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Dear Editor Diabetes mellitus (DM) has become a significant chronic condition that seriously affects human health.[1] Nowadays, China has become the country with the largest number of DM patients worldwide, of which more than 90% are type 2 diabetes mellitus (T2DM).[2] The increasing prevalence of DM exacerbates the incidence of end-stage renal disease (ESRD).[3] T2DM-related ESRD not only reduces survival rate and health-related quality of life but also places a significant cost on patients as well as society.[[4], [6]] To identify clinical predictive factors and develop prediction models for ESRD risk in T2DM patients, we used the study population extracted from the China Renal Data System, a database containing the information of more than seven million patients attended at 19 hospitals in the Chinese mainland, as previously described.[7] ESRD, including an eGFR of 15 mL/min/1.73 m SP 2 sp or less, or the commencement of dialysis or kidney transplantation due to ESRD, was classified as the outcome. These prediction models were further provided as simple bedside tools, including a risk score and a nomogram, which could be extensively applied to assess T2DM patients' ESRD risk in clinical practice, to aid clinical decision-making and sensible resource allocation. Using a randomized approach, 55 824 patients with T2DM from 10 medical centers were included in the derivation cohort, and 25 745 patients from seven additional medical institutions were included for external validation. [Extracted from the article]

Introduction: Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). Methods: Gene Expression Omnibus database was searched for microarray data including distinct types of CKD. Gene co-expression network was constructed, and modules specific for DN were identified by WGCNA. Gene ontology (GO) analysis was performed, and the hub genes were screened out within the selected gene modules. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. Results: Dataset GSE99339 was selected, and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control, and 7 groups of other glomerular diseases. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (r = 0.54, p = 9e−15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5, and FMOD were identified as hub genes. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. Conclusion: By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN. [ABSTRACT FROM AUTHOR]