The Impact of PCSK9 Gene Polymorphisms on Ischemic Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Single-nucleotide polymorphisms (SNPs) in the proprotein convertase subtilisin/kexin type 9 (PCSK9) gene are known to be associated with susceptibility to several cerebrovascular diseases, including ischemic stroke (IS). The aims of this study was to evaluate associations between PCSK9 gene polymorphisms and the risk of IS. Based on previous reports linking PCSK9 SNPs to plasma lipid levels and to atherosclerosis, and to inconsistencies in the reported associations between the SNPs, plasma lipid levels and IS risk, we choose the PCSK9 rs505151, rs529787, and rs17111503 to performe the association analysis. Methods: Using multiple databases, all relevant case-control and cohort studies that matched our search criteria were collected. Quality assessment of included studies was performed using the Newcastle-Ottawa Scale. Demographic and genotype data were extracted from each study, and meta-analysis was performed using Stata/MP 17.0. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using fixed and random effects models. Results: A critical evaluation was conducted on ten case-control studies, involving a total of 2426 cases and 2424 controls. Pooled results from the allelic models indicated the PCSK9 rs505151 G allele (OR: 1.41, 95% CI: 1.06–1.87, p = 0.019, I2 = 53.9%) and the PCSK9 rs17111503 A allele (OR: 1.38, 95% CI: 1.22–1.55, p < 0.001, I2 = 43.5%) were significantly associated with IS. Study qualities ranged from moderate (n = 4) to good (n = 6). Begg’s and Egger’s tests results indicated there was no evidence of publication bias in the findings (p > 0.05). Conclusions: This meta-analysis demonstrated that G allele variant of PCSK9 rs505151 and A allele variant of PCSK9 rs17111503 were associated with an increased risk of IS. Based on our findings, these SNPs could serve as potential targets for the diagnosis and treatment of IS. The integration of information on genetic polymorphism into IS risk prediction model may be beneficial in routine clinical practice.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".