Effect of metformin on metabolic improvement and gut microbiota - PubMed (original) (raw)
Effect of metformin on metabolic improvement and gut microbiota
Heetae Lee et al. Appl Environ Microbiol. 2014 Oct.
Abstract
Metformin is commonly used as the first line of medication for the treatment of metabolic syndromes, such as obesity and type 2 diabetes (T2D). Recently, metformin-induced changes in the gut microbiota have been reported; however, the relationship between metformin treatment and the gut microbiota remains unclear. In this study, the composition of the gut microbiota was investigated using a mouse model of high-fat-diet (HFD)-induced obesity with and without metformin treatment. As expected, metformin treatment improved markers of metabolic disorders, including serum glucose levels, body weight, and total cholesterol levels. Moreover, Akkermansia muciniphila (12.44%±5.26%) and Clostridium cocleatum (0.10%±0.09%) abundances increased significantly after metformin treatment of mice on the HFD. The relative abundance of A. muciniphila in the fecal microbiota was also found to increase in brain heart infusion (BHI) medium supplemented with metformin in vitro. In addition to the changes in the microbiota associated with metformin treatment, when other influences were controlled for, a total of 18 KEGG metabolic pathways (including those for sphingolipid and fatty acid metabolism) were significantly upregulated in the gut microbiota during metformin treatment of mice on an HFD. Our results demonstrate that the gut microbiota and their metabolic pathways are influenced by metformin treatment.
Copyright © 2014, American Society for Microbiology. All Rights Reserved.
Figures
FIG 1
Effects of dietary changes and metformin treatment on body weight and glucose, TC, and HDL levels. Mice were induced to develop metabolic disorders while on an HFD for 18 weeks and then subjected to metformin treatment and a dietary change to an ND for the following 10 weeks (weeks 19 to 28 of the study). Several metabolic biomarkers, including body weight and glucose, TC, and HDL levels, were measured at the indicated times (n = 41). (A) Body weights of male and female mice over 28 weeks. Dotted line, time of metformin treatment and dietary changes. (B) Serum glucose level and glucose tolerance (determined by OGTTs) at 21 weeks. Glucose tolerance was expressed on the basis of the area under the curve (AUC). (C) HOMA-IR and HOMA-β were calculated from the levels of glucose and insulin measured at 21 weeks. (D) Both TC and HDL were measured at 28 weeks. Different superscript letters represent significant differences (P < 0.05) according to Duncan's post hoc test.
FIG 2
Metabolic and inflammatory biomarkers in the liver and fat pads. Relative levels of mRNA for metabolic and inflammatory biomarkers were analyzed using qPCR. (A) Expression in the livers of male and female mice; (B) expression in the epididymal fat pads of male mice and parametrial fat pads of female mice. The 2−ΔΔ_CT_ relative quantification method, described in Materials and Methods, was used for analysis of the level of biomarker expression compared to the level of GAPDH expression as an internal control. Different superscript letters indicate significant differences (P < 0.05) according to Duncan's post hoc test.
FIG 3
Effects of dietary change and metformin treatment on mucin expression in small intestine tissue and the histology of small intestine and liver tissue. (A) The levels of mRNA for MUC2 and MUC5 were increased after metformin treatment in female mice on an HFD. The 2−ΔΔ_CT_ relative quantification method, described in Materials and Methods, was used to analyze the level of biomarker expression compared to the level of GAPDH expression as an internal control. Statistical significance was assessed using the Mann-Whitney U test. (B) Confirmation of thickened intestinal mucosa after metformin treatment based on an immunohistochemistry assay. Arrows, MUC5AC stained by anti-MUC5AC. Magnification, ×20. (C) Weight of the liver after a dietary change to an ND and metformin treatment. (D) The extension score (0 to 3) of steatosis was evaluated by a pathologist, as follows: 0, no involvement; 1, mild involvement; 2, moderate involvement; 3, severe involvement. Each dot signifies a liver sample in which steatosis was diagnosed. Steatosis of the liver was observed using an optical microscope. Magnification, ×20. Steatosis was improved while the mice were on an ND and during metformin treatment.
FIG 4
Microbial diversity and difference in the bacterial community between groups categorized according to diet and metformin treatment. (A) Rarefaction curve of bacterial diversity according to dietary change and metformin treatment in mice on an HFD and ND; (B) PCoA of weighted and unweighted UniFrac distances from 40 mouse stool samples; (C) visualized UniFrac distances between groups; (D) bacterial classification at the genus level; (E) cladogram from the LEfSe results between the HFD, HFD-Met, and HFD-ND groups of mice; (F) cladogram from the LEfSe results between the ND and ND-Met groups of mice. The differences were significant (P < 0.05) both among classes (Kruskal-Wallis test) and between subclasses (Wilcoxon's test). The threshold of the logarithmic LDA score was 4.0. *, bacterial species.
FIG 5
Differences in the bacterial communities between males and females. A taxonomic comparison of the bacterial communities in male and female mice in the HFD (A), HFD-Met (B), HFD-ND (C), and ND (D) groups was performed. Significant differences in LDA scores (P < 0.05) were produced among classes (Kruskal-Wallis test) and between subclasses (Wilcoxon's test). There was no significant difference between males and females in the ND-Met group. The threshold of the logarithmic LDA score was 3.0. *, bacterial species.
FIG 6
Comparison of KEGG pathways predicted using PICRUSt according to diet and metformin treatment. Groups categorized according to metformin treatment and diet were clearly clustered on the basis of the KEGG pathways predicted using PICRUSt, as well as the bacterial diversity. A total of 245 KEGG pathways were generated, and the KEGG pathways that were significantly increased during metformin treatment were further analyzed using PCoA and LEfSe. (A) Clustering of five groups by KEGG pathways using PCoA. (B) Predicted KEGG pathways during metformin treatment and a dietary change to an ND. A total of 30 KEGG pathways were increased during metformin treatment of mice on an HFD. Among them, 2 and 12 KEGG pathways that were increased overlapped those that were increased in the HFD-ND and ND-Met groups, respectively. Finally, 18 unique KEGG pathways were predicted to be increased during metformin treatment of mice on an HFD. (C) LEfSe results showed a statistically significant increase in the abundance of KEGG pathways in the HFD-ND, HFD-Met, and HFD groups. LEfSe results showed a sequentially significant ranking (P < 0.05) among classes (Kruskal-Wallis test) and between subclasses (Wilcoxon's test). The threshold for the logarithmic LDA score was 3.0.
FIG 7
Correlation between metabolic biomarkers and bacterial abundance during metformin treatment in mice with HFD-induced obesity. *, statistical significance based on a P value of <0.05 (Spearman's correlation coefficient). The heat map was generated using MultiExperiment Viewer software (MEV; v4.8.1).
FIG 8
Effects of metformin and phenformin on growth of Akkermansia muciniphila as a proportion of all bacteria in BHI medium. Mixed stool, original stool samples from HFD-Met mice prior to culture. The amount of Akkermansia muciniphila bacteria as a proportion of all bacteria, considered 100%, was calculated, *, statistical significance based on a P value of <0.05 (Mann-Whitney U test).
References
- Foretz M, Hebrard S, Leclerc J, Zarrinpashneh E, Soty M, Mithieux G, Sakamoto K, Andreelli F, Viollet B. 2010. Metformin inhibits hepatic gluconeogenesis in mice independently of the LKB1/AMPK pathway via a decrease in hepatic energy state. J. Clin. Invest. 120:2355–2369. 10.1172/JCI40671 - DOI - PMC - PubMed
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