Background: The gut microbiota is recognized as a major modulator of metabolic disorders such as type 2 diabetes. Dapagliflozin, sodium glucose cotransporter 2 inhibitors (SGLT2i), enhances renal glucose excretion, and lowers blood glucose levels. The study aimed to determine the effects of dapagliflozin on fecal microbiota in a type 2 diabetic rat model. Methods: Four-week-old male Sprague Dawley rats (n = 24) were fed a high-fat diet (HFD) for 8 weeks and then given a single dose of STZ injection (30 mg/kg, i.p). They were randomly divided into three groups (n = 8). Each group received intragastric infusion of normal saline (2 ml, 0.9%) or metformin (215.15 mg/kg/day) or dapagliflozin (1 mg/kg/day) for 4 weeks. Blood glucose levels and plasma insulin levels were detected during intragastric glucose tolerance. Fecal samples were collected to access microbiome by 16S ribosomal RNA gene sequencing. Results: Dapagliflozin significantly decreased fasting and postprandial blood glucose levels as metformin in type 2 diabetic rats (P < 0.001). Enterotype was composed of Ruminococcaceae after treatment of dapagliflozin, whereas Ruminococcaceae and Muribaculaceae were the main enterotypes following metformin treatment. Dapagliflozin did not increase the abundance of beneficial bacteria including Lactobacillaceae and Bifidobacteriaceae. However, these were increased in the metformin group. It is surprising to find that Proteobacteria (especially Desulfovibrionaceae) were enriched in the dapagliflozin group. Conclusion: Dapagliflozin and metformin exerted complementary effects on the main beneficial bacteria. A combination of these two drugs might be beneficial to improve the structure of fecal microbiota in the treatment of type 2 diabetes.
Pubmed ID: 33312157 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.
View all literature mentionsRepository of gene phenotype associations for phenotypes derived from electronic health records, questionnaire data, and continuous traits computed on exomes released by UK Biobank. Repository was made available by AstraZeneca for public research.
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