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

## TL;DR

This study uses multi-omics to show that gut microbiota and metabolic changes are linked to coronary heart disease, identifying potential biomarkers for diagnosis.

## Contribution

The study integrates microbiome, metabolome, and proteome data to reveal novel biomarkers and mechanisms in coronary heart disease.

## Key findings

- CHD patients show gut microbiota dysbiosis with increased Pseudomonadota and decreased Bacillota and Actinomycetota.
- Metabolomic and proteomic analyses identified 32 and 38 differentially expressed metabolites and proteins, respectively.
- Functional interactions between metabolites and proteins suggest roles in inflammation, coagulation, and oxidative stress.

## Abstract

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.

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.

At the phylum level, the CHD group exhibited an increased abundance of Pseudomonadota and a decreased abundance of Bacillota and Actinomycetota. At the genus level, Escherichia–Shigella, Bacteroides, and Klebsiella were significantly enriched, whereas Bifidobacterium and Faecalibacterium were decreased in abundance. Shotgun metagenomic analysis revealed functional remodeling of gut microbiota in CHD, with upregulation of KEGG pathways related to energy metabolism, inflammatory signaling, and host–microbe interactions. Serum metabolomics and proteomic analyses identified 32 differentially expressed metabolites and 38 differentially expressed proteins, respectively. Correlation analysis revealed significant associations between phospholipid metabolites and apolipoproteins, inflammatory mediators and the complement system, asymmetric dimethylarginine and endothelial function–related proteins, and oxidative stress metabolites and antioxidant proteins. ROC analysis identified several potential biomarkers with high diagnostic value.

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.