Microbiome-Based Clustering Identifies Glycemic Control-Related Subtypes in Youth With Recent-Onset Type 1 Diabetes

Abstract

Type 1 diabetes (T1D) in children exhibits substantial heterogeneity in glycemic control, yet the biological mechanisms underlying this variation remain unclear. We aimed to explore endotype heterogeneity in youth with recent-onset T1D using unsupervised clustering based on multi-omics data, and to identify associated molecular signatures and underlying mechanisms. In a discovery cohort of 69 children and adolescents with recent-onset T1D, unsupervised clustering of fecal metagenomic profiles revealed two robust subgroups distinguished by hemoglobin A1c (HbA1c) levels. The High-HbA1c group was enriched in Bacteroidota, while the Low-HbA1c group was enriched in Firmicutes and certain Bacteroides species (Bacteroides ovatus, Bacteroides xylanisolvens, Bacteroides nordii, and Bacteroides cellulosilyticus). Metabolomics revealed significant enrichment of tryptophan-derived metabolites in the Low-HbA1c group. Bacteroides species signatures are positively correlated with tryptophan metabolite skatole. In an independent validation cohort, Bacteroides signatures discriminated individuals with good versus poor glycemic control (AUC = 0.854). Similar microbial patterns were observed in healthy children stratified by glycemic risk, indicating broader relevance of these signatures. Together, microbiome-based clustering identified glycemic control-related subtypes in T1D youth and suggested a potential role of Bacteroides and skatole in glycemic control. Mechanistic studies are warranted to confirm its role as a glycemic control-related endotype with distinct pathophysiology.

Keywords

endotype / glycemic control / gut microbiota / machine learning / multi-omics / type 1 diabetes

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