Association Between Gut Microbiota Composition and Statin Responsiveness in Hyperlipidemic Patients: A Meta-Analysis
Bibliographic record
Abstract
Background: Statin therapy remains a cornerstone in the management of hyperlipidemia and prevention of cardiovascular events. However, interindividual variability in statin response poses a major clinical challenge. Emerging evidence suggests that the gut microbiota may influence lipid-lowering efficacy by modulating drug metabolism, bile acid composition, and systemic lipid pathways. This meta-analysis aims to evaluate the association between gut microbiota composition and responsiveness to statin therapy among hyperlipidemic patients. Methods: A systematic search of PubMed, Scopus, Embase, and Cochrane CENTRAL databases was conducted through May 2019. Studies were included if they assessed lipid outcomes (LDL-C, total cholesterol, HDL-C, triglycerides) in statin-treated patients, with or without gut microbiota analysis. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Risk of bias was assessed using the Cochrane Risk of Bias tool and Newcastle-Ottawa Scale. Publication bias was evaluated via funnel plot inspection. Results: Three studies (n = 884 participants) met the inclusion criteria. Pooled analysis showed that rosuvastatin significantly reduced LDL-C (SMD = -0.62; 95% CI: -1.00, -0.23; p = 0.002) and total cholesterol (SMD = -0.36; 95% CI: -0.60, -0.13; p = 0.003) compared to control groups. No significant differences were observed in HDL-C or triglyceride levels. One included study demonstrated that statin responders had distinct gut microbiota profiles, including reduced alpha diversity and increased abundance of Blautia species. Conclusion: This meta-analysis supports the superior lipid-lowering efficacy of rosuvastatin and highlights the potential role of gut microbiota in modulating statin response. Integration of microbiome profiling into lipid management strategies may advance personalized therapy for hyperlipidemia. Further large-scale studies are needed to validate these findings and explore microbiota-targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".