Muscle damage, physiological changes, and energy balance in ultra-endurance mountain-event athletes
Bibliographic record
Abstract
The biological response to ultra-endurance mountain race events is not yet well understood. The aim of this study was to determine the biochemical and physiological changes after performing an ultra-endurance mountain race in runners. We recruited 11 amateur runners (age: 29.7 ± 10.2 years; height: 179.7 ± 5.4 cm; body mass: 76.7 ± 10.3 kg). Muscle damage, lactate concentration, energy balance, rating of perceived exertion (RPE), heart rate (HR), heart rate variability (HRV), body composition changes, and jump performance were analyzed before, during (only lactate, HR, and HRV), and after the race. Athletes completed 54 km in 6 h, 44 min (±28 min). After the race, myoglobin and creatine kinase concentration increased from 14.9 ± 5.2 to 1419.9 ± 1292.1 μg/L and from 820.0 ± 2087.3 to 2421.1 ± 2336.2 UI/L, respectively (p < 0.01). In addition, lactate dehydrogenase and troponin I significantly increased after the race (p < 0.01). Leukocyte and platelet count increased by 180.6% ± 68.9% and 23.7% ± 11.2%, respectively (p < 0.001). Moreover, after the competition, athletes presented a 3704 kcal negative energy balance; a significant increase in RPE values; a decrease in countermovement and squat jump height; and a decrease in body mass and lower limb girths. During the event, lactate concentration did not change and subjects presented a mean HR of 158.8 ± 17.7 beats/min, a significant decrement in vagal modulation, and a significant increase in sympathetic modulation. Despite the relative "low" intensity achieved, ultra-endurance mountain race is a stressful stimulus that produces a high level of muscle damage in the athletes. These findings may help coaches to design specific training programs that may improve nutritional intake strategies and prevent muscle damage.
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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".