Characterisation of the gut microbiome in hypertensive and type II diabetic populations in different regions of Xinjiang

Background This study aimed to explore the diversity of gut microbial profiles and their associations with dietary habits and metabolites in different ethnic groups and disease states. By conducting gut microbiome and metabolomic analyses on 192 healthy and diseased individuals (including those with hypertension, type II diabetes, and their co - morbidities) in Xinjiang, it strived to offer new insights into the role of gut microbes in metabolic diseases, which could potentially contribute to early diagnosis and personalized treatment. Results The dominant genus in the Hui group was Faecalibacterium, while Prevotella dominated in the Uyghur group, differing from previously reported enterotype distributions. Hypertensive patients had a significantly higher abundance of Prevotella, which was positively correlated with a high - salt diet. In type II diabetes patients, the abundance of Bifidobacterium adolescentis was significantly higher. Through integrative multi - omics data analysis, it was found that changes in the proportion of specific microorganisms (such as Coriobacteriales_bacterium and Dorea_formicigenerans) in disease - comorbid states were strongly associated with significant differences in urinary metabolites (such as L - carnitine and hydroxycinnamic acid). Metabolic pathway analyses also revealed significant alterations in glycolysis/glycolysis, phenylalanine metabolism, and other pathways in the disease state. Conclusions This study systematically and for the first time reveals the gut microbiome and metabolome characteristics of healthy and diseased populations of different ethnic groups in the Xinjiang region. It offers a new perspective for understanding the role of gut microbes in metabolic diseases and provides a potential scientific basis for early disease diagnosis and personalized treatment. Future research should further integrate multi - omics technology and longitudinal design to comprehensively disclose the interactions among factors and specific mechanisms.