Metabolic rate and body composition -
Natalie A. Delgadillo , Midwestern State University Follow Frank Wyatt , Midwestern State University Follow Michael W. Olson , Midwestern State University Follow Soon-Mi Choi , Midwestern State University Follow. Resting metabolic rate RMR is the measure of daily energy expenditure while the body is at rest.
RMR is becoming more useful in order to measure the energy demands in both athletes and non-athletes. PURPOSE : The purpose of this study is to analyze the RMR among college aged students, along with their body compositions in order to determine if there is a correlation between body fat percentage and RMR.
METHODS: Participants in this study included 19 students at Midwestern State University between the ages of Participants were asked prior to their initial measurements about their fitness status and were placed into either a physically active PA group or a sedentary group S.
A Pearson Product R Correlation Coefficient was run to determine association between RMR, body fat percentage and other variables. Because there were only four 4 participants determined to be sedentary, statistical analysis was run as one sample size of 19 rather than two separate groups.
The mean RMR of the participants was A post hoc analysis with BMI was then conducted. Mean SD BMI of the participants was However, body composition does affect RMR, specifically through LBM. Delgadillo, Natalie A. Health and Physical Education Commons , Medical Education Commons , Sports Sciences Commons.
The difference between the predicted power and the device-recorded power was then compared to ensure consistency in the power meter recordings across time. Power comparison data was not utilised for any other purpose than assessing for drift in the predicted-actual power relationship.
On eight occasions during the monitored laboratory sessions Fig 1 , venous blood samples 1 x 8. Samples were taken before and after a standardised exercise, i.
at rest PRE and immediately following POST the standardised warm-up, in an attempt to mitigate the large variability in the assessment of leptin and free thyroid hormone triiodothyronine, fT3. Raw data were then assessed as the percentage change between PRE and POST, per session.
The present study design involved repeated measures of multiple variables at specific time points, and a number of proposed inter-variable relationships.
A multivariate structural equation model SEM was initially employed, however the complexity of the study design and irregularity of measurement points meant that the SEM did not achieve convergence.
A linear mixed modelling approach was thus utilised, with independent regressions defined based on the previously predicted SEM relationships. These models allowed us to investigate the time evolution of the dependent variables, associations with other variables covariates , as well as modelling the substantial amount of heterogeneity amongst subjects and varying baseline levels.
All analyses were carried out using the lme4 package [ 39 ] in R [ 40 ]. The technical specifications of the models are: 1 inclusion of a random intercept for participants, 2 Restricted Maximum Likelihood REML estimation, and 3 significance testing of the fixed effects using Type II F tests with Kenward-Roger degrees of freedom approximation.
The selection of independent variables included in the models was initially based on a visual assessment of descriptive plots assessing the relationship between the dependent and independent variables. Only those variables that presented the strongest relationship with the dependent variable were included as fixed effects in the linear mixed models.
This procedure was adopted to avoid issues with multi-collinearity e. including similar variables that highly correlate and to avoid over-testing, thus minimizing inflated Type I errors. After fitting an initial full model, a backward model selection procedure was carried out to remove non-significant variables, which helped in the interpretation of the models.
Each of the models included evolution over time as a fixed effect i. Training Block , regardless of whether there were any visible changes over time in the visual assessment. Linear mixed model data are available in Supporting Information Tables 1 to 7 S1 — S7 Tables , and presented as the F-statistic and p-value, with significance set at 0.
Raw data are available in Supporting Information Tables 8 to 18 S8 — S18 Tables , and presented as individual values for each time point, and group mean ± SD. A significant positive association was observed between HRV and fT3 levels [F 1, An interaction effect on HRV was also observed between relative RMR and the training block [F 2, Peak power output for the 15 s sprint decreased in 12 participants by the end of Loading 2, and returned toward baseline levels by Recovery 2 S6 Table.
An interaction effect on mean power output was also observed between TSS and RESTQ Total Stress [F 5, Increases in both MTDS Total Mood Disturbance TMD and RESTQ Total Stress were significantly associated with the training block.
Raw data comparisons for each variable across the study period as a percentage change from Day 1 are presented in Fig 3. The present period of intensified training elicited a state of overreaching in trained male cyclists, and significantly decreased both absolute and relative RMR, body mass, fat mass and HRV, with concomitant increases in mood disturbance, and declines in anaerobic performance, aerobic performance and associated peak HR; all of which improved following a period of recovery.
It is likely that the increased energetic demands of training, coupled with insufficient energy intake, are contributing factors to these results; supporting recent evidence from elite rowers that significant decreases in RMR, body composition and performance can occur with heavy training loads when energy intake does not keep up with a greater energy output [ 20 , 24 ].
The present data do not support the notion that RMR might be a useful marker to monitor training adaptation. Instead, we advocate the proactive monitoring of validated markers of training distress, including subjective wellness, energy intake, power output, body mass and HRV to attenuate fatigue and the potential for a decline in RMR; promoting athlete health, wellbeing and training ability.
Relative RMR decreased in the present participants from ~ to kJ. day -1 ~29 to 26 cal. day -1 at the end of the intensified training weeks, supporting a likely decrease in EA as a result of the training load, and an increased risk of physiological dysfunction.
This notion is supported by the negative linear relationship between both absolute and relative RMR and training load, whereby the greater the training load, the lower the RMR. The novel data in the present study demonstrate that male athletes can also suffer a low relative RMR and potentially a low EA, which is supported by previous data from our group [ 20 , 24 ].
These data suggest that there is potential benefit in monitoring RMR within the daily training environment. Such knowledge, along with the proactive monitoring of energy intake and body mass, may help to ensure that athletes do not suffer energy restriction from a mismatch between energy intake and output during heavy training, and promote a more optimal EA.
Importantly, by maintaining a more optimal RMR and EA, athletes are more likely to have sufficient energy for training as well as crucial physiological functions including bone health, growth and repair, cardiovascular, gastrointestinal and haematological function; ultimately promoting athletic performance [ 23 ].
Training distress in the present cohort was demonstrated by small but significant reductions in both aerobic m TT, These data agree with other studies reporting performance decrements [ 16 — 18 , 46 — 48 ], however a handful of studies have observed either no decline [ 49 ], or even improvements in TT performance in highly-trained and elite cyclists following an overload training period [ 50 , 51 ].
Such discrepancies may relate to the degree of overload imposed and the training status of the participants; with more highly trained participants being more resilient to increased training volume and intensity. It must also be acknowledged that whilst fatigue is more than likely the driving factor for the observed decreases in peak HR values, a simple explanation could be that these lowered values are directly related to the lowered peak power output from the performance trials.
Further investigation is required to ascertain the mechanisms for such changes. Interestingly, as shown in Fig 3H , the decline in 15 s peak power output occurred prior to the decline in mean power output for the m TT, and was of a greater magnitude.
As we did not indirectly measure muscle activation by integrated electromyography activity nor undertake specific measurements examining changes in neuromuscular function we can only speculate if this decline in anaerobic performance was due to i peripheral fatigue ii central fatigue, or iii a combination of both.
That said, the earlier decline in 15 s peak power output data in the current investigation might indicate that predominantly anaerobic efforts are a more sensitive marker of training distress than a short-term endurance effort such as a m TT. Rietjens and Kuipers [ 52 ] have proposed that a decline in reaction time to a finger pre-cuing test was strongly suggestive of central fatigue preceding peripheral fatigue.
In that study, the training load was significantly increased from baseline for two weeks, however no changes in hormonal responses, body composition or physiological assessments power output, HR, BLa were observed, which may imply that their participants did not suffer sufficient training-induced distress to stimulate both central and peripheral fatigue.
The present data suggests that regular monitoring of power output during both aerobic and anaerobic efforts may aid in the assessment of training-related distress within the daily training environment.
Present perceptions of stress and recovery were consistent with increased training volume, and participants demonstrated a worsened mood state through the loading weeks. The perceptual responses provide additional confirmation that the training prescription was sufficient to induce a state of overreaching.
These findings are not unique, but rather support recent research in elite rowers from the present group [ 20 ] and others [ 1 , 16 , 17 , 19 , 46 , 49 , 53 — 55 ]. Interestingly, RPE for the m TT decreased through the loading weeks, which may suggest that, even though participants were instructed to complete a maximal effort, they were unable or less motivated to do so as a result of their fatigue state.
RPE is also well-correlated with HR during steady-state and high-intensity cycle training [ 8 ], and so the reduction in RPE might be related to the lowered maximal HR values observed. De Koning et al [ 56 ] has postulated that RPE in a closed-loop trial is dependent on the magnitude and rate of homeostatic disturbance, as well as the knowledge of duration or distance remaining.
It is plausible that participants experienced a greater homeostatic disturbance earlier in the m TT during the loading weeks and subconsciously adjusted their pacing, which led to a subtle reduction in power output, heart rate and RPE.
However, it should also be noted that post-exercise RPE scores are also prone to variability as physiological feedback is diminishing as soon as exercise is terminated and so there can be significant measurement error [ 57 ].
A number of statistical associations were also observed between mood disturbances, perceived recovery and HRV, providing a potential link between training load, mood responses and autonomic nervous system activity. Being some of the earliest to change, these data further reinforce the importance of subjective assessments like RPE as some of the easiest and more reliable markers to monitor athlete wellbeing and training adaptation, particularly within ecological situations such as training camps [ 1 , 46 , 58 , 59 ].
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 ].
In the present study, we suggest that the small but significant changes in FFM between Baseline and the end of the loading period In addition, participants would have demonstrated some muscle damage and EPOC during the intensified training periods but they did not demonstrate an increase in RMR.
A possible explanation for these contradictory findings might be due to the timing of training on the day prior to the RMR measurement, however, in our study, training activity was standardised, and so we are confident our results were not affected in this way. We propose that the decreases in both absolute and relative RMR were due to a compensatory response to the intensified training load or insufficient energy intake, or both.
Taken together with the finding of a reduction in both absolute and relative RMR, these data support earlier studies which have suggested energy conservation under intensified training circumstances [ 20 , 71 ]. One contrasting study found no change in body mass or fat mass in competitive cyclists undertaking two weeks of intensified training [ 18 ], however the study estimated body composition from skinfold measurements, which typically have lower test-retest reliability than the DXA method used in the present study, and so might account for the disparity in the findings.
Nonetheless, our findings emphasize the critical nature of maintaining energy intake, independent of feelings of appetite which might be relatively insensitive , in order to maintain body mass and RMR; each of which are strongly linked [ 72 ].
This notion is particularly important for athletes who cannot afford to lose lean mass, risking a decline in performance from a decrease in muscular strength and power capabilities.
Supplementary CHO ingestion throughout a training cycle has been reported to assist in alleviating the symptoms of overreaching [ 73 , 74 ], and may mitigate the stress hormone response to exercise [ 75 ].
If total energy intake is insufficient, however, acute ingestion of CHO immediately before and after a training session may not provide an attenuation of fatigue-induced decreases in maximal power output or immunological disturbance [ 76 ].
The present cohort attempted to increase their CHO intake by the end of the loading weeks; however such compensation appears not to have been sufficient to attenuate a reduction in RMR or fatigue.
However, we acknowledge that individual appetite responses were highly variable, and so these findings must be interpreted with caution. Leptin is a hormone secreted by the adipose tissue, and is reported to regulate neuroendocrine function, appetite perception and energy homeostasis through a series of complex interactions within the hypothalamus, the mesolimbic dopamine system and hindbrain [ 77 — 81 ].
High leptin levels are associated with increased satiety and energy expenditure, whilst low leptin levels, as seen in the present cohort, are consistent with low levels of body fat and chronic energy restriction [ 77 , 81 — 85 ].
In particular, leptin has been suggested as a marker of training stress in male rowers [ 86 ], and is widely reported to decrease following heavy training periods [ 71 , 87 , 88 ]. In contrast to previous research, leptin levels in the present study tended to increase through the loading weeks, indicating greater satiety; however the responses were highly variable between individuals and so not statistically significant.
Anecdotal reports from athletes within the Australian Institute of Sport cite a loss of appetite with heavy training, but these reflections, and our data, are not consistent with the literature. Another explanation of our findings might relate to dietary intake.
In overweight and obese populations, overfeeding is reported to increase circulating levels of leptin [ 81 ]. More applicable to the present context, perhaps, is that leptin levels are highly correlated with carbohydrate intake [ 89 ], and can be influenced by circulating insulin and pro-inflammatory cytokines such as tumor necrosis factor and interleukin-6 [ 81 ], so it is possible that the observed trend of increased carbohydrate intake during intensified training had some effect.
Despite this, the present data suggest that, in a practical sense, it is crucial for athletes to maintain sufficient energy intake to support their training load. It is possible that athletes should be instructed to eat in relation to the training undertaken, rather than appetite, to fuel optimal performance and recovery.
Free triiodothyronine fT3 has been proposed as a key regulator of metabolic rate and overall energy expenditure by modulating a number of regulatory pathways in skeletal muscle and other tissues [ 90 — 92 ].
Increases in circulating thyroid hormones are broadly associated with an increase in RMR, with the opposite trend occurring in response to lowered hormone levels [ 89 ]. Total T3 tended to decrease in response to chronic energy restriction and high-energy expenditure in a military setting [ 93 ]; and in females, T3 is lower in association with an increased severity of exercise-associated menstrual disturbances, reflective of energy conservation [ 85 ].
In the present study, the percentage change in fT3 demonstrated varied responses throughout the loading and recovery weeks, which did not result in statistical significance. Nonetheless, the substantial changes illustrated in Fig 3F might indicate an altered thyroid and hypothalamic—pituitary—thyroid HPT -axis activity as a result of the intervention, which may have practical implications for energy production and thermogenesis, nutrient metabolism, and the regular functioning of the cardiovascular system [ 94 ].
We were unable to measure these axes directly, however, and so this notion remains speculative and requires further investigation. The observed reduction in LnRMSSD might be attributed to accumulated fatigue as a result of the training load, and may reflect the decreased ability of the ANS to respond to exercise training, stress and illness [ 95 ].
Reductions in LnRMSSD may further indicate parasympathetic hyperactivity or saturation and reduced sympathetic tone [ 96 ] if accompanied by increases in inter-beat intervals [ 97 ], which has been reported in response to periods of intensive training in elite and well-trained endurance athletes [ 97 — ].
We propose that alterations in ANS activity might have influenced metabolic activity, as evidenced by the similar pattern of RMR and HRV responses, and the statistical association between fT3 and HRV.
Fig 3 illustrates a decrease in RMR immediately prior to a decrease in HRV, so it is possible that an increase in parasympathetic activity, with ensuing reduction in sympathetic activity, may influence or be influenced by changes in RMR.
Further research is needed to fully understand this potential association. The present investigation was applied in nature, and whilst scientific rigour was paramount, there remain some limitations that must be acknowledged. Firstly, we acknowledge that our findings need to be interpreted with caution given that individuals, when training intensively, can exhibit highly variable responses, and also the statistically significant changes lay close to both the technical error of measurement and normal day-to-day variability.
The study design consisted of multiple measurements across a number of time points, which resulted in difficulty in applying a statistical model; the power of which would have been improved with both a greater number of participants, as well as simultaneous measurements.
The combination of biological and measurement error further adds complexity, and as such we have focused on the broad trends observed between variables.
We also acknowledge the lack of an independent pair-matched control group, however the difficulty in retaining participants for the course of the six weeks meant it was not possible to recruit a separate cohort for comparison. Whilst this means that it is difficult to conclude with certainty that the changes observed are truly due to the training intervention applied, we are confident that by monitoring the participants for four weeks prior to the study beginning, we were able to gauge an accurate representation of their routine training.
We are thus confident that the physiological changes observed during the study period can indeed be attributed to the increased training load. As such, a number of different central responses might have been produced which we were not able to predict and subsequently assess.
Finally, we recognize that the participants were free-living, trained cyclists, but not elite athletes. As such, they were subject to stressors outside of our control including work and study commitments, family duties, and lifestyle factors which may have added to the imposed training load.
The present data suggest that during periods of intensified training, practitioners should employ a series of monitoring tools—early, and often—to avoid detrimental levels of training-related distress and ensure sufficient energy intake to support the greater energetic demands.
In the daily training environment, athletes should specifically be encouraged to increase their energy intake in relation to training load, rather than appetite, to support a more optimal EA.
The proactive monitoring of subjective wellness, energy intake, power output, body mass and HRV during intensified training may further support athlete health, wellbeing and training ability before a detrimental decline in RMR, and likely EA, becomes apparent. Importantly, a more optimal RMR and EA will, in turn, ensure sufficient energy is available for training, recovery and adaptation, and ultimately, athletic performance.
Athletes often undertake periods of intensified training in order to improve performance following a period of recovery. The present study demonstrates, however that exercising with an increased training load, without sufficient energy intake, can risk significant reductions in both absolute and relative RMR, body mass, HRV and performance, and increased mood disturbance.
Such physiological disturbance and maladaptation to training may be problematic in athletes who cannot afford to lose mass, or those undertaking intense training prior to competition. The proactive monitoring of subjective wellness, energy intake, power output, body mass and HRV during intensified training periods may alleviate fatigue and attenuate any decreases in RMR, and subsequently provide more optimal conditions for a positive training adaptation.
From the initial full model, variables considered non-significant following a backward model selection procedure and subsequently removed are denoted by. Data are presented as individual values for each time point, and group mean ± SD.
We would like to sincerely thank the athletes for their participation in the study, and the staff and students from AIS Physiology, AIS Nutrition, and UCRISE for their assistance with testing sessions.
We would also like to thank Professor Romain Meeusen, Professor Peter Hassmen and Dr Nathan Versey for your advice in designing the study, John Cardinal and Victor Vuong for your assistance with biochemical analysis and Jamie Plowman for your technical expertise.
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Abstract Background Recent 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. Results The intensified training period elicited significant decreases in RMR F 5, Conclusion Intensified 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.
Introduction Periods of intensified training are deliberately programmed to foster physiological and psychological adaptations to potentially improve physical performance. Method Study design Thirteen trained male cyclists completed a six-week training program designed to achieve an overreached state followed by a recovery period.
Download: PPT. Fig 1. Study design showing the training load undertaken in TSS points per week, the training sessions prescribed, and the corresponding physiological and perceptual measures taken.
Participants Fourteen male cyclists were recruited from local cycling and triathlon clubs in Canberra, Australia between December and March for participation in the six-week program.
Preliminary testing In the two weeks prior to the study beginning, participants completed an incremental cycling test to exhaustion using an electromagnetically braked cycle ergometer Lode Excalibur Sport, Groningen, Netherlands to assess V̇O 2max and MAP, as has been described previously [ 33 — 35 ].
Resting metabolic rate RMR was assessed on eleven mornings across the six-week period Fig 1 using the criterion Douglas Bag method of indirect calorimetry, which has been described previously [ 30 ].
Body composition Body composition was assessed immediately following three of the RMR measurements Baseline, end of Loading 2, end of Recovery 2; Fig 1 via Dual-Energy X-Ray Densitometry Lunar iDXA; GE Healthcare Asia-Pacific. Energy intake Dietary intake was recorded either by paper diary record or iPhone application Easy Diet Diary, Xyris Software Pty Ltd, Australia for the three days immediately prior to each RMR measurement Fig 1 , and analysed for total energy intake and macronutrient consumption by an accredited practising dietitian using nutrient analysis software FoodWorks Professional v7.
Appetite Subjective feelings of appetite were assessed prior to breakfast following each RMR measurement via 1—10 Likert visual analogue scale VAS, Fig 1 , adapted from [ 36 ] S1 Fig. Heart rate variability HRV was assessed during the minute rest period of each RMR measurement, for eleven measurements in total Fig 1.
Monitored laboratory sessions and cycling performance Following an initial familiarization on Day 1, 12 monitored laboratory sessions were performed across the six-week period Fig 1 , inclusive of a standardised warm-up, assessment of cycling performance, and a high-intensity interval training HIIT session option 1, 2 or 3 with varied work-rest ratios Table 1.
Table 1. Outline of the monitored laboratory sessions and assessment of cycling performance. On-road cycling On alternate days to the laboratory sessions Fig 1 , participants completed two on-road rides in their own time, with a minimum of five hours between each: 1 long duration, aerobic-based session and 2 a series of hill repeats at FTP in order to induce fatigue.
Biochemical markers PRE-POST ergometer On eight occasions during the monitored laboratory sessions Fig 1 , venous blood samples 1 x 8. Data analysis The present study design involved repeated measures of multiple variables at specific time points, and a number of proposed inter-variable relationships.
Linear mixed models Resting metabolic rate. Table 2. Linear mixed model data for the resting metabolic rate RMR model. Body composition. Energy intake. Biochemical markers. Heart rate variability. Cycling performance. Mood questionnaires. Time course of change Raw data comparisons for each variable across the study period as a percentage change from Day 1 are presented in Fig 3.
Fig 3. Percentage change in measured variables from baseline in relation to training load across the study duration for A RMR, B Body mass, C Total energy intake, D Appetite, E Mood disturbance, F Biochemical markers leptin and fT3, G Heart rate variability LnRMSSD , and H Cycling performance.
Discussion Main findings The present period of intensified training elicited a state of overreaching in trained male cyclists, and significantly decreased both absolute and relative RMR, body mass, fat mass and HRV, with concomitant increases in mood disturbance, and declines in anaerobic performance, aerobic performance and associated peak HR; all of which improved following a period of recovery.
RMR, energy availability and intensified training Relative RMR decreased in the present participants from ~ to kJ. Evidence that overreaching occurred Performance decline. Mood disturbance.
Possible mechanisms for the observed changes in RMR Body composition. Energy intake and appetite. Thyroid hormone. Limitations The present investigation was applied in nature, and whilst scientific rigour was paramount, there remain some limitations that must be acknowledged.
Practical application The present data suggest that during periods of intensified training, practitioners should employ a series of monitoring tools—early, and often—to avoid detrimental levels of training-related distress and ensure sufficient energy intake to support the greater energetic demands.
Conclusion Athletes often undertake periods of intensified training in order to improve performance following a period of recovery. Supporting information. S1 Fig. Subjective feelings of appetite assessment via 1—10 Likert visual analogue scale.
s JPG. S1 Table. Linear mixed model data for the body composition model. s DOCX. S2 Table. Linear mixed model data for the energy intake model.
S3 Table. Linear mixed model data for the appetite model. S4 Table. Linear mixed model data for the biochemical markers model. S5 Table. Linear mixed model data for the heart rate variability model.
S6 Table. Linear mixed model data for the cycling performance model. S7 Table. Linear mixed model data for the mood questionnaire tesponses model. S8 Table. Raw data: Absolute RMR. S9 Table. Raw data: Relative RMR. S10 Table.
Raw data: Minute ventilation [VE STPD ]. S11 Table. Raw data: Body composition. S12a-d Tables. Raw data: Energy intake. S13a-d Tables. Raw data: Appetite. S14a-b Tables. Raw data: Biochemical markers PRE-POST ergometer warm-up.
S15a-b Tables. Raw data: Heart rate variability. S16a-e Tables. Raw data: Cycling performance. S17 Table. Raw data: Mood questionnaires—Multicomponent training distress scale. S18 Table. Raw data: Mood questionnaires—RESTQ sport.
Acknowledgments We would like to sincerely thank the athletes for their participation in the study, and the staff and students from AIS Physiology, AIS Nutrition, and UCRISE for their assistance with testing sessions.
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