Dysbiosis of urinary microbiota is positively correlated with type 2 diabetes mellitus

Abstract

Type 2 diabetes mellitus (T2DM) may be associated with altered urinary microbiota in female patients. We investigated alterations of urinary microbiota in Chinese female T2DM patients, and explored the associations between urinary microbiota and a patient's fasting blood glucose (FBG), urine glucose (UGLU), age, menstrual status, and body mass index (BMI). Midstream urine was collected from 70 female T2DM patients and 70 healthy females. Microbial diversity and composition were analyzed using the Illumina MiSeq sequencing platform by targeting the hypervariable V3-V4 regions of the 16S rRNA gene. We found that bacterial diversity was decreased in T2DM patients. Increased Actinobacteria phylum was positively correlated with FBG, UGLU, and BMI; Lactobacillus abundance decreased with age and menopause; and increased Lactobacillus correlated positively with FBG and UGLU. Decreased Akkermansia muciniphila was associated with FBG and UGLU. Escherichia coli abundance did not differ between the two cohorts. Carbohydrate and amino acid metabolism was reduced in T2DM patients, which were associated with bacterial richness indices such as Chao1 and ACE. Detailed microbiota analysis of well-characterized T2DM patients and healthy controls indicate that Chinese T2DM female patients exhibit dysbiosis of urinary microbiota.

Keywords: Akkermansia muciniphila; Escherichia coli; Immune response; Immunity; Immunology and Microbiology Section; lactobacillus; type 2 diabetes mellitus; urinary microbiota.

Figure 1. Structural comparison of urinary microbiota between two cohorts

A. The Good’s coverage was used to assess sequencing depth. B. and C. The Shannon and Simpson Rarefaction curves were applied to estimate diversity. D. Venn diagram demonstrating overlap of OTUs in the urinary microbiota between the two cohorts. E. Principal coordinate analysis plot of the urinary microbiota based on the unweighted UniFrac metric. Red and blue lines and dots represent healthy controls and T2DM patients, respectively. H and Pt stand for healthy controls and T2DM patients, respectively.

Figure 2. Genus-level OTUs different between the two cohorts (Mean ± SD)

Welch’s t-test was used to compare the abundance at the bacterial genus level between HCs and T2DM patients. The different genera were assigned only to those presenting a minimum variation at a significant level [p (corrected) < 0.05)]. H and Pt represent healthy controls and T2DM patients, respectively.

Figure 3. Cladogram showing differentially abundant taxa of microbiota

A. LEfSe cladogram showed the most differentially abundant taxa between the two cohorts. Taxonomic cladogram obtained from LEfSe analysis of 16S sequences. Taxa enriched for HCs in red; T2DM enriched taxa in Green. The brightness of each dot is proportional to its effect size. B. Only taxa meeting an LDA threshold > 1.8 are shown. H and Pt represent healthy controls and T2DM patients, respectively.

Figure 4. Relative abundance of Lactobacillus associated with age and menstrual status

A. Box plot showing the distribution in the proportion of Lactobacillus assigned to samples from H elderly, H non-elderly, Pt elderly, and Pt non-elderly. Triangles represent a significant difference was found between H elderly and H non-elderly, and between H elderly and Pt elderly. The median value is shown as a line within the box, and the mean value as a star. B. The difference among pre-menopausal status (PRE), hysterectomy (HE), and post-menopausal (POST) status groups. ANOVA test was applied, and Benjamini-Hochberg FDR was used as a correction approach to control the false discovery rate, p (corrected) < 0.05 was considered significant. H and Pt mean healthy controls and T2DM patients, respectively.

Figure 5. Clusters of orthologous group categories

Clusters of orthologous group categories reveal metabolic functions that were enriched in urinary microbiota from the two cohorts. P values were based on White's nonparameteric t-test with the Benjamini-Hochberg FDR false discovery rate correction approach.