Evidence map›Paper›PMID 39601293›Full record

ArticleGut microbes

miMatch: a microbial metabolic background matching tool for mitigating host confounding in metagenomics research.

Lei Liu, Suqi Cao, Weili Lin, Zhigang Gao, Liu Yang, Lixin Zhu, Bin Yang, Guoqing Zhang, Ruixin Zhu, Dingfeng Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Gut microbes. 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. Article
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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

10 authors.

Lei LiuDepartment of Gastroenterology, The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.ORCID 0000-0002-9378-6342
Suqi CaoNational Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China.
Weili LinDepartment of Gastroenterology, The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.
Zhigang GaoDepartment of General Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China.
Liu YangNational Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China.
Lixin ZhuGuangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-Sen University, Guangzhou, P. R. China.
Bin YangShanghai Southgene Technology Co., Ltd., Shanghai, China.
Guoqing ZhangNational Genomics Data Center & Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Shanghai, P. R. China.ORCID 0000-0001-8827-7546
Ruixin ZhuDepartment of Gastroenterology, The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.ORCID 0000-0002-5070-6453
Dingfeng WuNational Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China.ORCID 0000-0002-9287-122X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metagenomic research faces a persistent challenge due to the low concordance across studies. While matching host confounders can mitigate the impact of individual differences, the influence of factors such as genetics, environment, and lifestyle habits on microbial profiles makes it exceptionally challenging to create fully matched cohorts. The microbial metabolic background, which modulates microbial composition, reflects a cumulative impact of host confounders, serving as an ideal baseline for microbial sample matching. In this study, we introduced miMatch, an innovative metagenomic sample-matching tool that uses microbial metabolic background as a comprehensive reference for host-related variables and employs propensity score matching to build case-control pairs, even in the absence of host confounders. In the simulated datasets, miMatch effectively eliminated individual metabolic background differences, thereby enhancing the accuracy of identifying differential microbial patterns and reducing false positives. Moreover, in real metagenomic data, miMatch improved result consistency and model generalizability across cohorts of the same disease. A user-friendly web server (https://www.biosino.org/iMAC/mimatch) has been established to promote the integration of multiple metagenomic cohorts, strengthening causal relationships in metagenomic research.

Indexed as

MetagenomicsBacteriaCase-Control StudiesGastrointestinal MicrobiomeHumansMetagenomePropensity ScoreSoftwarecausalityMetagenomicsmicrobial metabolic backgroundpropensity score matching

Identifiers

PMID39601293
PMCPMC11610556

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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.