Evidence map›Paper›PMID 41696869›Full record

ArticleGut microbes2026

GutMIND: A multi-cohort machine learning framework for integrative characteristics of the microbiota-gut-brain axis in neuropsychiatric disorders.

Yanmei Ju, Shutian Lin, Shaohua Hu, Xin Jin, Liang Xiao, Tao Zhang, Yudan Zhang, Liping Zhang, Xiancang Ma, Feng Zhu and 1 more

Abstract read
In one paragraph

Article in Gut microbes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Yanmei JuCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Shutian LinCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Shaohua HuDepartment of Psychiatry, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.ORCID 0000-0003-0570-670X
Xin JinCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Liang XiaoBGI Research, Wuhan, People's Republic of China.
Tao ZhangBGI Research, Wuhan, People's Republic of China.
Yudan ZhangShaan Probiomicros Co. Ltd., Suzhou, People's Republic of China.
Liping ZhangNingbo mBioU Biopharma Co. Ltd., Ningbo, People's Republic of China.
Xiancang MaShaanxi Provincial Key Laboratory of Biological Psychiatry, Xi'an, People's Republic of China.ORCID 0000-0002-7826-305X
Feng ZhuShaanxi Provincial Key Laboratory of Biological Psychiatry, Xi'an, People's Republic of China.ORCID 0000-0003-3986-8803
Ruijin GuoBGI Research, Wuhan, People's Republic of China.ORCID 0000-0001-7863-3797

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emerging evidence underscores bidirectional communication along the microbiota-gut-brain axis in neuropsychiatric disorders. However, the field lacks dedicated metagenomic resources with standardized phenotyping for these conditions. Existing single-cohort studies face inherent limitations due to restricted sample sizes, confounding heterogeneity, and methodological fragmentation, compromising reproducibility and mechanistic insights. To overcome these challenges, we constructed the Gut Microbiome in Multinational Integrated Neuropsychiatric Disorders (GutMIND) database, a comprehensive resource integrating shotgun metagenomic data with harmonized metadata. Adhering to a standardized preprocessing protocol and rigorous quality control workflow, this dataset represents the largest gut-brain microbiome repository to date, encompassing 31 studies across 12 countries (

Indexed as

BrainGastrointestinal MicrobiomeMachine LearningMental DisordersBacteriaBiomarkersCohort StudiesHumansMetagenomicsBiomarkersmachine learningmicrobial biomarkersMicrobiota-gut-brain axisneuropsychiatric disorders

Identifiers

PMID41696869
PMCPMC12915850

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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.