Exercise-induced dehydration with and without environmental heat stress results in increased oxidative stress
Bibliographic record
Abstract
While in vitro work has revealed that dehydration and hyperthermia can elicit increased cellular and oxidative stress, in vivo research linking dehydration, hyperthermia, and oxidative stress is limited. The purpose of this study was to investigate the effects of exercise-induced dehydration with and without hyperthermia on oxidative stress. Seven healthy male, trained cyclists (power output (W) at lactate threshold (LT): 199 ± 19 W) completed 90 min of cycling exercise at 95% LT followed by a 5-km time trial (TT) in 4 trials: (i) euhydration in a warm environment (EU-W, control), (ii) dehydration in a warm environment (DE-W), (iii) euhydration in a thermoneutral environment (EU-T), and (iv) dehydration in a thermoneutral environment (DE-T) (W: 33.9 ± 0.9 °C; T: 23.0 ± 1.0 °C). Oxidized glutathione (GSSG) increased significantly postexercise in dehydration trials only (DE-W: p < 0.01, DE-T: p = 0.03), and while not significant, total glutathione (TGSH) and thiobarbituric acid reactive substances (TBARS) tended to increase postexercise in dehydration trials (p = 0.08 for both). Monocyte heat shock protein 72 (HSP72) concentration was increased (p = 0.01) while lymphocyte HSP32 concentration was decreased for all trials (p = 0.02). Exercise-induced dehydration led to an increase in GSSG concentration while maintenance of euhydration attenuated these increases regardless of environmental condition. Additionally, we found evidence of increased cellular stress (measured via HSP) during all trials independent of hydration status and environment. Finally, both 90-min and 5-km TT performances were reduced during only the DE-W trial, likely a result of combined cellular stress, hyperthermia, and dehydration. These findings highlight the importance of fluid consumption during exercise to attenuate thermal and oxidative stress during prolonged exercise in the heat.
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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".