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.
目的： 通过孟德尔随机化（Mendelian randomization，MR）分析探讨血清代谢物与耳鸣之间的关系。 方法： 本研究以486种血清代谢物为暴露因素，与其显著相关的单核苷酸多态性（single nucleotide polymorphisms，SNP）作为工具变量（instrumental variables），以耳鸣为结果变量，进行了两样本MR研究。血清代谢物数据通过公共数据库（http：//metabolomips.org/gwas/index.php）获取，耳鸣的全基因组关联分析（genome-wide association study，GWAS）汇总关联统计数据通过芬兰数据库（https：//r10.finngen.fi/pheno/H8_TINNITUS）获得。以逆方差加权法（inverse-variance weighted，IVW）作为MR分析的主要判定方法，采用错误发现率（false discovery rate，FDR）进行多重比较校正。通过MR-Egger回归、孟德尔随机多态性残差和离群值（MR-PRESSO）等方法进行敏感性检验。对已鉴定的血清代谢物进行连锁不平衡回归分析（linkage disequilibrium score regression，LDSC）和代谢途径分析。为了研究反向因果关系的可能性，进行了反向MR分析。采用R软件（版本4.3.1）进行以上分析。 结果： 共确定与耳鸣相关的17种血清代谢物，包括10种已知代谢物和7种未知代谢物。已知代谢物中包括乙酰肉毒碱、羟基异戊酰基肉碱、甘氨酸、单油酸甘油酯和甘氨酰-L-缬氨酸等保护性代谢物以及尿囊素、1-二十碳三烯酸甘油磷酸胆碱、肌醇、15-甲基棕榈酸酯和假尿苷等危险性代谢物；其中因果关系最强的保护性代谢物是乙酰肉毒碱，其次是羟基异戊酰基肉碱和甘氨酸；因果效应最强的危险性代谢物是假尿苷，其次是肌醇和15-甲基棕榈酸酯；经过FDR校正，仅羟基异戊酰基肉碱（PFDR=0.04）和单油酸甘油酯（PFDR=0.04）达到显著值。研究结果不存在异质性、多效性及反向因果关联。代谢途径主要富集在抗坏血酸和醛酸代谢等途径。 结论： 血清代谢物与耳鸣风险之间存在一定的因果关系。血清代谢物水平可能影响耳鸣相关的代谢途径。.