<i>N</i>-acetylcysteine alters substrate metabolism during high-intensity cycle exercise in well-trained humans
Bibliographic record
Abstract
We investigated the effects of N-acetylcysteine (NAC) on metabolism during fixed work rate high-intensity interval exercise (HIIE) and self-paced 10-min time-trial (TT10) performance. Nine well-trained male cyclists (V̇O2peak, 69.4 ± 5.8 mL · kg(-1) · min(-1); peak power output (PPO), 385 ± 43 W; mean ± SD) participated in a double-blind, repeated-measures, randomised crossover trial. Two trials (NAC supplementation and placebo) were performed 7 days apart consisting of 6 × 5 min HIIE bouts at 82% PPO (316 ± 40 W) separated by 1 min at 100 W, and then after 2 min of recovery at 100 W, TT10 was performed. Expired gases, venous blood, and electromyographic (EMG) data were collected. NAC did not influence blood glutathione but decreased lipid peroxidation compared with the placebo (P < 0.05). Fat oxidation was elevated with NAC compared with the placebo during HIIE bouts 5 and 6 (9.9 ± 8.9 vs. 3.9 ± 4.8 μmol · kg(-1) · min(-1); P < 0.05), as was blood glucose throughout HIIE (4.3 ± 0.6 vs. 3.8 ± 0.6 mmol · L(-1); P < 0.05). Blood lactate was lower with NAC after TT10 (3.3 ± 1.3 vs. 4.2 ± 1.3 mmol · L(-1); P < 0.05). Median EMG frequency of the vastus lateralis was lower with NAC during HIIE (79 ± 10 vs. 85 ± 10 Hz; P < 0.05), but not TT10 (82 ± 11 Hz). Finally, NAC decreased mean power output 4.9% ± 6.6% (effect size = -0.3 ± 0.4, mean ± 90% CI) during TT10 (305 ± 57 W vs. 319 ± 45 W). These data suggest that NAC alters substrate metabolism and muscle fibre type recruitment during HIIE, which is detrimental to time-trial performance.
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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) | 0.001 | 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 | 0.001 | 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".