No effect of grape juice on exercise-induced muscle damage or performance in male runners: a randomized, placebo-controlled, triple-blind clinical trial
Bibliographic record
Abstract
This study aimed to verify the effect of grape juice ( Vitis Labrusca) intake on exercise-induced muscle damage (EIMD) and exercise performance parameters (5 km running time-trial (TT), running economy, and countermovement jump (CMJ)). Twenty trained male runners were randomized into two blinded groups and consumed either placebo ( n = 9) or grape juice ( n = 11) for six consecutive days (600 mL/day). On the fourth day, the participants performed a downhill running (−15%) at speed that elicited 70% V̇O2max for 20 min, to induce muscle damage, followed by assessment of running economy, 5 km TT, and CMJ tests. Blood samples were obtained before and after the exercise tests for quantifying total phenols, creatine kinase (CK), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH). On the sixth day, blood parameters and CMJ were evaluated. A two-way Analysis of covariance (ANCOVA) mixed model was employed for data analysis, the effects were the juice groups, measurement and a interaction between the factors. EIMD was confirmed by increased levels of indirect markers (serum AST and LDH activities) and an impairment in TT and CMJ performances after 48 h. The 5 km TT, economy, and CMJ were compromised after EIMD, to a similar extent in the groups. Blood concentrations of CK, LDH, AST, and total phenolic compounds presented similar time course behavior between the groups, showing no group × time interaction effects. In conclusion, grape juice consumption over 6 days did not attenuate EIMD markers or the impairment in running performance in trained male runners. (ReBEC number: RBR-9jkkvbb).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".