Fat adaptation in well-trained athletes: effects on cell metabolism
Bibliographic record
Abstract
The performance of prolonged (>90 min), continuous, endurance exercise is limited by endogenous carbohydrate (CHO) stores. Accordingly, for many decades, sports nutritionists and exercise physiologists have proposed a number of diet-training strategies that have the potential to increase fatty acid availability and rates of lipid oxidation and thereby attenuate the rate of glycogen utilization during exercise. Because the acute ingestion of exogenous substrates (primarily CHO) during exercise has little effect on the rates of muscle glycogenolysis, recent studies have focused on short-term (<1-2 weeks) diet-training interventions that increase endogenous substrate stores (i.e., muscle glycogen and lipids) and alter patterns of substrate utilization during exercise. One such strategy is "fat adaptation", an intervention in which well-trained endurance athletes consume a high-fat, low-CHO diet for up to 2 weeks while undertaking their normal training and then immediately follow this by CHO restoration (consuming a high-CHO diet and tapering for 1-3 days before a major endurance event). Compared with an isoenergetic CHO diet for the same intervention period, this "dietary periodization" protocol increases the rate of whole-body and muscle fat oxidation while attenuating the rate of muscle glycogenolysis during submaximal exercise. Of note is that these metabolic perturbations favouring the oxidation of fat persist even in the face of restored endogenous CHO stores and increased exogenous CHO availability. Here we review the current knowledge of some of the potential mechanisms by which skeletal muscle sustains high rates of fat oxidation in the face of high exogenous and endogenous CHO availability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".