Background: Gut dysbiosis is postulated to participate in the pathogenesis of IgA nephropathy (IgAN). However, the key bacterial taxa closely associated with IgAN onset and treatment response have not been identified. Methods: We recruited 127 patients with IgAN who were treatment naive and 127 matched healthy controls (HCs) who were randomly divided into discovery and validation cohorts to investigate the characteristics of their gut microbiota and establish a bacterial diagnosis model for IgAN. A separate cohort of 56 patients and HCs was investigated to assess crossregional validation. A further 40 patients with primary membranous nephropathy (MN) were enrolled to probe disease-specific validation. A subgroup of 77 patients was prospectively followed to further dissect the association between alterations in gut microbiota and treatment response after 6 months of immunosuppressive therapy. Fecal microbiota samples were collected from all participants and analyzed using 16S ribosomal RNA sequencing. Results: Decreased α-diversity (Shannon, P=0.03), altered microbial composition (Adonis, P=0.0001), and a striking expansion of the taxonomic chain Proteobacteria–Gammaproteobacteria–Enterobacteriales–Enterobacteriaceae–Escherichia-Shigella (all P<0.001) were observed in patients with IgAN who were treatment naive, which reversed only in patients who achieved clinical remission after 6 months of immunosuppressive therapy. Importantly, seven operational taxa units, of which Escherichia-Shigella contributed the most, were determined to be the optimal bacterial classifier of IgAN (AUC=0.8635, 0.8551, 0.8026 in discovery, validation, and cross-regional validation sets, respectively), but did not effectively distinguish patients with IgAN versus those with MN (AUC=0.6183). Bacterial function prediction further verified enrichment of the shigellosis infection pathway in IgAN. Conclusion: Gut dysbiosis, characterized by a striking expansion of genus Escherichia-Shigella, is a hallmark of patients with IgAN and may serve as a promising diagnostic biomarker and therapeutic target for IgAN. Further studies are warranted to investigate the potential contribution of Escherichia-Shigella in IgAN pathogenesis.

Short‐chain fatty acids (SCFAs), pivotal metabolites derived from microbial fermentation of dietary fiber, serve as critical modulators of glucose and lipid metabolism. Dysregulation of SCFA levels, often stemming from inadequate fiber intake or dysbiosis of SCFA‐producing microbiota, correlates with heightened susceptibility to diverse pathologies, including autoimmune disorders, metabolic syndromes, and malignancies. Emerging evidence underscores the pleiotropic roles of SCFAs in orchestrating gut and systemic homeostasis, positioning them as novel therapeutic candidates for immune dysregulation, inflammatory conditions, and transplant rejection. This review synthesizes current knowledge on SCFA biosynthesis, absorption dynamics, and their multifaceted regulatory mechanisms, spanning epigenetic modulation, G protein–coupled receptor (GPR) signaling, and immune cell crosstalk. We further elucidate their therapeutic potential in clinical contexts, emphasizing their capacity to recalibrate immune responses, suppress chronic inflammation, and mitigate oncogenesis. By integrating recent advances in microbiome research and translational applications, this work highlights the imperative for precision interventions targeting SCFA pathways to bridge the gap between microbial ecology and clinical innovation.

Introduction: Immunoglobulin A nephropathy (IgAN) patients with lower estimated glomerular filtration rate (eGFR) and higher proteinuria are at a higher risk for end-stage kidney disease (ESKD) and their prognosis is still unclear. We aim to develop and validate prognostic models in IgAN patients with CKD stage 3 or 4 and proteinuria≥1.0 g/d. Methods: Patients who came from Xijing Hospital, spanning December 2008 to January 2020 were divided into training and test cohorts randomly, with a ratio of 7:3, achieving ESKD and death as study endpoints. Created prediction models for IgAN patients based on 66 clinical and pathological characteristics using the random survival forests (RSF), survival support vector machine (SSVM), eXtreme Gradient Boosting (XGboost), and Cox regression models. The concordance index (C-index), integrated Brier scores (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification respectively. Results: A total of 263 patients were enrolled. The median follow-up time was 57.3 months, with 124 (47.1%) patients experiencing combined events. Age, blood urea nitrogen, serum uric acid, serum potassium, glomeruli sclerosis ratio, hemoglobin, and tubular atrophy/interstitial fibrosis were identified as risk factors. The RSF model predicted the prognosis with a C-index of 0.871 (0.842, 0.900) in training cohort and 0.810 (0.732, 0.888) in test cohort, which was higher than the models built by SSVM model (0.794 (0.753, 0.835) and 0.805 (0.731, 0.879) respectively), XGboost model (0.840 (0.797, 0.883) and 0.799 (0.723, 0.875) respectively) and Cox regression (0.776 (0.727, 0.825) and 0.793 (0.713, 0.873) respectively). NRI and IDI showed that the RSF model exhibited superior performance than the Cox model. Conclusion: Our model introduced seven risk factors that may be useful in predicting the progression of IgAN patients with CKD stage 3 or 4 and proteinuria ≥1.0g/d. The RSF model is applicable for identifying the progression of IgAN and has outperformed than SSVM, XGboost, and Cox models.

Background: The spectrum of biopsy-confirmed kidney disease varies with regions and periods. We describe the distribution ofpathological types and epidemiological characteristics of kidney diseases in Northwest China due to regional differences in geographicalenvironment, social economy, and dietary habits.Methods: Kidney biopsy cases from 2005 to 2020 in Xijing Hospital were retrospectively analyzed. Pathological characteristics of patientsin different periods were analyzed using the t test or chi-square test. Joinpoint regression was used to analyze trends in pathologicaltypes and disease spectrum.Results: A total of 10,528 eligible patients were included. Primary glomerular disease (PGD) accounted for the majority of the casesand exhibited an obvious downward trend, whereas secondary glomerular disease (SGD) showed an obvious upward trend. AmongPGD, immunoglobulin A nephropathy (IgAN) remained the most common pathological type, and the detection rate of membranousnephropathy (MN) was significantly increased. Among SGD, Henoch-Schönlein purpura nephritis (HSPN) was the most commonpathological type and may present a significant characteristic of Northwest China. Diabetic nephropathy (DN) exhibited the most obviousupward trend in the whole process, whereas the fastest growth since 2012 was in hypertensive nephropathy.Conclusion: The proportion of SGD increased whereas PGD declined. IgAN remained the most common PGD, and HSPN was themost common SGD. MN and DN showed the most obvious upward trend among PGD and SGD, respectively. Changes in the spectrumof kidney disease, especially the constituent ratio of SGD, pose a great challenge to public health.

Objective: The objective of this study is to compare the clinical features and survival outcomes of class IV ± V lupus nephritis (LN) patients, identify risk factors, and develop an accurate prognostic model.Methods: This study enrolled patients diagnosed with class IV ± V LN by renal biopsy at Xijing Hospital from December 2013 to June 2023. The composite endpoint of the study was defined as a decline in the estimated glomerular filtration rate (eGFR) by more than 50%, progression to end stage renal disease, or death, whichever came first. The eGFR was calculated utilizing the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. ESRD is defined as an eGFR less than 15ml/min/1.73m², necessitating the commencement of chronic dialysis (hemodialysis or peritoneal dialysis) or kidney transplantation. We compared the baseline features and survival prognosis between patients with class IV ± V LN. The prognostic model was developed using machine learning algorithms and Cox regression. The model’s performance was evaluated in terms of discrimination, calibration, and risk classification using the concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI), respectively.Results: A total of 313 patients were enrolled for this study, including 156 class IV and 157 class IV+V LN. During the median follow-up period of 42.6 (17.0, 83.4) months, 35 (22.4%) class IV and 38 (24.2%) class IV+V LN patients experienced combined events. Class IV and class IV+V patients have similar clinical manifestations, treatment strategies, and long-term prognosis, despite class IV having a higher chronic index (CI) score (P < 0.001). Seven eligible variables (eGFR, CI, age, basophil percentage, red blood cell count, mean arterial blood pressure, and uric acid) were selected to develop the random survival forest (RSF) model. This model demonstrated the best performance with a C-index of 0.771 (0.667, 0.848) and an IBS of 0.144 (0.132, 0.154). The IDI and NRI in the testing set further confirmed that the RSF model exhibited superior risk classification and discrimination capabilities.Conclusion: Class IV ± V LN was similar in clinical manifestations, treatment strategies, and long-term prognosis, despite differences in pathological features. The RSF model we established for class IV ± V LN patients, incorporating seven risk factors, exhibits superior survival prediction and provides more precise prognostic stratification.