting energy expenditure (REE) variation between measured REE (mREE) and calculated REE (cREE). However, the REE variation during ALS is a prognostic factor for the survival in patients with a REE variation over +20%. The present study aimed to study the relevant threshold of REE variation according to several cREE formulas to screen patients with the higher evolving risk.

Survival according to resting energy expenditure (REE) variation using two ree formulas and without indirect calorimetry in patients with amyotrophic lateral sclerosis (ALS)

Rationale: The increase of resting energy expenditure (REE) variation in ALS is a prognostic factor for the survival mainly in patients with a REE variation over +20%. Measurement of REE by indirect calorimetry (IC) compared to REE predicted with formula (Harris and Benedict [HB] 1919, HB 1984 or Mifflin) is necessary to obtain the REE variation. However, IC is not available in many centres. Jésus et al. formula is a validate formula to predict REE in ALS and can be used in the absence of an IC. The aim of this work was to study the survival of ALS patients according to REE variation with IC or Jésus formula.

Nutritional and metabolic status at diagnosis and survival of patients with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) compared to patients with ALS alone

Rationale: About 5%e10% of ALS patients suffer from frank ALS/FTD. In ALS, the increase of resting energy expenditure (REE) is a prognostic factor for the survival. No studies have investigated nutritional and metabolic status in ALS/FTD and its impact on survival. The aims of this study were i) to study nutritional and metabolic status of ALS/FTD compared to ALS patients, ii) to study the survival according to the disease and the metabolic status. Methods: Three months after diagnosis, nutritional assessments and body composition (by bioimpedance analysis) were performed in all patients. REE measured (mREE) by indirect calorimetry and calculated (cREE) by Harris and Benedict 1919 formula were used for the calculation of REE variation. Neurological (bulbar onset), functional (ALS Functional Rating Scale e Revised [ALSFRS-R]) and respiratory (Forced Vital Capacity [FVC]) assessments were collected. Survival by Log-rank test (Hazard Ratio [HR], confidence interval 95%) and multivariate analysis with Cox model were also used. Results were expressed as median (interquartile range). Results: 446 patients were included, 421 ALS patients and 25 (5.6%) ALS/FTD patients. Age at diagnosis and sex were not significantly different. ALS/ FTD patients had more bulbar onset (100.0% vs. 34.5%, p<0.0001). ALSFRSR was not significantly different but FVC was lower in ALS/FTD patients (79.5% (61.0 e 95.3) vs. 93.0% (70.8 e 106.3), p¼0.042). ALS/FTD patients had a lower body mass index (21.8 kg/m2 (19.8 e 25.8) vs. 24.3 kg/m2 (22.2e 27.5), p¼0.014), were more malnourished (24.0% vs. 8.3%, p¼0.008) and with a higher weight loss (-10.3% (-15.4 e -5.7) vs -5.0% (-11.3 e -0.2), p¼0.004). Body composition was not significantly different between the two groups. However, ALS/FTD had less often a REE variation over 10% (32.0% vs. 53.9%, p¼0.03). ALS/FTD patients had more often gastrostomy placement (68.0% vs. 40.1%, p¼0.006). In univariate analysis, ALS/FTD patients had an increased risk of death compared to ALS patients (HR ¼ 1.97 [1.31 e 2.97], p¼0.001) but not in multivariate analysis. In multivariate analysis, regardless of type of ALS (ALS and ALS/DFT) a higher fat mass (þ1%)was associated with a reduced risk of death (HR ¼ 0.98 [0.96 e 0.99], p¼0.03) and a greater weight loss (-1%) with an increased risk of death (HR ¼ 1.03 [1.02 e 1.05], p<0.0001). Conclusion: ALS/FTD patients would seem to have a lower survival compared to patients with ALS alone. However, this is not found after adjustment in multivariate analysis. Regardless of the form of ALS, as described previously in literature, nutritional status and body composition were associated with the risk of death. Early nutritional assessment and management are essential in this severe neurodegenerative disease whatever the form of ALS

Effects Of Adiposity And Body Composition On Adjusted Resting Energy Expenditure In Women

PURPOSE: To compare adjusted resting energy expenditure (REEa) and contributions of skeletal lean mass (SM) and fat mass (FM) to REEa in women at varying levels of adiposity. METHODS: Resting energy expenditure (REE) was measured via indirect calorimetry (REEm). Body composition data was obtained from dual-energy X-ray absorptiometry in 182 female subjects (Mean ± SD; Age 24.2 ± 9.1 yrs, Ht 164.6 ± 6.0 cm, Wt 69.0 ± 22.5 kg, BMI 25.5 ± 8.3 kg·m-2). The sample was divided into tertiles by body fat percent (%fat); Tertile 1 (T1): %fat=18.5-28.4%; Tertile 2 (T2): %fat=28.5-33.8%; Tertile 3 (T3): %fat=34.0-61.0%. To normalize and compare groups, REE was predicted (REEp) to account for differences in FFM and age. Adjusted REE was then calculated from REEm, REEp, and mean REEm per tertile (REEt) using REEa=REEt+REEm-REEp. Contributions (mass × metabolic rate, kcal/kg/day) of FM and SM were calculated as percentages of REEa. One-way ANOVAS and a two-way ANOVA were conducted to analyze differences in body composition and differences between REEm and REEa [(REE (REEm vs REEa) × Group (T1 vs T2 vs T3)], respectively. RESULTS: FM in T3 was significantly higher than T1 (Mean Difference (MD) ± SD; 22.6 ± 25.0 kg, p=0.001) and T2 (MD ± SD; 17.9 ± 25.0 kg, p=0.001). No differences occurred in SM (p>0.05). When evaluating REE and group, there was no significant interaction (p=0.191), or main effect for REE (REEm vs REEa) (p=0.995). There was a significant main effect for group (T1 vs T2 vs T3, p=0.001); REE was significantly higher in T3 versus T1 (MD ± SD; 281 ± 396 kcal/day, p=0.001) and T2 (MD ± SD; 215 ± 396 kcal/day, p=0.001). Expenditure attributed to SM in T1 was significantly higher than T3 (MD ± SD; 3.2 ± 8.6%, p=0.001). Difference in FM contribution between T1 and T2 lacked significance (p=0.210), but T3 had a significantly higher FM contribution than T1 (MD ± SD; 5.1 ± 7.0%, p=0.001) and T2 (MD ± SD: 3.9 ± 7.0%, p=0.001). CONCLUSIONS: Despite similarities in SM, women with elevated %fat experienced lower SM contribution and higher FM contribution to REEa. As adiposity increases, REE increases; FM may explain more of the variance in REE between women of different levels of adiposity.

Changes in Resting Energy Expenditure following Gastric Bypass Surgery: Impact on Total Body Weight.

AbstractBariatric surgery, including Gastric Bypass (GBP) Surgery, is the most efficient modality to manage severe obesity. Resting Energy Expenditure (REE) is an area of interest in the context of weight loss (WL) as it has been postulated to be an independent predictor of WL success following GBP. As such, the aim of this study is to investigate the impact of REE on WL following GBP. 31 GBP patients (77.4% females BMI 45.5 ± 7.0kg/m2 ; 47.3 ± 11.6y) and 32 weight-stable controls (46.9% females; BMI 27.0 ± 4.6kg/m2 ; 41.1 ± 13.5y) were assessed at one-month pre-surgery and at 3 and 12-months post-surgery. Fat mass (FM) and fat-free mass (FFM) were measured using dual energy X-ray absorptiometry (Lunar iDXA, GE Healthcare). REE was measured under standardised conditions using indirect calorimetry (ECAL, Metabolic Health Solutions). Statistical analyses were performed with SPSS v24.0, Armonk, NY. Multiple regression analysis showed that FM (P = 0.001), FFM (P &lt; 0.0001) and gender (P = 0.012) significantly predicted the interindividual variability in REE. Total body weight (TBW) was removed from the model due to collinearity. Adjusted-REE values were then generated using the above predictor variables. Low-REE and high-REE groups were created using within-group adjusted-REE split. At both follow-ups (3- and 12-months post-surgery), patients had a greater reduction in TBW, FM, FFM, measured-REE and adjusted-REE values compared with controls (P &lt; 0.0001). There was also no significant difference between measured and adjusted-REE values at all time-points (P &gt; 0.05). Patients with high REEs at baseline lost more TBW than those in the low-REE group at 3-months post-surgery (-24.9 ± 6.5 kg vs. -16.6 ± 7.0 kg; P = 0.005) and 12-months post-surgery (-41.3 ± 12.5 kg vs. -25.8 ± 10.4.0 kg; P = 0.003). There was no significant difference in mean TBW changes for controls in the low-and high-REE groups at both follow-ups. Patients with high REEs at 3-months post-surgery did not lose more TBW than those in the low-REE group at 12-months post-surgery (-30.1 ± 12.8 kg vs. -38.6 ± 14.4 kg; P = 0.155). Similarly, there was no difference in mean TBW reduction between controls in the low- and high-REE groups (P = 0.115). Thus while patients with a high adjusted-REE value at baseline (&gt; 9746.6kJ/day) lost more weight at 3- and 12-months post-GBP, it is plausible that from the third to the 12th month post-surgery, other key drivers of weight loss, particularly the reduction in energy intake are more important in predicting WL. Further research with a larger sample size is required to increase the chances of detecting a true effect.

Determinants of change in resting energy expenditure in patients with stage III/IV colorectal cancer

Resting energy expenditure (REE) is variable in cancer and might be influenced by changes in tumor burden, systemic inflammation, and body composition. The objective of this study was to assess REE change and the predictors of such in patients with stage III or IV colorectal cancer. REE was measured via indirect calorimetry and fat mass and fat-free mass (FFM) were assessed using dual X-ray absorptiometry as part of a unique analysis of two studies. C-reactive protein (CRP) was measured as an inflammatory marker. Linear regression was used to assess the determinants of REE at baseline and REE change, with days between baseline and follow-up measures included as a covariate. One-hundred and nine patients were included at baseline (59.6% male; 67±12 years; body mass index 24.1±4.3kg/m2); 49 had follow-up data (61.2% male; 65±12 years; body mass index 25.4±4.3kg/m2), with median follow-up of 119 days (interquartile range: 113-127 days). At baseline, age, FFM, and CRP explained 68.9% of the variability in REE. A wide variability in REE change over time was observed, ranging from-156 to 370kcal/day, or-13.0 to 15.7%/100 days. CRP change (1.7±0.4mg/L, p<0.001) and stage (81.3±38.7, p=0.042) predicted REE change in multivariate analysis, controlling for age, FFM change, and days between visits (R2: 0.417±88.2, p<0.001). Age, FFM, and CRP predicted REE at a single time point. REE change was highly variable and explained by inflammation and stage. Future research should investigate the validity and feasibility of incorporating these measures into energy needs recommendations.

Energy expenditure is associated with age, anthropometric indicators and body composition in children with spastic cerebral palsy.

proper estimation of energy requirements in children with cerebral palsy (CP) is essential in ensuring that their energy needs are optimally met. therefore, the purpose of this study was to demonstrate that resting energy expenditure (REE) and total energy expenditure (TEE) are associated with age, anthropometric indicators and body composition in children with spastic cerebral palsy. a cross-sectional study included 79 participants with spastic CP from 24 months to 16 years nine months. Weight and height (estimated by lower leg length) were obtained; body composition and energy expenditure were estimated by bioelectrical impedance analysis. ANOVA, post hoc tests, the Pearson correlation and determination coefficients (R2) were performed. significant gradual increases according to age in REE and TEE (both in kcal/d) were observed. There were highly significant positive correlations between REE and TEE (kcal/d, kcal/cm/d) with fat-free mass (FFM) and fat mass (FM), but negative correlations between REE (kcal/ kg/d) with body composition and energy indicators. FFM and total body water, and to a lesser extent FM, explained a high percentage of the direct variability of REE and TEE in kcal/d and the inverse in kcal/kg/d. as age increased, energy expenditure also increased. The estimated energy expenditure in kcal/cm/d did not differ with age and sex. The estimated energy expenditure, based on height, would be a practical and reliable method for estimating energy expenditure and ensuring adequate nutritional status.

Normalizing resting energy expenditure across the life course in humans: challenges and hopes

Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.

Hypermetabolism is a deleterious prognostic factor in patients with amyotrophic lateral sclerosis.

The aim of this study was to investigate patients with amyotrophic lateral sclerosis in order to determine their nutritional, neurological and respiratory parameters, and survival according to metabolic level. Nutritional assessment included resting energy expenditure (REE) measured by indirect calorimetry [hypermetabolism if REE variation (ΔREE) > 10%] and fat mass (FM) using impedancemetry. Neurological assessment included the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised score. Survival analysis used the Kaplan-Meier method and multivariate Cox model. A total of 315 patients were analysed. Median age at diagnosis was 65.9 years and 55.2% of patients were hypermetabolic. With regard to the metabolic level (ΔREE: < 10%, 10-20% and >20%), patients with ΔREE > 20% initially had a lower FM(29.7% vs. 32.1% in those with ΔREE ≤10%; P = 0.0054). During follow-up, the median slope of Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised tended to worsen more in patients with ΔREE > 20% (-1.4 vs. -1.0 points/month in those with ΔREE ≤10%; P = 0.07). Overall median survival since diagnosis was 18.4 months. ΔREE > 20% tended to increase the risk of dying compared with ΔREE ≤10% (hazard ratio, 1.33; P = 0.055). In multivariate analysis, an increased REE:FM ratio was independently associated with death (hazard ratio, 1.005; P = 0.001). Hypermetabolism is present in more than half of patients with amyotrophic lateral sclerosis. It modifies the body composition at diagnosis, and patients with hypermetabolism >20% have a worse prognosis than those without hypermetabolism.

Contribution of structural brain phenotypes to the variance in resting energy expenditure in healthy Caucasian subjects.

Brain gray (GM) and white matter (WM) volumes are related to weight changes. The impact of structural variations in GM and WM on the variance in resting energy expenditure (REE) and the REE-on-fat-free mass (FFM) association is unknown. The aim of this study was to address this in healthy Caucasian subjects. Cross-sectional data analysis of 493 healthy Caucasian subjects (age range 6-80 years; 3 age groups) was conducted with comprehensive information on FFM, organ and tissue masses, and detailed brain composition as assessed by whole body magnetic resonance imaging and REE (assessed by indirect calorimetry). REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. FFM was the major determinant of REE (70.6%); individual masses of liver, total brain, and heart explained a further 2.1% of the variance in REE. Replacing total brain with GM and WM did not change the total R2. Nevertheless, GM added more to the variance in REE (5.6%) and corresponding residuals (12.5%) than did total brain. Additionally, up to 12% was explained by age and sex (<2%). There was a systematic bias between REE and REEc with positive values in younger subjects but negative values in older ones. This bias remained after substituting the specific metabolic rate of brain with the specific metabolic rates of GM and WM. In healthy Caucasian subjects, GM and WM contributed to the variance in REE. Detailed brain structures do not explain the bias between REE and REEc. NEW & NOTEWORTHY Detailed brain composition (gray and white matter) contributed to the variances of resting energy expenditure (REE) and REE-on-fat-free mass residuals. Gray matter explained most of the variances, and for future studies on energy expenditure, brain compartments should be analyzed separately with regard to their different energy needs.

PT03.3: Hypermetabolism is a Deleterious Prognostic Factor in Patients with Amyotrophic Lateral Sclerosis

Rationale: Hypermetabolism, defined as an excessive level of resting energy expenditure (REE) has been reported in 50 –60% of patients with amyotrophic lateral sclerosis (ALS). The aim of this study was to investigate a large cohort of ALS patients in order to determine their nutritional, neurological and respiratory parameters and their survival according to the metabolic level. Methods: Nutritional, neurological and respiratory assessments were prospectively recorded. Nutritional evaluationincluded body mass index, REE measured by indirect calorimetry (hypermetabolism if REE variation [ΔREE] >10%) and fat mass (FM) using impedancemetry. Neurological evaluation included ALS phenotype at time of diagnosis, site at onset, ALSFRS-R score. Respiratory evaluation included vital capacity and SNIFF test. Survival analysis used the Kaplan-Meier method and the multivariate Cox model. Results: 315 patients were analysed. Median age at diagnosis was 65.9 years. 55.2% of patients were hypermetabolic. According to the metabolic level (Δ REE 20%), patients with a Δ REE >20% initially had a lower FM, 29.7% vs. 32.1% in those ≤ 10% (p = 0.0054). SNIFF test tended to be lower in ALS patients with Δ REE >20% (p = 0.07). During follow-up, nutritional status did not differ between the three groups, The median slope of ALSFRS-R tended to evolve more severely in patients with Δ REE >20%, −1.4 points/month vs. − 1.0 points/month in those ≤ 10% (p = 0.07). Overall median survival since diagnosis was 18.4 months. Δ REE >20% tended to increase the risk of dying compared to ≤ 10% (HR = 1.33, p = 0.055). In multivariate analysis, an increased REE/FM ratio was independently associated with death (HR = 1.005, p = 0.001). Conclusion: Hypermetabolism is present in more than half of ALS patients. It modifies the body composition at diagnosis, and patients with hypermetabolism >20% have a worse prognosis.

Impact of Fat-Free Mass Quality and Detailed Body Composition on Changes of Resting Energy Expenditure with Age

Resting energy expenditure (REE) and fat-free mass (FFM) change from childhood to adulthood. Since FFM is the major predictor of REE, age-related changes in body composition have been proposed to explain changes in REE. Beside its mass, FFM composition also changes with age. FFM is composed of (i) chemical components (protein, total water, and minerals) and (ii) anatomical components, i.e., organ and tissue masses, both of which change with age. In addition, during aging, specific metabolic rates of organs and tissues also decline. In elderly subjects, about 88.0% of the variance in REE is explained by age-related changes in masses, their composition, and specific metabolic rates. However, about 12% of the variance in REE remains unexplained. The residuals in the REE-FFM relationship are associated with metabolic disturbances, and negative residuals are related to “healthy” aging. Age-related changes in body composition explain most of the variance in metabolic changes. The variances in REE are associated with health.

Daily variability of resting energy expenditure in acute kidney injury patients on dialysis.

It is needed for nutrition prescription correct estimate of resting energy expenditure (REE), which is a challenge given the possible daily variation in critically ill patients with acute kidney injury (AKI). To evaluate the daily variability of REE measured by indirect calorimetry (IC) in patients with AKI and dialysis indication and identify clinical variables associated with REE. The REE was measured on the time of dialysis indication and the subsequent four days. We also evaluated parameters that can influence the REE. The daily differences were analyzed by generalized linear model for repeated measures. We also used Spearman correlation and multiple linear regression. There were 301 IC measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were male. The average REE was 2081 ± 645 kcal, rising on day 5 (2270 ± 556 kcal) compared to the days 2 and 3 (2022 ± 754; 2022 ± 660 kcal, respectively, p = 0,04). When normalized to weight, there was no significant difference in REE (kcal/kg/day) during follow-up. REE was positively correlated with total leukocyte count, C-reactive protein, minute volume (MV), fraction of inspired oxygen (FiO2) urea nitrogen appearance (UNA), weight and height and inversely with age. After multiple regression, MV, FiO2, weight and age are correlated independently with REE. Patients with AKI have REE stable. The REE was associated independently with FiO2, MV, body weight and age. Thus, ventilatory parameters should be evaluated each day for the necessary dietary changes may be made.

Feasibility and Acceptability of Implementing Indirect Calorimetry Into Routine Clinical Care of Patients With Spinal Cord Injury.

Background: In the absence of reliable predictive equations, indirect calorimetry (IC) remains the gold standard for assessing energy requirements after spinal cord injury (SCI), but it is typically confined to a research setting. The purpose of this study is to assess the feasibility and acceptability of implementing IC into routine clinical care in an Australian SCI rehabilitation facility. Methods: Bedside IC (canopy hood) was performed, and patients completed an IC acceptability questionnaire (open-ended; yes/no; 5-point Likert scale). Fasted resting energy expenditure (REE) steady-state criteria were applied to assess data quality, and adherence to a test ≥20 minutes was recorded. Staff were surveyed to assess impact of IC on usual care. Results: Of 35 eligible patients, 9 declined (7 reported claustrophobia). One patient could not be tested before discharge and 25 underwent IC (84% male, injury level C2-L2, AIS A-D). Anxiety prevented one patient from completing IC, while another failed to fast. The remaining 23 patients achieved a steady-state REE (≥5 consecutive minutes with ≤10% coefficient of variation for VO2 and VCO2). Test-retest (n = 5) showed <10% variation in REE. Patients deemed the procedure acceptable, with 88% reporting a willingness to repeat IC. Eighty percent of patients and 90% of staff agreed it was acceptable for IC to be integrated into usual care. Conclusion: This study found that IC is a feasible and acceptable addition to the routine clinical care of patients recovering from SCI and may serve to improve accuracy of nutrition interventions for this patient population.

Comparison of Resting Energy Expenditure Between Cancer Subjects and Healthy Controls: A Meta-Analysis

ABSTRACTThere is conflicting evidence surrounding the extent of changes in resting energy expenditure (REE) in cancer. This meta-analysis aimed to establish the mean difference in REE, as kilojoules per kilogram fat-free mass, among cancer patients when compared to healthy control subjects. The secondary aim was to determine differences among different cancer types. PubMed, Cochrane Library, Medline, Science Direct, Scopus, Web of Science, Wiley Online Library, and ProQuest Central were searched from the earliest records until March 2014. Studies were included if measured REE was reported as kilojoules or kilocalories per kilogram fat-free mass (FFM) in adult subjects with cancer. Twenty-seven studies were included in the meta-analysis. Fourteen studies included both cancer (n = 1453) and control (n = 1145) groups. The meta-analysis shows an average increase in REE of 9.66 (95% confidence interval: 3.34, 15.98) kJ/kgFFM/day in cancer patients when compared to control subjects. Heterogeneity was detected (P < 0.001) which suggest variations in REE among cancer types. Elevations are most noticeable in patients with cancers of metabolically demanding organs.