Correlation between plasma long non-coding RNA MEG-3 and inflammatory cytokines in patients with diabetic nephropathy.

To identify the correlation between the plasma long non-coding RNA maternally expressed gene 3 (lncRNA MEG-3) and inflammatory cytokines in patients with diabetic nephropathy (DN) and search for a potential index for the diagnosis of DN. Quantitative real-time PCR (qPCR) was used to assess lncRNA MEG-3 expression. The levels of plasma cytokines were detected via enzyme-linked immunosorbent assay (-ELISA). 20 patients with type 2 diabetes (T2DM) and DN (DM+DN+ group), 19 patients with T2DM (DM+DN- group), and 17 healthy subjects (DM-DN- group) were finally enrolled. The expression of lncRNA MEG-3 was significantly upregulated in the DM+DN+ group compared to the DM+DN- group (p < 0.05) and the DM-DN- group (p < 0.001). Pearson's correlation analysis showed a positive correlation of lncRNA MEG-3 levels with cystatin C (Cys-C) (r = 0.468, p < 0.05), albumin-creatinine ratio (ACR) (r = 0.532, p < 0.05), and creatinine (Cr) (r = 0.468, p < 0.05), and a negative correlation with estimated glomerular filtration rate (eGFR) (r = -0.674, p < 0.01). Furthermore, the expression level of plasma lncRNA MEG-3 had a significantly positive correlation with the level of interleukin 1ß (IL-1ß) (r = 0.524, p < 0.05) and interleukin 18 (IL-18) (r = 0.230, p < 0.05). Binary regression analysis showed that lncRNA MEG-3 was a risk factor for DN with odds ration (OR) value of 1.71 (p < 0.05). The area under receiver operation characteristic (ROC) curve (AUC) of DN identified by lncRNA MEG-3 was 0.724. LncRNA MEG-3 was highly expressed in DN patients and showed a positive correlation with IL-1ß, IL-18, ACR, Cys-C, and Cr.

Identification of autophagy-related genes as potential biomarkers for type 1 diabetes mellitus.

Background: Type 1 diabetes mellitus (T1DM) is a metabolic disease in which the autoimmune destruction of pancreatic islet ß-cells occurs. This study sought to investigate the role of autophagy-related genes and immune cells in the development of T1DM. Methods: We acquired the raw gene expression profiles of 302 T1DM and 422 normal control peripheral blood samples from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the Limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The Search Tool for the Retrieval of Interacting Genes/Proteins (https://string-db.org/) and Cytoscape autophagy genes were intersected with the DEGs for the immune cell analysis and the correlation analysis. Results: A total of 568 DEGs were identified in the T1DM and normal samples, of which 301 were upregulated and 267 were downregulated. The results of the functional and pathway enrichment analyses showed that the DEGs were closely associated with autophagy and immunity. Member RAS oncogene family (RAB11A), protein tyrosine phosphatase non-receptor type 11, lamin A/C, heat shock protein70, heat shock protein family A member 4, cluster of differentiation 8A, caspase 3 (CASP3), exportin 1, proto-oncogene, non-receptor tyrosine kinase, SMAD family member 4, and sirtuin 1 (SIRT1) were located at the center of the protein-protein interaction network as the core genes. The peripheral blood T cells were more elevated in the T1DM subjects than the normal subjects. RAB11A, CASP3, and SIRT1 are autophagy-associated genes. RAB11A and CASP3 were positively correlated with most immune cells, while SIRT1 was negatively correlated with most immune cells. Conclusions: Autophagy-related genes (i.e., RAB11A, CASP3, and SIRT1) and immune cells (i.e., T and B cells) may play important regulatory roles in the development of T1DM. Our findings provide novel insights into and potential targets for T1DM prediction and treatment.

Predicting the Risk of Diabetic Foot Ulcers From Diabetics With Dysmetabolism: A Retrospective Clinical Trial.

Background: Diabetic foot ulcer (DFU) in patients with type 2 diabetes mellitus (T2D) often leads to amputation. Early intervention to prevent DFU is urgently necessary. So far, there have been no studies on predictive models associated with DFU risk factors. Our study aimed to quantify the predictive risk value of DFU, promote health education, and further develop behavioral interventions to reduce the incidence of DFU. Methods: Data from 973 consecutive patients with T2D was collected from two hospitals. Patients from the Guangxi Medical University First Affiliated Hospital formed the training cohort (n = 853), and those from the Wuming Hospital of Guangxi Medical University formed the validation cohort (n = 120). Independent variable grouping analysis and multivariate logistic regression analysis were used to determine the risk factors of DFUs. The prediction model was established according to the related risk factors. In addition, the accuracy of the model was evaluated by specificity, sensitivity, predictive value, and predictive likelihood ratio. Results: In total, 369 of the 853 patients (43.3%) and 60 of the 120 (50.0%) were diagnosed with DFUs in the two hospitals. The factors associated with DFU were old age, male gender, lower body mass index (BMI), longer duration of diabetes, history of foot disease, cardiac insufficiency, no use of oral hypoglycemic agent (OHA), high white blood cell count, high platelet count, low hemoglobin level, low lymphocyte absolute value, and high postprandial blood glucose. After incorporating these 12 factors, the nomogram drawn achieved good concordance indexes of 0.89 [95% confidence interval (CI): 0.87 to 0.91] in the training cohort and 0.84 (95% CI: 0.77 to 0.91) in the validation cohort in predicting DFUs and had well-fitted calibration curves. Patients who had a nomogram score of ≥180 were considered to have a low risk of DFU, whereas those having ≥180 were at high risk. Conclusions: A nomogram was constructed by combining 12 identified risk factors of DFU. These 12 risk factors are easily available in hospitalized patients, so the prediction of DFU in hospitalized patients with T2D has potential clinical significance. The model provides a reliable prediction of the risk of DFU in patients with T2D.

IL-1B can serve as a healing process and is a critical regulator of diabetic foot ulcer.

Background: Diabetic foot ulcer (DFU) is the main cause of disability in diabetic patients. However, the molecular changes underlying the occurrence and progression of DFU remain unclear. We conducted this study to examine gene alterations in different DFU patients. Methods: GSE143735 and GSE134431 transcriptome data sets were acquired from the Gene Expression Omnibus database, and differential expression analyses of the genes in these data sets were performed. A functional enrichment analysis of the differentially expressed genes (DEGs) was performed using clusterProfiler package in R. To examine the correlations between DEGs and significant immune-related genes, we identified the intersecting ulcer-related DEGs, healing-related DEGs, and immune-related DEGs. Finally, we further investigate the relationship between the selected genes with immune cell regulation via a single-sample gene set enrichment analysis, and the infiltration of 28 immune cells in common diabetes samples, unhealed DFU samples, and healed samples DFU were compared. Results: We found 238 upregulated genes and 207 downregulated genes in the diabetic foot (DF) patients with ulcers compared to the DF patients without ulcers, and 74 upregulated genes and 28 downregulated genes in the healed samples compared to the unhealed samples. To examine the main biological functions, we conducted a functional enrichment analysis. The results showed that the biological functions of functional enrichment analysis included neutrophil degranulation, leukocyte chemotaxis, myeloid leukocyte migration, phagosome, cytokine-cytokine receptor interaction, and the chemokine signaling pathway. Interleukin (IL)-1B was more highly expressed in patients with ulcers and healed DFU patients than those without ulcers and unhealed DFU patients. Finally, the immune cell abundance difference results showed that activated cluster of differentiation (CD)8 T cells, central memory CD8 T cells, T follicular helper cells, myeloid-derived suppressor cells, natural killer T cells and monocytes were more highly infiltrated in normal diabetes patients and healed DFU patients than unhealed DFU patients. However, no difference was found between DF patients with and without ulcers. Conclusions: IL-1B is an inflammation gene that can be used to assess and regulate DFU progression.