The Effect of Ginseng Supplementation on Lipid Profile: GRADE-assessedSystematic Review and Dose-response Meta-analysis of Randomized ControlledTrials
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis aimed to evaluate the overall impact of Panax ginseng on lipid profile by synthesizing existing evidence. Cardiovascular Disease (CVD) is the leading cause of morbidity and mortality among the elderly population, and serum lipids play a crucial role in its development. Maintaining optimal levels of triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total cholesterol is essential in reducing the risk of CVD. Ginsenosides, the active constituents in ginseng, have shown positive effects on lipid metabolism. This review aimed to provide a comprehensive understanding of the potential benefits of ginseng in managing dyslipidemia, which could have significant implications for the prevention and treatment of CVD. METHODS: A comprehensive analysis of 29 Randomized Controlled Trials (RCTs) was conducted to assess the effects of ginseng supplementation on lipid profile, including Triglyceride (TG), Total Cholesterol (TC), High-density Lipoprotein Cholesterol (HDL-C), and Low-density Lipoprotein Cholesterol (LDL-C) levels. A systematic search was done in online databases, such as MEDLINE, Scopus, and Clarivate Analytics Web of Science, using relevant keywords and MeSH terms to identify relevant studies until January 2024. RESULTS: The Weighted Mean Differences (WMD) and 95% Confidence Intervals (CI) for TG, TC, LDL-C, and HDL-C did not show significant changes with ginseng supplementation. CONCLUSION: Taking into account the results, using ginseng did not have a statistically significant influence on lipid profile parameters in individuals with different health conditions. Further, well-designed RCTs focusing on specific diseases are needed to clarify the potential beneficial effects of ginseng and its derivatives on lipid profile.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".