ABSTRACT The gut microbiota plays a crucial role in the development of hyperuricemia (HUA) and gout. However, the variability in study designs and analytical methods has led to inconsistent conclusions across different studies. Here, we conducted a comprehensive analysis of the gut microbiota associated with HUA and gout by examining 368 16S rRNA sequencing data from four Chinese cohorts, including 159 healthy controls (HC), 136 HUA patients, and 73 gout patients. Our findings indicate that there were significant differences in the gut microbiota composition between the three groups. Specifically, the HUA and gout groups demonstrated an increased abundance of pro-inflammatory bacteria, such as Fusobacterium and Bilophila , while beneficial bacteria known for their anti-inflammatory properties and metabolic benefits, including Christensenellaceae R-7 group, Anaerostipes, and Collinsella , are relatively reduced. Additionally, we developed a predictive model using microbial markers that achieved a high accuracy (area under the curve AUC > 0.8) in distinguishing between the HC, HUA, and gout groups. Notably, further metagenomic analysis identified a species-level genome bin (SGB), designated as Phil1 sp00194085 , belonging to the order Christensenellales . For the first time, we discovered that this SGB carries a uric acid metabolic gene cluster and possesses enzymes associated with purine metabolism, suggesting its potential role in uric acid metabolism. Overall, our study deepens the understanding of the gut microbiota’s role in HUA and gout and lays a foundation for developing innovative therapeutic strategies to effectively control uric acid levels through gut microbiota modulation. In this study, we conducted a comprehensive analysis of gut microbiota across multiple cohorts, identifying distinct microbial signatures in healthy controls, hyperuricemia (HUA), and gout patients. We observed an increase in pro-inflammatory bacteria and a decrease in beneficial bacteria for host metabolism in both the HUA and gout groups. Additionally, we developed a predictive model with high accuracy (area under the curve AUC > 0.8) based on microbial markers and discovered a novel species with potential for uric acid metabolism, providing new therapeutic targets for HUA and gout.
Qie et al. (2025) studied this question.