Fan Mingyuan, Yun Zhangjun, Yuan Jiushu, Lu Dingyi, Xie Hongyan, Yuan Haipo, Gao Hong
Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Dongzhimen Hospital, Beijing University of Chinese Medicine (BUCM), Beijing, China.
Clin Rheumatol. 2025 May;44(5):2053-2065. doi: 10.1007/s10067-025-07402-2. Epub 2025 Mar 28.
Metabolic disorders represent a hallmark feature of gout. However, evidence on the causality of blood metabolites and gout remains lacking. We performed a Mendelian randomization (MR) analysis to systematically evaluate the causality from genetically proxied 1398 blood metabolites to gout.
Genome-wide association study (GWAS) data for 1398 blood metabolites were extracted from 8299 European subjects. The discovery stage was performed using gout data from FinnGen consortium R9 to initially explore causal associations between metabolites and gout. The significant associations identified in the discovery stage were validated in the replication stage employing gout GWAS data from the IEU database. Random-effect inverse variance weighted was chosen as the main method for causality analysis, with MR-Egger, weighted median, robust adjusted profile score, and maximum likelihood as complementary analysis methods. Then, a series of sensitivity analyses were performed.
Results showed that there was a significant causal relationship between eight metabolites and gout, including 21-hydroxypregnenolone disulfate levels, carnitine levels, ethyl beta-glucopyranoside levels, gamma-glutamylglycine levels, glycine levels, glycine-to-alanine ratio, glycolithocholate sulfate levels, and propionylglycine levels. Colocalization analysis evidence strongly supported a causal relationship between 21-hydroxypregnenolone disulfate levels and carnitine levels and gout. In addition, four metabolic pathways were involved in the biological process of gout (carnitine synthesis, beta oxidation of very long-chain fatty acids, alanine metabolism, glutathione metabolism).
The current study provides evidentiary support for the causal relationship between eight blood metabolites and gout and identifies four significant metabolic pathways. These findings hold the potential to inform future research, clinical interventions, and therapeutic strategies for gout.
代谢紊乱是痛风的一个标志性特征。然而，关于血液代谢物与痛风因果关系的证据仍然不足。我们进行了一项孟德尔随机化（MR）分析，以系统评估1398种经基因代理的血液代谢物与痛风之间的因果关系。
从8299名欧洲受试者中提取了1398种血液代谢物的全基因组关联研究（GWAS）数据。发现阶段使用来自芬兰基因组联盟R9的痛风数据，初步探索代谢物与痛风之间的因果关联。在复制阶段，采用来自IEU数据库的痛风GWAS数据对发现阶段确定的显著关联进行验证。随机效应逆方差加权法被选为因果关系分析的主要方法，MR-Egger法、加权中位数法、稳健调整轮廓得分法和最大似然法作为补充分析方法。然后，进行了一系列敏感性分析。
结果显示，8种代谢物与痛风之间存在显著因果关系，包括21-羟基孕烯醇酮二硫酸盐水平、肉碱水平、β-葡萄糖苷酸乙酯水平、γ-谷氨酰甘氨酸水平、甘氨酸水平、甘氨酸与丙氨酸比值、甘氨胆酸硫酸盐水平和丙酰甘氨酸水平。共定位分析证据有力地支持了21-羟基孕烯醇酮二硫酸盐水平和肉碱水平与痛风之间的因果关系。此外，四条代谢途径参与了痛风的生物学过程（肉碱合成、超长链脂肪酸的β氧化、丙氨酸代谢、谷胱甘肽代谢）。
本研究为8种血液代谢物与痛风之间的因果关系提供了证据支持，并确定了四条重要的代谢途径。这些发现有可能为痛风的未来研究、临床干预和治疗策略提供参考。
