Diagnostic model for predicting hyperuricemia based on alterations of the gut microbiome in individuals with different serum uric acid levels

Abstract

BackgroundWe aimed to assess the differences in the gut microbiome among participants with different uric acid levels (hyperuricemia [HUA] patients, low serum uric acid [LSU] patients, and controls with normal levels) and to develop a model to predict HUA based on microbial biomarkers.MethodsWe sequenced the V3-V4 variable region of the 16S rDNA gene in 168 fecal samples from HUA patients (n=50), LSU patients (n=61), and controls (n=57). We then analyzed the differences in the gut microbiome between these groups. To identify gut microbial biomarkers, the 107 HUA patients and controls were randomly divided (2:1) into development and validation groups and 10-fold cross-validation of a random forest model was performed. We then established three diagnostic models: a clinical model, microbial biomarker model, and combined model.ResultsThe gut microbial α diversity, in terms of the Shannon and Simpson indices, was decreased in LSU and HUA patients compared to controls, but only the decreases in the HUA group were significant (P=0.0029 and P=0.013, respectively). The phylum Proteobacteria (P<0.001) and genus Bacteroides (P=0.02) were significantly increased in HUA patients compared to controls, while the genus Ruminococcaceae_Ruminococcus was decreased (P=0.02). Twelve microbial biomarkers were identified. The area under the curve (AUC) for these biomarkers in the development group was 84.9% (P<0.001). Notably, an AUC of 89.1% (P<0.001) was achieved by combining the microbial biomarkers and clinical factors.ConclusionsThe combined model is a reliable tool for predicting HUA and could be used to assist in the clinical evaluation of patients and prevention of HUA.

Ameliorative Effect of Mannuronate Oligosaccharides on Hyperuricemic Mice via Promoting Uric Acid Excretion and Modulating Gut Microbiota

“…[ 74 , 75 ]. In the study of Liang et al [ 76 ], the abundance of Ruminococcus in the intestinal flora of HUA patients was significantly decreased and was considered a microbiological marker of HUA, while in the present study, this bacterium could be upregulated by MOS.…”

Modulation of gut microbiota and serum metabolome by Apostichopus japonicus derived oligopeptide in high-fructose diet-induced hyperuricemia in mice

“…And the increase of Bacteroides has also been proved to be positively correlated with uric acid content [46] . In addition, some studies have also confirmed that the increase of Prevotella and Bacteroides is common in the intestines of hyperuricemic animals [47][48] . And Prevotella stimulates dendritic cells (DC) to release IL-1β, IL-6, etc., so we speculate that the imbalance of Prevotella was related to the occurrence of intestinal inflammation [45] .…”

Bibliometric analysis of intestinal microbiota and lung diseases

“…Future research aims to delve deeper into understanding the gut microbiota and its metabolic products, airway microbiota and their metabolites, and to develop suitable bioinformatics and machine learning models. These models are intended to predict and assess various gut-lung axis diseases and health statuses, leading to the development of personalized prevention and treatment strategies ( Zheng et al., 2020 ; Zheng et al., 2023 ; Liang et al., 2022 ). Recent discoveries indicate significant differences in the gut microbiota of lung cancer patients compared to their lung microbiome communities.…”