Effect of training in the fasted state on metabolic responses during exercise with carbohydrate intake
Bibliographic record
Abstract
Skeletal muscle gene response to exercise depends on nutritional status during and after exercise, but it is unknown whether muscle adaptations to endurance training are affected by nutritional status during training sessions. Therefore, this study investigated the effect of an endurance training program (6 wk, 3 day/wk, 1-2 h, 75% of peak Vo(2)) in moderately active males. They trained in the fasted (F; n = 10) or carbohydrate-fed state (CHO; n = 10) while receiving a standardized diet [65 percent of total energy intake (En) from carbohydrates, 20%En fat, 15%En protein]. Before and after the training period, substrate use during a 2-h exercise bout was determined. During these experimental sessions, all subjects were in a fed condition and received extra carbohydrates (1 g.kg body wt(-1) .h(-1)). Peak Vo(2) (+7%), succinate dehydrogenase activity, GLUT4, and hexokinase II content were similarly increased between F and CHO. Fatty acid binding protein (FABPm) content increased significantly in F (P = 0.007). Intramyocellular triglyceride content (IMCL) remained unchanged in both groups. After training, pre-exercise glycogen content was higher in CHO (545 +/- 19 mmol/kg dry wt; P = 0.02), but not in F (434 +/- 32 mmol/kg dry wt; P = 0.23). For a given initial glycogen content, F blunted exercise-induced glycogen breakdown when compared with CHO (P = 0.04). Neither IMCL breakdown (P = 0.23) nor fat oxidation rates during exercise were altered by training. Thus short-term training elicits similar adaptations in peak Vo(2) whether carried out in the fasted or carbohydrate-fed state. Although there was a decrease in exercise-induced glycogen breakdown and an increase in proteins involved in fat handling after fasting training, fat oxidation during exercise with carbohydrate intake was not changed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".