Quercetin Alleviates Insulin Resistance and Repairs Intestinal Barrier in db/ db Mice by Modulating Gut Microbiota

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease which seriously affects public health. Gut microbiota remains a dynamic balance state in healthy individuals, and its disorder may affect health status and even results in metabolic diseases. Quercetin, a natural flavonoid, has been shown to have biological activities that can be used in the prevention and treatment of metabolic diseases. This study aimed to explore the mechanism of quercetin in alleviating T2DM based on gut microbiota. db/db mice were adopted as the model for T2DM in this study. After 10 weeks of administration, quercetin could significantly decrease the levels of body weight, fasting blood glucose (FBG), serum insulin (INS), the homeostasis model assessment of insulin resistance (HOMA-IR), monocyte chemoattractant protein-1 (MCP-1), D-lactic acid (D-LA), and lipopolysaccharide (LPS) in db/db mice. 16S rRNA gene sequencing and untargeted metabolomics analysis were performed to compare the differences of gut microbiota and metabolites among the groups. The results demonstrated that quercetin decreased the abundance of Proteobacteria, Bacteroides, Escherichia-Shigella and Escherichia_coli. Moreover, metabolomics analysis showed that the levels of L-Dopa and S-Adenosyl-L-methionine (SAM) were significantly increased, but 3-Methoxytyramine (3-MET), L-Aspartic acid, L-Glutamic acid, and Androstenedione were significantly decreased under quercetin intervention. Taken together, quercetin could exert its hypoglycemic effect, alleviate insulin resistance, repair the intestinal barrier, remodel the intestinal microbiota, and alter the metabolites of db/db mice.

Keywords: gut microbiota; insulin resistance; intestinal metabolites; intestinal permeability; quercetin; type 2 diabetes mellitus.

Figure 1

Schematic overview of experimental protocol.

Figure 2

Effects of quercetin on glucose homeostasis and insulin resistance. (A) Body weight. (B) Feed consumption. (C) FBGs at the baseline and after 10 weeks of treatment. (D) Insulin levels. (E) The HOMA-IR index. Data are expressed as means ± SEM (n = 10/group). ** p < 0.01, *** p < 0.001 vs. control; ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 vs. model.

Figure 3

Quercetin reduced inflammation and repaired intestinal barrier. (A) MCP-1 levels in serum. (B) D-LA levels in serum. (C) LPS levels in serum. (D) DAO activity. Data are expressed as means ± SEM (n = 10/group). * p < 0.05, *** p < 0.001 vs. control; ^## p < 0.01, ^### p < 0.001 vs. model.

Figure 4

The diversity analysis. (A) Alpha diversity indices (observed species, Chao1 index, ACE index, Shannon index, Simpson index, and Fisher index) of the gut microbial communities in the feces (n = 5/group). (B) Beta diversity (n = 5/group). PCoA was used to assess beta diversity.

Figure 5

Effects of quercetin on gut microbiota in db/db mice after 10 weeks of treatment. (A) Clustering heat map of relative abundance at phylum level (top 25). (B) The relative abundance of Proteobacteria. (C) Evolutionary tree at the genus level. (D) The relative abundance of Bacteroides, Escherichia-Shigella, and Lachnoclostridium. (E) The relative abundance of microbiota at species level (top 20). (F) The relative abundance of Escherichia_coli and Lachnospiraceae_bacterium_28-4. (G) Histogram of LDA value distribution. Microbiota with an LDA score greater than 4 were shown in the histogram. The length of the bars represents the effect size of the differing species. (H) Cladogram generated from LEfSe analysis. Data are expressed as means ± SEM (n = 5/group). * p < 0.05 vs. control; ^# p < 0.05 vs. model.

Figure 6

Quercetin altered the metabolites of gut microbiota in db/db mice. (A) PLS-DA scatter diagram. The R2Y metric represents the interpretation rate of the PLS-DA model, while Q2Y evaluates the model’s predictive ability. When R2Y exceeds Q2Y, it indicates the PLS-DA model is well established. (B) Sort verification diagram. (C) Volcano map. The X-axis indicates the variation in the multiplicity of differences of metabolites among the groups, while the Y-axis indicates the statistical significance level of those differences. (D) Z-score analysis. The Z-score, also known as the standard score, is used to quantify the relative content of metabolites on a standardized scale. These figures display the Z-score values for the top 30 metabolites, ranked from smallest to largest p-value. (E) KEGG enrichment scatterplot.

Figure 7

Association of gut microbiome and biochemical indicators with metabolites. The metabolites are on the underside, and the biochemical indicators and gut microbiome are on the upside.