Regional pattern and signatures of gut microbiota in rural residents with coronary heart disease: A metagenomic analysis.

Coronary heart disease (CHD) is tightly associated with gut microbiota, but microbiota heterogeneity limits the application of microbial biomarkers and personalized interventions demand regional-specific features. The purpose of this study was to comprehensively characterize the regional pattern of gut microbiota in rural residents with CHD and assess the predictive value and clinical correlations of local microbial signatures. We profiled the gut microbiota by shotgun metagenomic sequencing from 19 CHD and 19 healthy residents in rural Xinxiang, China, and tested the physiological parameters. The results indicated that microbial diversity, as well as KEGG orthology (KO) and carbohydrate-active enzymes (CAZymes) functions, deserved no significant disparities between CHD and healthy residents. The relative abundance of Bacteroidetes phylum was significantly lower and unclassified genus, and species were markedly higher in CHD residents compared with the healthy control. Co-occurrence network revealed a more diverse and scattered ecology in CHD residents. LEfSe identified 39 potential biomarkers and butanoate metabolism and glycosyltransferases families were the enhanced KO and CAZymes in CHD residents, respectively. Twenty key signatures were determined by the random forest algorithm and most of them belonged to the cluster. These key signatures harbored a superior accuracy of 83.9% to distinguish CHD and healthy residents and, fasting serum insulin, diastolic blood pressure, and body mass index were the top three clinical parameters influencing the gut bacterial community. Furthermore, we also found that low-density lipoprotein and waist circumference had significantly positive correlations with the members of the cluster. These findings expand our knowledge in the regional-specific pattern of gut microbiota for rural CHD residents and highlight the non-invasive diagnostic value and clinical correlations of microbial signatures.