Trial reportFrontiers in endocrinology2022
Diagnostic model for predicting hyperuricemia based on alterations of the gut microbiome in individuals with different serum uric acid levels.
Trial report in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 24 citations in OpenAlex.
- Butyrate and Butyrate-Producing Bacteria in Cardiovascular-Kidney-Metabolic Syndrome.Antioxidants (Basel, Switzerland) · 2026Review
- The roles of gut microbiota and their metabolites in uric acid-related metabolic diseases: mechanisms and therapeutic targets.Frontiers in microbiology · 2026Review
- Research progress on the correlation between gut microbiota and the occurrence of hyperuricemia.Frontiers in microbiology · 2026Review
- Association between plant dietary fiber intake and hyperuricemia risk in Chinese children aged 6-17 years.Asia Pacific journal of clinical nutrition · 2025Article
- Multi-cohort analysis unveils novel microbial targets for the treatment of hyperuricemia and gout.mSystems · 2025Article
- The Role of the Intestinal Microbiome in the Pathogenesis and Treatment of Hyperuricemia: A Review.Food science & nutrition · 2025Review
- Limosilactobacillus fermentum M5e Attenuates Hyperuricemia and Renal Injury via Dual Modulation of Gut Microbiota and Purine Metabolic Pathways.ACS pharmacology & translational science · 2025Article
- Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome.BMC microbiology · 2025Article
- Sex-specific associations ofiMetaOmics · 2025Article
- Gut Microbiome Diversity and Uric Acid in Serum and Urine.Kidney international reports · 2025Article
- Whole-genome analysis of Bacillus paranthracis YC06 isolated from healthy individual feces for biodegrading inosine and guanosine.BMC microbiology · 2025Article
- Aggression and Justice Involvement: Does Uric Acid Play a Role?Brain sciences · 2025Article
- Mechanism of intestinal flora affecting SLC2A9 transport function to promote the formation of hyperuricemia.Heliyon · 2024Article
- An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.Scientific reports · 2024Article
- Association of resistome abundance with hyperuricaemia in elderly individuals: a metagenomics study.Frontiers in microbiomes · 2024Article
- One-Year Risk Prediction of Elevated Serum Uric Acid Levels in Older Adults: A Longitudinal Cohort Study.Clinical interventions in aging · 2024Article
- Bibliometric analysis of intestinal microbiota and lung diseases.Frontiers in cellular and infection microbiology · 2024Article
- Gut microecology: effective targets for natural products to modulate uric acid metabolism.Frontiers in pharmacology · 2024Review
- Understanding Hyperuricemia: Pathogenesis, Potential Therapeutic Role of Bioactive Peptides, and Assessing Bioactive Peptide Advantages and Challenges.Foods (Basel, Switzerland) · 2023Review
- Ameliorative Effect of Mannuronate Oligosaccharides on Hyperuricemic Mice via Promoting Uric Acid Excretion and Modulating Gut Microbiota.Nutrients · 2023Article
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We 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. Methods: We 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. Results: The 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 Conclusions: The 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.
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