A Meta-Analysis Of The Effect Of Quercetin Supplementation On Endurance Performance And Maximal Oxygen Consumption
Bibliographic record
Abstract
Recently, the effect of quercetin supplementation (QS) upon endurance performance (EP) and maximal oxygen consumption (VO2max) has been the object of several studies. To this day, however, the effect of QS on these parameters remains unclear. PURPOSE: Determine the magnitude of the effect of QS on EP and VO2max using a meta-analytic approach. METHODS: Database searches and cross-referencing used to locate articles. Inclusion criteria: 1) data necessary to compute the effect estimates and variances; 2) protocols were placebo-controlled, double-blinded; 3) laboratory-based exercise protocols; 4) exercise protocols duration > 5 min (for EP); 5) studies published in English and in peer-reviewed journals. Exclusion criteria: 1) QS < 5 days; 2) research with animals. All EP outcomes were put on the same scale and converted to mean changes in power output (PO), when necessary. A random-effects model was used to determine the mean weighted summary effects, along with a random-effects meta-regression (method-of-moments) to establish the relationship between fitness level and QS-induced EP changes. Ninety-five percent confidence intervals (CI) were calculated and exclusion of 0 indicated a statistically significant effect. Magnitude-based inferential statistics were also used: the smallest worthwhile % change in VO2max was set at 2.5%, and for half-marathon and marathon-runners and long-distance cyclists the changes in PO were set at 2.15, 2.7 and 1.6%, respectively. RESULTS: Ten research articles were retrieved, providing 4 (VO2max) and 9 (EP) effect estimates from 4 and 6 research articles meeting the inclusion criteria, respectively. Supplementation duration was 21 ± 18 days (EP) and 18 ± 23 days (VO2max), with a daily amount of quercetin of 1000 mg. For EP, mean exercise duration was 82 ± 87 min. QS increased mean PO by 0.73% (95% CI: 0.08-1.37%) and VO2max by 2.06% (95% CI: 0.43-3.70%), compared with the placebo. The meta-regression established no relationship between the changes in EP and VO2max (P=0.74). Under real-world conditions, the effect of QS on VO2max, half-marathon, marathon and long-distance cycling performances is very likely to be trivial. CONCLUSION: QS (1000 mg/day) for 18-21 days is very unlikely to provide an EP advantage or incur a meaningful physiological change in VO2max under field conditions.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".