Quercetin with vitamin C and niacin does not affect body mass or composition
Bibliographic record
Abstract
In vitro and animal data suggest that quercetin affects adipogenesis and basal metabolism; however, whether this metabolic effect translates to reductions in body mass or improvement in body composition in humans is unknown. This study investigated 12-week supplementation of 2 different doses of quercetin, combined with vitamin C and niacin, on body mass and composition in a large, heterogeneous group of adults (n = 941; 60% female, 40% male; 18-85 years of age; 45% normal body mass index, 30% overweight, 25% obese). Subjects were randomized into 3 groups, with supplements administered in double-blind fashion: Q500 = 500 mg quercetin·day(-1), Q1000 = 1000 mg quercetin·day(-1), and placebo. Quercetin supplements were consumed twice daily over a 12-week period, and pre- and poststudy body mass and composition measurements were taken in an overnight fasted state. A general linear model was used to predict change in body mass and composition across groups with adjustment for demographic and lifestyle factors. Plasma quercetin increased in a dose-responsive manner in both Q500 and Q1000 groups relative to placebo. After adjustment for confounders, no significant differences in body mass (males interaction p value = 0.721, females p = 0.366) or body composition (males p = 0.650, females p = 0.639) were found between Q500 or Q1000 groups compared with placebo. No group differences in body mass or body composition were found in a subgroup of overweight and obese subjects. High-dose quercetin supplementation (500 and 1000 mg·day(-1)) for 12 weeks in a large, heterogeneous group of adults did not affect body mass or composition.
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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 | 0.001 |
| 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) | 0.002 | 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".