Validity of Predictive Equations for Resting Energy Expenditure in Greek Adults

2018 ◽  
Vol 72 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Maria I. Maraki ◽  
Demosthenes B. Panagiotakos ◽  
Lisa T. Jansen ◽  
Costas Anastasiou ◽  
Anastasios Papalazarou ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Age Groups ◽  
Resting Energy Expenditure ◽  
Type I ◽  
Group Level ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
The Individual ◽  
Greek Adults ◽  
Resting Energy

Aim: To examine the validity of published resting energy expenditure (REE) equations in Greek adults, and if indicated, develop new cohort-specific predictive REE equations. Methods: Indirect calorimetry and anthropometric data were obtained from 226 adult volunteers of diverse age groups and body mass index ranges (18–60 years, 16.6–67.7 kg·m–2). Measured REE was compared to preexisting prediction equations via correlation, regression, and Bland-Altman analysis. Then, cohort-specific REE equations were developed using curve estimation and nonlinear regression. To reduce type I error, presently derived equations were validated by splitting the sample into a training and validation group. Results: Preexisting equations over-predicted in-cohort REE. Equations by Livigston and Kohlstadt were most accurate at the individual level (63% accuracy), while formulas by Owen and collaborators elicited highest accuracy at the group level (–1.8% bias). Bland-Altman analysis showed proportional bias for most equations. Currently developed equations showed highest overall accuracy with 70% at the individual and group level (1.0% bias), with small differences between measured and predicted REE values (mean, 95% CI 36 [–15 to 88] kcal·day–1). Conclusion: Data indicate currently developed equations to be the most accurate and valid for estimating REE in Greek adults. Further studies should examine the developed equations in an independent sample.

Osteogenesis Imperfecta: The Impact of Genotype and Clinical Phenotype on Adiposity and Resting Energy Expenditure

2021 ◽  
Author(s):  
Kaitlin L Ballenger ◽  
Nicol Tugarinov ◽  
Sara K Talvacchio ◽  
Marianne M Knue ◽  
An N Dang Do ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Osteogenesis Imperfecta ◽  
Type I Collagen ◽  
Resting Energy Expenditure ◽  
Type I ◽  
Healthy Controls ◽  
Type Iv ◽  
Reduced Mobility ◽  
The Impact ◽  
Resting Energy

Abstract Context Mutations in type I collagen or collagen-related proteins cause Osteogenesis Imperfecta (OI). Energy expenditure and body composition in OI could reflect reduced mobility, or intrinsic defects in osteoblast differentiation increasing adipocyte development. Objective Compare adiposity and resting energy expenditure (REE) in OI and healthy controls (HC), for OI genotype- and Type-associated differences. Design/Setting/Participants We studied 90 participants, 30 with OI (13 COL1A1 Gly, 6 COL1A2 Gly, 3 COL1A1 non-Gly, 2 COL1A2 non-Gly, 6 non-COL; 8 Type III, 16 Type IV, 4 Type VI, 1 Type VII, 1 Type XIV) and 60 HC with sociodemographic characteristics/BMI/BMIz similar to the OI group. Participants underwent dual-energy X-ray absorptiometry to determine lean mass and fat mass percentage (FM%) and REE. FM% and REE were compared, adjusting for covariates to examine the relationship of OI genotypes and phenotypic Types. Results FM% did not differ significantly in all patients with OI versus HC (OI: 36.6±1.9%, HC: 32.7±1.2%, p =0.088). FM% was, however, greater than HC for those with non-COL variants (p=0.018). FM% did not differ from HC among OI Types (p’s>0.05). Overall, covariate-adjusted REE did not differ significantly between OI and HC (OI: 1376.5±44.7 kcal/d, healthy controls: 1377.0±96 kcal/d p=0.345). However, those with non-COL variants (p=0.034) and Type VI OI (p=0.04) had significantly lower REE than HC. Conclusions Overall, patients with OI did not significantly differ in either extra-marrow adiposity or REE from BMI-similar HC. However, reduced REE among those with non-COL variants may contribute to greater adiposity.

scholarly journals Energy expenditure in critically ill surgical patients. Comparative analysis of predictive equation and indirect calorimetry

2011 ◽  
Vol 26 (suppl 2) ◽  
pp. 51-56 ◽  
Author(s):  
Maria Auxiliadora Martins ◽  
Mayra Gonçalves Menegueti ◽  
Edson Antônio Nicolini ◽  
Michele Ferreira Picolo ◽  
Alessandra Fabiane Lago ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Energy Expenditure ◽  
Critically Ill ◽  
Indirect Calorimetry ◽  
Resting Energy Expenditure ◽  
Surgical Patients ◽  
Altman Analysis ◽  
Limits Of Agreement ◽  
Bland Altman Analysis ◽  
Critically Ill Surgical Patients

PURPOSE: The aim of this investigation was to compare the resting energy expenditure (REE) calculated by the Harris-Benedict equation (REE HB) with the REE measured by indirect calorimetry (REE IC) in critically ill surgical patients under mechanical ventilation. METHODS: Thirty patients were included in this work. REE was calculated by the Harris-Benedict equation (REE HB) using real body weight, and it was also measured by indirect calorimetry (REE IC), which was performed for 30 minutes. RESULTS: REE HB had significant (p < 0.0005) but low correlation (Spearman r = 0.57) with REE IC, with a mean bias of 12 kcal.d-1 and limits of agreement ranging from - 599.7 to 623.7 kcal.d-1 as detected by the Bland-Altman analysis. CONCLUSION: These findings suggest that REE IC seems to be more appropriate than REE HB for accurate measurement of REE in critically ill surgical patients under mechanical ventilation.

665 Reliability of Resting Energy Expenditure in Major Burns: Comparison Between Measured and Predictive Equations

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S177-S178
Author(s):  
Jun Hur ◽  
Dohern Kym
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Concordance Correlation Coefficient ◽  
Altman Analysis ◽  
Concordance Correlation ◽  
Predictive Equations ◽  
Severe Burns ◽  
The Mean ◽  
Poor Outcomes ◽  
Resting Energy

Abstract Introduction Poor outcomes can result from inadequate energy intake. We aimed to investigate the reliability of resting energy expenditure (REE) measured by indirect calorimetry (IC) with REE calculated using predictive equations for nutritional support in patients with major burns. Methods REE was measured using IC and compared with predictive equations in 215 adult severe burns patients from Jan 2011 to Jun 2015. Agreement between IC and predictive equations was assessed using Bland-Altman methods. Results All predictive equations were compared with REE measured using IC. The mean measured REE was 1712 kcal/d. Bland-Altman analysis showed that 1.2 times HBE, Thumb 25, and Ireton-Jones equations had higher accuracy and reliability. The concordance correlation coefficient was higher (0.49) in the Ireton-Jones equation, and root mean square error (RMSE) was lowest (471.5) in the Thumb 25 equation. The proportion of patients with predicted REE within ±10% of measured REE was highest with Thumb 25 (52.5%). Other equations for burns patients had higher mean bias and overestimated REE when compared with IC results. Conclusions This study suggests that Thumb 25 can be used as an alternative method for estimating energy requirements of patients with major burns when IC is not available or applicable. Applicability of Research to Practice Harrison-Benedict known as standard equation failed to show superiority to others in burns. Burn-specific equations are tend to overestimate ebergy requirements.

scholarly journals Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

Nutrients ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 216 ◽  
Author(s):  
Maurizio Marra ◽  
Iolanda Cioffi ◽  
Rosa Sammarco ◽  
Lidia Santarpia ◽  
Franco Contaldo ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Group Equation ◽  
Bioimpedance Analysis ◽  
Specific Group ◽  
Predictive Equations ◽  
Individual Level ◽  
The Individual ◽  
Resting Energy

This study aimed to develop and validate new predictive equations for resting energy expenditure (REE) in a large sample of subjects with obesity also considering raw variables from bioimpedance-analysis (BIA). A total of 2225 consecutive obese outpatients were recruited and randomly assigned to calibration (n = 1680) and validation (n = 545) groups. Subjects were also split into three subgroups according to their body mass index (BMI). The new predictive equations were generated using two models: Model 1 with age, weight, height, and BMI as predictors, and Model 2 in which raw BIA variables (bioimpedance-index and phase angle) were added. Our results showed that REE was directly correlated with all anthropometric and raw-BIA variables, while the correlation with age was inverse. All the new predictive equations were effective in estimating REE in both sexes and in the different BMI subgroups. Accuracy at the individual level was high for specific group-equation especially in subjects with BMI > 50 kg/m2. Therefore, new equations based on raw-BIA variables were as accurate as those based on anthropometry. Equations developed for BMI categories did not substantially improve REE prediction, except for subjects with a BMI > 50 kg/m2. Further studies are required to verify the application of those formulas and the role of raw-BIA variables for predicting REE.

scholarly journals The Diagnostic-Measurement Method—Resting Energy Expenditure Assessment of Polish Children Practicing Football

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Edyta Łuszczki ◽  
Anna Bartosiewicz ◽  
Katarzyna Dereń ◽  
Maciej Kuchciak ◽  
Łukasz Oleksy ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Composition ◽  
Energy Expenditure ◽  
Muscle Mass ◽  
Indirect Calorimetry ◽  
Energy Demand ◽  
Body Height ◽  
Resting Energy Expenditure ◽  
Blood Pressure Monitor ◽  
Resting Energy

Establishing the amount of energy needed to cover the energy demand of children doing sport training and thus ensuring they achieve an even energy balance requires the resting energy expenditure (REE) to be estimated. One of the methods that measures REE is the indirect calorimetry method, which may be influenced by many factors, including body composition, gender, age, height or blood pressure. The aim of the study was to assess the correlation between the resting energy expenditure of children regularly playing football and selected factors that influence the REE in this group. The study was conducted among 219 children aged 9 to 17 using a calorimeter, a device used to assess body composition by the electrical bioimpedance method by means of segment analyzer and a blood pressure monitor. The results of REE obtained by indirect calorimetry were compared with the results calculated using the ready-to-use formula, the Harris Benedict formula. The results showed a significant correlation of girls’ resting energy expenditure with muscle mass and body height, while boys’ resting energy expenditure was correlated with muscle mass and body water content. The value of the REE was significantly higher (p ≤ 0.001) than the value of the basal metabolic rate calculated by means of Harris Benedict formula. The obtained results can be a worthwhile suggestion for specialists dealing with energy demand planning in children, especially among those who are physically active to achieve optimal sporting successes ensuring proper functioning of their body.

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