Quercetin’s effect on cycling efficiency and substrate utilization
Bibliographic record
Abstract
Previous evidence suggests that quercetin supplementation increases performance in humans. We examined the effects of 3 weeks of quercetin supplementation on fuel utilization, gross efficiency (GE), and perceived effort during 3 h of cycling over 3 successive days. Forty cyclists were randomized into quercetin and placebo groups and tested for maximal oxygen consumption (53.2 +/- 1.2 and 54.7 +/- 1.1 mL.kg(-1).min(-1)). For 3 weeks following maximal oxygen consumption testing, subjects supplemented either 1000 mg.day(-1) quercetin or placebo during normal training. Following supplementation, subjects cycled at 57% maximum power for 3 h, on 3 successive days, using their own bicycles fitted to CompuTrainer Pro Model trainers (RacerMate, Seattle, Wash.). Metabolic measurements were taken every 30 min for each 3-h ride. Muscle biopsies obtained from the vastus lateralis immediately pre-exercise and postexercise on days 1 and 3 were analyzed for muscle glycogen content. Power output remained constant for all 3 exercise trials, but significant decreases over time were measured for GE, cadence, respiratory exchange ratio, blood glucose, and muscle glycogen. Significant increases were measured for heart rate and volume of oxygen consumption over time. No quercetin treatment effect was observed for any of the outcome measures in this study. These data indicate that GE is reduced during an exhausting 3-h bout of exercise. However, quercetin did not significantly affect any outcomes in these already well-trained subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".