Wenge Li

Podocyte injury is a central pathological event in diabetic kidney disease (DKD), but the mitochondrial regulatory mechanisms underlying podocyte dysfunction remain incompletely defined. We integrated single-nucleus RNA-seq data from human renal biopsies in GSE195460 with podocyte-specific differential expression, functional enrichment, mitochondrial gene annotation, co-expression network analysis, machine learning feature selection, pseudotime analysis, in silico knockout, transcription factor enrichment, motif analysis, and molecular docking; findings were externally evaluated in GSE209781. Podocytes showed prominent disease-associated transcriptomic perturbation, with 677 exploratory cell-level differentially expressed genes and enrichment of cytoskeletal, adhesion, developmental, oxidative phosphorylation, and metabolic pathways. Thirty-one mitochondrial-related differentially expressed genes were identified, and OXR1 was prioritized as a candidate regulatory hub based on consistent downregulation, network centrality, machine learning selection, and external validation. HNF4A was predicted as an upstream regulator linked to OXR1 and other mitochondrial targets. In silico OXR1 perturbation increased the predicted injured-state probability from 15% to 82%, while promoter motif analysis provided additional computational support for a putative HNF4A–OXR1 regulatory relationship. These findings highlight OXR1 as a candidate associated with podocyte mitochondrial dysfunction in DKD and suggest a putative HNF4A–OXR1 regulatory relationship.

Background: Chronic low-grade inflammation drives cardiovascular-kidney-metabolic (CKM) syndrome. Clonal hematopoiesis of indeterminate potential (CHIP), an age-related driver of systemic inflammation, is linked to several cardiometabolic disorders. However, whether CHIP modifies CKM progression and contributes to heterogeneity in cardiovascular disease (CVD) risk within the CKM framework remains uninvestigated. Methods: This cohort study included 307,025 UK Biobank participants at CKM stages 0–3 free of baseline CVD. CHIP status was identified via whole-exome sequencing (WES). The association between CHIP and baseline CKM severity was examined, along with the independent and joint effects of CHIP and CKM stages on incident CVD risk. The joint effects of CHIP and polygenic risk scores (PRS) were further assessed, and the incremental predictive value of incorporating CHIP into the AHA PREVENT equations was evaluated. Results: CHIP carriers were more likely to present with advanced CKM stages [OR 1.14 (1.09-1.20), P < 0.001] and exhibited higher incident CVD risk during follow-up [HR 1.13 (1.08-1.18), P < 0.001]. Significant joint effects between CHIP and CKM stages were observed, with the highest risk among CHIP carriers at CKM stage 3 [HR 1.63 (1.50-1.78), P < 0.001]. Large or multiple CHIP mutations conferred greater hazards, with distinct gene-specific effects observed. Moreover, CHIP and high genetic risk also jointly amplified CVD susceptibility. Most importantly, incorporating CHIP into AHA PREVENT significantly improved risk discrimination. Conclusions: CHIP is a significant risk factor associated with more advanced CKM stages and amplifies incident CVD risk. Integrating CHIP into existing prevention strategies may refine CVD risk stratification. Graphic Clinical Perspective: What Is New?: This study provides large-scale evidence that clonal hematopoiesis of indeterminate potential (CHIP) is associated with more advanced cardiovascular-kidney-metabolic (CKM) stages and jointly amplifies future cardiovascular disease (CVD) risk within the CKM staging framework. Somatic CHIP and germline polygenic susceptibility were jointly associated with CKM severity and CVD risk, with the greatest risk observed among individuals with both high polygenic risk and CHIP. What Are the Clinical Implications?: CHIP significantly refines CVD risk stratification across AHA PREVENT risk categories and provides incremental predictive value. Incorporating CHIP into CKM-based prevention strategies may help identify individuals with excess residual cardiovascular risk before the development of clinical CVD.

Patients with diabetic kidney disease (DKD) at chronic kidney disease (CKD) stages 3 to 4 are at high risk for rapid renal function decline within 1 year. However, owing to the multifactorial complexity of the disease, effective prognostic tools that integrate multidimensional clinical and pathological information are currently lacking for this specific population. We conducted a retrospective cohort study involving 322 patients with biopsy-proven DKD (CKD stages 3 to 4) from the China-Japan Friendship Hospital and Hebei University Affiliated Hospital. Their clinical data and 2,576 renal biopsy pathology images were used to develop and validate a multimodal model. Machine learning was applied to integrate clinical composite indices and renal biopsy images to develop a prognostic prediction tool. Four key clinical predictors were identified: estimated glomerular filtration rate, 24-h urinary protein, systemic immune inflammation index, and estimated pulse wave velocity. Among the 6 machine learning algorithms used to develop the prediction models, the random forest algorithm achieved the best performance in the test set, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 and a precision–recall AUC (PR-AUC) of 0.921 for predicting the 1-year composite renal endpoint. The integration of pathological features led to a marked improvement in the performance of the model (ROC-AUC: 0.923 vs. 0.898). External validation demonstrated that incorporating pathological information into the model increased the ROC-AUC from 0.885—achieved when clinical composite indices alone were used as predictors—to 0.930. In this study, machine learning-based automated image analysis of glomerular crescent-shaped changes and renal interstitial fibrosis was integrated with established clinical composite indices to construct an accurate model for predicting short-term renal prognosis of DKD at CKD stages 3 to 4 and to provide a potential tool for improved risk stratification.

This study aimed to identify pyroptosis-related hub genes and investigate immune cell infiltration in IgA nephropathy (IgAN) using bioinformatics and experimental validation. Gene expression data from the GEO database (GSE93798) were analyzed, and differential expression analysis was performed using the limma R package. LASSO regression and SVM algorithms were employed to select feature genes. A total of 19 pyroptosis-related differentially expressed genes (nine up-regulated and 10 down-regulated) were identified, with six hub genes (BHLHE40, JUN, CEBPB, ACE2, IL1B, and CD14) showing high diagnostic accuracy for discriminating IgAN from normal samples (AUC > 0.9). Gene Ontology (GO) and KEGG analyses revealed involvement in immune response, cell migration, and inflammation-related signaling pathways. Immune cell infiltration analysis showed higher levels of CD8+ T cells, macrophages, and neutrophils in IgAN. Validation using GSE99339 confirmed these findings, with the hub genes also demonstrating high AUC values in this independent dataset. The differential expression of these genes was validated in both human kidney tissues and serum samples. This study provides potential biomarkers and therapeutic targets for IgAN by elucidating pyroptosis-related genes and immune cell infiltration profiles.

Background: To explore the potential benefits and risks of glucose-lowering drugs on cardiovascular-kidney-metabolic (CKM) syndrome outcomes using drug-target Mendelian randomization (MR).

Air pollution exposure is increasingly recognized as a risk factor for chronic kidney disease (CKD), but the underlying mechanisms, especially the complex gene-environment interactions as reflected in genetic susceptibility, transcriptomic, and proteomic signatures, remain to be elucidated. We conducted a large-scale prospective cohort study including 330,002 UK Biobank participants with an average follow-up of 13.0 years. Annual average concentrations of PM_2.5, PM_2.5-10, PM₁₀, NO₂, and NO_X were assessed. Cox proportional hazards models were applied to estimate CKD risk associated with long-term air pollution exposure. We further evaluated non-linear relationships using restricted cubic splines (RCS), potential mediators via mediation analyses, and CKD susceptibility through additive interaction analyses with baseline comorbidities and polygenic risk scores (PRS). Additionally, transcriptome-wide association study (TWAS) and proteome-wide two-step Mendelian randomization (MR) were integrated to explore potential molecular pathways. Higher exposures to PM_2.5 (HR: 1.36, 95% CI: 1.22-1.51, per 5 µg/m³), PM_2.5-10 (HR: 1.25, 95% CI: 1.07-1.46, per 5 µg/m³), PM₁₀ (HR: 1.20, 95% CI: 1.06-1.36, per 10 µg/m³), and NO_X (HR: 1.04, 95% CI: 1.02-1.07, per 20 µg/m³) were significantly associated with increased CKD risk, whereas NO₂ showed no significant association (HR: 0.98, 95% CI: 0.95-1.00, per 10 µg/m³). RCS revealed non-linear relationships for PM_2.5-10 and PM₁₀. Mediation analyses indicated that incident hypertension and type 2 diabetes mellitus (T2DM) acted as potential mediators in these associations. Crucially, additive interaction analyses revealed that participants with pre-existing hypertension or type 1 diabetes mellitus (T1DM) were significantly more vulnerable to specific pollution-driven CKD. Compared to individuals with low genetic risk and low air pollution exposure, those with both high genetic risk and high exposure exhibited the highest CKD risk, demonstrating a clear gradient effect across categories. TWAS identified shared genes potentially linking air pollutants with CKD, including upregulated transcripts (STX2, PHOSPHO2, NECAB3) and downregulated transcripts (CDK3, MEIOB, NDUFAF1, CRIPAK). Furthermore, proteome-wide MR analyses identified ALDH3A1, F12, and SNCG as potential risk proteins, and GNLY and MEGF10 as protective proteins. This study provides comprehensive evidence that long-term air pollution exposure is associated with increased CKD risk and offers exploratory insights into the potential molecular pathways underlying this association, advocating the incorporation of renal health considerations into air quality control policies.

The Cholesterol, High-Density Lipoprotein, and Glucose (CHG) index is a recently proposed composite metric, but its utility in predicting the development a

Diabetic kidney disease (DKD) is among the most common and severe complications of diabetes mellitus, with current treatments providing limited efficacy. Yi‐Shen‐Qu‐Yu prescription (YSQYP), a traditional Chinese herbal formula, has demonstrated clinical efficacy in the treatment of DKD; however, its bioactive constituents and underlying mechanisms remain poorly defined. This study proposes a structure‐based and deep learning–guided screening strategy to identify active compounds within YSQYP that target the vitamin D receptor (VDR). An integrated screening strategy was established, combining virtual screening using conventional docking methods and deep learning tools (CarsiDock and RTMScore), as well as biophysical validation through surface plasmon resonance (SPR). Subsequently, the protective effects of candidate compounds against high‐glucose‐induced podocyte injury were further validated using biological assays. Among the screened constituents, isorhamnetin, hederagenin, and (+)‐catechin exhibited the strongest binding affinities for VDR. Functional assays confirmed their capacity to mitigate high‐glucose‐induced cytotoxicity and apoptosis in podocytes, with isorhamnetin showing the most potent protective effect. Collectively, this study establishes an efficient strategy for natural product screening and identifies promising VDR‐targeting compounds from YSQYP, providing novel therapeutic candidates for the treatment of DKD.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disorder, characterized by progressive cyst formation, kidney enlargement, and eventual loss of kidney function. Tolvaptan, a selective vasopressin V2 receptor antagonist, is the only disease-modifying treatment for ADPKD, demonstrating efficacy in slowing cyst growth and preserving kidney function. However, its efficacy and safety in Chinese patients are not well studied due to limited clinical data and regulatory approval gaps. This study aimed to evaluate the efficacy and safety of tolvaptan in kidney volume and kidney function in Chinese patients with rapidly progressing ADPKD. This single-center, prospective, single-arm study enrolled patients with rapidly progressing ADPKD (Mayo classification types 1C–1E). Participants received tolvaptan at doses tailored to baseline estimated glomerular filtration rate based on serum creatinine (eGFR_cr), with follow-up assessments at 3, 6, 9, and 12 months. The primary outcome was the difference between expected and actual height-adjusted total kidney volume (htTKV) growth rates (eHTKV-α − annual change in htTKV), while secondary outcomes included changes in eGFR_cr and safety profiles. Statistical analyses included hierarchical clustering to explore comorbidity patterns and their association with outcomes. Among 145 analyzed patients, htTKV growth stabilized over time, with the median annual change decreasing from 16.8% at 3 months to −2.5% at 12 months. The eHTKV-α − annual change in htTKV shifted from −12.6% at 3 months to 7.6% at 12 months, indicating slower kidney growth than predicted. eGFR_cr remained stable overall, although subgroups (e.g., females) showed slight declines. Comorbidity clustering revealed that patients with proteinuria and pelviectasis had poorer outcomes. Safety monitoring indicated mild liver enzyme elevations [alanine aminotransferase (ALT) > upper limit of normal (ULN): 6.3% at baseline to 9.1% at 12 months] and minimal hyponatremia, with no severe hepatic toxicity. Tolvaptan effectively slowed kidney volume growth and stabilized kidney function in Chinese patients with rapidly progressing ADPKD. The safety profile was favorable, with no severe adverse events observed. These results support tolvaptan as a viable therapeutic option for Chinese patients with ADPKD.