国家神经系统疾病临床医学研究中心数据管理与共享系统

Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

Relationship between PPARγ Gene Polymorphisms and the Clinical Outcome of Ischemic Stroke.

第一作者:Xin Qiu, Ran Yan

通讯作者:Yongjun Wang ,Zixiao Li

PPARγ Ischemic stroke

基本信息

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已发表文章

发表杂志:ournal of Cerebral Blood Flow & Metabolism

发表类型: Original Contributions

影响因子(IF): 4.5

PMID: 39161254 Pubmed原文链接 全文下载

主要研究背景及目的

Pioglitazone is one of the thiazolidinedione class of Peroxisome proliferator-activated receptor-γ (PPARγ) agonists. PPARγ belongs to the nuclear receptor superfamily, which modulatied the transcriptional regulation of many genes involved in metabolism and energyhomeostasis. PPARγ gene has been confirmed to be associated with insulin sensitivity and the risk of diabetes in different ethnic groups. Regieli et al found that the minor allele of rs1801282 in PPARγ gene has protective effect on clinical outcome in patients with manifest coronary or other vascular disease. Animal research of neuron-specific PPARγ knock-out mice also indicated PPARγ gene plays a protective role in brain injury after stroke. Limited by the small sample size, current studies have not reached definite conclusions on the relationship between PPARγ and stroke. The role of PPARγ on the prognosis of stroke is still unknown. Our study thus aimed to evaluate the association between PPARγ gene polymorphisms and the clinical outcome of acute ischemic stroke.

方法

Continuous variables were presented as medians with interquartile ranges and categorical variables as percentages. Baseline characteristics for patients with and without PPARγrs1801282 CC/CG+GG genotype stratified by diabetes were described separately. The linkage disequilibrium block and haplotype structure were measured by D’ among the rs1801282 SNPs. Hardy-Weinberg equilibrium was evaluated with a χ2 test. All tests were 2-sided, and P < 0.05 was considered to be statistically significant. The linkage disequilibrium block and haplotype structure were estimated using the genetics package version 3.5.1 in R (R Development Core Team). All other analyses were conducted with SAS, version 9.4 (SAS Institute Inc).

结论

主要结果