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Diabetic nephropathy, as a severe microvascular complication of diabetes, manifests in four clinical types: classic, albuminuria regression, a rapid decline in kidney function (RDKF), and non-proteinuric or non-albuminuric DKD. Rapidly progressive diabetic nephropathy advances to end-stage renal disease more swiftly than the typical form, posing significant risks. However, a comprehensive understanding of rapidly progressive diabetic nephropathy is currently lacking. This article reviewed latest developments in genetic and clinical risk factors associated with rapidly progressive diabetic nephropathy, aiming to broad perspectives concerning the diagnosis and interventions of this condition.

Abstract The pathogenesis of diabetic kidney disease (DKD) is complicated. Current clinical treatments fail to achieve satisfactory efficacy in the prevention of DKD progression, it urgently needs novel and effective treatment for DKD. In this study, we firstly demonstrated that renal lipid metabolism abnormality and inflammation significantly changed in DKD conditions by mining public transcriptomic data of DKD patient samples. KEGG analysis further exhibited the critical role of vascular endothelial growth factor B (VEGF-B) and interleukin 17A (IL-17A) signal pathways in DKD progression, indicating that VEGF-B and IL-17A might be the promising targets for DKD treatment. Then the potential of a novel combination therapy, anti-VEGF-B plus anti-IL-17A antibody, was evaluated for DKD treatment. Our results demonstrated that simultaneous blockade of VEGF-B and IL-17A signaling with their neutralizing antibodies alleviated renal damage and ameliorated renal function. The therapeutic effectiveness was not only related to the reduced lipid deposition especially the neutral lipids in kidney but also associated with the decreased inflammation response. Moreover, the therapy alleviated renal fibrosis by reducing collagen deposition and the expression of fibronectin and α-SMA in kidney tissues. RNA-seq analysis indicated that differential expression genes (DEGs) in db/db mice were significantly clustered into lipid metabolism, inflammation, fibrosis and DKD pathology-related pathways, and 181 of those DEGs were significantly reversed by the combinatory treatment, suggesting the underlying mechanism of administration of anti-VEGF-B and anti-IL-17A antibodies in DKD treatment. Taken together, this study identified that renal lipid metabolism abnormality and inflammation were critically involved in the progression of DKD, and simultaneous blockade of VEGF-B and IL-17A signaling represents a potential DKD therapeutic strategy.

Background and aimProtein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectively. We performed a cross-sectional study in hemodialysis patients to propose a novel PEW prediction model.Materials and methodsA total of 380 patients who underwent maintenance hemodialysis were enrolled in this cross-sectional study. The data were analyzed with univariate and multivariable logistic regression to identify influencing factors of PEW. The PEW prediction model was presented as a nomogram by using the results of logistic regression. Furthermore, receiver operating characteristic (ROC) and decision curve analysis (DCA) were used to test the prediction and discrimination ability of the novel model.ResultsBinary logistic regression was used to identify four independent influencing factors, namely, sex (P = 0.03), triglycerides (P = 0.009), vitamin D (P = 0.029), and NT-proBNP (P = 0.029). The nomogram was applied to display the value of each influencing factor contributed to PEW. Then, we built a novel prediction model of PEW (model 3) by combining these four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and BMI, while the ISRNM diagnostic criteria served as model 1 and model 2. ROC analysis of model 3 showed that the area under the curve was 0.851 (95%CI: 0.799–0.904), and there was no significant difference between model 3 and model 1 or model 2 (all P > 0.05). DCA revealed that the novel prediction model resulted in clinical net benefit as well as the other two models.ConclusionIn this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objective than traditional diagnostic criteria.