Zeng Huiqiong, Lai Junda, Liu Zhihang, Liu Wei, Zhang Ye
Traditional Chinese Medicine Department of Immunology, Women & Children Health Institute Futian Shenzhen, #2002 Jintian Road, Shenzhen, 518000, China.
Department of Human Life Sciences, Beijing Sport University, Haidian district, Beijing, #48 Xinxi Road, 100029, China.
Eur J Clin Nutr. 2025 Jan;79(1):24-32. doi: 10.1038/s41430-024-01497-7. Epub 2024 Aug 30.
Gout, common metabolic disorders, have poorly understood links with blood metabolites. Exploring these relationships could enhance clinical prevention and treatment strategies.
We applied bidirectional two-sample Mendelian randomization (MR) analysis, using data from a genome-wide association (GWAS) study of 486 blood metabolites. Gout data was obtained from FinnGen R8 (7461 gout and 221,323 control cases). We implemented the inverse variance-weighted (IVW) method for main analytical approach. Extensive heterogeneity, pleiotropy tests, leave-one-out analysis, and reverse MR were conducted to validate the robustness of our findings. Both Bonferroni and False Discovery Rate (FDR) corrections were used to adjust for multiple comparisons, ensuring stringent validation of our results.
Initial MR identified 31 candidate metabolites with potential genetic associations to gout. Following rigorous sensitivity analysis, 23 metabolites as potential statistical significance after final confirmation. These included metabolites enhancing gout risk such as X-11529 (OR = 1.225, 95% CI 1.112-1.350, P < 0.001), as well as others like piperine and stachydrine, which appeared to confer protective effects. The analysis was strengthened by reverse MR analysis. Additionally, an enrichment analysis was conducted, suggesting that 1-methylxanthine may be involved in the metabolic process of gout through the caffeine metabolism pathway.
Identifying causal metabolites offers new insights into the mechanisms influencing gout, suggesting pathways for future research and potential therapeutic targets.
痛风是常见的代谢紊乱疾病，其与血液代谢物之间的联系尚不清楚。探索这些关系可以加强临床预防和治疗策略。
我们应用双向双样本孟德尔随机化（MR）分析，使用来自对486种血液代谢物的全基因组关联（GWAS）研究的数据。痛风数据来自芬兰基因库R8（7461例痛风患者和221,323例对照）。我们采用逆方差加权（IVW）方法作为主要分析方法。进行了广泛的异质性、多效性检验、留一法分析和反向MR，以验证我们研究结果的稳健性。同时使用Bonferroni校正和错误发现率（FDR）校正来调整多重比较，确保对我们的结果进行严格验证。
初始MR确定了31种与痛风有潜在遗传关联的候选代谢物。经过严格的敏感性分析，最终确认有23种代谢物具有潜在统计学意义。其中包括增加痛风风险的代谢物，如X-11529（OR = 1.225，95% CI 1.112 - 1.350，P < 0.001），以及胡椒碱和水苏碱等似乎具有保护作用的代谢物。反向MR分析加强了该分析。此外，进行了富集分析，表明1-甲基黄嘌呤可能通过咖啡因代谢途径参与痛风的代谢过程。
确定因果代谢物为影响痛风的机制提供了新见解，为未来研究和潜在治疗靶点指明了方向。
