SYSTEMATIC REVIEW article
Front. Cell. Infect. Microbiol.
Sec. Intestinal Microbiome
Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1610523
This article is part of the Research TopicImpact of Gut Probiotic Metabolites on Human Metabolic DiseasesView all 7 articles
Meta-Analysis of H. pylori and the Gut Microbiome Interactions and Clinical Outcomes
Provisionally accepted
- First Affiliated Hospital of Gannan Medical University, Ganzhou, China
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Helicobacter pylori (H. pylori), a globally prevalent gastric pathogen associated with the chronic gastritis, peptic ulcers, and gastric cancer. Its interaction with gut microbiome (GM) which is dynamic microbial community within gastrointestinal tract plays a critical role in modulating host immune responses and disease progression. This study aimed to investigate the complex interactions between H. pylori infection and gut microbiome and to evaluate how microbiome alterations relate to the clinical outcomes such as gastritis, ulcers, and gastric cancer. A meta-analysis (MA) was conducted using publicly available 16S rRNA and shotgun metagenomic datasets. Microbiome composition differences were assessed using differential abundance analysis, alpha and beta diversity metrics, and principal component analysis (PCA). Random Forest models were employed to predict the clinical outcomes based on microbiome and clinical data. Hyperparameter tuning and cross-validation ensured model robustness.The analysis revealed the significant microbial shifts associated with H. pylori infection, including enrichment of Proteobacteria, Fusobacterium spp., and Prevotella spp., and depletion of a beneficial taxa like Lactobacillus spp. and Faecalibacterium prausnitzii. Microbial diversity declined progressively with disease severity. Predictive models demonstrated high accuracy (89.3%) in classifying the disease states, identifying key microbial biomarkers such as Fusobacterium spp. and Bacteroides fragilis with a strong predictive power. This study highlights critical role of the gut microbiome dysbiosis in H. pylori-related disease progression. The identified microbial signatures and predictive models offer promising tools for early diagnosis, risk stratification, and personalized treatment of H. pylori-associated gastrointestinal disorders. Future integration of the multi-omics data may further unravel the microbial mechanisms and support microbiome-based precision medicine.
Keywords: Meta-analysis (MA), H.pylori, Gut microbiome (GM), predictive model (PM), clinical applications
Received: 12 Apr 2025; Accepted: 27 Jun 2025.
Copyright: © 2025 Wu, Zhu, Hu, Zhang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiongjian Wu, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
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