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Clinical Trial Summary

Helicobacter pylori affects the gut microbiome in ways that are only partially understood. In which patients H. pylori causes severe disease and in whom it merely colonizes, possibly even with beneficial effects, is not understood. The investigators are pursuing the hypothesis that changes in the gut microbiome that can be easily measured in stool have such predictive value.


Clinical Trial Description

Helicobacter pylori colonizes the stomach of about half of the world's population, including about 20-30% of adults in Germany. In some cases, this colonization can lead to chronic inflammation of the gastric mucosa, which can lead to various serious diseases such as ulcer disease and gastric cancer. It has been reported several times in the literature that Helicobacter pylori infection negatively affects the human intestinal flora and can lead to microbial imbalance (dysbiosis). Recent studies, mostly from mouse models, reveal new roles and interactions of the microbiome: host immune response may influence bacterial activity; bacterial metabolites may determine microbiome functions. Differences in the microbiome were also found between Helicobacter pylori-infected patients and were associated with treatment success. On the other hand, beneficial microbial symbiosis may prevent intestinal inflammation. The reasons for these differences in the microbiome of Helicobacter pylori-infected patients, which may also contribute to treatment failure, remain to be investigated. Therefore, this project aims to investigate how Helicobacter pylori affects the bacteria and fungi of the human gastrointestinal microbiome and how the suspected microbial imbalance may influence treatment success. In this project, The investigators aim to answer the question of how these newly discovered mechanisms alter the course of human H. pylori infection. The investigators will analyze H. pylori itself in colonized patients and asymptomatic individuals (whole genome sequencing), determine the immune response of the carrier (RNA expression in lymphocytes), and composition of the gut microbiome (DNA sequencing) and activity (RNA expression in the bacteria/fungi and identification of metabolites). Using bioinformatics approaches, particularly machine learning, The investigators will determine the parameters that predict disease progression and eradication success. The results will provide important decision support for H. pylori-infected patients. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05121025
Study type Observational
Source University Hospital Freiburg
Contact Mohamed Tarek Badr, M.D.
Phone 00497612036585
Email mohamed.tarek.badr@uniklinik-freiburg.de
Status Recruiting
Phase
Start date March 11, 2022
Completion date June 1, 2025

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