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Life sciences · Journal article
Russian Journal of Evidence-based Gastroenterology · October 6, 2026
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Alterations in the gut microbiota (GM) are increasingly recognized as a key contributor to the pathogenesis of type 2 diabetes mellitus (T2DM). However, its taxonomic composition, as well as the structural characteristics of enterotypes (ETs) and microbiota-based cooperative networks, remain incompletely characterized. Objective. To characterize the GM composition in treatment-naïve patients with newly diagnosed T2DM. Material and methods. This observational study assessed GM composition using 16S rRNA gene amplicon sequencing. Enterotypes were identified by Dirichlet multinomial mixture modeling, and microbial cooperative networks were inferred using the SPIEC-EASI algorithm. Results. No significant differences were observed at the phylum, class, or order levels compared with reference populations. At the family level, Lachnospiraceae predominated over Bacteroidaceae. At the genus level, Bacteroides, Blautia, and Alistipes were dominant, while the species-level profile was characterized by a high relative abundance of Bacteroides vulgatus and Prevotella copri, along with numerous unclassified taxa. Three enterotypes were identified. ET-1 exhibited a mixed profile, including taxa negatively associated with T2DM and obesity (Faecalibacterium, Christensenella) and taxa positively associated with T2DM risk (Bacteroides). ET-2 was characterized by the predominance of Blautia and Bacteroides, taxa associated with metabolic dysregulation and atherosclerosis. ET-3 was enriched in Prevotella and Faecalibacterium and was associated with a more favorable metabolic profile. Four dominant microbial cooperative networks were identified, ranging from an adverse profile (MC-1, associated with T2DM, obesity, and atherosclerosis) to a potentially protective profile (MC-4, enriched in butyrate-producing taxa). Conclusion. In newly diagnosed, treatment-naïve T2DM, the GM is characterized by distinct taxonomic features and heterogeneity in enterotype structure and microbial networks. These findings may inform the development of microbiota-based predictive models.