Achieving energy balance with a high‐fat meal does not enhance skeletal muscle adaptation and impairs glycaemic response in a sleep‐low training model

What is the central question of this study? Does achieving energy balance mainly with ingested fat in a ‘sleep‐low’ model of training with low muscle glycogen affect the early training adaptive response during recovery? What is the main finding and its importance? Replenishing the energy expended during exercise mainly from ingested fat to achieve energy balance in a ‘sleep‐low’ model does not enhance the response of skeletal muscle markers of early adaptation to training and impairs glycaemic control the morning after compared to training with low energy availability. These findings are important for optimizing post‐training dietary recommendations in relation to energy balance and macronutrient intake.


INTRODUCTION
Training with low endogenous and exogenous carbohydrate availability (LCHO) by means of strategically restricting carbohydrate intake can acutely enhance skeletal muscle response to endurance exercise (Bartlett, Hawley, & Morton, 2015). The physiological response to LCHO has been researched intensely over the past decade and the improved adaptation has been attributed to enhanced intracellular events increasing muscle oxidative capacity, triggered by low glycogen regulation of key kinases (e.g. AMP-activated protein kinase (AMPK)), transcription factors (e.g. p53) and transcriptional co-activators (e.g. peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α)) (Bartlett et al., 2015). However, the majority of the studies reporting beneficial metabolic adaptations to LCHO have concomitantly induced an acute state of low energy intake in the LCHO experimental groups (Bartlett et al., 2013;Impey et al., 2016;Lane et al., 2015;Morton et al., 2009;Yeo et al., , 2010. Provided that energy deficit can increase muscle oxidative capacity (Civitarese et al., 2007;Coen et al., 2015), it is unclear if a negative energy balance plays a role skeletal muscle adaptation during LCHO training.
Chronic energy deficit (ED)/low energy availability (LEA) has also been associated with systemic physiological effects that negatively affect health and may impair training adaptation (De Souza et al., 2014;De Souza, Koltun, & Williams, 2019;Mountjoy et al., 2018).
ED can affect skeletal muscle physiological response and we have shown that 5 days of ED down-regulates skeletal muscle protein synthesis (Areta et al., 2014), but how ED affects adaptation to endurance-type training is unclear. If ED impairs training adaptation during LCHO-training, increasing fat intake could rescue the energy balance to ultimately restore the normal adaptive response. Moreover, increasing circulating fatty acids through diet could enhance skeletal muscle oxidative capacity through up-regulation of AMPK activity , carnitine-acyl transferase activity (Goedecke et al., 1999), and fatty-acid translocase (FAT/CD36) (Cameron-Smith et al., 2003).
Short-term high-fat diets have, however, also been shown to negatively affect metabolism and training adaptation to endurancetype training. Research using various experimental protocols shows that short-term high-fat diets down-regulate pyruvate dehydrogenase (PDH) activity and skeletal muscle capacity for glycogenolysis (Stellingwerff et al., 2006) and mitochondrial respiration (Leckey et al., 2018); ultimately impairing high-intensity exercise capacity (Havemann et al., 2006). Research from our group has also shown that an acute (15 h) high-fat low-carbohydrate diet impairs the early adaptive response during recovery from endurance training, compared to a high-CHO energy-matched group (Hammond et al., 2016).
Based on these previous findings, therefore, it is not possible to rule out if achieving energy balance from ingested fat can enhance the early response to skeletal muscle in LCHO training. Recent findings from our group show no difference in markers of skeletal muscle adaptation between LCHO low energy and LCHO high fat (energy balance) during two consecutive aerobic training sessions

New Findings
• What is the central question of this study?
Does achieving energy balance mainly with ingested fat in a 'sleep-low' model of training with low muscle glycogen affect the early training adaptive response during recovery?
• What is the main finding and its importance?
Replenishing the energy expended during exercise mainly from ingested fat to achieve energy balance in a 'sleep-low' model does not enhance the response of skeletal muscle markers of early adaptation to training and impairs glycaemic control the morning after compared to training with low energy availability. These findings are important for optimizing post-training dietary recommendations in relation to energy balance and macronutrient intake. (Hammond et al., 2019). However, the lack of differences observed in this study can be due to the overriding effect of the short time frame (2.5 h) between the two consecutive training sessions of the 'twice-a-day' model used (Andrade-Souza et al., 2019). To eliminate this confounding factor, the 'sleep-low, train-low' model has emerged as a particularly potent strategy to prolong the period of CHO and energy restriction between two sessions (Bartlett et al., 2013;Lane et al., 2015).

Ethical approval
The study was approved by the Norwegian School of Sport Sciences Ethics Committee (Application ID 01-020517) and conformed to the standards of the Declaration of Helsinki. The study was registered in the Norwegian Center for Research Data (NSD) with reference number 54131/3/ASF. All subjects were informed about the nature of the study and possible risks involved and gave written consent.

Subjects
Nine well-trained male endurance cyclists/triathletes completed the study. The participants characteristics were as follows: age:

Cycling gross efficiency
Following 10-15 min recovery after PPO test, subjects cycled for three consecutive 5-min stages at 50, 67 and 85% of PPO, respectively, for determination of cycling gross efficiency as reported by Moseley & Jeukendrup (2001) using the respiratory data of the last minute of each stage.

Resting metabolic rate
On a separate day, resting metabolic rate (RMR) was assessed in the morning (6.30-7.30 am). Participants were instructed to keep exercise to ∼1 h of moderate intensity in the afternoon at the latest the day prior, remain fasted in the morning, not to consume caffeine or nicotine on the day, and to arrive at the laboratory by car or public transport.
Following 10 min of lying supine, a face-mask was fitted to the subjects and breath-by-breath respiratory data were collected for 25 min using a calibrated Oxycon Pro metabolic system, while the subjects remained still. The last 20 min of data were used to estimate 24 h RMR, calculated using oxygen equivalents (Weir, 1949). Pilot testing of this protocol in 10 individuals tested twice on consecutive days showed a coefficient of variation of 4.8 ± 2.7% for RMR and 2.2 ± 2.8% for respiratory exchange ratio (RER).

Body composition
Following RMR assessment, body composition was assessed with dualenergy X-ray absorptiometry (Lunar iDXA, GE Healthcare, Madison, WI, USA with GE Healthcare enCORE software version 14.10.022).

Study overview
In a randomized, counterbalanced, cross-over design, participants visited the laboratory twice for overnight 'sleep-low' interventions (Lane et al., 2015), with a period of 1-2 weeks between the two visits Upon waking, sleep quality rating was assessed, and RMR immediately assessed using the same protocol as in baseline testing in a dedicated room. Afterwards, a catheter (18 G, BD, Franklin Lakes, NJ, USA) was placed in the antecubital vein for serial blood sampling, and a baseline muscle biopsy taken from the vastus lateralis using a 6 mm Bergström needle modified for manual suction, following local anaesthesia (1% lidocaine, AstraZeneca, Cambridge, UK). Then, participants completed a 75 min exercise session, during which respiratory gases and venous blood were sampled. A recovery drink was provided at 30 min recovery, immediately after the second muscle biopsy was taken. Blood was collected 30, 60, 120 and 180 min post-drink, and at 3.5 h recovery a final muscle biopsy was collected. Muscle biopsies were therefore taken pre-exercise and at 0.5 and 3.5 h recovery. The blood sampling, biopsies and recovery drink timing are all reported relative to the end of the 75 min exercise session, which is the time 'zero' (0 min). All blood samples were collected in 6 ml EDTA-containing vacuum sealed tubes (BD), immediately spun at 3000 g for 10 min at 4 • C, and plasma aliquoted and stored at −80 • C for later analysis.
F I G U R E 1 Schematic overview of experimental trials. On the evening of day 1, participants undertook a glycogen-depleting session until reaching 30 kcal kg −1 fat free mass (FFM) of metabolic energy expenditure. Immediately after the session, participants were provided with low carbohydrate (CHO) meals to completely (energy balance, high-fat) or partially (energy deficit, low-fat) restore the energy expended during exercise. In the morning of day 2, resting metabolic rate (RMR) was assessed upon waking. Subsequently a 75 min training session incorporating changes of intensity was completed and a CHO + protein (PROT) drink was ingested to optimize recovery. Skeletal muscle biopsies and venous blood samples were obtained during the morning. Visual analogue scales (VAS), at indicated time points were used to assess subjective appreciation of hunger and sleeping quality (upon waking only) Metabolic energy expenditure was estimated with a custom-made automated spreadsheet based on the duration, absolute power output of each interval and gross efficiency of each individual determined during baseline testing.
If an individual could not complete a 2 min interval at 85% of PPO, intensity was decreased 5% for each subsequent higher intensity interval. This pattern was repeated up until the participant failed to maintain 60% PPO, after which intensity was clamped at 50% PPO.
Note that the total metabolic workload of 30 kcal (kg FFM) −1 is not related to the alleged threshold for energy availability (Ihle & Loucks, 2004). This workload was determined from piloting as a manageable workload capable of both significantly increasing total energy expenditure and depleting muscle glycogen given the duration and intensity of the session (Areta & Hopkins, 2018).

Morning exercise session
The exercise session undertaken on day 2 lasted a total of 75 min.
Provided the participants would have not been able to complete a full high intensity session due to the lack of fuel substrate to match the energy demands (i.e. glycogen)  Treatment meals macronutrient intake Values of RMR, exercise energy expenditure and energy balance are means ± SD. Energy availability is defined as (total exercise energy intake -exercise energy expenditure)/FFM. *Based on baseline resting metabolic rate measurement. †Estimated for each individual during glycogendepletion session based on individual efficiency, does not include estimation of incidental physical activity during the day. EB, energy balance; ED, energy deficit; FFM, fat free mass; RMR, resting metabolic rate.
of fat, carbohydrates and protein, respectively, providing 40 kcal (kg FFM) −1 day −1 . The last meal was consumed in the laboratory ∼45-60 min prior to the glycogen-depleting session and contained 0.1, 2 and 0.3 g (kg FFM) −1 of fat, carbohydrates and protein, respectively.

Dietary treatments
The EB-HF or ED-LF treatment meals were protein and carbohydratematched meals that were approximately isovolumetric. Details of diets including the average energy intake of day 1 in addition to macronutrient composition of treatment meals are reported in Table 1.
All calculations were made to target divergent energy availability values in each group, provided energy availability is likely to be a key parameter in relation to the physiological effect of energy restriction (Loucks, Kiens, & Wright, 2011). References to 'energy balance' values are made due to its being a more widespread concept and for ease of interpretation to the reader and these were calculated retrospectively.

Recovery drink
A recovery drink was provided after the second muscle biopsy in the morning exercise session, and contained 1.2 and 0.38 g (kg FFM) −1 of carbohydrates and protein (maltodextrin and whey protein isolate), respectively. The purpose of this drink was to stimulate recovery processes, as supported by current evidence (Moore, Camera, Areta, & Hawley, 2014), which is also in accordance to current practice in endurance sports.

Subjective hunger and sleep quality ratings
Visual analogue scales (VAS) were used for assessment of participants' feeling of hunger 45-60 min post-dinner (day 1), immediately post-RMR assessment and at 2.5 h recovery from exercise (day 2), and sleep quality rating immediately upon waking. The VAS were 10 cm lines displaying the extremes 'no hunger at all' and 'worst possible hunger' and 'worse sleep I ever had' and 'best sleep I ever had' for hunger and sleep ratings, respectively.

Calculation of substrate utilization
Substrate utilization was determined using non-protein RER calculations and described in detail elsewhere (Torrens et al., 2016).

Muscle glycogen content
Freeze-dried muscle samples were hydrolysed with 1.8 M HCl (100 • C, 2.5 h), neutralized with 6 M NaOH and determined fluorometrically as glucose units using an enzymatic assay (Jensen et al., 2012).

Immunoblotting
Muscle was processed and analysed as previously described (Stocks, Dent, Ogden, Zemp, & Philp, 2019). All primary antibodies were prepared in Tris-buffered saline-Tween 20 at a dilution of 1:1000 except for p-p38, which was prepared in 5% BSA. Antibodies used were

RT-qPCR
RT-qPCR was employed for the determination of relative mRNA abundance of targeted genes (

Statistical analyses
Data were analysed using one-or two-way repeated-measures ANOVA, as appropriate, with Student-Newman-Keuls post hoc analysis to correct for the family-wise error during multiple post hoc tests (SigmaPlot for Windows Version 13, Systat Software, Inc., San Jose, CA, USA). Grouped data were analysed using Student's paired t test and magnitudes of difference in area under the curve for blood parameters were analysed with effect sizes (ES) with an on-line-available tool (sportsci.org/resource/stats/xcrossover.xls) following guidelines explained therein. All data are presented as means ± standard deviation (SD) and the level of statistical significance was set at P < 0.05.

Glycogen depleting/energy expenditure sessions
The glycogen-depleting sessions were completed with average values of: 266 ± 26 W absolute power, 65.3 ± 1.3% of PPO, 15.2 ± 1.2 RPE at 82 ± 4% of maximal heart rate with a total duration of 109 ± 8 min, and estimated metabolic energy expended was 2031 ± 177 kcal (30 ± 0.1 kcal (kg FFM) −1 ), displaying no differences in any of these variables between groups.

Physiological parameters during morning exercise
The average heart rate for the morning exercise session was 142 ± 10 bpm (76 ± 4% max), and the maximal values were 166 ± 10 bpm (88 ± 3% max). Physiological and metabolic parameters during the submaximal portion of exercise are reported in Table 3. None of these variables showed a treatment or time effect.

Free fatty acids, glycerol and βHB
There

Glucose, insulin and lactate
There was a main effect of time for glucose (P < 0.001) and a time × treatment interaction (P = 0.033) with no significant main effect of treatment (P = 0.069). Thirty minutes post-recovery drink, plasma glucose reached higher values in EB versus ED (8.4 ± 1.6 vs.

F I G U R E 3
Skeletal muscle glycogen (a), and phosphorylation status of AMPK Thr172 (b), ACC Ser79 (c), 4EBP-1 Thr47/46 (d), S6K Thr389 (e) and eEF2 Thr56 (f) relative to pre-ex EB and representative blots. Samples were taken pre-exercise, and at 0.5 and 3.5 h recovery from exercise, including a recovery drink (1.2 and 0.38 g (kg FFM) −1 of carbohydrates and protein, respectively) at 0.5 h recovery. Values are means ± SD. Signalling data are fold change relative to pre-EX EB-HF. Time point difference from: a Pre-EX; b 0.5 h post-EX and c 3.5 h post-EX (P < 0.05). EB-HF, energy balance-high fat; ED-LF, energy deficit-low fat Values are means ± SD. Main effects that are significant (P < 0.05) are indicated in bold.*Significantly different from EB within time point, P = 0.005.

Muscle mRNA expression
Genes related to mitochondrial biogenesis and mitochondrial proteins were mainly responsive to exercise (Table 4). PGC-1α showed a time effect (P = 0.02) and p53 showed a time effect (P = 0.005), and a time × treatment interaction (P = 0.027) with no significant treatment effect (P = 0.073). For p53, within ED, mRNA was higher at 3.5 h compared to 0 h (P < 0.001), and within the 3.5 h time point ED-LF was higher than EB-HF (P = 0.005).

Subjective ratings of hunger and sleep quality
There was a main effect of time for subjective ratings of hunger (P < 0.001) with values increasing from 21 ± 18 mm post-dinner to 60 ± 17 mm post-RMR and 65 ± 19 mm at 3.5 h recovery. There was no significant difference in perception of increased sleep quality in EB-HF (64 ± 20 mm) versus ED-LF (52 ± 21 mm; P = 0.078).

DISCUSSION
The main finding of this study is that restoring the energy expended after glycogen-depleting exercise with an EB-HF meal has no positive effect on training adaptations and impairs glycaemic regulation posttraining the following day compared to an ED-LF meal. Specifically, there were no meaningful or large differences between acute ED-LF and EB-HF on early markers of muscle adaptation to endurancetype training such as intracellular signalling and mRNA expression of pathways associated to substrate utilization and mitochondrial biogenesis. In addition, glucose regulation and the insulin response were impaired in EB-HF compared to ED-LF after a post-training carbohydrate-containing meal. To our knowledge this is the first study addressing if replenishing the energy expended during exercise during a low-carbohydrate with fat benefits adaptation to endurance training using a 'sleep-low' model. This extends our recent findings using a 'twice-a-day' model where we showed no differences in the adaptive response between low-CHO energy deficit and low-CHO energy balance (high fat) treatment groups (Hammond et al., 2019).
Moreover, these results suggest that replenishing energy expended during exercise with a high-fat meal is more likely to impair than enhance the adaptive response to training with low skeletal muscle glycogen.
Using a sleep-low train-low model in which a high-fat meal was consumed prior to sleep, we studied trained cyclists who 12 h later commenced morning exercise with reduced but comparable pre-exercise muscle glycogen but divergent amounts of energy availability (Table 1), all undertaken in a strictly controlled setting.
The morning skeletal muscle glycogen concentrations group means of ∼350 mmol kg −1 dry mass ( Figure 3) were in line with expected values for this population after training and low carbohydrate diet (Areta & Hopkins, 2018), and are in accordance with the diminished morning RERs indicating low carbohydrate availability (Bergström, Hermansen, Hultman, & Saltin, 1967). Despite the divergent energy availability between groups (Table 1), there were no differences in RMR, sleep or hunger, and substrate utilization, and perceptual responses were comparable between trials. There was, additionally, a large use of fat of ∼1.1 g min −1 exercising at 60% ofV O 2 max (Table 3) in both groups, which is close to twofold that of the maximal rate of fat oxidation we observed in athletes under normal conditions (Areta, Austarheim, Wangensteen, & Capelli, 2018). Data therefore are in agreement with classic literature (Bergström et al., 1967)  Genes associated to carbohydrate metabolism (pyruvate dehydrogenase kinase 4 (PDK4)) and mitochondrial biogenesis (PGC-1α and p53) were up-regulated post-training in both groups and despite no effect of diet on genes associated to fat metabolism, with a small response of the p53 gene in the energy deficit group (Table 4). The magnitude in the change of PDK4 mRNA was lower than that observed in previous studies (Lane et al., 2015;Pilegaard et al., 2005) probably due to being dampened by the carbohydrate ingested in the recovery drink, which has been shown to completely blunt the exercise-induced increase in PDK4 mRNA 4 h post-exercise (Cluberton, McGee, Murphy, & Hargreaves, 2005). This may also have affected the fat metabolism genes post-exercise, as the lack of change in CD36 or carnitine palmitoyltransferase I (CPT1) mRNA in EB-HF was surprising given the previously documented increase of these in high-fat interventions (Hammond et al., 2016;Pilegaard et al., 2005). Instead, the most prominent gene responses to exercise was that of p53 and PGC-1α, which is similar to previous observations in response to exercise (Bartlett et al., 2013;Hammond et al., 2016;Impey et al., 2016). Overexpression of p53 has been linked to increased mitochondrial content and aerobic capacity (Park et al., 2009) and is thought to play a key role in mitochondrial biogenesis (Bartlett et al., 2013). Additionally, as a further benefit in the ED-LF intervention we also observed improved glucose control in the morning.
We show for the first time that glucose regulation is impaired after a second bout of exercise when a high-fat meal is ingested following a glycogen-depleting exercise bout the day before. Prior research has shown that an acute exercise session followed by a 36 h high-fat diet (Sparti & Décombaz, 1992) or 7 h intralipid infusion (Pehmoller et al., 2012) impaired exogenous glucose and insulin regulation, but these studies incorporated no exercise prior the provision of carbohydrates.
Using a similar design to these, Newsom et al. (2010) showed that low muscle glycogen, rather than energy deficit, was related to improved intravenous glucose disposal. Our findings were unexpected given we show no difference in muscle glycogen between groups and other studies showed no effect of large amounts of fat provision intravenously and orally after exercise on glucose regulation (Fox, Kaufman, & Horowitz, 2004;Schenk, Cook, Kaufman, & Horowitz, 2005).
Therefore, we did not expect to observe this effect from a single highfat meal given the large energy expenditure on both training sessions and the training status of our participants, allegedly with a superior insulin sensitivity compared to the normal population (Steenberg et al., 2019). Our data may partially be explained by the recent findings that chronic training reduces the acute insulin-sensitizing effect of acute exercise, which has been linked to AMPK signalling in muscle (Steenberg et al., 2019). However, we could not link AMPK signalling to blood glucose regulation.
In relation to the activation of the energy-sensing AMPK pathway, showed increased phosphorylation early post-exercise in both groups ( Figure 3c) returning to baseline afterwards as we (Bartlett et al., 2013) and others (Wojtaszewski et al., 2003) have previously shown.
Altogether, these results suggest that ED-LF, did not seem to further up-regulate the 'energy-sensing' AMPK signalling at these time points suggesting no interference, synergistic or additive effect between muscle glycogen and energy status in the phosphorylation status of key proteins in this pathway.
The energy status or macronutrient intake did not affect skeletal muscle protein synthesis intracellular markers as evidenced by the phosphorylation status of the mechanistic target of rapamycin (mTOR) pathway. We have previously shown that 5 days of reduced energy availability had no effect on mTOR or its downstream effector p70S6K (Areta et al., 2014), and the current results expand those findings by showing also no effect with endurance exercise. However, in that study (Areta et al., 2014) there was a disconnect between the early signalling response and a measured reduction in myofibrillar protein synthesis. Moreover, it is possible that p70S6K activity may have been down-regulated by high-fat feeding as we previously documented (Hammond et al., 2016). Nevertheless, p-eEF2 Thr56 decreased at all time points post-exercise likely responding to exercise, and p-S6K Thr389 increased 3.5 h post-exercise likely due to increased amino-acidaemia from the recovery drink. However, 4EBP-1 Thr47/46 phosphorylation remained suppressed throughout recovery.
In conclusion, these signalling pathways showed no differences between groups, but our data expand current knowledge on the effects of LEA and are the first to directly investigate its effect on status of adaptation markers for oxidative phenotype shift in skeletal muscle.
Concordant with our hypothesis, achieving energy balance with a high fat meal did not enhance skeletal muscle response and wholebody metabolism in response to endurance training compared to energy deficit-low fat. While there is growing evidence that LEA can have negative effects on health and potentially adaptation to training through down-regulating muscle protein synthesis (Areta et al., 2014), bone metabolism (Ihle & Loucks, 2004), reproductive function and other physiological systems (Mountjoy et al., 2018), its effect on oxidative capacity and endurance capacity is less clear. Our findings suggest that, while LEA may down-regulate some physiological systems, this might happen at the expense of maintaining (or even improving) functional capacity of other physiological systems (e.g. maintaining tissues' oxidative capacity). If such is the case, it may be possible that LEA/energy deficit could be a stressor independent from, and additive to, training and macronutrient manipulation as a trigger for adaptation. While there can be negative psycho-physiological effects of chronic LEA such as those associated with relative energy deficiency in sport syndrome (Mountjoy et al., 2018) and the triad models (De Souza et al., 2014), evidence suggests that the severity of energy deficit and the duration (Ihle & Loucks, 2004) may be key factors in determining a positive or negative outcome of LEA as a stressor. We, therefore, do not advocate inducing LEA chronically, but highlight the potential for periodized energy/nutrition interventions as part of a balanced training/nutrition plan (Areta, 2020;Stellingwerff, 2018), taking care not to induce a chronic state of LEA which may be detrimental in the short and long-term for the individual (Areta, 2020;Mountjoy et al., 2018).
We believe this is the first study examining the effect of LEA and macronutrient composition on endurance-type training and muscle oxidative capacity early adaptive response and provides an initial observation on the responses to short-term (<24 h) LEA using a 'sleep-low' model. This study informs the best choice of macronutrient composition to maximize adaptations to endurance-type training. As such, our findings indicate that when training with low carbohydrate availability, transient energy deficit may be more beneficial than replacing the expended energy with ingested fat and supports and extends previous findings using isoenergetic low-CHO high-protein versus low-CHO high-fat diets (Leckey et al., 2018) and using a twice-a-day model (Hammond et al., 2019). Future studies should address whether more prolonged duration of LEA would result on a different response, provided the 'dose' and duration of reduced energy availability to affect physiological responses in males is still unknown.
Additionally, the inclusion of a high-carbohydrate energy balance group in future studies would provide further insights for unravelling the effects of interaction between macronutrient and energy. Finally, it is important to highlight that the current findings represent preliminary results focusing on the early responses of markers associated to key metabolic pathways. Further research will be required to determine if these early-response results are representative of real-world outcomes, which are a product of complex interactions between an intricate network of metabolic responses and other factors.
In conclusion, our findings are the first to indicate that replenishing energy after glycogen-depleting exercise with a low-carbohydrate high-fat meal in a 'sleep-low' model does not enhance skeletal muscle adaptation and metabolic response in comparison to a low-carbohydrate low-fat (low energy) meal. Interventions aiming at maximizing aerobic training adaptation through restricting carbohydrate availability should incorporate the concept that achieving energy balance through means of increasing fat intake will likely impair rather than enhance the response to training.