A Dynamic Prediction Model for Renal Progression in Primary Membranous Nephropathy.

Objective: This study aimed to build and validate a practical web-based dynamic prediction model for predicting renal progression in patients with primary membranous nephropathy (PMN). Method: A total of 359 PMN patients from The First Affiliated Hospital of Fujian Medical University and 102 patients with PMN from The Second Hospital of Longyan between January 2018 to December 2023 were included in the derivation and validation cohorts, respectively. Renal progression was delineated as a decrease in eGFR of 30% or more from the baseline measurement at biopsy or the onset of End-Stage Renal Disease (ESRD). Multivariable Cox regression analysis was employed to identify independent prognostic factors. A web-based dynamic prediction model for renal progression was built and validated, and the performance was assessed using. An analysis of the receiver operating characteristic and the decision curve analysis. Results: In the derivation cohort, 66 (18.3%) patients experienced renal progression during the follow-up period (37.60 ± 7.95 months). The final prediction rule for renal progression included hyperuricemia (HR=2.20, 95%CI 1.26 to 3.86), proteinuria (HR=2.16, 95%CI 1.47 to 3.18), significantly lower serum albumin (HR=2.34, 95%CI 1.51 to 3.68) and eGFR (HR=1.96, 95%CI 1.47 to 2.61), older age (HR=1.85, 95%CI 1.28 to 2.61), and higher sPLA2R-ab levels (HR=2.08, 95%CI 1.43 to 3.18). Scores for each variable were calculated using the regression coefficients in the Cox model. The developed web-based dynamic prediction model, available online at http://imnpredictmodel1.shinyapps.io/dynnomapp, showed good discrimination (C-statistic = 0.72) and calibration (Brier score, P = 0.155) in the validation cohort. Conclusion: We developed a web-based dynamic prediction model that can predict renal progression in patients with PMN. It may serve as a helpful tool for clinicians to identify high-risk PMN patients and tailor appropriate treatment and surveillance strategies.

NET-Related Gene as Potential Diagnostic Biomarkers for Diabetic Tubulointerstitial Injury.

Background: Tubulointerstitial injury plays a pivotal role in the progression of diabetic kidney disease (DKD), yet the link between neutrophil extracellular traps (NETs) and diabetic tubulointerstitial injury is still unclear. Methods: We analyzed microarray data (GSE30122) from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with DKD's tubulointerstitial injury. Functional and pathway enrichment analyses were conducted to elucidate the involved biological processes (BP) and pathways. Weighted gene coexpression network analysis (WGCNA) identified modules associated with DKD. LASSO regression and random forest selected NET-related characteristic genes (NRGs) related to DKD tubulointerstitial injury. Results: Eight hundred ninety-eight DEGs were identified from the GSE30122 dataset. A significant module associated with diabetic tubulointerstitial injury overlapped with 15 NRGs. The hub genes, CASP1 and LYZ, were identified as potential biomarkers. Functional enrichment linked these genes with immune cell trafficking, metabolic alterations, and inflammatory responses. NRGs negatively correlated with glomerular filtration rate (GFR) in the Neph v5 database. Immunohistochemistry (IHC) validated increased NRGs in DKD tubulointerstitial injury. Conclusion: Our findings suggest that the CASP1 and LYZ genes may serve as potential diagnostic biomarkers for diabetic tubulointerstitial injury. Furthermore, NRGs involved in diabetic tubulointerstitial injury could emerge as prospective targets for the diagnosis and treatment of DKD.