Evidence map›Paper›PMID 40935888›Full record

ArticleJournal of gastroenterology2025

Development of gastric mucosa-associated microbiota in autoimmune gastritis with neuroendocrine tumors.

Koji Otani, Geicho Nakatsu, Kosuke Fujimoto, Daichi Miyaoka, Noriaki Sato, Yuji Nadatani, Yu Nishida, Hirotsugu Maruyama, Masaki Ominami, Shusei Fukunaga and 6 more

Abstract read
In one paragraph

Article in Journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Koji OtaniDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan. kojiotani@omu.ac.jp.ORCID 0000-0001-9075-6415
Geicho NakatsuDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Room 904, Building 1, 665 Huntington Avenue, Boston, MA, 02115, USA.
Kosuke FujimotoDepartment of Immunology and Genomics, Osaka Metropolitan University Graduate School of Medicine, 18/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Daichi MiyaokaDepartment of Immunology and Genomics, Osaka Metropolitan University Graduate School of Medicine, 18/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Noriaki SatoDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
Yuji NadataniDepartment of Premier Preventive Medicine, Osaka Metropolitan University Graduate School of Medicine, 12/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Yu NishidaDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Hirotsugu MaruyamaDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Masaki OminamiDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Shusei FukunagaDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Shuhei HosomiDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Fumio TanakaDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Seiya ImotoDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
Satoshi UematsuDepartment of Immunology and Genomics, Osaka Metropolitan University Graduate School of Medicine, 18/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Toshio WatanabeDepartment of Premier Preventive Medicine, Osaka Metropolitan University Graduate School of Medicine, 12/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
Yasuhiro FujiwaraDepartment of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, 10/F, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.

Funding

Japan Society for the Promotion of Science JP22K08040
6 · The paper itself

Abstract

backgroundAutoimmune gastritis (AIG) is a chronic atrophic gastritis that affects the gastric corpus, leading to achlorhydria, hypergastrinemia, and a precursor of neuroendocrine tumors (NETs). This study aimed to elucidate the underlying mechanisms of gastric NET formation in AIG by analyzing gastric mucosa-associated microbiota and host tissue-derived metabolite profiles.

methodsA total of 19 patients diagnosed with AIG and 12 controls uninfected with Helicobacter pylori underwent gastric mucosal biopsies for microbiome analysis using next-generation sequencing with primers targeting the V3-V4 region of the 16S rRNA gene, and metabolome analysis using capillary electrophoresis time-of-flight mass spectrometry.

resultsMicrobiome analysis revealed significantly reduced α-diversity indices in patients with AIG when compared with the control group. β-Diversity analysis showed distinct microbial compositions among the control, NET-negative, and NET-positive groups. The NET-positive group exhibited a significantly higher abundance of Proteobacteria and Fusobacteriota, particularly Haemophilus parainfluenzae, Fusobacterium periodonticum, and Fusobacterium nucleatum, whereas Firmicutes, including Streptococcus salivarius and Veillonella atypica, were significantly decreased compared with the NET-negative group. Metabolome analysis revealed a shift away from glycolysis and tricarboxylic acid cycle activity toward alternative metabolic pathways in patients with AIG. Integrated analysis of gastric microbiota signatures (GMS) and tissue metabotypes demonstrated significant associations among GMS, tissue metabotypes, and NET diagnosis.

conclusionsThese findings highlight marked shifts in gastric mucosa-associated microbiota profiles in patients with AIG who developed gastric NETs. Tissue-specific metabolic alterations may precede mucosal dysbiosis in patients with AIG and promote the development of a microenvironment implicated in NET formation.

Indexed as

Autoimmune DiseasesGastric MucosaGastritis, AtrophicGastrointestinal MicrobiomeNeuroendocrine TumorsStomach NeoplasmsAdultAgedCase-Control StudiesFemaleHumansMaleMetabolomeMiddle AgedRNA, Ribosomal, 16SRNA, Ribosomal, 16SAutoimmune gastritisDysbiosisGastric microbiotaMetabolomicsNeuroendocrine tumors

Identifiers

PMID40935888
PMCPMC12630263

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.