Background: Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship between diabetic retinopathy and DKD has been reported; thus, we aimed to develop a novel detection algorithm for DKD using artificial intelligence technology based on retinal vascular parameters combined with several easily available clinical parameters in patients with type-2 diabetes. Methods: A total of 515 consecutive patients with type-2 diabetes mellitus from Xiangyang Central Hospital were included. Patients were stratified by DKD diagnosis and split randomly into either the training set (70%, N = 360) or the testing set (30%, N = 155) (random seed = 1). Data from the training set were used to develop the machine learning algorithm (MLA), while those from the testing set were used to validate the MLA. Model performances were evaluated. Results: The MLA using the random forest classifier presented optimal performance compared with other classifiers. When validated, the accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model were 84.5%(95% CI 83.3–85.7), 84.5%(82.3–86.7), 84.5%(82.7–86.3), 0.845(0.831–0.859), and 0.914(0.903–0.925), respectively. Conclusions: A new machine learning algorithm for DKD diagnosis based on fundus images and 8 easily available clinical parameters was developed, which indicated that retinal vascular changes can assist in DKD screening and detection.

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD. To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis. Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases. Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation.

Aims: Scavenger receptor class B type I (SRBI) promotes cell cholesterol efflux and the clearance of plasma cholesterol. Thus, SRBI deficiency causes abnormal cholesterol metabolism and hyperlipidemia. Studies have suggested that ferroptosis is involved in lipotoxicity; however, whether SRBI deficiency could induce ferroptosis remains to be investigated. Results: We knocked down or knocked out SRBI in renal HK-2 cells and C57BL/6 mice to determine the expression levels of ferroptosis-related regulators. Our results demonstrated that SRBI deficiency upregulates transferrin receptor 1 (TFR1) expression and downregulates ferroportin (FPN) expression, which induces iron overload and subsequent ferroptosis in renal tubular epithelial cells. TFR1 is known to be regulated by hypoxia-inducible factor-1α (HIF-1α). Next, we investigated whether SRBI deletion affected HIF-1α. SRBI deletion upregulated the mRNA and protein expression of HIF-1α and promoted its translocation to the nucleus. To determine whether HIF-1α plays a key role in SRBI-deficiency-induced ferroptosis, we used HIF-1α inhibitor and siHIF-1α in HK-2 cells and found that downregulation of HIF-1α prevented SRBI-silencing-induced TFR1 upregulation and iron overload, and eventually reduced ferroptosis. The underlying mechanism of HIF-1α activation was explored next, and the results showed that SRBI knockout or knockdown may upregulate the expression of HIF-1α and promote HIF-1α translocation from the cytoplasm into the nucleus via the PKC-β/NF-κB signaling pathway. Innovation and Conclusion: Our study showed, for the first time, that SRBI deficiency induces iron overload and subsequent ferroptosis via the HIF-1α/TFR1 pathway.

Chronic kidney disease (CKD) represents a global health epidemic, with approximately one-third of affected individuals ultimately necessitating renal replacement therapy or transplantation. The kidney, characterized by its exceptionally high energy demands, exhibits significant sensitivity to alterations in energy supply and mitochondrial function. In CKD, a compromised capacity for mitochondrial ATP synthesis has been documented. As research advances, the multifaceted roles of mitochondria, extending beyond their traditional functions in oxygen sensing and energy production, are increasingly acknowledged. Empirical studies have demonstrated a strong association between mitochondrial dysfunction and the pathogenesis of fibrosis and cellular apoptosis in CKD. Targeting mitochondrial dysfunction holds substantial therapeutic promise, with emerging insights into its epigenetic regulation in CKD, particularly involving non-coding RNAs and DNA methylation. This article presents a comprehensive review of contemporary research on mitochondrial dysfunction in relation to the onset and progression of CKD. It elucidates the associated molecular mechanisms across various renal cell types and proposes novel research avenues for CKD treatment.

Diabetic nephropathy (DN) and obesity bring a huge burden to society. Obesity plays a crucial role in the progression of type 2 DN, but the pathophysiology remains unclear. Thus, we aimed the explore the association between type 2 DN and obesity using bioinformatics method. The gene expression profiles of type 2 DN (GSE96804) and obesity (GSE94752) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened with the thresholds defined as |log2FC| ≥1 and P<0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Subsequently, a protein-protein interaction network was constructed based on the STRING database. Hub genes were identified, and the co-expression network was constructed. Finally, the hub genes were verified in clinical samples of 24 patients by immunohistochemistry. A total of 17 common DEGs were identified. Finally, two overlapping hub genes were identified (CCL18, C1QC). C1QC has been verified in clinical specimens. Using bioinformatics methods, the present study analyzed the common DEGs and the potential pathogenic mechanisms involved in type 2 DN and obesity. C1QC was the hub gene. Further studies are needed to clarify the specific relationships among C1QC, type 2 DN and obesity.