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. 2013 Aug 27;8(8):e71108.
doi: 10.1371/journal.pone.0071108. eCollection 2013.

Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance

Free PMC article

Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance

Xiuying Zhang et al. PLoS One. .
Free PMC article


To explore the relationship of gut microbiota with the development of type 2 diabetes (T2DM), we analyzed 121 subjects who were divided into 3 groups based on their glucose intolerance status: normal glucose tolerance (NGT; n = 44), prediabetes (Pre-DM; n = 64), or newly diagnosed T2DM (n = 13). Gut microbiota characterizations were determined with 16S rDNA-based high-throughput sequencing. T2DM-related dysbiosis was observed, including the separation of microbial communities and a change of alpha diversity between the different glucose intolerance statuses. To assess the correlation between metabolic parameters and microbiota diversity, clinical characteristics were also measured and a significant association between metabolic parameters (FPG, CRP) and gut microbiota was found. In addition, a total of 28 operational taxonomic units (OTUs) were found to be related to T2DM status by the Kruskal-Wallis H test, most of which were enriched in the T2DM group. Butyrate-producing bacteria (e.g. Akkermansia muciniphila ATCCBAA-835, and Faecalibacterium prausnitzii L2-6) had a higher abundance in the NGT group than in the pre-DM group. At genus level, the abundance of Bacteroides in the T2DM group was only half that of the NGT and Pre-DM groups. Previously reported T2DM-related markers were also compared with the data in this study, and some inconsistencies were noted. We found that Verrucomicrobiae may be a potential marker of T2DM as it had a significantly lower abundance in both the pre-DM and T2DM groups. In conclusion, this research provides further evidence of the structural modulation of gut microbiota in the pathogenesis of diabetes.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Figure 1
Figure 1. Principal component analysis (PCA) analysis of the similarity of microbiota (OTUs) between the NGT, Pre-DM, and T2DM groups.
Data for NGT (n = 44), Pre-DM (n = 64) and T2DM (n = 13) subjects were plotted on the first two principal components of the OTU profiles. The first 2 components (contributing 19.5% of variance) were plotted. Lines connect individuals belonging to the same group and colored circles cover individuals near the center of gravity for each cluster (<1.5σ). The top 7 OTUs (labeled with their annotations) for the main contributors to these groups were determined and plotted by their loadings for these 2 components. PCA was performed with R package ‘ade4’. OTU = operational taxonomic unit.
Figure 2
Figure 2. Alpha diversity of the 3 groups.
The Shannon index (left panel) and Chao1 index (right panel) were computed for all 121 subjects. The box depicts the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively) and the line inside the box denotes the median.
Figure 3
Figure 3. Co-inertia analysis (CIA) of the relationship between microbiota at the OTU level, clinical parameters, and disease group.
The upper left panel shows the CIA of the clinical parameter principal component analysis (PCA) and the microbiota PCA; arrows indicate where samples in the clinical parameter dataset are relative to the microbiota dataset. Red lines represent the NGT group, green lines the Pre-DM group, and blue lines the T2DM group. The upper right panel shows clinical parameter loading data; the lower panel displays the associated microbiota at the OTU level (labeled with their annotations). Only OTUs present in at least 1% of the samples were used in the analysis. CIA was performed with R package ‘ade4’. OTU = operational taxonomic unit.
Figure 4
Figure 4. Correlations between the fasting insulin (FINS) concentration (mIU/L) and microbiota diversity.
The dashed line was derived from a simple regression model (P-value for the model was 0.034; R = −0.22).

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Grant support

Financial support from the National High Technology Research and Development Program (2006AA02A409). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.