Evidence map›Paper›PMID 40153197›Full record

ArticleClinical rheumatology2025

Integration of metabolomics and genomics implicates a causality between 1398 blood metabolites and gout.

Mingyuan Fan, Zhangjun Yun, Jiushu Yuan, Dingyi Lu, Hongyan Xie, Haipo Yuan, Hong Gao

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Article in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Mingyuan Fan *Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Zhangjun Yun *Dongzhimen Hospital, Beijing University of Chinese Medicine (BUCM), Beijing, China.
Jiushu YuanHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Dingyi LuHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Hongyan XieHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Haipo YuanDepartment of Endocrinology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Hong GaoHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China. 0530@cdutcm.edu.cn.ORCID http://orcid.org/0009-0002-5808-6276

Funding

Chengdu University of Traditional Chinese Medicine 2023KCY16Hospital of Chengdu University of Traditional Chinese Medicine 20Y13Hospital of Chengdu University of Traditional Chinese Medicine 21ZS01Sichuan Provincial Administration of Traditional Chinese Medicine 2023zd012
6 · The paper itself

Abstract

introductionMetabolic 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.

methodGenome-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.

resultsResults 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).

conclusionsThe 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.

Indexed as

GoutMetabolomicsGenome-Wide Association StudyGenomicsHumansMaleMendelian Randomization AnalysisPolymorphism, Single NucleotideBlood metabolitesCausalityColocalization analysisGoutMendelian randomization

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