医学
孟德尔随机化
联想(心理学)
随机化
眩晕
梅德林
随机对照试验
内科学
遗传学
外科
基因
遗传变异
基因型
哲学
认识论
政治学
法学
生物
作者
Bin Zhang,Sulan Chen,Xin Teng,Qi Han,Wu T,Yin Liu,Ke Xiang,Wu T
出处
期刊:Medicine
[Ovid Technologies (Wolters Kluwer)]
日期:2024-09-20
卷期号:103 (38): e39688-e39688
标识
DOI:10.1097/md.0000000000039688
摘要
Metabolic disorders have been identified as an important factor causing nervous system diseases. However, due to the interference of confounding factors, the causal relationship between them has not been clearly elucidated, so it is necessary to study the causal relationship between them. To explore the causal relationship between blood metabolites and vertigo by Mendelian randomization. To assess causality, the inverse variance weighting method was employed as the primary analytical approach, complemented by additional sensitivity analyses. Metabolic pathway enrichment analysis and genetic correlation analysis were employed to further assess the metabolites. All statistical analyses were conducted using the R software. The study employed metabolite Genome Wide Association Study and vertigo diseases summary data sets to examine the causal relationship between 486 blood metabolites and 3 types of vertigo. A total of 55 potential metabolites associated with the 3 types of vertigo were identified, with 22, 16, and 13 candidate metabolites showing relatively reliable MR Evidence for Vestibular Dysfunction, Peripheral Vertigo, and Central Vertigo, respectively. Enrichment analysis was conducted to investigate the biological significance of these candidate metabolites, resulting in the identification of 7 key metabolic pathways across the 3 diseases, the metabolic pathway known as "Valine, leucine, and isoleucine biosynthesis" was found to be associated with all 3 types of vertigo, suggesting its potential influence on the vestibular system. Genetic correlation analysis revealed a genetic correlation between X-10510 and dodecanedioate with Vestibular Dysfunction. This study offers novel perspectives on the causal impact of blood metabolites on vertigo through the integration of genomics and metabolomics. Identifying metabolites that contribute to vertigo could serve as potential biomarkers and contribute to a better understanding of the underlying biological mechanisms associated with vertigo.
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