[系统评估血清代谢物与耳鸣之间的相关性]。

Y P Zuo, H Xie, T T Zhao, X Y Zhang, H Jiang
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引用次数: 0

摘要

研究目的我们采用孟德尔随机分析法(MR)来探讨血清代谢物与耳鸣之间的关系。方法在本研究中,486种血清代谢物被视为暴露因素,与之显著相关的单核苷酸多态性(SNP)被用作工具变量(IV)。血清代谢物数据来自公共数据库(http://metabolomips.org/gwas/index.php),而耳鸣的全基因组关联研究(GWAS)关联统计摘要来自芬兰数据库(https://r10.finngen.fi/pheno/H8_TINNITUS)。反方差加权法(IVW)被用作 MR 分析的主要判定方法,并使用错误发现率(FDR)对多重比较进行校正。使用 MR-Egger 回归、孟德尔随机多态性残差和异常值(MR-PRESSO)方法进行了敏感性测试。对确定的血清代谢物进行了链式不平衡回归分析(LDSC)和代谢途径分析。为了研究反向因果关系的可能性,还进行了反向 MR 分析。分析在 R 软件(4.3.1 版)中进行。结果共鉴定出 17 种与耳鸣相关的血清代谢物(包括 10 种已知代谢物和 7 种未知代谢物)。已知代谢物包括乙酰肉碱、羟基异戊酰肉碱、甘氨酸、单不饱和甘油酯和甘氨酸-L-缬氨酸等保护性代谢物,以及尿囊素、甘油磷酸胆碱 1-二十碳三烯酸酯、肌醇、15-甲基棕榈酸酯和假尿苷等有害代谢物;因果保护作用最强的代谢物是乙酰肉碱,其次是羟基异戊酰肉碱和甘氨酸;因果作用最强的危险代谢物是假尿苷,其次是肌醇和 15-甲基棕榈酸酯;只有羟基异戊酰肉碱(PFDR=0.04)和单油酸甘油酯(PFDR=0.04)经 FDR 校正后达到显著值。研究结果不存在异质性、多效性和反向因果关联。代谢途径主要富集在抗坏血酸和醛酸代谢等途径中。结论研究表明,血清代谢物与耳鸣风险之间存在因果关系。血清代谢物水平可能会影响耳鸣相关的代谢途径。
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[Systematic evaluation of the correlation between serum metabolites and tinnitus].

Objective: We performed a Mendelian randomisation (MR) analysis to explore the relationship between serum metabolites and tinnitus. Methods: In this study, 486 serum metabolites were considered as exposure factors, and single nucleotide polymorphisms (SNP) significantly associated with them were used as instrumental variables (IV). The serum metabolite data were obtained from a public database (http://metabolomips.org/gwas/index.php), while the genome-wide association study (GWAS) summary association statistics for tinnitus were obtained from a Finnish database (https://r10.finngen.fi/pheno/H8_TINNITUS). The inverse variance weighting (IVW) method was employed as the primary determination method for MR analysis, with corrections for multiple comparisons made using the false discovery rate (FDR). Sensitivity tests were conducted using the MR-Egger regression, Mendelian random polymorphism residuals and outliers (MR-PRESSO) methods. The identified serum metabolites were subjected to chained disequilibrium regression analysis (LDSC) and metabolic pathway analysis. Reverse MR analysis was performed to investigate the possibility of reverse causality. Analyses were performed in R software (version 4.3.1). Results: A total of 17 serum metabolites (including 10 known and 7 unknown metabolites) associated with tinnitus were identified. The known metabolites included protective metabolites such as acetylcarnitine, hydroxyisovaleryl carnitine, glycine, monounsaturated glycerol ester, and glycine-L-valine, and hazardous metabolites such as allantoin, glycerylphosphorylcholine 1-eicosatrienoate, myo-inositol, 15-methylpalmitate, and pseudouridine; the strongest causally protective metabolites were acetylcarnitine, the followed by hydroxyisopentanoyl carnitine and glycine; the hazardous metabolite with the strongest causal effect was pseudouridine, followed by inositol and 15-methylpalmitate; and only hydroxyisopentanoyl carnitine (PFDR=0.04) and glycerol monooleate (PFDR=0.04) reached significance values after FDR correction. The findings were free of heterogeneity, pleiotropy and reverse causal associations. The metabolic pathways were mainly enriched in pathways such as ascorbic acid and aldolac metabolism. Conclusions: The study suggests a causal relationship between serum metabolites and tinnitus risk. Serum metabolite levels may influence tinnitus-related metabolic pathways.

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