Impact of indexing resting metabolic rate against fat-free mass determined by different body composition models

LaForgia, Joe; Ploeg, Grant E. van der; Withers, R. T.; Gunn, Simon M.; Brooks, A.; Chatterton, B. E.

doi:10.1038/sj.ejcn.1601941

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…An individual’s FFM is the greatest determinant of RMR, thus a greater amount of FFM results in a higher energy requirement due to a greater proportion of metabolically active tissue [ 60 , 61 ]. Previous research has largely demonstrated increases in RMR following exercise, possibly related to increases in FFM [ 62 , 63 ], increased metabolic demand in response to exercise-induced muscle damage [ 64 – 68 ], and excess post-exercise O 2 consumption (EPOC), which may elevate energy expenditure for up to 24 hours following training [ 69 , 70 ].…”

Section: Discussionmentioning

confidence: 99%

The effects of intensified training on resting metabolic rate (RMR), body composition and performance in trained cyclists

Woods

Rice²,

Garvican-Lewis

et al. 2018

PLoS ONE

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BackgroundRecent research has demonstrated decreases in resting metabolic rate (RMR), body composition and performance following a period of intensified training in elite athletes, however the underlying mechanisms of change remain unclear. Therefore, the aim of the present study was to investigate how an intensified training period, designed to elicit overreaching, affects RMR, body composition, and performance in trained endurance athletes, and to elucidate underlying mechanisms.MethodThirteen (n = 13) trained male cyclists completed a six-week training program consisting of a “Baseline” week (100% of regular training load), a “Build” week (~120% of Baseline load), two “Loading” weeks (~140, 150% of Baseline load, respectively) and two “Recovery” weeks (~80% of Baseline load). Training comprised of a combination of laboratory based interval sessions and on-road cycling. RMR, body composition, energy intake, appetite, heart rate variability (HRV), cycling performance, biochemical markers and mood responses were assessed at multiple time points throughout the six-week period. Data were analysed using a linear mixed modeling approach.ResultsThe intensified training period elicited significant decreases in RMR (F(5,123.36) = 12.0947, p = <0.001), body mass (F(2,19.242) = 4.3362, p = 0.03), fat mass (F(2,20.35) = 56.2494, p = <0.001) and HRV (F(2,22.608) = 6.5212, p = 0.005); all of which improved following a period of recovery. A state of overreaching was induced, as identified by a reduction in anaerobic performance (F(5,121.87) = 8.2622, p = <0.001), aerobic performance (F(5,118.26) = 2.766, p = 0.02) and increase in total mood disturbance (F(5, 110.61) = 8.1159, p = <0.001).ConclusionIntensified training periods elicit greater energy demands in trained cyclists, which, if not sufficiently compensated with increased dietary intake, appears to provoke a cascade of metabolic, hormonal and neural responses in an attempt to restore homeostasis and conserve energy. The proactive monitoring of energy intake, power output, mood state, body mass and HRV during intensified training periods may alleviate fatigue and attenuate the observed decrease in RMR, providing more optimal conditions for a positive training adaptation.

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Section: Discussionmentioning

confidence: 99%

The effects of intensified training on resting metabolic rate (RMR), body composition and performance in trained cyclists

Woods

Rice²,

Garvican-Lewis

et al. 2018

PLoS ONE

View full text Add to dashboard Cite

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“…Gender was also incorporated in the equation providing for both sexes reliable estimates (determined coefficients greater than 90%). Many studies have supported the fact that gender affects metabolic rate [ 20 ], due to the different allocation of FM [ 32 ] and FFM [ 32 , 46 ], which also plays an important role in metabolic rate [ 47 , 48 ]. It is also supported that hormonal factors lead to additional differences between genders [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate

et al. 2018

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BackgroundMany studies have been performed over time in order to determine the reliability of metabolic rate prediction equations.PurposeTo evaluate the agreement, in terms of bias, absolute bias and accuracy between metabolic rate prediction equations and measured metabolic rate using indirect calorimetry system (IC), investigating also the factors affecting this agreement.MethodsThe anthropometric features of 383 Caucasian participants of all Body Mass Index (BMI) classes were recorded and Resting Metabolic Rate (RMR) was measured by using the IC Fitmate portable device. The resulting values were compared with the predictive values of Harris & Benedict, Schofield, Owen, FAO-WHO-UNU, Mifflin and Harrington equations.ResultsA closer approximation in agreement was obtained using the Harrington equation (based on BMI, age and gender). The equations using variables, such as weight, height, age and gender demonstrated higher agreement than the equations using merely weight and gender. Higher educational level was associated with normal weight, while higher calorific ratio was found in the class of normal-weighted individuals. An inverse relationship between ΒΜΙ and RMR was also observed and a logarithmic equation for calculating RMR was created, which was differentiated in relation to BMI classes, using the weight and gender variables.ConclusionA better measurement agreement between RMR prediction equations and IC may be achieved due to BMI consideration. The present findings contributed to a better understanding of the measured parameters, confirming the inverse relationship between BMI and RMR. Age group and gender variables may also exert significant role on the bias response of some RMR equations.Electronic supplementary materialThe online version of this article (10.1186/s12986-018-0278-7) contains supplementary material, which is available to authorized users.

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“…the gold standard) to assess FFM (Withers et al, 1999). The idea that discrepancies between different REE prediction algorithms are based on differences in body composition analysis is supported by recent findings of LaForgia et al (2004) who measured REE and FFM in 104 male subjects, and found considerable differences between REE indexed by FFM as assessed by a 2C-model when compared to REE indexed by FFM measured by a 4C-model. The inaccuracy of 2C models for body composition analysis may also explain the observation that FFM-based REE prediction was not superior to body weight-based REE prediction when including height, age and sex .…”

Section: Introductionmentioning

confidence: 88%

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

et al. 2006

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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. . 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.

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Impact of indexing resting metabolic rate against fat-free mass determined by different body composition models

Cited by 9 publications

References 36 publications

The effects of intensified training on resting metabolic rate (RMR), body composition and performance in trained cyclists

The effects of intensified training on resting metabolic rate (RMR), body composition and performance in trained cyclists

Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

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