• Pol. Arch. Med. Wewn. · Jun 2018

    Characteristics of gut microbiota in adult patients with type 1 and type 2 diabetes based on next‑generation sequencing of the 16S rRNA gene fragment.

    • Dominika Salamon, Agnieszka Sroka-Oleksiak, Przemysław Kapusta, Magdalena Szopa, Sandra Mrozińska, Agnieszka H Ludwig-Słomczyńska, Paweł P Wołkow, Małgorzata Bulanda, Tomasz Klupa, Maciej T Małecki, and Tomasz Gosiewski.
    • Pol. Arch. Med. Wewn. 2018 Jun 30; 128 (6): 336343336-343.

    AbstractIntroduction Scientific data indicate a possible influence of gut microbiota on the development of type 1 and type 2 diabetes mellitus (T1DM and T2DM, respectively). Sequence analysis of 16S ribosomal RNA identified several hundred bacterial species of the intestinal ecosystem, most of which cannot be cultured. Objectives We aimed to evaluate gut microbiota composition in adult patients with T1DM and T2DM and establish a link between microbiological test results and patients' clinical data. Patients and methods We examined DNA isolated from fecal samples in 3 groups: healthy volunteers (n = 23), patients with T1DM (n = 22), and patients with T2DM (n = 23). Next‑generation sequencing was performed on the MiSeq platform. Results At the phylum level, the Firmicutes bacteria prevailed (>77%) in all groups. At the taxonomic levels L2 (phylum) and L6 (genus), significant differences were demonstrated in bacterial profiles, particularly in the T2DM group. A negative correlation was observed between several genera of bacteria and the percentage of glycated hemoglobin A1c in the T2DM group, while a positive correlation was revealed between bacteria belonging to the genus Bifidobacterium and high‑density lipoprotein cholesterol levels in both T1DM and T2DM groups. Conclusions Our results provide grounds for conducting research in the field of gut microbiota in order to develop individualized therapy for patients with diabetes based on modifying the microbiota composition, as a new method for controlling glycemia. Next‑generation sequencing allows a rapid identification of the DNA of all bacteria present in the sample and their taxonomic classification.

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