Effects of Quercetin Supplementation on Endurance Performance and Maximal Oxygen Consumption: A Meta-Analysis
Bibliographic record
Abstract
Lately, the effect of quercetin supplementation (QS) on endurance performance (EP) and maximal oxygen consumption (VO2max) has been receiving much scientific and media attention. Therefore, a meta-analysis was performed to determine QS's ergogenic value on these variables. Studies were located with database searches (PubMed and SPORTDiscus) and cross-referencing. Outcomes represent mean percentage changes in EP (measured via power output) and VO2max between QS and placebo. Random-effects model meta-regression, mixed-effects model analog to the ANOVA, random-effects weighted mean effect summary, and magnitude-based inferences analyses were used to delineate the effects of QS. Seven research articles (representing 288 subjects) were included, producing 4 VO2max and 10 EP effect estimates. Mean QS daily intake and duration were, respectively, 960 ± 127 mg and 26 ± 24 d for the EP outcome and 1,000 ± 0 mg and 8 ± 23 d for the VO2max outcome. EP was assessed during exercise with a mean duration of 79 ± 82 min. Overall, QS improved EP by 0.74% (95% CI: 0.10-1.39, p = .02) compared with placebo. However, only in untrained individuals (0.83% ± 0.78%, p = .02) did QS significantly improve EP (trained individuals: 0.09% ± 2.15%, p = .92). There was no relationship between QS duration and EP (p = .69). Overall, QS increased VO2max by 1.94% (95% CI: 0.30-3.59, p = .02). Magnitude-based inferences suggest that the effect of QS on EP and VO2max is likely to be trivial for both trained and untrained individuals. In conclusion, this meta-analysis indicates that QS is unlikely to prove ergogenic for aerobic-oriented exercises in trained and untrained individuals.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.033 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".