Evidence map›Paper›PMID 40785047›Full record

ArticleGut microbes2025

Multi-omics integration reveals functional signatures of gut microbiome in atherosclerosis.

Hongbo Shi, Meitao Wu, Xiaoliang Wu, Zhuoxin Liu, Shuo Jiang, Gen Li, Yetong Yang, Yanghe Fu, Qiuping Wang, Guangde Zhang and 1 more

Abstract read
In one paragraph

Article in Gut microbes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

11 authors.

Hongbo ShiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Meitao WuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xiaoliang WuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Zhuoxin LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Shuo JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Gen LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yetong YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yanghe FuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Qiuping WangDepartment of Cardiology, Department of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Guangde ZhangDepartment of Cardiology, Department of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Liang ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID 0000-0002-6665-6710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atherosclerosis (AS), a predominant contributor to global cardiovascular disease burden, exhibits complex interplay with gut microbiota dysbiosis. While the associations between microbial imbalance and AS pathogenesis are well-documented, the pathophysiological mechanisms governing microbe-host crosstalk remain incompletely characterized. Current research limitations stem from methodological heterogeneity across studies and the absence of consensus regarding disease-specific microbial signatures. In this study, we conducted an integrated multi-omics analysis to characterize the functional signatures of gut microbiome in AS. We collected all public AS-related 6 microbiome datasets and 8 peripheral blood host transcriptomic datasets from across the world, comprising 456 metagenomic samples and 111 16S rRNA gene sequencing samples for microbial profiling, alongside 118 RNA-Seq samples and 302 microarray samples. We systematically characterized AS microbial taxa and computationally inferred the metabolic potential for the gut microbiome using metabolomic-related data. Metabolite-host gene interactions were further predicted based on the synergistic effects between microbiome and host transcriptome in AS. Five "microbe-metabolite-host gene" tripartite associations related to AS were identified involving 5 microbial genera (

Indexed as

AtherosclerosisBacteriaGastrointestinal MicrobiomeDysbiosisHumansMetabolomicsMetagenomicsMultiomicsRNA, Ribosomal, 16STranscriptomeRNA, Ribosomal, 16SAtherosclerosisdiagnostic biomarkergut microbiomehost transcriptomemulti-omics

Identifiers

PMID40785047
PMCPMC12341049

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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.