Background: Diabetic nephropathy (DN) is the leading cause of end-stage renal disease, but it remains relatively underdiagnosed. Objective: In this study, we aimed to explore the key regulatory pathways and potential biomarkers related to DN using integrated bioinformatics analysis and validation. Methods: First, the microarray data of the GSE30528 and GSE96804 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. Then, weighted gene coexpression network analysis (WGCNA), gene ontology (GO) annotation, gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify key pathways and genes. qRT-PCR and receiver operating characteristic (ROC) curves were used to validate our results. Furthermore, single-cell RNA sequencing (scRNA-seq) data were reanalyzed to investigate the expression specificity of C7 in DN cells. An online database search and luciferase reporter assay identified the target relationship between miRNAs and C7. Results: The "complement and coagulation cascades" were significantly enriched, and complement C3 and C7 were candidate markers. The receiver operating characteristic (ROC) curve revealed that C7 had significant diagnostic value (AUC=0.865) in DN. Through scRNA-seq reanalysis, we found that C7 was specifically elevated in mesangial (MES) cells of DN. Moreover, we found that the expression of C7 was regulated by miR-494-3p and miR-574-5p. Conclusion: This is the first study to reveal that C7 is specifically expressed in mesangial cells, is a potential diagnostic biomarker for diabetic nephropathy, and is regulated by miR-494-3p and miR-574-5p.
Purpose The purpose of this study is to evaluate whether thyroid hormone in euthyroid patients with type 2 diabetes mellitus (T2DM) is associated with macrovascular complications. Patients and Methods The authors examined 311 patients enrolled from February 2019 to December 2019 in Tianjin Medical University Chu Hsien-I Memorial Hospital. A medical record review enabled the collection of demographic and anthropometric information. We classified the patients into two groups based on the echocardiography and vascular ultrasonography results, namely, non-macrovascular complications (n=131) group and macrovascular complications (n=180) group. Odds ratios (OR) and 95% confidence intervals (CI) were calculated, adjusting for potential confounders, the prevalence of macrovascular complications was determined using multivariate logistic regression. Results A significant association was observed for diabetic macrovascular complications with normal free triiodothyronine (FT3) (OR=0.534, 95% CI 0.358–0.796, p = 0.002) and free thyroxine (FT4) (OR= 0.844, 95% CI 0.760–0.937, p = 0.001). Nevertheless, there was no evidence of any association between thyroid-stimulating hormone (TSH) and the development of diabetic macrovascular complications. When stratified by the body mass index (BMI), a similar relationship existed with the overall results. The positive association remained in restricted analyses involving only patients with HbA1c abnormalities. Conclusion Overweight or obese T2DM patients are at high risk due to the implicit association between low but clinically normal thyroid hormone levels and elevated risk of macrovascular complications. However, there were no statistically significant associations between TSH and diabetic macrovascular complications.
Purpose: This study aimed to determine whether insomnia is associated with hypertension (HBP) and coronary artery disease (CAD) in a hospital-based sample of patients with type 2 diabetes mellitus (T2DM).Methods: Our present study included 354 patients with T2DM. According to the diagnostic criteria of insomnia, the participants were assigned to three groups based on the duration of T2DM and insomnia diagnosis. Patients with T2DM alone were placed in group A; patients with T2DM longer than insomnia were placed in group B; and patients with insomnia longer than T2DM were placed in group C. Medical history was collected from all the patients in detail. Besides, the participants underwent thorough physical examinations and laboratory measurements. Propensity score matching (PSM) was applied to evaluate the associations of insomnia with HBP and CAD. The univariate and multivariate logistic regression analysis was used to explore whether insomnia was a risk factor for HBP and CAD in patients with T2DM.Results: Of 354 patients, 225 patients were included in group A, 62 patients were included in group B, and 67 patients were included in group C. Compared with groups B and C, group A showed a lower prevalence of HBP and CAD (p < 0.05). In addition, compared with group B, group C showed no difference in the prevalence of HBP and CAD (p > 0.05). After PSM was performed, groups B and C had a higher prevalence of HBP and CAD (p < 0.05) than group A with no significant difference between groups B and C (p > 0.05). In the univariate and multivariate logistic regression analysis, insomnia was a risk factor for HBP [univariate: odds ratio (OR) = 3.376, 95% CI 2.290–6.093, p < 0.001; multivariate: OR = 2.832, 95% CI 1.373–5.841, p = 0.005] and CAD (univariate: OR = 5.019, 95% CI 3.148–8.001, p < 0.001; multivariate: OR = 5.289, 95% CI 2.579–10.850, p < 0.001).Conclusion: T2DM combined with insomnia was related to HBP and CAD and insomnia was a risk factor for HBP and CAD in patients with T2DM. However, larger, prospective studies are required to confirm our findings.
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