Hou Xiaowen, Zheng Jiaqi, Zhang Jiajun, Tao Lin, Cen Kaiwen, Cui Ying, Wu Ji
School of Public Health, Shenyang Medical College, Shenyang, 110034, China.
School of International Education, Shenyang Medical College, Shenyang, 110034, China.
Iran J Public Health. 2024 Feb;53(2):397-403. doi: 10.18502/ijph.v53i2.14924.
Ischemic stroke (IS) is the leading cause of disability and mortality worldwide. Low-density lipoprotein cholesterol (LDL-C) levels hadno potential risk on ischemic stroke. However, higher LDL-C levels were closely related to IS. Based on two antagonistic viewpoints, a Mendelian randomization (MR) study was designed to evaluate the causal effects of LDL-C levels on IS.
Datasets of LDL-C levels and ischemic stroke were acquired from genome-wide association studies (GWAS). Weighted median method was conducted for main analysis, and MR-Egger and inverse-variance weighted (IVW) methods were performed for auxiliary analyses. Heterogeneity and pleiotropic tests were utilized to confirm the reliability of this study.
A total of 359 single nucleotide polymorphisms (SNPs) were associated with LDL-C levels ( < 5 × 10) and 337 SNPs were available in ischemic stroke with eliminating outliers. LDL-C levels were significantly associated with ischemic stroke (OR = 1.104, 95%CI = 1.019 - 1.195, = 1.52 × 10). MR-Egger and IVW showed directionally similar estimates (MR-Egger: OR = 1.120, 95%CI = 1.040 - 1.207, = 3.12 × 10; IVW: OR = 1.120, 95%CI = 1.064 - 1.178, = 1.17 × 10).
LDL-C levels had causal effects on IS, providing insights into the design of future interventions to reduce the burden of ischemic stroke.
缺血性中风(IS)是全球致残和致死的主要原因。低密度脂蛋白胆固醇(LDL-C)水平对缺血性中风没有潜在风险。然而,较高的LDL-C水平与缺血性中风密切相关。基于两种对立观点,设计了一项孟德尔随机化(MR)研究来评估LDL-C水平对缺血性中风的因果效应。
从全基因组关联研究(GWAS)中获取LDL-C水平和缺血性中风的数据集。采用加权中位数法进行主要分析,采用MR-Egger法和逆方差加权(IVW)法进行辅助分析。利用异质性和多效性检验来确认本研究的可靠性。
共有359个单核苷酸多态性(SNP)与LDL-C水平相关(<5×10),在排除异常值后,有337个SNP可用于缺血性中风研究。LDL-C水平与缺血性中风显著相关(OR = 1.104,95%CI = 1.019 - 1.195, = 1.52×10)。MR-Egger法和IVW法显示出方向相似的估计值(MR-Egger法:OR = 1.120,95%CI = 1.040 - 1.207, =