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.
Background Coronary heart disease (CHD) is one of the most common causes of morbidity and mortality in type 2 diabetes mellitus (T2DM). Oxidative stress is one of the important contributors to the pathogenesis of CHD. Sestrin2 is a stress-induced antioxidant protein that plays a important role in T2DM and CHD. However, the relationship between serum Sestrin2 levels and T2DM with CHD remains unclear. Aim This study aimed to investigate the relationship between serum Sestrin2 levels and CHD in patients with type 2 diabetes. Methods A total of 70 T2DM patients with CHD and 69 T2DM patients were enrolled in this study. Clinical features and metabolic indices were identified. Serum Sestrin2 was measured by ELISA. Results Serum Sestrin2 levels in T2DM-CHD groups were significantly lower compared with the T2DM group (11.17 (9.79, 13.14) ng/mL vs 9.46 (8.34, 10.91) ng/mL). Bivariate correlation analysis revealed that serum Sestrin2 levels were negatively correlated with age (r = − 0.256, P = 0.002), BMI (r = − 0.206, P = 0.015), FBG (r = − 0.261, P = 0.002) and Tyg index (r = − 0.207, P < 0.014). Binary logistic regression suggested that low serum Sestrin2 levels were related to the increased risk of T2DM-CHD (P < 0.05). In addition, the receiver operating characteristic analysis revealed that the area under the curve of Sestrin2 was 0.724 (95% CI 0.641–0.808, P < 0.001) to predict T2DM-CHD patients (P < 0.001). Conclusion The Sestrin2 levels were highly associated with CHD in diabetes patients. Serum Sestrin2 may be involved in the occurrence and development of diabetic with CHD.
Purpose Asprosin is a newly discovered adipose factor secreted by white fat, which is involved in glucose metabolism and inflammation. Neuregulin-4 (Nrg-4) is a new adipose factor released from brown adipose tissue and is considered to play an important role in metabolism. This study aims to explore the association between serum Asprosin, Nrg-4 level and coronary heart disease(CHD) in patients with type 2 diabetes mellitus(T2DM) and the diagnostic value. Patients and methods 157 patients with T2DM were enrolled from Affiliated Hospital of Chengde Medical University between December 2020 to July 2021. These patients were divided into T2DM without CHD group (T2DM-0, n = 80) and T2DM with CHD (T2DM-CHD, n = 77). Serum Asprosin and Nrg-4 expression was detected by enzyme-linked immunosorbent assay, and the correlations between Asprosin or Nrg-4 and clinical and biochemical indicators were analyzed. A receiver operating characteristics curve analysis and area under the curve (AUC) were used to evaluate diagnostic accuracy. Results Serum Asprosin level of the T2DM-CHD group were significantly higher and Nrg-4 level significantly lower than those of the T2DM-0 group.Spearman correlation analysis showed that serum Asprosin levels were significantly positively correlated with diabetes course,history of hypertension, fasting plasma glucose(FPG), glycosylated hemoglobin A1c(HbA1C), triglycerides(TG),triglyceride glucose index(TyG index) and urea, and negatively correlated with ALT (all p < 0.05). Nrg-4 was negatively correlated with history of hypertension, body mass index(BMI), FPG, HbA1C, TG, and TyG indexes (all p < 0.05), and positively correlated with high-density lipoprotein cholesterol(HDL-C)(p < 0.05).Logistic regression analysis showed that after adjusting potential confounders, Asprosin was a risk factor for diabetes mellitus, Nrg-4 was a protective factor.The AUC of Asprosin for diagnosing T2DM-CHD was 0.671 (95% confidence interval [CI] 0.584–0.759), and the AUC of the Nrg4 index for diagnosing T2DM-CHD was 0.772 (95% CI 0.700-0.844). The AUC of Asprosin and Nrg-4 for the combined diagnosis of T2DM-CHD was 0.796 (95% CI 0.726–0.864). Conclusion Asprosin and Nrg-4 may be novel diagnostic biomarkers for T2DM with CHD, as they effectively improved the diagnostic accuracy for T2DM-CHD.
In this study, we aimed to determine whether liraglutide could effectively reduce insulin resistance (IR) by regulating Sestrin2 (SESN2) expression in L6 rat skeletal muscle cells by examining its interactions with SESN2, autophagy, and IR. L6 cells were incubated with liraglutide (10–1000 nM) in the presence of palmitate (PA; 0.6 mM), and cell viability was detected using the cell counting kit-8 (CCK-8) assay. IR-related and autophagy-related proteins were detected using western blotting, and IR and autophagy-related genes were analyzed using quantitative real-time polymerase chain reaction. Silencing SESN2 was used to inhibit the activities of SESN2. A reduction in insulin-stimulated glucose uptake was observed in PA-treated L6 cells, confirming IR. Meanwhile, PA decreased the levels of GLUT4 and phosphorylation of Akt and affected SESN2 expression. Further investigation revealed that autophagic activity decreased following PA treatment, but that liraglutide reversed this PA-induced reduction in autophagic activity. Additionally, silencing SESN2 inhibited the ability of liraglutide to up-regulate the expression of IR-related proteins and activate autophagy signals. In summary, the data showed that liraglutide improved PA-induced IR in L6 myotubes by increasing autophagy mediated by SESN2.
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