Evidence map›Paper›PMID 41883806›Full record

ArticleFrontiers in microbiology2026

Integrated multi-omics analysis reveals gut microbiota and metabolic characteristics in coronary heart disease.

Liqi Peng, Yuanting Zhang, Xudong Li, Zongren Hu

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Liqi Peng *Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Yuanting Zhang *Postgraduate Medical Education Center, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China.
Xudong LiSchool of Traditional Chinese Medicine, Hunan University of Medicine, Huaihua, Hunan, China.
Zongren HuSchool of Traditional Chinese Medicine, Hunan University of Medicine, Huaihua, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Coronary heart disease (CHD) is a leading cause of morbidity and mortality worldwide. Increasing evidence indicates that gut microbiota dysbiosis contributes to CHD pathogenesis through metabolic, inflammatory, and coagulation-related mechanisms. However, comprehensive multi-omics investigations of individuals with CHD remain limited. In this study, we aimed to characterize the multi-omics features of CHD and to identify potential diagnostic biomarkers. Methods: The study included 10 patients with clinically diagnosed CHD and 10 healthy controls. Blood and fecal samples were collected for further analysis. The gut microbiota composition was assessed using 16S ribosomal RNA high-throughput sequencing, and shotgun metagenomic sequencing was further performed to evaluate microbial functional potential through the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and differential pathway analysis. Non-targeted metabolomic profiling was performed using ultra-high-performance liquid chromatography coupled with Orbitrap mass spectrometry, and quantitative proteomic analysis was conducted using liquid chromatography-tandem mass spectrometry. Functional interaction networks between differentially expressed metabolites and proteins were constructed using Spearman correlation analysis, and the diagnostic potential of candidate biomarkers was evaluated using receiver operating characteristic (ROC) curve analysis. Results: At the phylum level, the CHD group exhibited an increased abundance of Conclusion: We demonstrate that individuals with CHD exhibit significant gut microbiota dysbiosis, distinct metabolic pathway alterations, and aberrant expression of coagulation- and inflammatory-related proteins. These findings provide novel insights into potential targets for CHD prevention and treatment strategies.

Indexed as

biomarkerscoronary heart diseasegut microbiotametabolomicsmulti-omicsproteomics

Identifiers

PMID41883806
PMCPMC13008861

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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.