Adiponectin, leptin, cortisol, neuropeptide Y and profile of mood states in athletes participating in an ultramarathon during winter: An observational study
Bibliographic record
Abstract
Background: The Montane ® Yukon Arctic Ultra (YAU) is one of the longest (690 km) and coldest (+10.6°C–43.9°C) ultramarathons worldwide. Taking part in an ultramarathon is associated with great physiological and psychological stress, which can affect one’s mood, level of hormones, and peptides. The current study aimed to identify relationships between peptides, hormones, and mood states in participants during this ultramarathon. Methods: The study cohort consisted of 36 participants (19 men, 17 women, 38.64 ± 9.12 years) split into a finisher ( n = 10), non-finisher ( n = 19), and control group ( n = 7). Data were collected at four time points: baseline (PRE), during (D1 after 277 km, D2 after 383 km), and after the race (POST). Questionnaires were used to assess ratings of perceived exertion (RPE), total quality of recovery (TQR), and profile of mood states (POMS-SF). Serum NPY, leptin, adiponectin, and cortisol were measured. Results: Among non-finishers, scores for confusion, anger, depression, and tension-anxiety (PRE vs. D2, p < 0.05) increased, while vigor decreased (PRE vs. D1, p < 0.05). In contrast, finishers’ tension-anxiety scores decreased (PRE vs. D1, p < 0.05). Fatigue increased in finishers (PRE vs. POST, p < 0.05) and non-finishers (PRE vs. D1, p < 0.05). In non-finishers, depressive mood correlated positively with leptin, anger, and confusion at several time points ( p < 0.001). In finishers, NPY correlated with TQR at PRE ( p < 0.05), while leptin correlated negatively with TQR at POST ( p < 0.05). Tension-anxiety correlated highly with perceived exertion in non-finishers ( p < 0.001) and with cortisol in finishers ( p < 0.05) and non-finishers ( p < 0.001). In finishers, confusion correlated negatively with NPY ( p < 0.01). Conclusion: The study reveals an essential interplay between hormones and mood states affecting performance: Leptin was associated with anger and a depressive mood state in non-finishers and worse recovery in finishers. In contrast, NPY appeared linked to a lower confusion score and heightened recovery in finishers. A simultaneous increase in depressed mood, anger, tension-anxiety, and confusion might harm performance and lead to race failure.
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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 | 0.001 | 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".