Locally applied heat stress during exercise training may promote adaptations to mitochondrial enzyme activities in skeletal muscle.

There is some evidence for temperature-dependent stimulation of mitochondrial biogenesis; however, the role of elevated muscle temperature during exercise in mitochondrial adaptation to training has not been studied in humans in vivo. The purpose of this study was to determine the role of elevating muscle temperature during exercise in temperate conditions through the application of mild, local heat stress on mitochondrial adaptations to endurance training. Eight endurance-trained males undertook 3 weeks of supervised cycling training, during which mild (~ 40 °C) heat stress was applied locally to the upper-leg musculature of one leg during all training sessions (HEAT), with the contralateral leg serving as the non-heated, exercising control (CON). Vastus lateralis microbiopsies were obtained from both legs before and after the training period. Training-induced increases in complex I (fold-change, 1.24 ± 0.33 vs. 1.01 ± 0.49, P = 0.029) and II (fold-change, 1.24 ± 0.33 vs. 1.01 ± 0.49, P = 0.029) activities were significantly larger in HEAT than CON. No significant effects of training, or interactions between local heat stress application and training, were observed for complex I-V or HSP70 protein expressions. Our data provides partial evidence to support the hypothesis that elevating local muscle temperature during exercise augments training-induced adaptations to mitochondrial enzyme activity.

Predicting daily recovery during long-term endurance training using machine learning analysis.

PURPOSE: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures. METHODS: Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model. RESULTS: Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5-23.6 and 0.05-0.44 for AM PRS and HRV change, respectively). CONCLUSION: At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.

Skeletal muscle proteins involved in fatty acid transport influence fatty acid oxidation rates observed during exercise.

Several proteins are implicated in transmembrane fatty acid transport. The purpose of this study was to quantify the variation in fatty acid oxidation rates during exercise explained by skeletal muscle proteins involved in fatty acid transport. Seventeen endurance-trained males underwent a (i) fasted, incremental cycling test to estimate peak whole-body fatty acid oxidation rate (PFO), (ii) resting vastus lateralis microbiopsy, and (iii) 2 h of fed-state, moderate-intensity cycling to estimate whole-body fatty acid oxidation during fed-state exercise (FO). Bivariate correlations and stepwise linear regression models of PFO and FO during 0-30 min (early FO) and 90-120 min (late FO) of continuous cycling were constructed using muscle data. To assess the causal role of transmembrane fatty acid transport in fatty acid oxidation rates during exercise, we measured fatty acid oxidation during in vivo exercise and ex vivo contractions in wild-type and CD36 knock-out mice. We observed a novel, positive association between vastus lateralis FATP1 and PFO and replicated work reporting a positive association between FABPpm and PFO. The stepwise linear regression model of PFO retained CD36, FATP1, FATP4, and FABPpm, explaining ~87% of the variation. Models of early and late FO explained ~61 and ~65% of the variation, respectively. FATP1 and FATP4 emerged as contributors to models of PFO and FO. Mice lacking CD36 had impaired whole-body and muscle fatty acid oxidation during exercise and muscle contractions, respectively. These data suggest that substantial variation in fatty acid oxidation rates during exercise can be explained by skeletal muscle proteins involved in fatty acid transport.

A three-minute all-out test performed in a remote setting does not provide a valid estimate of the maximum metabolic steady state.

PURPOSE: The three-minute all-out test (3MT), when performed on a laboratory ergometer in a linear mode, can be used to estimate the heavy-severe-intensity transition, or maximum metabolic steady state (MMSS), using the end-test power output. As the 3MT only requires accurate measurement of power output and time, it is possible the 3MT could be used in remote settings using personal equipment without supervision for quantification of MMSS. METHODS: The aim of the present investigation was to determine the reliability and validity of remotely performed 3MTs (3MTR) for estimation of MMSS. Accordingly, 53 trained cyclists and triathletes were recruited to perform one familiarisation and two experimental 3MTR trials to determine its reliability. A sub-group (N = 10) was recruited to perform three-to-five 30 min laboratory-based constant-work rate trials following completion of one familiarisation and two experimental 3MTR trials. Expired gases were collected throughout constant-work rate trials and blood lactate concentration was measured at 10 and 30 min to determine the highest power output at which steady-state [Formula: see text] (MMSS-[Formula: see text]) and blood lactate (MMSS-[La-]) were achieved. RESULTS: The 3MTR end-test power (EPremote) was reliable (coefficient of variation, 4.5% [95% confidence limits, 3.7, 5.5%]), but overestimated MMSS (EPremote, 283 ± 51 W; MMSS-[Formula: see text], 241 ± 46 W, P = 0.0003; MMSS-[La-], 237 ± 47 W, P = 0.0003). This may have been due to failure to deplete the finite work capacity above MMSS during the 3MTR. CONCLUSION: These results suggest that the 3MTR should not be used to estimate MMSS in endurance-trained cyclists.

Pre-Exercise Nutrition Habits and Beliefs of Endurance Athletes Vary by Sex, Competitive Level, and Diet.

OBJECTIVE: The purpose of this study was to determine the self-reported beliefs and practices relating to pre-exercise nutrition intake among endurance athletes of varying ages and competitive levels and examine differences based on sex, competitive level, and habitual dietary pattern. METHOD: An anonymous online survey was circulated internationally in English and completed by 1950 athletes of varying competitive levels (51.0% female, mean age 40.9 years [range 18:78]). Survey questions included training background, determinants of pre-exercise nutrition intake and composition, and timing relative to exercise. RESULTS: Prior to morning exercise, 36.4%, 36.0%, and 27.6% of athletes consumed carbohydrate-containing food/drinks before almost every workout, some of the time, and never/rarely, respectively, with significant effects of sex (p < 0.001, Cramer's V (ϕc) = 0.15) and competitive level (p < 0.001, ϕc = 0.09). Nutritional intake before exercise varied based on workout duration for 47.6% of athletes, with significant effects of sex (ϕc = 0.15) and habitual diet (ϕc = 0.19), and based on workout intensity for 39.1% of athletes, with significant effects of sex (ϕc = 0.13) and habitual diet (ϕc = 0.17, all p < 0.001). Additionally, 89.0% of athletes reported using at least some type of dietary supplement (including caffeine from coffee/tea) within 1 hour before exercise. CONCLUSIONS: Overall, nearly all factors measured relating to pre-exercise nutrition intake varied by sex, competitive level, habitual dietary pattern, and/or intensity/duration of the training session and suggest a large number of athletes may not be following current recommendations for optimizing endurance training adaptations.

Prevalence and Determinants of Fasted Training in Endurance Athletes: A Survey Analysis.

Athletes may choose to perform exercise in the overnight-fasted state for a variety of reasons related to convenience, gut comfort, or augmenting the training response, but it is unclear how many endurance athletes use this strategy. We investigated the prevalence and determinants of exercise performed in the overnight-fasted state among endurance athletes using an online survey and examined differences based on sex, competitive level, and habitual dietary pattern. The survey was completed by 1,950 endurance athletes (51.0% female, mean age 40.9 ± 11.1 years). The use of fasted training was reported by 62.9% of athletes, with significant effects of sex (p < .001, Cramer's V [φc] = 0.18, 90% CI [0.14, 0.22]), competitive level (p < .001, φc = 0.09, 90% CI [0.5, 0.13]), and habitual dietary pattern noted (p < .001, φc = 0.26, 90% CI [0.22, 0.29]). Males, nonprofessional athletes, and athletes following a low-carbohydrate, high-fat diet were most likely to perform fasted training. The most common reasons for doing so were related to utilizing fat as a fuel source (42.9%), gut comfort (35.5%), and time constraints/convenience (31.4%), whereas the most common reasons athletes avoided fasted training were that it does not help their training (47.0%), performance was worse during fasted training (34.7%), or greater hunger (34.6%). Overall, some athletes perform fasted training because they think it helps their training, whereas others avoid it because they think it is detrimental to their training goals, highlighting a need for future research. These findings offer insights into the beliefs and practices related to fasted-state endurance training.

International society of sports nutrition position stand: ketogenic diets.

POSITION STATEMENT: The International Society of Sports Nutrition (ISSN) provides an objective and critical review of the use of a ketogenic diet in healthy exercising adults, with a focus on exercise performance and body composition. However, this review does not address the use of exogenous ketone supplements. The following points summarize the position of the ISSN.1. A ketogenic diet induces a state of nutritional ketosis, which is generally defined as serum ketone levels above 0.5 mM. While many factors can impact what amount of daily carbohydrate intake will result in these levels, a broad guideline is a daily dietary carbohydrate intake of less than 50 grams per day.2. Nutritional ketosis achieved through carbohydrate restriction and a high dietary fat intake is not intrinsically harmful and should not be confused with ketoacidosis, a life-threatening condition most commonly seen in clinical populations and metabolic dysregulation.3. A ketogenic diet has largely neutral or detrimental effects on athletic performance compared to a diet higher in carbohydrates and lower in fat, despite achieving significantly elevated levels of fat oxidation during exercise (~1.5 g/min).4. The endurance effects of a ketogenic diet may be influenced by both training status and duration of the dietary intervention, but further research is necessary to elucidate these possibilities. All studies involving elite athletes showed a performance decrement from a ketogenic diet, all lasting six weeks or less. Of the two studies lasting more than six weeks, only one reported a statistically significant benefit of a ketogenic diet.5. A ketogenic diet tends to have similar effects on maximal strength or strength gains from a resistance training program compared to a diet higher in carbohydrates. However, a minority of studies show superior effects of non-ketogenic comparators.6. When compared to a diet higher in carbohydrates and lower in fat, a ketogenic diet may cause greater losses in body weight, fat mass, and fat-free mass, but may also heighten losses of lean tissue. However, this is likely due to differences in calorie and protein intake, as well as shifts in fluid balance.7. There is insufficient evidence to determine if a ketogenic diet affects males and females differently. However, there is a strong mechanistic basis for sex differences to exist in response to a ketogenic diet.

Factors Influencing Substrate Oxidation During Submaximal Cycling: A Modelling Analysis.

BACKGROUND: Multiple factors influence substrate oxidation during exercise including exercise duration and intensity, sex, and dietary intake before and during exercise. However, the relative influence and interaction between these factors is unclear. OBJECTIVES: Our aim was to investigate factors influencing the respiratory exchange ratio (RER) during continuous exercise and formulate multivariable regression models to determine which factors best explain RER during exercise, as well as their relative influence. METHODS: Data were extracted from 434 studies reporting RER during continuous cycling exercise. General linear mixed-effect models were used to determine relationships between RER and factors purported to influence RER (e.g., exercise duration and intensity, muscle glycogen, dietary intake, age, and sex), and to examine which factors influenced RER, with standardized coefficients used to assess their relative influence. RESULTS: The RER decreases with exercise duration, dietary fat intake, age, VO2max, and percentage of type I muscle fibers, and increases with dietary carbohydrate intake, exercise intensity, male sex, and carbohydrate intake before and during exercise. The modelling could explain up to 59% of the variation in RER, and a model using exclusively easily modified factors (exercise duration and intensity, and dietary intake before and during exercise) could only explain 36% of the variation in RER. Variables with the largest effect on RER were sex, dietary intake, and exercise duration. Among the diet-related factors, daily fat and carbohydrate intake have a larger influence than carbohydrate ingestion during exercise. CONCLUSION: Variability in RER during exercise cannot be fully accounted for by models incorporating a range of participant, diet, exercise, and physiological characteristics. To better understand what influences substrate oxidation during exercise further research is required on older subjects and females, and on other factors that could explain additional variability in RER.

Factors Influencing AMPK Activation During Cycling Exercise: A Pooled Analysis and Meta-Regression.

BACKGROUND: The 5' adenosine monophosphate (AMP)-activated protein kinase (AMPK) is a cellular energy sensor that is activated by increases in the cellular AMP/adenosine diphosphate:adenosine triphosphate (ADP:ATP) ratios and plays a key role in metabolic adaptations to endurance training. The degree of AMPK activation during exercise can be influenced by many factors that impact on cellular energetics, including exercise intensity, exercise duration, muscle glycogen, fitness level, and nutrient availability. However, the relative importance of these factors for inducing AMPK activation remains unclear, and robust relationships between exercise-related variables and indices of AMPK activation have not been established. OBJECTIVES: The purpose of this analysis was to (1) investigate correlations between factors influencing AMPK activation and the magnitude of change in AMPK activity during cycling exercise, (2) investigate correlations between commonly reported measures of AMPK activation (AMPK-α2 activity, phosphorylated (p)-AMPK, and p-acetyl coenzyme A carboxylase (p-ACC), and (3) formulate linear regression models to determine the most important factors for AMPK activation during exercise. METHODS: Data were pooled from 89 studies, including 982 participants (93.8% male, maximal oxygen consumption [[Formula: see text]] 51.9 ± 7.8 mL kg-1 min-1). Pearson's correlation analysis was performed to determine relationships between effect sizes for each of the primary outcome markers (AMPK-α2 activity, p-AMPK, p-ACC) and factors purported to influence AMPK signaling (muscle glycogen, carbohydrate ingestion, exercise duration and intensity, fitness level, and muscle metabolites). General linear mixed-effect models were used to examine which factors influenced AMPK activation. RESULTS: Significant correlations (r = 0.19-0.55, p < .05) with AMPK activity were found between end-exercise muscle glycogen, exercise intensity, and muscle metabolites phosphocreatine, creatine, and free ADP. All markers of AMPK activation were significantly correlated, with the strongest relationship between AMPK-α2 activity and p-AMPK (r = 0.56, p < 0.001). The most important predictors of AMPK activation were the muscle metabolites and exercise intensity. CONCLUSION: Muscle glycogen, fitness level, exercise intensity, and exercise duration each influence AMPK activity during exercise when all other factors are held constant. However, disrupting cellular energy charge is the most influential factor for AMPK activation during endurance exercise.

Racing and Training Physiology of an Elite Ultra-Endurance Cyclist: Case Study of 2 Record-Setting Performances.

PURPOSE: To present a case report of an elite ultra-endurance cyclist, who was the winner and course record holder of 2 distinct races within a 4-month span: a 24-hour solo cycling race and a 2-man team multiday race (Race Across America). METHODS: The athlete's raw data (cycling power, heart rate [HR], speed, and distance) were obtained and analyzed for 2 ultra-endurance races and 11 weeks of training in between. RESULTS: For the 24-hour race, the athlete completed 861.6 km (average speed 35.9 km·h-1, average power 210 W [2.8 W·kg-1], average HR 121 beats per minute) with a 37% decrease in power and a 22% decrease in HR throughout the race. During the 11 weeks between the 24-hour race and Race Across America, training intensity distribution (Zone 1/2/3) based on HR was 51%/39%/10%. For the Race Across America, total team time to complete the 4939-km race was 6 days, 10 hours, 39 minutes, at an average speed of 31.9 km·h-1. Of this, the athlete featured in this case study rode 75.2 hours, completing 2532 km (average speed 33.7 km·h-1, average power 203 W [2.7 W·kg-1]), with a 12% decrease in power throughout the race. Power during daytime segments was greater than nighttime (212 [25] vs 189 [18] W, P < .001, ηp2=.189). CONCLUSIONS: This case report highlights the performance requirements of elite ultra-endurance cycling. Although average power was similar when riding for 24 hours continuously and 75 hours intermittently over 6.5 days, there were large differences in pacing strategies and within-day power-output changes.

Pre-Exercise Carbohydrate or Protein Ingestion Influences Substrate Oxidation but Not Performance or Hunger Compared with Cycling in the Fasted State.

Nutritional intake can influence exercise metabolism and performance, but there is a lack of research comparing protein-rich pre-exercise meals with endurance exercise performed both in the fasted state and following a carbohydrate-rich breakfast. The purpose of this study was to determine the effects of three pre-exercise nutrition strategies on metabolism and exercise capacity during cycling. On three occasions, seventeen trained male cyclists (VO2peak 62.2 ± 5.8 mL·kg-1·min-1, 31.2 ± 12.4 years, 74.8 ± 9.6 kg) performed twenty minutes of submaximal cycling (4 × 5 min stages at 60%, 80%, and 100% of ventilatory threshold (VT), and 20% of the difference between power at the VT and peak power), followed by 3 × 3 min intervals at 80% peak aerobic power and 3 × 3 min intervals at maximal effort, 30 min after consuming a carbohydrate-rich meal (CARB; 1 g/kg CHO), a protein-rich meal (PROTEIN; 0.45 g/kg protein + 0.24 g/kg fat), or water (FASTED), in a randomized and counter-balanced order. Fat oxidation was lower for CARB compared with FASTED at and below the VT, and compared with PROTEIN at 60% VT. There were no differences between trials for average power during high-intensity intervals (367 ± 51 W, p = 0.516). Oxidative stress (F2-Isoprostanes), perceived exertion, and hunger were not different between trials. Overall, exercising in the overnight-fasted state increased fat oxidation during submaximal exercise compared with exercise following a CHO-rich breakfast, and pre-exercise protein ingestion allowed similarly high levels of fat oxidation. There were no differences in perceived exertion, hunger, or performance, and we provide novel data showing no influence of pre-exercise nutrition ingestion on exercise-induced oxidative stress.

Effects of Dietary Supplements on Adaptations to Endurance Training.

Endurance training leads to a variety of adaptations at the cellular and systemic levels that serve to minimise disruptions in whole-body homeostasis caused by exercise. These adaptations are differentially affected by training volume, training intensity, and training status, as well as by nutritional choices that can enhance or impair the response to training. A variety of supplements have been studied in the context of acute performance enhancement, but the effects of continued supplementation concurrent to endurance training programs are less well characterised. For example, supplements such as sodium bicarbonate and beta-alanine can improve endurance performance and possibly training adaptations during endurance training by affecting buffering capacity and/or allowing an increased training intensity, while antioxidants such as vitamin C and vitamin E may impair training adaptations by blunting cellular signalling but appear to have little effect on performance outcomes. Additionally, limited data suggest the potential for dietary nitrate (in the form of beetroot juice), creatine, and possibly caffeine, to further enhance endurance training adaptation. Therefore, the objective of this review is to examine the impact of dietary supplements on metabolic and physiological adaptations to endurance training.

What Should I Eat before Exercise? Pre-Exercise Nutrition and the Response to Endurance Exercise: Current Prospective and Future Directions.

The primary variables influencing the adaptive response to a bout of endurance training are exercise duration and exercise intensity. However, altering the availability of nutrients before and during exercise can also impact the training response by modulating the exercise stimulus and/or the physiological and molecular responses to the exercise-induced perturbations. The purpose of this review is to highlight the current knowledge of the influence of pre-exercise nutrition ingestion on the metabolic, physiological, and performance responses to endurance training and suggest directions for future research. Acutely, carbohydrate ingestion reduces fat oxidation, but there is little evidence showing enhanced fat burning capacity following long-term fasted-state training. Performance is improved following pre-exercise carbohydrate ingestion for longer but not shorter duration exercise, while training-induced performance improvements following nutrition strategies that modulate carbohydrate availability vary based on the type of nutrition protocol used. Contrasting findings related to the influence of acute carbohydrate ingestion on mitochondrial signaling may be related to the amount of carbohydrate consumed and the intensity of exercise. This review can help to guide athletes, coaches, and nutritionists in personalizing pre-exercise nutrition strategies, and for designing research studies to further elucidate the role of nutrition in endurance training adaptations.

Effect of low- and high-carbohydrate diets on swimming economy: a crossover study.

BACKGROUND: Swimming economy refers to the rate of energy expenditure relative to swimming speed of movement, is inversely related to the energetic cost of swimming, and is as a key factor influencing endurance swimming performance. The objective of this study was to determine if high-carbohydrate, low-fat (HCLF) and low-carbohydrate, high-fat (LCHF) diets affect energetic cost of submaximal swimming. METHODS: Eight recreational swimmers consumed two 3-day isoenergetic diets in a crossover design. Diets were tailored to individual food preferences, and macronutrient consumption was 69-16-16% and 16-67-18% carbohydrate-fat-protein for the HCLF and LCHF diets, respectively. Following each 3-day dietary intervention, participants swam in a flume at velocities associated with 50, 60, and 70% of their maximal aerobic capacity (VO2max). Expired breath was collected and analyzed while they swam which enabled calculation of the energetic cost of swimming. A paired t-test compared macronutrient distribution between HCLF and LCHF diets, while repeated-measures ANOVA determined effects of diet and exercise intensity on physiological endpoints. RESULTS: Respiratory exchange ratio was significantly higher in HCLF compared to LCHF (p = 0.003), but there were no significant differences in the rate of oxygen consumption (p = 0.499) or energetic cost of swimming (p = 0.324) between diets. Heart rate did not differ between diets (p = 0.712), but oxygen pulse, a non-invasive surrogate for stroke volume, was greater following the HCLF diet (p = 0.029). CONCLUSIONS: A 3-day high-carbohydrate diet increased carbohydrate utilization but did not affect swimming economy at 50-70% VO2max. As these intensities are applicable to ultramarathon swims, future studies should use higher intensities that would be more relevant to shorter duration events.