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1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked.
- Source :
-
Diabetes . 2020 Supplement, Vol. 69, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- Gut microbiota plays a role in human health and autoimmunity. Among environmental factor linked to type 1 diabetes (T1DM) pathogenesis, gut microbiota impairment seems to be involved. We evaluated gut microbial fingerprinting in 31 pediatric patients with new-onset T1DM and 25 healthy children using multiple polymorphic region of the 16S rRNA. We performed machine learning and metagenome functional analyses to identify significant taxa and metabolic pathways and correlate with clinical and metabolic parameters. Inclusion criteria were living in Northern Italy, born from Caucasian parents, singleton birth, personal history negative for acute/chronic gastrointestinal diseases and/or antibiotic or probiotics administration. Different Bacteroidetes species i.e., B.stercoris (q=1,473E-4), B.intestinalis (q=0.010), B.fragilis (q=0.0452) were significantly more frequent in patients as well as B.bifidum, Gammaproteobacteria, Holdemania, Synergistetes and their descendants. Abundance of the Synergistetes (q=0.001), Synergistia (q=4.798E-5), Synergistales (q=2.64E-4) and Synergistaceae (q=0.0017) was reported. Among Deltaproteobacteria descendants, the Desulfovibrionales (q=0.0016), Desulfovibrionaceae (q=0.016) and the Bilophila genus (q=0.036) were significantly lower in patients as well as B.vulgatus, Deltaproteobacteria, Parasutterella and Lactobacillus, Turicibacter genera. BMI-SDS, insulin autoantibodies, glycemia, HbA1c, Tanner and age were the most significant positively or negatively parameters related to specific clusters of taxa. The supervised analyses confirmed the importance of B. stercoris in T1DM patients and a relevant role of Synergistetes and its descendants. The robustness and coherence of our results underline the relevance of studying microbioma using multiple polymorphic regions, different types of analysis and approaches within each analysis. Disclosure: G. d'Annunzio: Consultant; Self; Sandoz. R. Biassoni: None. M. Squillario: None. E. Ugolotti: None. A. Barla: None. G. Piccolo: None. N. Minuto: None. M. Maghnie: None. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00121797
- Volume :
- 69
- Database :
- Academic Search Index
- Journal :
- Diabetes
- Publication Type :
- Academic Journal
- Accession number :
- 152329386
- Full Text :
- https://doi.org/10.2337/db20-1290-P