<sup>1</sup> H NMR urinary metabolomic analysis in recreational athletes: Impact of physical exercise, high intensity interval training and whole body cryostimulation
Bibliographic record
Abstract
Introduction: Physical exercise induces various metabolic changes, influencing energy expenditure and substrate utilization. Metabolomics provides a comprehensive understanding of the metabolic adaptations occurring in response to physical exercise and recovery. This study aimed to investigate metabolic adaptations in recreational athletes by analyzing the urine metabolome following a high intensity interval training (HIIT) program with or without repeated cryotherapy recovery. Method: H NMR spectroscopy was used to investigate the impact of sub-maximal cycling bout (SMC) at 60 % of power aerobic peak on urine metabolome before and after 4 weeks HIIT with or without cryostimulation recovery (WBC, N = 11; CTL, N = 12). Results: PCA analysis revealed a distinct separation between the urine NMR profiles of the WBC and the CTL groups induced by SMC. Targeted analyses showed no significant metabolic differences before SMC. However, post-SMC analysis revealed marked changes in lactate, acetate, acetone, urea, formate, citrate and adenine levels. The training program amplified these metabolic alterations in both groups. The WBC group exhibited significant changes in alanine, acetone and 2-hydroxyisobutyric acid, while the CTL group showed alterations in citrate Conclusion: SMC triggers a variety of metabolic changes that reflect the body's efforts to maintain energy balance under stress. When combined with WBC, HIIT further enhances these adaptations, improving glycolytic capacity, fat metabolism, and the regulation of energy homeostasis.
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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) | 0.000 | 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".