Intra- and interindividual variability of resting energy expenditure in healthy male subjects – biological and methodological variability of resting energy expenditure (original) (raw)
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European Journal of Clinical Nutrition, 2006
Objective: There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. Design: In a cross-sectional design measurements of REE and body composition were performed. Subjects: The study population consisted of 50 men (age 37.1715.1 years, body mass index (BMI) 25.974.1 kg/m 2 ) and 54 women (age 35.3715.4 years, BMI 25.574.4 kg/m 2 ). Interventions: REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D 2 O). A 4-compartment (4C)-model was used as a reference. Results: When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFM ADP ) to 1645 kJ/24 h (FFM SF ) and the slopes ranged between 100.3 kJ (FFM SF ) and 108.1 kJ/FFM (kg) (FFM ADP ). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFM BIA ) to 75% (FFM DXA ) and was only 46% for body weight. Conclusion: Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population-and/or investigator specificity of algorithms for REE prediction.
Nutrients, 2016
Age-related changes in organ and tissue masses may add to changes in the relationship between resting energy expenditure (REE) and fat free mass (FFM) in normal and overweight healthy Caucasians. Secondary analysis using cross-sectional data of 714 healthy normal and overweight Caucasian subjects (age 18-83 years) with comprehensive information on FFM, organ and tissue masses (as assessed by magnetic resonance imaging (MRI)), body density (as assessed by Air Displacement Plethysmography (ADP)) and hydration (as assessed by deuterium dilution (D₂O)) and REE (as assessed by indirect calorimetry). High metabolic rate organs (HMR) summarized brain, heart, liver and kidney masses. Ratios of HMR organs and muscle mass (MM) in relation to FFM were considered. REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. REE, FFM, specific metabolic rates, the REE-FFM relationship, HOMA, CRP, and thyroid hormone levels change with age. The age-related decreas...
Methods for data analysis of resting energy expenditure measured using indirect calorimetry
Nutrition, 2019
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Reduced steady-state methods during 10 min of measurement overestimate the REE. Interval methods during 10 and 30 min of measurement overestimate the REE. We recommend 5 min in steady-state during 30 min of measurement to estimate the REE.
2012
O BESITY IS ONE OF THE MAJOR RISK FACTORS FOR development of other chronic diseases and disabilities. 1,2 Its prevention and appropriate treatments are essential for the maintenance of good health status. A dietary prescription based on individual energy expenditure is the first step for obesity prevention and treatment. Resting energy expenditure (REE) contributes from 50% to 75% of total energy expenditure, depending on the physical activity level. Therefore, the assessment of REE provides useful information for weight management. There are many methods for REE evaluation, including direct and indirect calorimetry (IC), bioelectrical impedance analysis (BIA), predictive equations, and others. 3,6-8 However, the use of some of these methods is limited because of their high costs and the shortage of trained personnel. For this reason, IC is hardly feasible in most clinical settings and is more frequently used in scientific research. It is still important to use accurate predictive equations to estimate REE in clinical practice. The low accuracy of some REE predictive equations contributes to the inappropriate estimation of energy requirements.
Examining Variations of Resting Metabolic Rate of Adults
Medicine & Science in Sports & Exercise, 2014
Purpose: There has not been a recent comprehensive effort to examine existing studies on the resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcalIkg j1 Ih j1 ) to determine the relationship of age, sex, and obesity status to RMR as compared with the commonly accepted value for the metabolic equivalent (MET; e.g., 1.0 kcalIkg j1 Ih j1 ). Methods: Using several databases, scientific articles published from 1980 to 2011 were identified that measured RMR, and from those, others dating back to 1920 were identified. One hundred and ninety-seven studies were identified, resulting in 397 publication estimates of RMR that could represent a population subgroup. Inverse variance weighting technique was applied to compute means and 95% confidence intervals (CI). Results: The mean value for RMR was 0.863 kcalIkg j1 Ih j1 (95% CI = 0.852-0.874), higher for men than women, decreasing with increasing age, and less in overweight than normal weight adults. Regardless of sex, adults with BMI Q 30 kgIm j2 had the lowest RMR (G0.741 kcalIkg j1 Ih j1 ). Conclusions: No single value for RMR is appropriate for all adults. Adhering to the nearly universally accepted MET convention may lead to the overestimation of the RMR of approximately 10% for men and almost 15% for women and be as high as 20%-30% for some demographic and anthropometric combinations. These large errors raise questions about the longstanding adherence to the conventional MET value for RMR. Failure to recognize this discrepancy may result in important miscalculations of energy expended from interventions using physical activity for diabetes and other chronic disease prevention efforts.
Issues in characterizing resting energy expenditure in obesity and after weight loss
Frontiers in Physiology, 2013
Limitations of current methods: Normalization of resting energy expenditure (REE) for body composition using the 2-compartment model fat mass (FM), and fat-free mass (FFM) has inherent limitations for the interpretation of REE and may lead to erroneous conclusions when comparing people with a wide range of adiposity as well as before and after substantial weight loss.
A new predictive equation for resting energy expenditure in healthy individuals�3
2000
A predictive equation for resting energy ex- penditure (REE) was derived from data from 498 healthy sub- jects, including females (n = 247) and males (n = 25 1), aged 19-78 y (45 ± 14 y, I ± SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was mea- sured by indirect calorimetry. Multiple-regression