Unveiling the Predictive Model for Macrovascular Complications in Type 2 Diabetes Mellitus: microRNAs Expression, Lipid Profile, and Oxidative Stress Markers

Figures

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  Figure 1. The relative expression of (a) miR-126, (b) miR-155, (c) miR-484, (d) miR-21, (e) miR-146a, (f) miR-27a, and (g) miR-210 in plasma of controls (CG), T2DM patients without complications (DG), diabetics with complications (DG+C). Data are presented as means ± standard error of the mean (SEM). p values from Kruskal–Wallis test. Comparisons between groups are indicated in the graphs. * p < 0.05, ** p < 0.01.

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  Figure 2. Assessment of oxidative stress markers in plasma from diabetic patients without complications (DG), with vascular complications (DG+C), and controls (CG): (a) Advanced oxidation protein products (AOPP), (b) lipid peroxidation (LPO), and (c) nitric oxide (NOx). Data are presented as means ± standard error of the mean (SEM). p values from Kruskal–Wallis test. Comparisons between groups are indicated in the graphs. * p < 0.05 and ** p < 0.01.

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  Figure 3. Assessment of oxidative stress markers in erythrocytes from diabetic patients without complications (DG), with vascular complications (DG+C), and controls (CG): (a) superoxide dismutase (SOD), (b) catalase (Cat), (c) glucose-6-phosphate dehydrogenase (G6PD), (d) glutathione reductase (GRd), (e) glutathione peroxidase (GPx), and (f) glutathione disulfide to reduced glutathione ratio (GSSH/GSH). Data are presented as means ± standard error of the mean (SEM). p values from Kruskal–Wallis test. Comparisons between groups are indicated in the graphs. * p < 0.05, ** p < 0.01, *** p < 0.001.

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  Figure 4. The concentration of pro- (IL-6, IL-8, IL-18, MCP-1, and TNF-α) and anti-inflammatory cytokines (IL-10) in plasma samples from diabetic and control groups. (a) IL-6; (b) IL-8; (c) IL-10; (d) IL-18; (e) MCP-1 and (f) TNF-α were assessed. Data are presented as means ± standard error of the mean (SEM). p values from Kruskal–Wallis test. Comparisons between groups are indicated in the graphs. * p < 0.05 and *** p < 0.001.

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  Figure 5. Correlations between relative (a) miR-126 and TG, (b) miR-146a and glucose, (c) miR-155 and glucose, and (d) miR-210 and TNF-α in diabetic patients. Pearson correlation coefficient (r) analysis was used.

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  Figure 6. Receiver operator characteristic (ROC) curves generated for sensitivity analysis show diagnostic performances of plasma markers for diabetes. AUC of proposed models: (a) AUC (95%) = 0.986; creatinine, HOMA-IR, HbA1c, cholesterol and BMI; (b) AUC (95%) = 0.978; miR-21, miR-155, AOPP, GRd, MCP-1; (c) AUC (95%) = 1.000, include miR-155, miR-210, LPO, GRd and BMI. AUC- area under the curve.

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  Figure 7. Receiver-operating characteristic (ROC) curve analysis of two models proposed for significantly predicting the risk of development macrovascular complications in the studied diabetic patients. Model I included a combination of assayed predictors: gender, total cholesterol, GPx, CAT, miR-484 (Exp(B) 0.528, p = 0.024, Chi2 = 28.214, AUC = 0.904(95%). Model II included classical variables: gender, total cholesterol, HbAc1, creatinine, HOMA-IR, BMI (Exp(B) 0.462, p = 0.007, Chi2 = 18.814, AUC = 0.850(95%).