Changes in glutamine and glutamate concentrations for tracking training tolerance
Bibliographic record
Abstract
PURPOSE: The purpose was to monitor high-performance athletes throughout training macrocycles and competitions and examine the changes in plasma glutamine (Gm) and glutamate (Ga) concentrations in order to develop a model of tolerance to training. METHODS: Plasma glutamine and glutamate concentrations of 52 National team athletes (31 male and 21 female) divided into male and female groups of speed skating, swimming, and cross-country skiing were measured in an early season rested condition to determine highest Gm and lowest Ga concentrations and over 2-4 macrocycles, which included heavy training to establish lowest Gm and highest Ga concentrations. RESULTS: In the rested condition, there were no differences within and between the male and female groups, excluding five athletes (OTA) who became overtrained in heavy training. The mean (+/-SD) Gm concentration was 585 +/- 54 micromol x L(-1), Ga concentration 101 +/- 16 micromol x L(-1), and Gm/Ga ratio 5.88 +/- 0.84 micromol x L(-1). The OTA had a significantly higher Ga concentration of 128 +/- 16 micromol x L(-1) and lower Gm/Ga ratio of 4.43 +/- 0.49 micromol x L(-1) than all the other groups. In heavy training, there was a significant decrease (P < 0.05) in Gm concentration to 522 +/- 53 micromol x L(-1), significant increase in Ga concentration to 128 +/- 19 micromol x L(-1) and significant decrease in Gm/Ga ratio to 4.15 +/- 0.57 micromol x L(-1). The OTA Gm concentration of 488 +/- 31 micromol x L(-1) was significant lower than only the male speed skating and swimming groups. However, the Ga concentration of 171 +/- 17 micromol x L(-1) and Gm/Ga ratio of 2.88 +/- 0.27 micromol x L(-1) were significantly higher and lower respectively than all other groups. CONCLUSIONS: Based on the changes in Gm and Ga concentration under different training conditions, we propose an athlete tolerance to training model where glutamine concentration reflects tolerance to volume of work and glutamate concentration reflects tolerance to high intensity training. We suggest that the Gm/Ga ratio may globally represent overall tolerance to 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) | 0.001 | 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".