The Impact of PCSK9 Gene Polymorphisms on Ischemic Stroke: A Systematic Review and Meta-Analysis.

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.