Influence of menstrual cycle on muscle glycogen utilization during high-intensity intermittent exercise until exhaustion in healthy women
Bibliographic record
Abstract
The present study investigated the effects of the menstrual cycle on muscle glycogen and circulating substrates during high-intensity intermittent exercise until exhaustion in healthy women who habitually exercised. In total, 11 women with regular menstrual cycles completed three tests, which comprised the early follicular phase (E-FP), late follicular phase (L-FP), and luteal phase (LP) of the menstrual cycle. High-intensity intermittent exercise until exhaustion was performed on each test day. Evaluation of muscle glycogen concentration by 13C-magnetic resonance spectroscopy and measurement of estradiol, progesterone, blood glucose, lactate, free fatty acids (FFA), and insulin concentrations were conducted before exercise (Pre) and immediately after exercise (Post). Muscle glycogen concentrations from thigh muscles at Pre and Post were not significantly different between menstrual cycle phases ( P = 0.57). Muscle glycogen decreases by exercise were significantly greater in L-FP (59.0 ± 12.4 mM) than in E-FP (48.3 ± 14.4 mM, P < 0.05). Nonetheless, blood glucose, blood lactate, serum FFA, serum insulin concentrations, and exercise time until exhaustion in E-FP, L-FP, and LP were similar. The study results suggest that although exercise time does not change according to the menstrual cycle, the menstrual cycle influences muscle glycogen utilization during high-intensity intermittent exercise until exhaustion in women with habitual exercise activity. Novelty: This study compared changes in muscle glycogen concentration across the menstrual cycle during high-intensity intermittent exercise until exhaustion using 13C-magnetic resonance spectroscopy. Our results highlight the influence of the menstrual cycle on muscle glycogen during high-intensity intermittent exercise in healthy women.
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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".