Background/Purpose: Gout is a metabolic disorder driven by abnormalities in urate metabolism and chronic inflammation. Recent studies suggest that gout is not limited to joint involvement but is also closely associated with various systemic metabolic disturbances, particularly metabolic syndrome (MS), which comprises central obesity, insulin resistance, hypertension, and dyslipidemia. MS significantly increases the risk of cardiovascular disease, type 2 diabetes, and mortality. The comorbidity between gout and MS suggests that the two conditions may share common pathophysiological mechanisms. As a critical interface between environment, immunity, and metabolism, the gut microbiome may play a key role in the interaction between gout and MS. However, there is currently a lack of systematic multi-omics research elucidating the characteristics of microbiota–metabolite interactions, which hinders the development of personalized prevention and treatment strategies.

Methods: This study included 77 patients with gout, stratified into two subgroups based on the presence of MS: gout + MS group (n = 34) and gout-only group (n = 43). Additionally, healthy controls (n = 73) were recruited for microbiome (n = 73) and metabolomics (n = 50) comparisons. We applied 16S rRNA sequencing, metagenomics, and untargeted metabolomics to characterize gut microbial composition and metabolic profiles. Multi-omics integration was used to construct a microbiota–metabolite interaction network.

Results: Metabolomic analysis revealed significant disturbances in short-chain fatty acid, purine, and bile acid metabolism pathways in gout patients, along with marked alterations in glycerophospholipid levels. The presence of MS further exacerbated abnormalities in glycerolipid, sphingolipid, steroid hormone biosynthesis, and carbohydrate metabolism, leading to the identification of six key differential metabolites. Microbial analysis showed a depletion of beneficial taxa such as Actinobacteria and Firmicutes, along with an enrichment of potentially pathogenic Bacteroidetes, particularly in the gout + MS group. These microbial changes were closely associated with metabolic indices. Multi-omics integration identified microbiota–metabolite modules specific to the gout + MS group. A random forest model based on differential taxa and metabolites demonstrated strong discriminative performance (AUC = 0.81).

Conclusion: This study provides a comprehensive characterization of gut microbiota and metabolic alterations in gout patients with and without MS, offering new insights into the microbiota–metabolite interaction networks underlying these conditions. These findings enhance our understanding of disease heterogeneity and may inform future biomarker-based stratified management strategies.

Key Pathways and Metabolites Revealed by Metabolomics and Their Correlation with Clinical Indicators

Analysis of Gut Microbiota Structure in Gout and Its Correlation with Clinical Indicators

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/multi-omics-integration-reveals-gut-microbiota-metabolite-dysregulation-in-gout-with-metabolic-syndrome/