Glycogen availability does not affect the TCA cycle or TAN pools during prolonged, fatiguing exercise
Bibliographic record
Abstract
The hypothesis that fatigue during prolonged exercise arises from insufficient intramuscular glycogen, which limits tricarboxylic acid cycle (TCA) activity due to reduced TCA cycle intermediates (TCAI), was tested in this experiment. Seven endurance-trained men cycled at approximately 70% of peak O(2) uptake (Vo(2 peak)) until exhaustion with low (LG) or high (HG) preexercise intramuscular glycogen content. Muscle glycogen content was lower (P < 0.05) at fatigue than at rest in both trials. However, the increase in the sum of four measured TCAI (>70% of the total TCAI pool) from rest to 15 min of exercise was not different between trials, and TCAI content was similar after 103 +/- 15 min of exercise (2.62 +/- 0.31 and 2.59 +/- 0.28 mmol/kg dry wt for LG and HG, respectively), which was the point of volitional fatigue during LG. Subjects cycled for an additional 52 +/- 9 min during HG, and although glycogen was markedly reduced (P < 0.05) during this period, no further change in the TCAI pool was observed, thus demonstrating a clear dissociation between exercise duration and the size of the TCAI pool. Neither the total adenine nucleotide pool (TAN = ATP + ADP + AMP) nor IMP was altered compared with rest in either trial, whereas creatine phosphate levels were not different when values measured at fatigue were compared with those measured after 15 min of exercise. These data demonstrate that altered glycogen availability neither compromises TCAI pool expansion nor affects the TAN pool or creatine phosphate or IMP content during prolonged exercise to fatigue. Therefore, our data do not support the concept that a decrease in muscle TCAI during prolonged exercise in humans compromises aerobic energy provision or is the cause of fatigue.
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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 | 0.001 |
| 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) | 0.001 | 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".