The gut microbiota plays an important role in the regulation of the immune system and the metabolism of the host. The aim of the present study was to characterize the gut microbiota of patients with type 2 diabetes mellitus (T2DM). A total of 118 participants with newly diagnosed T2DM and 89 control subjects were recruited in the present study; six clinical parameters were collected and the quantity of 10 different types of bacteria was assessed in the fecal samples using quantitative PCR. Taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (AUC) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). The abundance of Bacteroides, Eubacterium rectale and Roseburia inulinivorans was significantly lower in the T2DM group compared with the control group. However, the abundance of Enterococcus was significantly higher in the T2DM group compared with the control group. In addition, Faecalibacterium prausnitzii, Enterococcus and Roseburia inulinivorans were significantly associated with sex status while Bacteroides, Bifidobacterium, Enterococcus and Roseburia inulinivorans were significantly associated with older age. In the training set, among the three models, support vector machine (SVM) and XGboost models obtained AUC values of 0.72 and 0.70, respectively. In the test set, only SVM obtained an AUC value of 0.77, and the precision and specificity were both above 0.77, whereas the accuracy, recall and npv were above 0.60. Furthermore, Bifidobacterium, age and Roseburia inulinivorans played pivotal roles in the model. In conclusion, the SVM model exhibited the highest overall predictive power, thus the combined use of machine learning tools with gut microbiome profiling may be a promising approach for improving early prediction of T2DM in the near feature.
Objective. To explore the characteristics and analyze the gut microbiota in female patients with diabetic microvascular complications (DMC). Methods. Thirty-seven female patients with type 2 diabetes mellitus (T2DM) were included in the study. These patients were divided into DM group with microvascular complications (T2DM-MC, n = 17 ) and no microvascular complications group (T2DM-0, n = 20 ). Patients in the microvascular group presented with the involvement of at least one of the following: kidney, retinal, or peripheral nerves. Using real-time fluorescence quantitative polymerase chain reaction, fecal samples from the two groups were tested for Bacteroides, Prevotella, Bifidobacterium spp, Lactobacillus, Faecalibacterium prausnitzii, Enterococcus spp, Eubacterium rectale, Veillonellaceae, Clostridium leptum, and Roseburia inulinivorans. Levels of fasting and 2 h postprandial blood glucose, glycosylated hemoglobin (HbA1c), lipids, and creatinine were determined to explore the correlation between gut microbiota and blood sugar. Mann–Whitney U test was used to analyze the differences between the two groups. Spearman correlation analysis was used to determine the correlation between gut microbiota and blood glucose. Multifactor logistic regression was used to analyze the risk factors for DMC. Results. The HbA1c levels in the T2DM-MC group were higher than those in the T2DM-0 group. The abundances of Bacteroides and Enterococcus spp in the T2DM-MC group were higher than that in the T2DM-0 group. The abundances of Bacteroides and Enterococcus spp in the T2DM-MC group were lower than that in the T2DM-0 group. Spearman’s correlation analysis showed that Bacteroides, Prevotella, Lactobacillus, C. leptum, and R. inulinivorans were related to the levels of HbA1c or blood glucose ( p < 0.05 ). Logistic regression analysis showed that after adjusting for confounding factors such as age, body mass index, family history, HbA1c, hypertension, dyslipidemia, and creatinine, Bacteroides remained an independent risk factor in female patients with DMC. Conclusion. Gut microbiota is related to blood glucose levels. Female patients with DMC experience gut microbiota disorders. The abundances of Bacteroidesare related to DMC, and the abundances of intestinal flora may affect the blood sugar levels of the body.
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