Exploring genetic links between blood metabolites and gout susceptibility - Zeng, Wenxing; Hu, Minhua; Zhou, Lin; Cun, Dejun; Ma, Luyao; Zhang, Jingtao; Huang, Feng; Jiang, Ziwei (Clinical rheumatology, 2024)
Background Gout, a prevalent form of inflammatory arthritis, has a complex etiology where the causal relationship between metabolites and the disease remains underexplored. This study aims to elucidate the impact of genetically determined blood metabolites on gout. Methods Employing a two-sample bidirectional Mendelian randomization analysis, we examined the association between 1400 blood metabolites and gout. Causal associations were determined using the inverse variance weighted (IVW) method with false discovery rate (FDR) correction. Sensitivity analyses encompassed weighted models, MR-Egger, weighted median, and MR-PRESSO approaches. MR-pleiotropy and Cochran’s Q statistic were utilized to evaluate potential heterogeneity and pleiotropy. Additionally, metabolic pathway analyses were conducted to pinpoint relevant pathways. Results Of the initial 4 serum metabolites identified, 3 known metabolites—hexanoylglutamine levels, mannose content, and the phosphate to mannose ratio—were found to be causally associated with gout, along with 55 serum metabolites identified as potential predictors of gout (PIVW < 0.05). Furthermore, we discovered 3 metabolic pathways implicated in gouty attacks. Conclusion Our findings, derived from Mendelian randomization, indicate that the identified metabolites and pathways may serve as biomarkers for clinical screening and prevention of gout. Additionally, they offer novel insights into the mechanisms of the disease and potential drug targets. Key points • Conducted a comprehensive Mendelian randomization study involving 1400 blood metabolites to explore their genetic impact on gout development and progression • Identified three key metabolites—hexanoylglutamine, mannose, and the phosphate-to-mannose ratio—with causal associations to gout, highlighting their potential use as biomarkers for early detection and risk stratification • Discovered 55 additional serum metabolites as potential predictors of gout, offering new insights into the pathophysiology of the disease and identifying high-risk individuals • Revealed three novel metabolic pathways involved in gout attacks, providing new therapeutic targets for precision medicine in gout treatment