Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked

AIMS The purpose is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes. METHODS The microbiome of 31 children with type 1 diabetes at onset and of 25 healthy children was determined using multiple polymorphic region of the 16S rRNA. We performed machine learning analyses and metagenome functional analysis in order to identify significant taxa and their metabolic pathways content. RESULTS Compared with healthy controls, patients showed a significantly higher relative abundance of the following most important taxa: B.stercoris, B.fragilis, B.intestinalis, B.bifidum, Gammaproteobacteria and its descendants, Holdemania, Synergistetes and its descendants. On the contrary the relative abundance of B.vulgatus, Deltaproteobacteria and its descendants, Parasutterella and the Lactobacillus, Turicibacter genera was significantly lower in patients with respects to healthy controls. The predicted metabolic pathway more associated with type 1 diabetes patients concerns "Carbon metabolism", sugar and iron metabolisms in particular. Among the clinical variables considered, BMI-SDS, anti insulin autoantibodies, glycemia, HbA1c, Tanner and age at onset emerged as the most significant positively or negatively correlated with specific clusters of taxa. CONCLUSIONS The relative abundance and the supervised analyses confirmed the importance of B. stercoris in type 1 diabetes patients at onset and showed a relevant role of Synergistetes and its descendants in patients with respect to healthy controls. In general the robustness and the coherence of the showed results underline the relevance of studying the microbioma using multiple polymorphic regions, different types of analysis and different approaches within each analysis.