The effects of Gymnema Sylvestre supplementation on lipid profile, glycemic control, blood pressure, and anthropometric indices in adults: A systematic review and meta‐analysis
Bibliographic record
Abstract
There is a growing interest in the considerable health benefits of Gymnema Sylvestre (GS) supplementation, as some studies have reported that it may improve cardiometabolic risk factors. However, the widespread impact of GS supplementation on the parameters mentioned above is not fully resolved. Consequently, this study aimed to examine the effects of GS supplementation on lipid profile, glycemic control, blood pressure, and anthropometric indices in adults. Eligible randomized controlled trials (RCT), published up to November 2021, were identified through PubMed, Scopus, and ISI Web of Science databases. Six studies were included and analyzed using a random-effects model to calculate weighted mean differences (WMDs) with 95% confidence intervals (CI). All studies were conducted in adults that used a GC supplement (>1 week) and assessed our selected cardiovascular risk factors. Outcomes revealed that GS supplementation significantly decreased triglyceride (p < .001), total cholesterol (p < .001), low-density lipoprotein (p < .001), fasting blood sugar (p < .001), and diastolic blood pressure (p = .003). Some limitations, including notable heterogeneity, low quality of studies, and lack of diversity among research participants, should be considered when interpreting our results. Our outcomes suggest that GS supplementation may improve cardiovascular risk factors. Future large-high-quality RCTs with longer duration and various populations are needed to firmly establish the clinical efficacy of the plant.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".