Alternations of Gut Microbiome and Serum Metabolome With Prolongation of the Course of Type 1 Diabetes Mellitus

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AIMS: We aimed to explore the gut microbial and serum metabolic disturbances associated with the course of type 1 diabetes mellitus (T1DM), and identify potential biomarkers for discriminating T1DM from normoglycemia individuals by machine learning. METHODS: We performed 16s ribosomal RNA gene sequencing and untargeted metabolomics in a cohort of 41 patients with T1DM of varying diabetes duration and 39 healthy controls (HCs) to characterise complex interactions between the gut microbiome and serum metabolome during T1DM progression. RESULTS: We identified 25 microbial genera that significantly altered in patients with T1DM, eight genera changed as the disease course, and 17 genera changed only in a specific T1DM-course group. Metabolomics analysis revealed that serum glycerophospholipid and amino acids levels were significantly changed in T1DM patients with varying disease course. Notably, we observed significantly higher levels of serum estrone, whereas lower levels of corticosterone and estrone glucuronide as the course of T1DM prolonged; Glycated haemoglobin and fasting blood glucose levels were positively correlated with estrone, and negatively correlated with corticosterone and estrone glucuronide. Furthermore, these notably changes in gut microbiota and serum metabolome were accompanied by functional alterations in sphingolipid, glutathione and taurine and hypotaurine metabolism pathways with T1DM progression. Finally, we successfully selected seven microbial and three metabolic biomarkers to differentiate T1DM from HCs. CONCLUSIONS: Perturbed diabetes course-related gut microbiota was highly correlated with the alternation of metabolic patterns in T1DM, and multi-omics coupled with machine learning algorithms can be used to develop diagnostic models based on selected biomarkers.

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