Diabetic kidney disease (DKD) is emerging rapidly as the leading cause of chronic kidney disease (CKD) worldwide. In this 3-year prospective, multicenter cohort study, a total of 1138 pre-dialysis CKD patients were recruited. Patients were categorized into two groups according to the etiologies of DKD and non-diabetic kidney disease (NDKD). Propensity score matching was performed to adjust for confounding factors, resulting in 197 patients being assigned to DKD and NDKD groups, respectively. The primary endpoints were 50% estimated glomerular filtration rate (eGFR) decline and initiation of kidney replacement therapy (KRT). The secondary endpoints were all-cause death and the development of cardiovascular disease (CVD) events. We found that DKD patients have a higher risk to develop 50% eGFR decline endpoint (HR:2.30, 95%CI [1.48–3.58], p < 0.001) and KRT endpoint (HR:1.64, 95%CI [1.13–2.37], p < 0.05) than NDKD patients. The 3-year cumulative incidence of 50% eGFR decline and KRT endpoint was significantly higher in DKD patients (26.90% vs. 13.71% and 35.03% vs. 22.34%, respectively). The Cox regression analyses showed that the increased systolic blood pressure (SBP), DKD, decreased serum albumin (Alb), and higher CKD stages were risk factors for the 50% eGFR decline endpoint; the increased SBP, DKD, decreased serum Alb, serum creatinine (Scr), higher CKD stages, presence of proteinuria and CVD were risk factors for KRT endpoint; the increased age, decreased hemoglobin (Hb), decreased serum Alb were risk factors for all-cause death endpoint; the increased age, decreased serum Alb were risk factors for CVD events endpoint. Appropriate preventive or therapeutic interventions should be taken to control these predictive factors to delay the development of CKD complications, thereby improving the prognosis and reducing the disease burden of the high-risk populations.

Background. Diabetic nephropathy (DN) is a common microvascular complication of diabetes and a major cause of end-stage renal disease, resulting in a substantial socioeconomic burden around the world. Some unknown biomarkers, mechanisms, and potential novel agents regarding DN are yet to be identified. Methods. GSE30528 and GSE1009 were downloaded as training datasets to identify differentially expressed genes (DEGs) of DN. Common DEGs were selected for further analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed to explore molecular mechanisms and pathways. Protein-protein interaction (PPI) network of DEGs was used to identify the top 10 hub genes of DN. Expression profiles of the hub genes were validated in GSE96804 and GSE47183 datasets. The clinical correlation analyses were conducted to confirm the association between key genes and clinical characteristics in the Nephroseq v5 database. The Drug Gene Interaction Database was used to predict potential targeted drugs. Results. 345 and 1228 DEGs were identified in GSE30528 and GSE1009, respectively; and 120 common DEGs were found. The biological process of DEGs was significantly enriched in kidney development. PI3K-Akt signaling pathway, focal adhesion, complement and coagulation cascades were significantly enriched KEGG pathways. The identified top10 hub genes were VEGFA, NPHS1, WT1, TJP1, CTGF, FYN, SYNPO, PODXL, TNNT2, and BMP2. VEGFA, NPHS1, WT1, CTGF, SYNPO, PODXL, and TNNT2 were significantly downregulated in DN. VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL were positively correlated with glomerular filtration rate. The targeted drugs or molecular compounds were enalapril, sildenafil, and fenofibrate target for VEGFA; losartan target for NPHS1; halofuginone, deferoxamine, curcumin, and sirolimus target for WT1; and purpurogallin target for TNNT2. Conclusions. VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL are promising biomarkers for diagnosing and evaluating the progression of DN. The drug-gene interaction analyses provide a list of candidate drugs for the precise treatment of DN.

Background Diabetic kidney disease (DKD) is a major complication of diabetes and the leading cause of end-stage renal disease worldwide. Renal inflammation and infiltration of immune cells contribute to the development and progression of DKD. Thus, the aim of the present study was to identify and validate immune-related biomarkers and analyze potential regulators including transcription factors (TFs), microRNAs (miRNAs), and drugs for DKD. Methods Immune-related genes from the ImmPort database and glomeruli samples from GSE1009 and GSE30528 were used to identify differentially expressed immune-related genes (DEIRGs) of DKD. The expression level and clinical correlation analyses of DEIRGs were verified in the Nephroseq database. Murine podocytes were cultured to construct the high glucose-induced podocyte injury model. The reliability of the bioinformatics analysis was experimentally validated by RT-qPCR in podocytes. Networks among DEIRGs, regulators, and drugs were constructed to predict potential regulatory mechanisms for DKD. Results DKD-associated DEIRGs were identified. CCL19 and IL7R were significantly upregulated in the DKD group and negatively correlated with glomerular filtration rate (GFR). GHR, FGF1, FYN, VEGFA, F2R, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly decreased in the DKD group and positively correlated with GFR. RT-qPCR showed that the relative mRNA expression levels of GHR, FGF1, FYN, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly down-regulated in the high glucose-induced podocyte injury group. The enriched regulators for DEIRGs included 110 miRNAs and 8 TFs. The abnormal expression of DEIRGs could be regulated by 16 established drugs. Conclusions This study identified immune-related biomarkers, regulators, and drugs of DKD. The findings of the present study provide novel insights into immune-related diagnosis and treatment of DKD.