Roberto Biassoni 1 , Eddi Di Marco 1 , Margherita Squillario 2 , Annalisa Barla 2 , Gianluca Piccolo 3 , Elisabetta Ugolotti 1 , Cinzia Gatti 1 , Nicola Minuto 3 , Giuseppa Patti 4,5 , Mohamad Maghnie 3,4,5 , Giuseppe d'Annunzio 3 Show Affiliations »
Abstract
AIMS: The purpose of this work 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 regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis 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: Bacteroides stercoris, Bacteroides fragilis, Bacteroides intestinalis, Bifidobacterium bifidum, Gammaproteobacteria and its descendants, Holdemania, and Synergistetes and its descendants. On the contrary, the relative abundance of Bacteroides vulgatus, Deltaproteobacteria and its descendants, Parasutterella and the Lactobacillus, Turicibacter genera were significantly lower in patients with respect 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, standardized body mass index, anti-insulin autoantibodies, glycemia, hemoglobin A1c, Tanner stage, and age at onset emerged as most significant positively or negatively correlated with specific clusters of taxa. CONCLUSIONS: The relative abundance and 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 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. © Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
AIMS: The purpose of this work 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 regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis 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: Bacteroides stercoris, Bacteroides fragilis, Bacteroides intestinalis, Bifidobacterium bifidum, Gammaproteobacteria and its descendants, Holdemania, and Synergistetes and its descendants. On the contrary, the relative abundance of Bacteroides vulgatus, Deltaproteobacteria and its descendants, Parasutterella and the Lactobacillus, Turicibacter genera were significantly lower in patients with respect 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, standardized body mass index, anti-insulin autoantibodies, glycemia, hemoglobin A1c, Tanner stage, and age at onset emerged as most significant positively or negatively correlated with specific clusters of taxa. CONCLUSIONS: The relative abundance and 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 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. © Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Entities: Chemical
Disease
Gene
Species
Keywords:
adolescent; autoimmunity; children; gut; machine learning algorithms; microbiota; type 1 diabetes mellitus
Mesh： See more »
Year: 2020
PMID： 32692360 DOI： 10.1210/clinem/dgaa407
Source DB: PubMed Journal: J Clin Endocrinol Metab ISSN： 0021-972X Impact factor: 5.958