Zhang Jingyuan, Wang Anning, Zhao Yanyan, Ma Luping, Shen Hui, Zhu Weikai
First Affiliated Hospital of Dalian Medical University, Dalian, China.
Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China.
Front Neurol. 2024 Aug 29;15:1417357. doi: 10.3389/fneur.2024.1417357. eCollection 2024.
Metabolomics is increasingly being utilized in IS research to elucidate the intricate metabolic alterations that occur during ischemic stroke (IS). However, establishing causality in these associations remains unclear between metabolites and IS subtypes. In this study, we employ Mendelian randomization (MR) to identify specific metabolites and investigate potential causal relationships between metabolites and IS subtypes.
MR analysis was conducted using genome-wide association study (GWAS) summary data. We obtained 1,091 blood metabolites and 309 metabolite ratios from the GWAS Catalog (GCST90199621-90201020), which gene sequencing data from 8,299 individuals from the Canadian Longitudinal Study. We obtained GWAS summary statistics for IS subtypes which include large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) from the MEGASTROKE consortium that included 446,696 cases of European ancestry and 406,111 controls of European ancestry. The primary analysis utilized inverse-variance weighted (IVW) method. To validate our results, we performed supplementary analyses employing the MR-Egger, weighted median, simple mode, and weighted mode methods. Heterogeneity and pleiotropy were assessed through Cochran's test, MR-Egger intercept test, and leave-one-out analysis.
The study assessed the possible causality of serum metabolites in the risk of IS subtypes. The discovery of significant causal links between 33 metabolites and 3 distinct IS subtypes.
Metabolites show significant potential as circulating metabolic biomarkers and offer promise for clinical applications in the prevention and screening of IS subtypes. These discoveries notably advance our comprehension of the molecular processes specific to IS subtypes and create avenues for investigating targeted treatment approaches in the future.
代谢组学在缺血性卒中(IS)研究中越来越多地被用于阐明缺血性卒中期间发生的复杂代谢变化。然而,这些关联中代谢物与IS亚型之间的因果关系仍不明确。在本研究中,我们采用孟德尔随机化(MR)来识别特定代谢物,并研究代谢物与IS亚型之间的潜在因果关系。
使用全基因组关联研究(GWAS)汇总数据进行MR分析。我们从GWAS目录(GCST90199621 - 90201020)中获得了1091种血液代谢物和309种代谢物比值,该目录来自加拿大纵向研究的8299名个体的基因测序数据。我们从MEGASTROKE联盟获得了IS亚型的GWAS汇总统计数据,其中包括446,696例欧洲血统的病例和406,111例欧洲血统的对照,IS亚型包括大动脉卒中(LAS)、心源性栓塞性卒中(CES)和小血管卒中(SVS)。主要分析采用逆方差加权(IVW)方法。为了验证我们的结果,我们采用MR - Egger、加权中位数、简单模式和加权模式方法进行了补充分析。通过 Cochr an检验、MR - Egger截距检验和留一法分析评估异质性和多效性。
该研究评估了血清代谢物在IS亚型风险中的可能因果关系。发现了33种代谢物与3种不同IS亚型之间的显著因果联系。
代谢物作为循环代谢生物标志物具有显著潜力,并为IS亚型的预防和筛查临床应用带来希望。这些发现显著推进了我们对IS亚型特定分子过程的理解,并为未来研究靶向治疗方法开辟了道路。