Usefulness of resveratrol supplementation in decreasing cardiometabolic risk factors comparing subjects with metabolic syndrome and healthy subjects with or without obesity: meta-analysis using multinational, randomised, controlled trials
Bibliographic record
Abstract
Introduction Resveratrol (RES), a natural polyphenolic compound, has been linked to some beneficial effects against cardiovascular disease (CVD). Material and methods We conducted a systematic search to conduct a meta-analysis on cardiometabolic risk factors modulated by RES targeting patients with metabolic syndrome (Met-S) and Obese/Healthy (O/H) subjects. The PICO (Patient, Intervention, Comparison, Outcome) research question was: Does RES among patients with Met-S and O/H subjects reduce the cardiometabolic risk? The first group was affected with MetS, which is defined as a clustering of abdominal obesity, dyslipidaemia, hyperglycaemia, and hypertension in a single individual. The second group was composed of ‘obese/healthy’ individuals, i.e. healthy subjects with or without obesity. We performed a literature search of MEDLINE/ PubMed, Scopus, and Google Scholar for randomised, controlled trials (RCT) that estimated the effects of RES on cardiometabolic risk factors. Results We found 780 articles, of which 63 original articles and reviews were identified. Data from 17 well-conducted RCT studies, comprising 651 subjects, were extracted for analysis. Overall, RES had a significant influence on Homeostatic Model Assessment-Insulin Resistance (HOMA-IR), resulting in a mean difference of –0.520665 (95% CI: –1.12791; –0.01439; p = 0.00113). In Met-S, RES significantly reduced glucose, low-density lipoprotein-cholesterol (LDL-C), and total cholesterol (T-Chol) as detected by the mean difference of –1.069 (95% CI: –2.107, –0.032; p = 0.043), –0.924 (95% CI: –1.804, –0.043; p = 0.040), and –1.246 (95% CI: –2.314, –0.178; p = 0.022), respectively. Conclusions Despite some heterogeneity in the populations, RES supplementation seems to improve cardiometabolic health, decreasing some risk factors (HOMA-IR, LDL-C, and T-Chol) associated with CVD.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".