The acute effects of green tea and carbohydrate coingestion on systemic inflammation and oxidative stress during sprint cycling
Bibliographic record
Abstract
Green tea (Camellia sinensis) has anti-oxidative and anti-inflammatory effects, which may be beneficial to athletes performing high-intensity exercise. This study investigated the effects of carbohydrate and green tea coingestion on sprint cycling performance and associated oxidative stress and immunoendocrine responses to exercise. In a crossover design, 9 well-trained male cyclists completed 3 sets of 8 repetitions of 100-m uphill sprint cycling while ingesting green tea and carbohydrate (TEA) (22 mg/kg body mass catechins, 6 mg/kg body mass caffeine, 230 mg/kg glucose, and 110 mg/kg fructose) or carbohydrate only (CHO) (230 mg/kg body mass glucose and 110 mg/kg body mass fructose) during each 10-min recovery period between sets. Blood samples were collected before exercise, 10 min after exercise, and 14 h after exercise. There was no effect of acute TEA ingestion on cycling sprint performance (p = 0.29), although TEA maintained postexercise testosterone and lymphocyte concentrations, which decreased significantly in the CHO group (p < 0.001). While there was a trend for lower postexercise neutrophil count with TEA (p = 0.05), there were no significant differences between TEA and CHO for circulating cytokines (p > 0.20), markers of oxidative stress and antioxidant capacity (p > 0.17), adiponectin concentration (p = 0.60), or muscle damage markers (p > 0.64). While acute green tea ingestion prevents the postexercise decrease in testosterone and lymphocytes, it does not appear to benefit cycling sprint performance or reduce markers of oxidation and inflammation when compared with carbohydrate alone.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".