The Effects of Quercetin Supplementation on Blood Pressures and Endothelial Function Among Patients with Metabolic Syndrome and Related Disorders: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis of randomized controlled trials (RCTs) were performed to determine the effect of quercetin administration on blood pressures and endothelial function among patients with metabolic syndrome (MetS) and related disorders. METHODS: We searched systematically online databases including Cochrane Library, EMBASE, MEDLINE, and Web of Science to identify the relevant RCTs until December 2018. Q-test and I2 statistics were applied to assess heterogeneity among the included studies. Data were pooled using a random-effects model and weighted mean difference (WMD) was considered as the overall effect size. RESULTS: Out of 284 citations, 8 RCTs were included in the meta-analysis. We found a significant reduction in systolic blood pressure (SBP) (WMD: -1.69; 95% CI: -3.22, -0.17) following the intake of quercetin supplements. However, quercetin supplementation did not significantly affect diastolic blood pressure (DBP) (WMD: -3.14; 95% CI: -8.24, 1.95), vascular cell adhesion molecule 1 (VCAM-1) (WMD: -24.49; 95% CI: -53.74, 4.77) and intercellular adhesion molecule 1 (ICAM-1) (WMD: -5.78; 95% CI: -12.93, 1.38). CONCLUSION: In summary, the current meta-analysis demonstrated that quercetin supplementation significantly reduced SBP, yet did not affect DBP, VCAM-1 and ICAM-1 among patients with MetS and related disorders.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".