BACKGROUND:Diabetic kidney disease (DKD) is a major cause of chronic kidney disease (CKD) worldwide, characterised by tubular injury, inflammation, and fibrosis. Increasing evidence suggests that mechanotransduction and innate immune activation contribute to DKD progression. However, the mechanistic link between mechanosensitive ion channels, cGAS-STING signalling, and pyroptosis remains incompletely understood. PURPOSE:This study aimed to investigate whether the modified Shen-Yan-Fang-Shuai formula (M-SYFSF) attenuates DKD and to explore its potential regulatory effects on Piezo1-mediated Ca²⁺ signalling, cGAS-STING activation, and tubular pyroptosis. METHODS:DKD was induced in rats by unilateral nephrectomy combined with streptozotocin injection, with valsartan as a positive control, and HK-2 cells were stimulated with advanced glycation end products (AGEs). Renal function, histopathology and fibrosis were assessed by biochemical assays and histological staining. Renal transcriptome sequencing, network pharmacology and analysis of public human renal transcriptomic datasets were used to identify candidate pathways. Western blotting, immunohistochemistry, immunofluorescence, qRT-PCR, Fluo-4 AM calcium imaging, ELISA, serum LDH activity and renal caspase-1 activity assays were performed to evaluate Piezo1 expression, cGAS-STING signalling and pyroptosis. Piezo1 knockdown and pharmacological modulation were used to assess pathway ordering, and Co-IP was used to examine the cGAS-STING association. Molecular docking and molecular dynamics simulation were performed as exploratory, hypothesis-generating analyses. RESULTS:DKD rats exhibited renal dysfunction, increased fibrosis, elevated Piezo1 expression, increased intracellular Ca²⁺ levels, activation of cGAS-STING signalling and upregulation of pyroptosis-associated proteins. Renal transcriptome sequencing and network pharmacology converged on mechanotransduction- and AGE-RAGE-associated pathways, and Piezo1 upregulation was further confirmed in db/db and HFD + STZ mice and in human tubulointerstitial datasets. M-SYFSF treatment improved renal function, attenuated fibrosis, reduced Piezo1 expression and intracellular Ca²⁺ accumulation, and suppressed cGAS-STING signalling and downstream inflammatory responses. Piezo1 knockdown attenuated STING and GSDMD-N upregulation, which was partially restored by Yoda1, whereas the STING inhibitor H-151 suppressed both STING and pyroptotic responses. Co-IP analysis showed an AGEs-induced cGAS-STING association that was reduced by M-SYFSF. CONCLUSION:M-SYFSF attenuated experimental DKD in association with reduced Piezo1-associated Ca²⁺ signalling, cGAS-STING activation and tubular pyroptosis-associated responses, and Piezo1 upregulation was reproducible across three rodent models and in human renal tissue. The data support an upstream contribution of Piezo1 under AGEs stress but do not establish the genetic necessity of STING, the proposed mitochondrial DNA intermediate, or the occurrence of fully executed pyroptosis.

Background: Cellular heterogeneity limits causal inference in complex diseases. We applied an established cell-type-stratified Mendelian randomization (csMR) framework, originally developed by Hao et al. (2024), to investigate whether lipid phenotypes affect kidney disease through distinct brain cell populations, extended by a proteome-wide mediation component. Methods: Using Bayesian colocalization (posterior probability of hypothesis 4 [PPH4], ≥0.8) and multidimensional instrumental variable selection, we evaluated cell-type-stratified MR-supported associations of five lipid traits across ten brain cell or tissue strata with kidney disease risk. Instrumental variables were derived from published genome-wide association studies and human brain single-cell expression quantitative trait locus (eQTL) maps. An exploratory proteomic mediation analysis using the Difference Method was performed with UKB-PPP as the discovery resource and deCODE as the external validation resource, with mediation signals interpreted as hypothesis-generating. For the primary csMR analysis, multiple testing was corrected separately within each lipid trait using a Bonferroni threshold of 3.33 × 10−4, corresponding to 150 cell/tissue-by-outcome tests per lipid trait. Results: In csMR analyses, cholesterol-related lipids showed cell-type-stratified associations with kidney disease, most prominently in oligodendrocyte-related analyses (max β on the log-odds scale = 1.025). LDL-related associations with broad chronic glomerular disease were most evident in excitatory neuron-related analyses. Dual-cohort proteomic analysis prioritized plasma proteins, including SNAP29 and ICAM4, as candidate protein-associated signals for exploratory mediation analysis. These findings represent genetic colocalization and Mendelian randomization-supported associations, not experimentally established mechanisms, and should be interpreted as hypothesis-generating. Conclusions: This study provides a cell-type-resolved genetic and proteomic association map linking lipid traits, brain cell strata, and kidney disease outcomes, offering hypotheses for future experimental validation.

Diabetic kidney disease (DKD) represents a major global health burden, affecting 20–40

BackgroundDiabetic kidney disease (DKD) is widely recognized as a major contributor to end-stage renal disease, in which podocyte injury serves as an important pathological basis for disease progression. ZhiXiaoSanZheng Formula (ZXSZF), an empirically derived traditional Chinese medicine prescription, has shown therapeutic potential in DKD; however, its molecular mechanisms remain unclear. This study investigated whether ZXSZF protects podocytes by modulating ferroptosis-related pathways.MethodsThe chemical profile of ZXSZF was analyzed by LC-MS/MS. Potential bioactive compounds were screened through SwissADME, and putative targets were predicted using SwissTargetPrediction. Overlapping targets among ZXSZF, DKD, and ferroptosis were identified and analyzed through protein-protein interaction and functional enrichment analyses. The predicted mechanisms were further validated in a unilateral nephrectomy plus STZ-induced DKD rat model and in AGEs-stimulated MPC5 podocytes.ResultsLC-MS/MS analysis identified 94 chemical constituents in ZXSZF. Network pharmacology analysis suggested that antioxidant and ferroptosis-related pathways centered on NRF2 may represent potential regulatory nodes of ZXSZF. In DKD rats, ZXSZF reduced albuminuria and improved renal histopathological changes, accompanied by restoration of podocyte markers and attenuation of ferroptosis-associated alterations. In AGEs-stimulated podocytes, ZXSZF decreased lipid peroxidation and iron accumulation while enhancing cellular antioxidant capacity. These effects were associated with increased NRF2 signaling and upregulation of SLC7A11 and GPX4. Pharmacological inhibition of NRF2 with ML385 partially attenuated the protective effects of ZXSZF.ConclusionsZXSZF alleviates podocyte injury in DKD and its renoprotective effects are associated with modulation of ferroptosis-related processes involving the NRF2/SLC7A11/GPX4 pathway. The present study provides experimental evidence for the mechanistic basis of ZXSZF and supports its potential role as a complementary therapeutic option in DKD management.

Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for metabolic disorders but also an “accelerator” of DKD progression. This study seeks to investigate the molecular pathways connecting air pollution to DKD. Multiple databases were integrated to obtain potential target genes of 10 common air pollutants. The gene expression omnibus (GEO) database was employed to acquire DKD datasets. Differential expression analysis and weighted correlation network analysis (WGCNA) were performed to identify DKD-related genes. 12 machine learning algorithms were utilized to generate 113 unique predictive models, which were employed to select hub genes. Subsequently, MR, single-cell, gene set enrichment analysis (GSEA), and immune infiltration analyses were performed, followed by molecular docking of hub genes with air pollutants. A total of 714 targets were identified from 10 air pollutants, and 80 potential targets were identified from DKD transcriptomic data. Machine learning methods identified 5 hub genes that are closely associated with DKD. Mendelian randomization (MR) analysis indicated that, among the five hub genes, only NOS3 demonstrated a statistically significant causal association with DKD. Immune infiltration analysis found that hub genes were closely related to immune cells. Molecular docking validation indicated that certain air pollutants can stably bind with hub genes such as NOS3 and PTGS2. Air pollutants may be linked to alterations in various biological processes, potentially involving key genes such as ADH5, CASP3, NOS3, PTGS2, and SDHB, including potential metabolic reprogramming, inflammatory processes, and immune microenvironment changes.

Background:Visceral obesity is a significant risk factor for metabolic disorders and is also an important risk factor for diabetic kidney disease (DKD). However, there is still insufficient research on the predictive value of different visceral obesity indices for DKD and the sex differences in this regard. This study aims to explore the correlations between the cardiac metabolic index (CMI), lipid accumulation product (LAP), triglyceride-glucose (TyG) index, visceral adiposity index (VAI), and the risks and mortality associated with DKD, with a focus on sex differences. Methods:This study utilized data from NHANES conducted from 2007 to 2016. Weighted logistic regression models were employed to investigate the relationships between four visceral obesity indices and DKD. Restricted cubic splines (RCSs) were also used to assess the dose-response relationship, and subgroup and interaction analyses were conducted. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the screening utility of each index for DKD risk identification. Kaplan-Meier (K-M) curves were used to analyze survival outcomes across different tertiles of the four visceral obesity indices. Results:A total of 6722 diabetic patients were included in the study, with 5301 in the diabetes-only group and 1421 in the DKD group. All four visceral obesity indices were significantly positively correlated with the risk of DKD, exhibiting a nonlinear relationship, with notable sex differences. Compared to males, TyG and LAP showed a stronger correlation in females. The area under the curve (AUC) was generally higher in females. Furthermore, all four visceral obesity indices showed significantly lower all-cause and cardiovascular mortality rates in the lowest tertile (Q1 group) compared to the higher tertiles (Q2-Q3 groups). Conclusion:CMI, LAP, TyG, and VAI are significantly associated with the risk of DKD in diabetic patients, with notable differences between male and female populations. These metabolic indices may serve as important indicators for risk assessment and potential intervention strategies for DKD.

BACKGROUND:Diabetic nephropathy (DN) is a serious microvascular complication of diabetes that urgently requires effective treatments with low toxicity. The traditional Chinese medicine formula Yiqi Yangyin Qingre decoction (YQYYQR) has demonstrated potential in alleviating DN, yet its pharmacological mechanism remains unclear. METHODS:UPLC-MS/MS combined with network pharmacology was utilized to qualitatively analyze YQYYQR's bioactive components and predict therapeutic targets. Integrating public databases and GEO-derived DN-related genes, core targets were identified via intersection and subjected to pathway enrichment. Molecular docking validated key component-core target interactions, with in vivo DN mouse experiments and transcriptome sequencing performed for verification. RESULTS:This study identified 376 bioactive components from YQYYQR, corresponding to 1284 potential therapeutic targets. Cross-analysis between these targets and DN-related genes yielded 57 overlapping targets, among which GSK3β and NFKB1 were screened as core hub genes. Network pharmacology and transcriptomic pathway enrichment analyses indicated that the mechanism of YQYYQR in intervening DN involves biological processes such as autophagy and inflammatory response. In vivo, YQYYQR significantly improved mouse proteinuria and renal function and alleviated pathological kidney damage. Mechanistically, YQYYQR enhanced the inhibitory phosphorylation of GSK3β at Ser9, thereby facilitating TFEB nuclear translocation and activating the autophagy-lysosomal pathway. Simultaneously, it suppresses the NLRP3/ASC/caspase-1/GSDMD-N-mediated pyroptosis pathway, ultimately reducing renal inflammation. CONCLUSION:YQYYQR exerts a protective effect against DN progression by targeting core genes including GSK3β, regulating the GSK3β-TFEB axis to restore autophagic flux via the autophagy-lysosomal pathway, and inhibiting NLRP3-mediated pyroptosis to mitigate excessive renal inflammation.

BACKGROUND:Diabetic kidney disease (DKD) is significantly impacting both quality of life and survival rates. The Shen-Yan-Fang-Shuai (SYFS) formula is a traditional Chinese medicine (TCM) compound widely used in the clinical treatment of DKD with proven efficacy, though its potential mechanism of action remains unclear. This study attempts to elucidate the therapeutic efficacy, mechanisms of action, and active compounds of the SYFS formula in the treatment of DKD. MATERIALS AND METHODS:The components of SYFS formula were identified by UHPLC-MS/MS. Differentially expressed genes (DEGs) and key module genes were selected based on the GEO database to obtain intersection targets. Protein-protein interaction (PPI) network and component-target network were constructed. Machine learning (ML) was employed to screen for hub genes, which were validated through nomogram, immune infiltration analysis, molecular docking and molecular dynamics (MD) simulation. Subsequently, our findings were validated through a combination of transcriptomic sequencing of renal tissue from animal models and real-time quantitative PCR (qPCR) analyses performed on both the animal tissues and HK-2 cells. RESULTS:154 chemical components and 994 targets were identified in the SYFS formula. Intersection with DEGs and WGCNA module genes identified 39 potential targets. Five hub genes (MMP3, MMP12, PTGES, SST, and DUSP1) were selected through ML and used to construct a nomogram. Multiple immune cell infiltration levels were significantly elevated in DKD, with hub genes showing correlations with specific immune cell types. Molecular docking and MD simulation validated the binding capacity between components of the SYFS formula and key targets. In addition, it has been further verified in animal experiments and cell experiments. CONCLUSIONS:The core components of the SYFS formula, including naringenin chalcone, palmatine, oleanonic acid, β-elemonic acid, and Naringenin, likely exert their effects through the MMP3, MMP12, PTGES, SST, and DUSP1 targets. This research offers empirical support for the application of the SYFS formula in DKD, establishing a crucial groundwork for subsequent clinical investigations.

Diabetic kidney disease (DKD) is a prevalent complication in individuals with diabetes. Efferocytosis plays a pivotal role in chronic diseases; however, the precise mechanisms involved in DKD are still not fully understood. DKD-related datasets were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. These DEGs subsequently intersected with efferocytosis-related genes (ERGs) to produce DKD efferocytosis‒related genes (DKD-ERGs). Potential hub genes were subsequently identified using protein‒protein interaction (PPI) network analysis in combination with machine learning (LASSO regression, Boruta algorithm, and random forest algorithm). Next, we employed transcriptomics, proteomics, and metabolomics analyses of DKD animal models, followed by validation with serum samples from patients with DKD. A nomogram was developed using hub genes to evaluate its predictive accuracy. Consensus clustering was utilized to categorize DKD patients and conduct immune infiltration analysis. A total of 15 DKD-related ERGs were identified. ANXA1, CASP3, IL33, and C3 were identified as potential hub genes. First, validation was performed using the GEO and Nephroseq databases. The hub genes were subsequently validated from multiple perspectives, including transcriptomics, metabolomics, and proteomics of DKD animal models, as well as serological analysis of DKD patients. A risk score model incorporating these 4 hub genes effectively predicted both the onset and progression of DKD. On the basis of these hub genes, DKD patients were classified into Cluster 1 and Cluster 2, with distinct subtypes and immune infiltration correlating with disease stages. This study reveals the potential diagnostic value of ERGs (ANXA1, CASP3, IL33, and C3) in DKD through multidimensional analysis. These genes may serve as promising biomarkers and therapeutic targets for DKD.

ETHNOPHARMACOLOGICAL RELEVANCE:Pholidota chinensis Lindl. (P. chinensis), a traditional medicinal plant, exhibits notable antioxidant, anti-inflammatory, and antidiabetic properties. In Traditional Chinese Medicine, its pseudobulbs are applied for treating cough, bronchitis, diabetes, and diabetic kidney disease (DKD). The potential mechanism through which P. chinensis alleviates DKD requires further investigation. AIM OF THE STUDY:This investigation was designed to evaluate the potential of P. chinensis to optimize mitochondrial function via the dual regulation of dynamic remodeling and mitophagic flux within DKD models. MATERIALS AND METHODS:A DKD animal model was induced through unilateral nephrectomy and streptozotocin administration. Various parameters were evaluated, such as renal function, histopathology, inflammatory and fibrotic markers, and proteins related to mitochondrial fission-fusion balance and mitophagy. In vitro, AGEs-treated HK-2 cell model was employed to assess autophagic flux and mitochondrial function via adenoviral mCherry-GFP-LC3B transduction, JC-1 assay, together with measurements of ROS and ATP production. RESULTS:P. chinensis showed clear renoprotective effects in DKD rats, mainly by alleviating renal inflammation and fibrotic changes. Treatment was associated with improved mitochondrial dynamics, characterized by increased expression of mitochondrial fusion-related proteins (Mfn1, Opa1) and reduced levels of fission-related proteins (Drp1, Fis1). Meanwhile, a pronounced activation of mitophagy was observed, evidenced by increased levels of core regulators involved in the PINK1/Parkin pathway and autophagosome formation, along with reduced p62 accumulation. In vitro, P. chinensis exerted antioxidative effects by enhancing SOD activity while reducing MDA levels. It also facilitated autophagy activation and autophagosome-lysosome fusion, leading to improved mitochondrial membrane potential, reduced ROS production, and increased ATP generation. CONCLUSION:Overall, P. chinensis improves mitochondrial quality control by promoting fusion, reactivating mitophagy, and enhancing autophagic flux, thereby facilitating mitochondrial clearance and contributing to the attenuation of kidney injury and the slowing of DKD progression.

Chronic kidney disease (CKD) is traditionally studied through an organ‑centric paradigm, despite its frequent coexistence with intestinal dysbiosis and metabolic dysfunction‑associated steatotic liver disease, which confers a 38% increased CKD risk. Multi‑organ crosstalk along the gut‑liver‑kidney axis remains inadequately addressed in current guidelines. The present study aimed to establish the gut‑liver‑kidney axis as an integrated systems biology framework for understanding CKD progression and to translate this framework into diagnostic, therapeutic and clinical trial strategies. The present review aimed to combine mechanistic summaries with systems biology perspectives, including weighted gene co‑expression network analysis, Bayesian causal inference and ordinary differential equation‑based dynamic modeling, to map bidirectional signaling across microbial, metabolic, inflammatory and hemodynamic dimensions, with diabetic kidney disease (DKD) as the principal exemplar. The axis operates through anatomically and molecularly defined positive feedback loops in which gut dysbiosis drives barrier failure and endotoxemia, amplifying hepatic lipotoxicity and bile acid dysregulation, precipitating renal tubular injury and fibrosis. This self‑perpetuating cycle, sustained by uremic toxin signaling, dysregulated peroxisome proliferator‑activated receptor/farnesoid X receptor (FXR)/Takeda G protein‑coupled receptor 5 (TGR5) pathways and trained immunity (a persistent hyperinflammatory state of innate immune cells driven by epigenetic and metabolic reprogramming), is most pronounced in DKD. Microbiome‑targeted interventions and FXR/TGR5 modulators are as the most clinically advanced axis‑directed strategies, though most remain at preclinical or early‑phase stages. Reframing CKD as gut‑liver‑kidney axis dysfunction enables systems‑level mechanistic integration, precision diagnostics through composite microbiome‑metabolomic signatures, and adaptive trial designs targeting upstream pathology, providing a foundation for incorporating axis‑based approaches into future CKD management.

Objectives:To develop the List of Clinical Needs for New Chinese Materia Medica Development(Second Batch),aiming to identify and highlight specific diseases with pressing,unmet therapeutic needs.The objective is to guide pharmaceutical research and development(R&D)by establishing development priorities based on clinical gaps,epidemiological burden,and the shortcomings of existing treatments. Methods:The list was developed by the China Association of Chinese Medicine using a methodology grounded in evidence and expert consensus. Results:The list identifies 8 diseases with significant clinical gaps:Myelodysplastic syndromes(MDS),fibromyalgia(FM),atrophic gastritis and its precancerous lesions,adenomyosis(AM),diabetes related foot ulcers(DFU),granulomatous mastitis(GLM),diabetic kidney disease(DKD),and metabolic-associated fatty liver disease(MAFLD).For each disease,the list provides a systematic analysis of epidemiological burden,limitations of current treatments,and specific clinical needs.It also proposes a strategic research framework focused on multi-target mechanisms,disease-modifying potential,and optimized combination therapies. Conclusions:The list provides a strategically structured framework to guide the development of new Chinese materia medica.It aligns traditional Chinese medicine(TCM)development with national public health priorities,emphasizes TCM's holistic and multi-target advantages,and promotes a patient-centered approach.By bridging clinical needs with TCM principles,the list aims to foster the creation of clinically valuable medicines to address chronic and complex diseases where current western medical approaches are insufficient.

OBJECTIVE:To assess the benefits of Qingre Xiaozheng formula (, QRXZF) as an adjunct to standard Western medical management on renal outcomes in patients with diabetic kidney disease (DKD). METHODS:This retrospective study included patients with DKD who received the QRXZF between May 2017 and May 2021. A total of 144 patients with DKD, 24 h urinary total protein (24 h-UTP) ≥ 0.5 g, and estimated glomerular filtration rate (eGFR) ≥ 30 mL/min per 1.73 m2 were divided into the treatment group or the control group based on whether they received QRXZF treatment. The long-term renal outcomes of patients with DKD were analyzed to evaluate the effectiveness of the QRXZF. Differences in overall survival (OS) were assessed using Kaplan-Meier curve analysis. Cox proportional hazards regression analysis was used to determine the independent risk factors for renal endpoints. RESULTS:The mean follow-up period was (28±15) months. Nine (12.5%) patients in the treatment group and 27 (37.5%) patients in the control group met the renal endpoints. Multivariate Cox regression analysis showed that 24 h-UTP ≥ 3.5 g [hazard ratio (HR) = 4.70, 95% confidence interval (CI) (1.83, 12.05), P = 0.001], combined coronary artery disease [HR = 3.39, 95% CI (1.65, 6.98), P = 0.001], total cholesterol [HR = 1.34, 95% CI (1.05, 1.70), P = 0.019] and low-density lipoprotein [HR = 1.65, 95% CI (1.111, 2.45), P = 0.013] were independent prognostic factors for renal endpoints in patients with DKD. Compared with the treatment group, the risk of renal endpoint events increased 2.68-fold in the control group [HR = 2.68, 95% CI (1.19, 6.02); P = 0.017]. We included 48 patients with 24 h-UTP ≥ 3.5 g in a further stratification analysis of patients with DKD. The independent risk factor for the renal endpoints in patients with 24h-UTP ≥ 3.5 g was smoking history [HR = 5.52, 95% CI (1.131, 26.92), P = 0.035]. Compared with the treatment group, the risk of renal endpoint events increased 3.01-fold in the control group [HR = 3.01, 95% CI (1.05, 8.67); P = 0.041]. CONCLUSIONS:The results show that QRXZF treatment improved renal outcomes and reduced proteinuria in patients with DKD. These results indicate that Traditional Chinese Medicine is likely to have a positive therapeutic effect on established and advanced DKD. Further well-designed clinical trials with longer follow-up periods are required.

BACKGROUND:Sepsis-associated acute lung injury (SALI) is a life-threatening respiratory condition with high mortality. Serum lactate is a key marker of oxygen metabolism and tissue hypoperfusion. This study aimed to investigate whether dynamic lactate trajectories in the early ICU period could predict 28-day mortality in SALI patients. METHODS:We retrospectively analyzed 340 patients with SALI from the MIMIC-IV database. Lactate trajectories over the first three ICU days were identified using latent growth mixture modeling (LGMM). Cox proportional hazards models and Kaplan-Meier survival analyses were used to evaluate the association between trajectory classes and 28-day mortality. The prognostic performance of SOFA score alone and in combination with lactate trajectory was compared using ROC curves. RESULTS:Three distinct lactate trajectory classes were identified: persistently elevated, low and stable, and initially elevated then declining. Patients in the low-stable and moderate-declining groups had significantly lower mortality risks compared to those with persistently high lactate. Adding lactate trajectory to SOFA improved AUC from 0.629 to 0.693 (P = 0.0074). CONCLUSION:Early lactate trajectory classification is an independent predictor of mortality in SALI patients and enhances prognostic performance beyond SOFA. Incorporating trajectory-based risk stratification may inform more personalized respiratory management strategies.

BackgroundDiabetic kidney disease (DKD) is one of the common microvascular complications of diabetes. The exploration of serum biomarkers holds promise for improving the efficiency and accuracy of early DKD diagnosis. This study aims to investigate the diagnostic value of transforming growth factor-β1 (TGF-β1) and cystatin C (CysC) in DKD patients.MethodsA total of 126 patients with type 2 diabetes mellitus (T2DM) diagnosed at Dongzhimen Hospital, Beijing University of Chinese Medicine, between May 2021 and March 2023 were enrolled. Patients were categorized based on proteinuria levels and estimated glomerular filtration rate (eGFR). Correlation analyses were conducted to examine the relationships between serum TGF-β1, CysC, and clinical parameters. Logistic regression was applied to identify correlation factors for DKD and renal function impairment in T2DM patients. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic efficacy.ResultsSignificant differences in TGF-β1 and CysC levels were observed across groups with varying proteinuria levels. CysC was positively correlated with TGF-β1 (r = 0.640, p < 0.001). TGF-β1 has been associated with proteinuria levels in T2DM patients. Each unit increase in TGF-β1 was associated with a 1.122-fold and 1.470-fold higher odds of the presence of microalbuminuria and proteinuria, respectively, in the normal proteinuria (NP) group. TGF-β1 and CysC showed varying diagnostic performance. TGF-β1 better distinguished microalbuminuria group (MP) from NP, while CysC alone was less effective. T2DM patients with impaired renal function exhibited significantly higher CysC and TGF-β1 levels compared to those with normal renal function. CysC emerged as an associated factor of renal function decline (OR = 2.255, p = 0.008). CysC demonstrated superior diagnostic efficacy compared to TGF-β1 in predicting renal function impairment (AUC = 0.974).ConclusionCysC and TGF-β1 can serve as potential biomarkers for assessing renal impairment and proteinuria in T2DM patients. Their combined evaluation demonstrates diagnostic value and clinical application potential.

Background:The gut microbiota-derived metabolite butyrate has been implicated in maintaining renal homeostasis through anti-inflammatory and immunomodulatory pathways. However, evidence from large-scale human studies, especially in high-risk diabetic populations, remains limited. This study aimed to investigate the association between butyrate exposure and renal function in adults with diabetes, using a dual-cohort design. Methods:We analyzed data from 7,723 adults with diabetes across ten NHANES cycles (1999-2018) to evaluate the association of dietary butyrate intake with estimated glomerular filtration rate (eGFR) and albuminuria. Multivariable linear regression, restricted cubic spline modeling, and subgroup analyses were performed with survey weighting. For external validation, we recruited a Chinese cohort of 70 patients with diabetic kidney disease (DKD) and measured serum butyrate and isobutyrate concentrations using UPLC-MS/MS. Associations with eGFR and 24-h urinary protein were assessed using adjusted regression models. Results:In the NHANES cohort, higher dietary butyrate intake was independently associated with a higher eGFR (β = 1.61; 95% CI: 0.29-2.92; p = 0.02), with a significant nonlinear dose-response (P for non-linearity = 0.0006). No significant associations were found with albuminuria. In the Chinese cohort, serum butyrate was positively associated with eGFR (β = 0.05; 95% CI: 0.01-0.08; p = 0.02), but not with proteinuria. Serum isobutyrate also showed a positive association with eGFR (β = 0.15; 95% CI: 0.02-0.28; p = 0.02). Sensitivity analyses confirmed the robustness of these findings among participants with both diabetes and CKD. Conclusion:This dual-cohort study provides the first epidemiological evidence that higher levels of butyrate-whether from dietary intake or serum concentration-are independently associated with better renal function in adults with diabetes. These findings underscore the relevance of the gut-kidney axis in diabetic kidney disease and suggest that enhancing endogenous butyrate production through diet or microbiota-targeted strategies may offer a novel avenue for renoprotection.

BackgroundActeoside (ACT), a natural phenylethanoid glycoside extracted from the Rehmannia glutinosa, has demonstrated renal protective effects against diabetic nephropathy (DN) through its confirmed antioxidant and anti-inflammatory properties. However, the underlying mechanisms by which ACT regulates DN progression via targeting ferroptosis remain to be elucidated.PurposeThis study aims to elucidate whether ACT ameliorates DN in mice by targeting ferroptosis and to uncover the underlying mechanisms involved.MethodsThis study first utilized network pharmacology approaches, integrating multiple databases and bioinformatics tools, to predict and screen the potential targets and pathways of ACT in DN. To validate the therapeutic efficacy of ACT, a DN model was established in C57BL/6J mice using streptozotocin (STZ). Subsequently, the therapeutic effect of ACT on DN was verified through molecular experiments. Finally, molecular docking was adopted to further verify the binding ability between ACT and key targets.ResultsNetwork pharmacology analysis identified potential targets of ACT related to DN and revealed that its therapeutic effects may be mediated through the regulation of ferroptosis. In vivo experiments demonstrated that ACT exerts significant renoprotective effects by improving renal function and alleviating pathological damage in DN mice. Furthermore, ACT was shown to attenuate oxidative stress by restoring mitochondrial homeostasis, a process closely associated with the regulation of ferroptosis.ConclusionIn summary, this study provides preclinical evidence that ACT ameliorates DN through ferroptosis inhibition, positioning it as a novel therapeutic candidate for DN treatment.

Background and aimsFerroptosis, a novel concept of programmed cell death proposed in 2012, in kidney disease, has garnered significant attention based on evidence of abnormal iron deposition and lipid peroxidation damage in the kidney. Our study aim to examine the trends and future research directions in the field of ferroptosis in kidney disease, so as to further explore the target or treatment strategy for clinical treatment of kidney disease.Material and MethodsA thorough survey using the Web of Science Core Collection, focusing on literature published between 2012 and 2024 examining the interaction between kidney disease and ferroptosis was conducted. VOSviewer, CiteSpace, and Biblioshiny were used for in-depth scientometric and visualized analyses.ResultsFrom 2012 to 2024, a total of 2,244 articles met the inclusion criteria for final analysis. The number of annual publications in this area of study showed a steady pattern at the beginning of the decade. The top 3 journals with the highest publication output were Renal Failure, Oxidative Medicine And Cellular Longevity, and Biomedicine & Pharmacotherapy. China and the United States had the highest number of publications. Central South University and Guangzhou Medical University as the most active and influential institutions. Documents and citation analysis suggested that Andreas Linkermann, Jolanta Malyszko, and Alberto Ortiz are active researchers, and the research by Scott J. Dixon and Jose Pedro Friedmann Angeli, as the most cited article, are more important drivers in the development of the field. Keywords associated with glutathione, lipid peroxidation, and nitric oxide had high frequency in the early studies. In recent years, however, there has been a shift towards biomarkers, inflammation and necrosis, which indicate current and future research directions in this area.ConclusionThe global landscape of the ferroptosis research in kidney disease from 2012 to 2024 was presented. Basic research and mechanism exploration for renal fibrosis and chronic kidney disease may be a hot spot in the future.

Background:The imbalance in macrophage phenotype transition is a central mechanism driving chronic inflammation in diabetic kidney disease (DKD). Macrophages can polarize toward the M2 phenotype via efferocytosis, exerting anti-inflammatory and pro-resolving effects. However, the identification and functional validation of regulatory genes governing M2 macrophage and efferocytosis in DKD remain to be thoroughly explored. Methods:Differentially expressed genes were obtained based on GSE96804 and GSE30122 data sets. Based on efferocytosis-related genes (ERGs) and M2 polarization-related genes (MRGs), ERG and MRG scores were computed in the GSE96804 dataset. Weighted gene co-expression network analysis (WGCNA) was carried out to identify critical module genes. Finally, macrophage-efferocytosis-related DEGs (MEDEGs) were identified. Further, machine learning (ML)-support vector machine (SVM), BORUTA, and lasso regression-were employed to identify hub genes and build Nomogram predictive model. Additionally, hub genes were confirmed through animal experiments. Results:A total of 35 MEDEGs were identified. ML recognized 3 hub genes-MCUR1, CYP27B1, and G6PC. Hub genes were notably downregulated in DKD group and exhibited high predictive ability. Furthermore, the Nomogram model based on key genes has shown potential in predicting DKD. The findings were further validated through transcriptome sequencing of DKD model. Conclusion:This study uncovered 3 hub genes-MCUR1, CYP27B1, and G6PC-linked to M2 polarization, efferocytosis, and DKD. These genes may contribute to DKD pathogenesis, providing novel targets for early diagnosis and therapeutic interventions in DKD.

BACKGROUND:Single-cell RNA sequencing (scRNA-seq) has revolutionized kidney disease research by enabling high-resolution transcriptomic analysis at the cellular level. This technology can overcome the limitations of traditional bulk-sequencing; reveal disease-progression trajectories, intercellular communication networks, and cellular heterogeneity; and provide crucial insights into disease mechanisms, thereby facilitating the development of targeted therapies and personalized treatment strategies. We conducted a bibliometric analysis of publications describing the use of scRNA-seq in kidney disease research from 2015 to 2024 using the Web of Science Core Collection (WoSCC) database. Data analysis was performed using the R packages Bibliometrix, VOSviewer, and CiteSpace to systematically evaluate the research landscape and emerging trends. RESULTS:A total of 1,210 publications on scRNA-seq in kidney diseases were identified. China was the largest contributor among the participating countries, demonstrating consistent annual growth in publication numbers. The major research institutions were Harvard Medical School, Sun Yat-sen University, and Shanghai Jiao Tong University. Most articles in this field were published by Frontiers in Immunology. In a list of 8,984 authors, the most productive authors were B. D. Humphreys, Haojia Wu, and Matthias Kretzler. The dominant categories identified in this search were scRNA-seq, disease progression/mechanisms, and gene regulation/expression. Several budding areas of investigation were also noted, including immunotherapy and scRNA-seq innovations, which allude to active evolution in the field. CONCLUSION:This bibliometric analysis revealed the rapid growth and evolving landscape of scRNA-seq applications in kidney disease research and highlighted promising opportunities for understanding disease mechanisms and developing personalized therapeutic strategies.