Effects of 7 wk of endurance training on human skeletal muscle metabolism during submaximal exercise
Bibliographic record
Abstract
This is the first study to examine the effects of endurance training on the activation state of glycogen phosphorylase (Phos) and pyruvate dehydrogenase (PDH) in human skeletal muscle during exercise. We hypothesized that 7 wk of endurance training (Tr) would result in a posttransformationally regulated decrease in flux through Phos and an attenuated activation of PDH during exercise due to alterations in key allosteric modulators of these important enzymes. Eight healthy men (22 +/- 1 yr) cycled to exhaustion at the same absolute workload (206 +/- 5 W; approximately 80% of initial maximal oxygen uptake) before and after Tr. Muscle biopsies (vastus lateralis) were obtained at rest and after 5 and 15 min of exercise. Fifteen minutes of exercise post-Tr resulted in an attenuated activation of PDH (pre-Tr: 3.75 +/- 0.48 vs. post-Tr: 2.65 +/- 0.38 mmol.min(-1).kg wet wt(-1)), possibly due in part to lower pyruvate content (pre-Tr: 0.94 +/- 0.14 vs. post-Tr: 0.46 +/- 0.03 mmol/kg dry wt). The decreased pyruvate availability during exercise post-Tr may be due to a decreased muscle glycogenolytic rate (pre-Tr: 13.22 +/- 1.01 vs. post-Tr: 7.36 +/- 1.26 mmol.min(-1).kg dry wt(-1)). Decreased glycogenolysis was likely mediated, in part, by posttransformational regulation of Phos, as evidenced by smaller net increases in calculated muscle free ADP (pre-Tr: 111 +/- 16 vs. post-Tr: 84 +/- 10 micromol/kg dry wt) and P(i) (pre-Tr: 57.1 +/- 7.9 vs. post-Tr: 28.6 +/- 5.6 mmol/kg dry wt). We have demonstrated for the first time that several signals act to coordinately regulate Phos and PDH, and thus carbohydrate metabolism, in human skeletal muscle after 7 wk of endurance training.
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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) | 0.000 | 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".