Green tea extract supplementation does not hamper endurance-training adaptation but improves antioxidant capacity in sedentary men
Bibliographic record
Abstract
The purpose of this study was to investigate the effect of green tea extract (GTE) supplementation combined with endurance training on endurance capacity and performance in sedentary men. Forty untrained men (age: 20 ± 1 years) participated in this study. Subjects were assigned to 1 of 4 treatments: (i) placebo-control (CTRL), (ii) GTE, (iii) endurance training (Ex), and (iv) endurance training with GTE (ExGTE). During the 4-week intervention, exercise training was prescribed as 75% oxygen uptake reserve for three 20-min sessions per week, and either GTE (250 mg/day) or placebo was provided. Endurance capacity, malondialdehyde (MDA), total antioxidant status (TAS), and creatine kinase (CK) were examined. Ex and ExGTE but not GTE improved exhaustive-run time (Ex: +8.2%, p = 0.031; ExGTE: +14.3%, p < 0.001); in addition, Ex and ExGTE significantly increased maximal oxygen uptake by ∼14% (p = 0.041) and ∼17% (p = 0.017) above the values of the CTRL group, respectively. Both Ex and ExGTE significantly decreased the increase of CK by ∼11%-32% below that of CTRL following an exhaustive run (Ex: p = 0.007; ExGTE: p = 0.001). Moreover, TAS levels increased by ∼11% in ExGTE after training (p = 0.040), and GTE, Ex, and ExGTE markedly attenuated exercise-induced MDA production (p = 0.01, p = 0.005, p = 0.011, respectively). In conclusion, this investigation demonstrated that daily ingestion of GTE during endurance training does not impair improvements in endurance capacity. Moreover, endurance training combined with GTE not only increases antioxidant capacity without attenuating endurance training adaptations, but also further attenuates acute exercise-induced CK release.
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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 | 0.000 | 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".