Correlation Network Analyses Based on Metagenomics and Multi-type Metabolomic Data Identified Biomarkers of Coronary Artery Disease

Background: Coronary artery disease (CAD) is a complex, multifactorial disease and the underlying pathogenesis is unclear. It is essential to improve our understanding of the aetiology and pathogenesis of CAD for developing effective methods of early diagnosis and treatment.Results: We recruited 190 participants including normal coronary artery (n = 49), stable coronary artery disease (n = 93) and acute myocardial infarction patients (n = 48). We combined metagenomics (16S rRNA sequencing and gut microbiome whole-genome sequencing) and multi-type metabolomics (serum, faeces and urine) analyses to determine the correlations among metabolites, the microbiota and clinical indexes to identify biomarkers of CAD. Compared with the faecal metabolites, serum and urine metabolites exhibited strong correlation with clinical indexes. Comparing the three types of metabolome, we discovered that the faecal and urine metabolome was more suitable than the serum metabolome for correlation analysis with the microbiota. Furthermore, we found that the serum and urine metabolome was more suitable than the faecal metabolome for correlation analysis with the clinical indexes. We constructed, for the first time, the relationship networks among the microbiota, metabolites and pathways. Through the relationship network analysis, we identified some important differential metabolites and delineated how these metabolites are related to the differentially abundant microbes.Conclusions: In our research, we used metagenome and multi-type metabolome profiling based on hundreds of samples to figure out the correlations among metabolites, microbiota and clinical indexes. In addition, we firstly constructed the relationship networks of microbiota, metabolites and pathways. We believe our findings can help the researchers to further understand the pathogenesis of CAD.