The estimation of the resting metabolic rate is affected by the method of gas exchange data selection in high-level athletes

Author(s):  
Raul Freire ◽  
Juan M.A. Alcantara ◽  
Matheus Hausen ◽  
Alex Itaborahy
Nutrients ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 487
Author(s):  
Juan M.A. Alcantara ◽  
Guillermo Sanchez-Delgado ◽  
Francisco J. Amaro-Gahete ◽  
Jose E. Galgani ◽  
Jonatan R. Ruiz

The method used to select representative gas exchange data from large datasets influences the resting metabolic rate (RMR) returned. This study determines which of three methods yields the lowest RMR (as recommended for use in human energy balance studies), and in which method the greatest variance in RMR is explained by classical determinants of this variable. A total of 107 young and 74 middle-aged adults underwent a 30 min RMR examination using a breath-by-breath metabolic cart. Three gas exchange data selection methods were used: (i) steady state (SSt) for 3, 4, 5, or 10 min, (ii) a pre-defined time interval (TI), i.e., 6–10, 11–15, 16–20, 21–25, 26–30, 6–25, or 6–30 min, and (iii) “filtering”, setting thresholds depending on the mean RMR value obtained. In both cohorts, the RMRs yielded by the SSt and filtering methods were significantly lower (p < 0.021) than those yielded by the TI method. No differences in RMR were seen under the different conditions of the SSt method, or of the filtering method. No differences were seen between the methods in terms of the variance in RMR explained by its classical determinants. In conclusion, the SSt and filtering methods return the lowest RMRs and intra-measurement coefficients of variation when using breath-by-breath metabolic carts.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Raul Freire ◽  
Glauber Pereira ◽  
Juan MA Alcantara ◽  
Ruan Santos ◽  
Matheus Hausen ◽  
...  

2018 ◽  
Vol 132 (16) ◽  
pp. 1741-1751 ◽  
Author(s):  
Jose E. Galgani ◽  
Mauricio Castro-Sepulveda ◽  
Cristian Pérez-Luco ◽  
Rodrigo Fernández-Verdejo

Background: There are several predictive equations for estimating resting metabolic rate (RMR) in healthy humans. Concordance of these equations against measured RMR is variable, and often dependent on the extent of RMR. Part of the discrepancy may be due to an insufficient accuracy of metabolic carts, but this accuracy can be improved via a correction procedure. Objective: To determine the validity of predictive RMR equations by comparing them against measured and corrected (i.e. the reference) RMR. Methods: RMR was measured, in 69 healthy volunteers (29 males/40 females; 32±8 years old; BMI 25.5±3.8 kg/m2) and then corrected by simulating gas exchange through pure gases and high-precision mass-flow regulators. RMR was predicted using 13 published equations. Bland–Altman analyses compared predicted vs. reference RMRs. Results: All equations correlated well with the reference RMR (r>0.67; P<0.0001), but on average, over-predicted the reference RMR (89–312 kcal/d; P<0.05). Based on Bland–Altman analyses, 12 equations showed a constant bias across RMR, but the bias was not different from zero for nine of them. Three equations stood out because the absolute difference between predicted and reference RMR was equal or lower than 200 kcal/d for >60% of individuals (the Mifflin, Oxford and Müller equations). From them, only the Oxford equations performed better in both males and females separately. Conclusion: The Oxford equations are a valid alternative to predict RMR in healthy adult humans. Gas-exchange correction appears to be a good practice for the reliable assessment of RMR.


Author(s):  
Habib Yarizadeh ◽  
Leila Setayesh ◽  
Caroline Roberts ◽  
Mir Saeed Yekaninejad ◽  
Khadijeh Mirzaei

Abstract. Objectives: Obesity plays an important role in the development of chronic diseases including cardiovascular disease and diabetes. A low resting metabolic rate (RMR) for a given body size and composition is a risk factor for obesity, however, there is limited evidence available regarding the association of nutrient patterns and RMR. The aim of this study was to determine the association of nutrient patterns and RMR in overweight and obese women. Study design: This cross-sectional study was conducted on 360 women who were overweight or obese. Method: Dietary intake was assessed using a semi-quantitative standard food frequency questionnaire (FFQ). Nutrient patterns were also extracted by principal components analysis (PCA). All participants were evaluated for their body composition, RMR, and blood parameters. Result: Three nutrient patterns explaining 64% of the variance in dietary nutrients consumption were identified as B-complex-mineral, antioxidant, and unsaturated fatty acid and vitamin E (USFA-vit E) respectively. Participants were categorized into two groups based on the nutrient patterns. High scores of USFA-vit E pattern was significantly associated with the increase of RMR (β = 0.13, 95% CI = 0.79 to 68.16, p = 0.04). No significant associations were found among B-complex-mineral pattern (β = −0.00, 95% CI = −49.67 to 46.03, p = 0.94) and antioxidant pattern (β = 0.03, 95% CI −41.42 to 22.59, p = 0.56) with RMR. Conclusion: Our results suggested that the “USFA-vit E” pattern (such as PUFA, oleic, linoleic, vit.E, α-tocopherol and EPA) was associated with increased RMR.


Author(s):  
Pathima Fairoosa ◽  
Indu Waidyatilaka ◽  
Maduka de Lanerolle-Dias ◽  
Pujitha Wickramasinghe ◽  
Pulani Lanerolle

Author(s):  
Andrew Clarke

The model of West, Brown & Enquist (WBE) is built on the assumption that the metabolic rate of cells is determined by the architecture of the vascular network that supplies them with oxygen and nutrients. For a fractal-like network, and assuming that evolution has minimised cardiovascular costs, the WBE model predicts that s=metabolism should scale with mass with an exponent, b, of 0.75 at infinite size, and ~ 0.8 at realistic larger sizes. Scaling exponents ~ 0.75 for standard or resting metabolic rate are observed widely, but far from universally, including in some invertebrates with cardiovascular systems very different from that assumed in the WBE model. Data for field metabolic rate in vertebrates typically exhibit b ~ 0.8, which matches the WBE prediction. Addition of a simple Boltzmann factor to capture the effects of body temperature on metabolic rate yields the central equation of the Metabolic Theory of Ecology (MTE). The MTE has become an important strand in ecology, and the WBE model is the most widely accepted physical explanation for the scaling of metabolic rate with body mass. Capturing the effect of temperature through a Boltzmann factor is a useful statistical description but too simple to qualify as a complete physical theory of thermal ecology.


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