Achieving energy balance with a high‐fat meal does not enhance skeletal muscle adaptation and impairs glycaemic response in a sleep‐low training model
Bibliographic record
Abstract
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. Abstract Training with low carbohydrate availability (LCHO) has been shown to acutely enhance endurance training skeletal muscle response, but the concomitant energy deficit (ED) in LCHO interventions has represented a confounding factor in past research. This study aimed at determining if achieving energy balance with high fat (EB‐HF) acutely enhances the adaptive response in LCHO compared to ED with low fat (ED‐LF). In a crossover design, nine well‐trained males completed a ‘sleep‐low’ protocol: on day 1 they cycled to deplete muscle glycogen while reaching a set energy expenditure (30 kcal (kg of fat free mass (FFM)) −1 ). Post‐exercise, low carbohydrate, protein‐matched meals completely (EB‐HF, 30 kcal (kg FFM) −1 ) or partially (ED‐LF, 9 kcal (kg FFM) −1 ) replaced the energy expended, with the majority of energy derived from fat in EB‐HF. In the morning of day 2, participants exercised fasted, and skeletal muscle and blood samples were collected and a carbohydrate–protein drink was ingested at 0.5 h recovery. Muscle glycogen showed no treatment effect ( P < 0.001) and decreased from 350 ± 98 to 192 ± 94 mmol (kg dry mass) −1 between rest and 0.5 h recovery. Phosphorylation status of the mechanistic target of rapamycin and AMP‐activated protein kinase pathway proteins showed only time effects. mRNA expression of p53 increased after exercise ( P = 0.005) and was higher in ED‐LF at 3.5 h compared to EB‐HF ( P = 0.027). Plasma glucose and insulin area under the curve ( P < 0.04) and peak values ( P ≤ 0.05) were higher in EB‐HF after the recovery drink. Achieving energy balance with a high‐fat meal in a ‘train‐low’ (‘sleep‐low’) model did not enhance markers of skeletal muscle adaptation and impaired glycaemia in response to a recovery drink following training in the morning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".