IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical heterogeneity results in divergent renal outcomes, making early identification of high-risk patients essential. Prognostic models are crucial for stratifying ESKD risk, guiding treatment intensity, optimizing timing of interventions such as immunosuppressive therapy, and informing clinical trial enrollment. Over recent decades, multiple prognostic approaches have emerged, ranging from traditional clinical and histopathological scoring systems to advanced machine learning (ML) and deep learning (DL) models designed to capture complex nonlinear interactions and improve predictive precision. Among them, the International IgA Nephropathy Prediction Tool (IIgAN-PT), endorsed by the 2021 KDIGO guidelines, represents a landmark in globally validated risk assessment and has set the foundation for standardized clinical decision support. However, classical models often rely on static baseline parameters and may not adequately reflect dynamic disease trajectories, limiting their utility in real-time clinical management. To overcome these limitations, ML- and DL-based models increasingly integrate multi-omics data, serial clinical measurements, and digital pathology features, offering enhanced accuracy, dynamic risk tracking, and potential for personalized response prediction. These data-driven approaches are progressively bridging the gap between prognostic research and precision nephrology. This review provides a comprehensive overview of the evolution of IgAN prognostic models, summarizes their strengths and limitations, and discusses considerations for clinical translation. By highlighting emerging trends toward explainable AI, dynamic time-series modeling, and multimodal prognostication, we outline how next-generation prediction tools may enable real-time, AI-driven decision support for individualized IgAN management.

Preservation of residual kidney function (RKF) is critical for patients undergoing peritoneal dialysis (PD). However, the relationship between the rate of RKF decline (RKF slope) and PD technique failure remains incompletely understood, and a clinically actionable threshold is lacking. This was a single-centre, retrospective cohort study that included 539 patients who underwent peritoneal dialysis catheter placement at the hospital from 1 May 2006 to 31 December 2022. The primary analysis used pre‑specified Cox models to assess the association between RKF decline rate and technique failure (death or transfer to HD). RCS analysis and Kaplan‑Meier curves were also performed. During a median follow‑up of 30 months, 152 patients (28.2

BackgroundThe interplay between inflammation and coagulation is central to the pathophysiology of acute kidney injury (AKI). This study aimed to explore the association between IBI and the incidence of AKI in the elderly, as well as the intricate connections between IBI, D-dimer, and AKI.MethodsThe data came from the clinical records of patients aged ≥ 65 years hospitalized at Wuhan Tongji Hospital between January 2018 and December 2022. We evaluated the predictive efficacy of IBI and other inflammatory indicators for AKI by receiver operating characteristic (ROC) analysis. Restricted cubic spline (RCS) and Logistic regression models were employed to examine the correlation between IBI, D-dimer, and AKI risk. Furthermore, ROC analysis, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were performed to assess the predictive value of the combined IBI-D-dimer index. The bidirectional mediating effects of IBI and D-dimer in AKI were validated using the bootstrap method.ResultsThis study involved 69,738 participants. The median age was 71.24 (67.75, 76.46) years, and 25,831 (37.0%) were female. During hospitalization, 3,755 (5.4%) elderly patients developed AKI. ROC analysis demonstrated that, compared to conventional inflammatory indicators, Log IBI had superior predictive value for AKI in elderly patients (AUC = 0.719, 95% CI 0.711–0.727, p < 0.001). In multivariate analyses, the highest quartile of Log IBI (OR = 2.526, 95% CI 2.193–2.918, p < 0.001) and D-dimer levels (OR = 2.493, 95% CI 2.184–2.854, p < 0.001) were independently linked to an elevated risk of AKI. A significant multiplicative interaction between Log IBI and D-dimer was observed (p-interaction < 0.001). The combined index further improved risk reclassification, with participants exhibiting concurrent elevations of both markers conferring the highest AKI risk (OR = 2.640, 95% CI 2.328–3.002, p < 0.001). Mediation analysis revealed bidirectional effects: D-dimer mediated 31.30% (p < 0.001) of Log IBI’s association with AKI, while Log IBI mediated 6.18% (p < 0.001) of D-dimer’s effect.ConclusionConcurrent elevation of IBI and D-dimer was associated with the greatest risk of elderly AKI. The dynamic monitoring of IBI and D-dimer may help identify high-risk elderly patients for AKI.

Introduction Chronic kidney disease (CKD) is a major global health burden characterized by immune dysregulation. Peripheral blood lymphocyte subsets can reflect immune status across CKD pathologies. We evaluated associations between lymphocyte subsets and clinical parameters in patients undergoing renal biopsy in a contemporary cohort. Methods We conducted a retrospective cross-sectional study of adults (≥16 years) who underwent native renal biopsy at Tongji Hospital, China, from January 2012 to December 2024. The lymphocyte subsets assessed by flow cytometry included combined T/B/NK cells (TBNK), total T cells (CD3⁺CD19⁻), total B cells (CD3⁻CD19⁺), helper T cells (CD3⁺CD4⁺), cytotoxic T cells (CD3⁺CD8⁺), and natural killer cells (CD3⁻CD16⁺CD56⁺). Clinical data included demographics, comorbidities, medications, clinical and immunological markers. Multivariable linear regression was employed to investigate the relationships between lymphocyte subsets as continuous variables and clinical parameters, and nested regression using quartile categories to assess dose-response relationships. Results In total, 1,033 individuals were enrolled. TBNK levels declined with age (β = −4.33, 95% CI: −7.35 to −1.32) and were lower in females than males (β = −84.26, 95% CI: −157.29 to −11.23). Lymphocyte subset distributions varied by nephritis pathology: ANCA-GN and LN showed lower levels than other types, whereas purpura nephritis was relatively higher. TBNK counts correlated positively with eGFR (β = 11.48, 95% CI: 6.78 to 16.19), with similar patterns for total B, total T, CD4+ T, and CD8+ T cells. Lymphocyte counts were also positively associated with complement 3. Among IgAN, TBNK levels correlated positively with IgA (β = 0.31, 95% CI: 0.06 to 0.57) and complement 3 (β = 0.10, 95% CI: 0.06 to 0.14). Conclusions Lymphocyte and subset counts relate to renal function, immune indices, and pathology. Their measurement may help evaluate disease severity and immune status in nephritis and inform clinical management.

BACKGROUND:Belimumab, a monoclonal antibody targeting B lymphocyte stimulator, has shown benefits in systemic lupus erythematosus and lupus nephritis, but its role in IgA nephropathy (IgAN) remains unclear. We evaluated the effectiveness and safety of belimumab in IgAN. METHODS:This retrospective cohort study included biopsy-proven IgAN patients at Tongji Hospital, China (July 2020-June 2024). 69 patients treated with belimumab were compared with 137 propensity score-matched controls receiving standard therapy. The primary outcome was proteinuria remission, defined as a reduction in 24-hour urinary protein excretion or urine protein-to-creatinine ratio (UPCR) to < 50% of baseline. RESULTS:Among 206 patients with IgAN (median age 37 years, baseline eGFR 79 ml/min/1.73 m², proteinuria 1.1 g/24 h), 173 (84%) achieved proteinuria remission within 12 months. Remission was higher in the belimumab group: 59% vs 46% at 3 months, 77% vs 66% at 6 months, and 93% vs 80% at 12 months (P = 0.025 at 12 months), with lower time-averaged UPCR at 6 months [371 (248 738) vs.463(262 855) mg/g] and 12 months [360 (234 560) vs.433(243 720) mg/g, P = 0.066]. In newly diagnosed patients, remission was significantly higher at 3 months (76.5% vs. 47.1%, P = 0.009) and 6 months (88.2% vs. 67.6%, P = 0.045), with similar trends at later time points. In patients with baseline UPCR >600 mg/g and those receiving RAAS inhibitors, belimumab demonstrated comparable or superior proteinuria reduction. No excess of common adverse events was observed in the belimumab group. CONCLUSION:Belimumab treatment was associated with higher proteinuria remission rates in patients with IgAN, without an increased incidence of common adverse events. These findings suggest that belimumab may represent a superior therapeutic approach for IgAN in real-world clinical practice.

Cardiovascular-kidney-metabolic (CKM) syndrome, a progressively advancing disorder involving metabolic, renal, and cardiovascular dysfunction, was newly defined by the American Heart Association in its latest recommendations. Life's Crucial 9 (LC9) is a novel cardiovascular health (CVH) metric that incorporates depression, thus integrating psychological health into an established framework of lifestyle and clinical factors. It builds upon and extends Life's Essential 8 (LE8). Although LC9 has demonstrated predictive value in other disease contexts, its association with CKM syndrome remains unexamined. A total of 7,776 individuals aged 20 and above were analyzed in this cross-sectional study, based on data collected from NHANES spanning 2005–2018. LC9 included nine components: depression, BMI, non-HDL cholesterol, glucose, blood pressure, sleep, physical activity, diet, and nicotine exposure. According to the American Heart Association's criteria, individuals in CKM stages 3 or 4 were considered to have progressed to advanced syndrome. The analysis used restricted cubic spline models and logistic regression adjusted for survey weights to examine key associations. Weighted quantile sum (WQS) regression was conducted to identify key contributors. To evaluate model performance, we employed the Area Under the Receiver Operating Characteristic Curve (AUROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) as metrics. Additional subgroup and sensitivity analyses assessed result robustness. Elevated LC9 and LE8 scores were both significantly linked to reduced likelihood of advanced CKM syndrome (per 10-point increase: OR = 0.61, 95

Serine β-lactamase-like protein (LACTB), a mitochondrial protease, has incompletely characterized roles in metabolic pathways. We employed Mendelian randomization to investigate LACTB's causal relationships with lipid metabolism, metabolic syndrome (MetS), and chronic kidney disease (CKD). We performed a comprehensive Mendelian randomization (MR) analysis using genome-wide association study summary statistics. Cis-expression quantitative trait loci from the eQTLGen consortium identified genetic instruments for LACTB. Two-sample MR approaches, including inverse variance weighted, MR-Egger, and weighted median methods, were applied. The cisMR-conditional maximum likelihood (cisMR-cML) method validated LACTB-related causal associations. GTEx Portal data independently replicated the LACTB-CKD relationship. LACTB exhibited significant negative causal effects on metabolic syndrome (95% CI: 0.91-0.99, p = 0.02) and chronic kidney disease (95% CI: 0.83-0.97, p = 0.009). cisMR-cML validation confirmed significant causal associations between LACTB and lipid profiles after Bonferroni correction. Metabolic syndrome demonstrated a robust positive causal effect on CKD (95% CI: 1.15-1.42, p = 8.45 × 10-6), with high-density lipoprotein showing a significant negative causal relationship with CKD (95% CI: 0.89-0.97, p = 0.0009). Mediation analysis revealed metabolic syndrome mediated 11.8% of the total effect between LACTB and CKD (mediation effect: -0.01, 95% CI: -0.024 to -0.0003). Our study elucidates LACTB's critical role in metabolic regulation, identifying a potential therapeutic target for preventing chronic kidney disease progression. By delineating complex interactions between LACTB, lipid metabolism, metabolic syndrome, and kidney function, we provide novel insights for precision medicine in metabolic and renal health.

ObjectiveTo accurately assess the importance of glomerular filtration rate in the elderly population， explore the clinical characteristics of the elderly population who need to monitor glomerular filtration rate， and verify the performance of the currently commonly used estimated glomerular filtration rate （eGFR） formula.MethodsA total of 313 patients aged 60 years and older who underwent 99mTc-DTPA renal dynamic imaging for measured glomerular filtration rate （mGFR） in Tongji Hospital， Tongji Medical College， Huazhong University of Science and Technology from December 1， 2012 to December 31， 2021 were enrolled. The characteristics of elderly patients were analyzed. The consistency， precision and accuracy of commonly used eGFR formulas and mGFR were analyzed. Bland-Altman plots were used to compare the deviations between eGFR values calculated by each formula and mGFR values. The accuracy of each formula was evaluated by the proportion of eGFR within mGFR （1 ± 10%） （P10）， the proportion within （1 ± 30%） （P30）， mean percent error （MPE）， and mean absolute percent error （MAPE）. The deviations between the formulas were compared using the t-test， and the differences in P10 and P30 between the formulas were compared using the McNemar test.ResultsResults The age of the elderly population in this study was 68.3（63.0，72.0） years. All the commonly used eGFR formulas overestimated mGFR to varying degrees. The interquartile range （IQR）， MPE， MAPE， and root mean square error （RMSE） of the residual （eGFR-mGFR） were used to evaluate the accuracy of the formula eGFR. Both the Cockroft-Gault formula and the EKFC formula showed higher accuracy， among which the Cockroft-Gault formula had the lowest MPE and IQR， and the EKFC formula had the lowest MAPE and RMSE. The percentage of patients with absolute percentage errors within 10% and 30% was used as an indicator to evaluate the accuracy performance of the eGFR formula. Both the EKFC formula and the BIS1 formula showed higher accuracy， among which the EKFC formula had the highest P10 （26.8%）， and the BIS1 formula had the highest P30 （67.4%）. The C-MDRD formula performed the worst in both accuracy and precision.ConclusionAll commonly used eGFR formulas overestimate mGFR in the elderly to varying degrees. In general， the EKFC formula has better performance in accuracy and precision， and the accuracy P30 of the EKFC formula is higher than that of the Cockroft-Gault formula， while the C-MDRD formula performs the worst in both accuracy and precision.

This study aims to explore the varying association of different bilirubin levels on the prognosis of peritoneal dialysis patients, with particular emphasis on the relationship between the initial occurrence of peritonitis and bilirubin levels, which has not been well elucidated in existing literature. This single-center retrospective study enrolled end-stage renal disease patients who underwent peritoneal dialysis catheter placement at Tongji Hospital from January 1, 2009, to December 31, 2023. Follow-up was conducted until October 31, 2024. To explore the relationship between the development of peritonitis and bilirubin levels, we utilized Cox proportional hazards regression models, Spearman correlation analysis, and restricted cubic spline plots. This study included 426 patients, with the sum of 106 episodes of peritonitis were documented, of which 57 were first-time occurrences, making for an incidence rate of 0.09 episodes per patient-year. Patients with total bilirubin (TBil) levels below 3.69 µmol/L (P = 0.008, HR = 1.919, 95

Studies have found that there is tertiary lymphoid structure (TLS) in IgA nephropathy (IgAN), and the existence of TLS has an impact on renal function, creatinine, and proteinuria in patients. We aim to explore the potential molecular mechanisms and therapeutic targets of TLS in IgA nephropathy by bioinformatics methods, hoping to provide treatment methods. The datasets GSE226840, GSE237120, and GSE116626 from the Gene Expression Omnibus (GEO) database were employed to investigate the potential therapeutic targets of TLS in IgAN. The R was used to obtain the differentially expressed genes (DEGs) of three datasets, and the Venny was used to intersect the above three parts of the DEGs to obtain the common DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on obtained genes using Metascape. Protein-Protein interaction (PPI) network was constructed. The intersection of the above common differential genes and IgAN differential genes was obtained by Venny tool. The Nephroseq platform was used to screen core genes and explore their relationship with clinical features. Meanwhile, CIBERSORT was utilized to further delve into the correlation between core genes and immune cells. 92 TLS-related genes and 486 IgAN related genes were obtained, and 6 common genes were obtained after crossing the two genes. The intersection genes were verified by Nephroseq, and CDKN1A, CD83, DUSP6, and CD48 were identified as core genes. At the same time, there were differences in the composition of immune cells between the disease group and the control group when the immune infiltration analysis was performed. And by further analyzing the correlation between core genes and immune cells, the study found that the four genes were positively correlated with T cells, B cells, plasma cells, and other immune cells. By exploring the relationship between core genes and clinical features, CDKN1A and DUSP6 were negatively correlated with Glomerular Filtration Rate (GFR) and positively correlated with proteinuria in IgAN patients. CD48 was negatively correlated with GFR and positively correlated with Blood Urea Nitrogen (BUN). The four genes highly associated with TLS and IgAN were screened using GEO database in study. And CDKN1A, CD83, DUSP6 and CD48 may provide potential therapeutic targets for the treatment of TLS in IgAN. At the same time, studies have found that T cells, B cells, and macrophages may be involved in the formation of TLS in IgAN.

Introduction The estimated glomerular filtration rate (eGFR) derived from either creatinine (eGFRcr) or cystatin C (eGFRcys) is common preoperative test in routine clinical practice. Recently, the difference between eGFRcys and eGFRcr (eGFRdiff) has been suggested to reflect health status and frailty. This study was aimed to determine the association of eGFRdiff with adverse events among adults undergoing major surgery. Methods We conducted a retrospective cohort study of adults undergoing major surgery from 19 academic health care centers across China from January 1, 2013 to December 31, 2020. The eGFRdiff was categorized based on previous studied, that is, negative eGFRdiff (< −15 mL/min/1.73 m2), midrange eGFRdiff (−15 to 15 mL/min/ 1.73 m2), and positive eGFRdiff (≥ 15 mL/min/1.73 m2). Multivariate logistic regression was performed to assess the association of eGFRdiff with 30-day mortality, 90-day mortality, admission to intensive care unit (ICU), and development of postoperative acute kidney injury (AKI) after surgery. Results Among 158,336 participants undergoing major surgery, the mean age was 57 years and 52.5% were male. The most frequent surgery type was general (47.5%), followed by the orthopedic (17.0%), and thoracic surgery (12.9%). The mean eGFRdiff was -7.6 mL/min/1.73 m2, negative (<-15 mL/min/1.73 m2) and positive (≥15 mL/min/1.73 m2) eGFRdiff values were observed in 36.1% and 11.6% participants, respectively. In multivariable analyses after adjustment for confounding factors, the negative eGFRdiff had OR of 1.34 (95% CI: 1.20-1.50) for 30-day mortality, 1.33 (95% CI: 1.23, 1.43) for 90-day mortality, 1.46 (95% CI: 1.41-1.50) for admission to ICU, and 1.39 (95% CI: 1.32-1.46) for postoperative AKI. Moreover, the positive eGFRdiff was associated lower risk of 90-days mortality, admission to ICU, and postoperative AKI. Conclusions Negative GFRdiff may be a valuable marker for identifying individuals at a higher risk of adverse events in participants undergoing major surgery.

A low-protein diet is essential for the nutritional management of chronic kidney diseases as it can reduce renal burden. However, the effect of low-protein diets on dialysis patients compared to pre-dialysis patients remains unclear. This study aims to compare residual renal function among dialysis patients following a low-protein diet versus a normal diet, offering valuable insights into the optimal nutritional strategy for preserving residual renal function. This meta-analysis has been registered on PROSPERO, an international registry of prospective systematic reviews. We conducted a comprehensive and systematic literature search using PubMed, Cochrane Library and Web of Science (WOS). Our search strategy was designed to discover all relevant studies investigating the influence of low-protein diets on residual renal function among dialysis patients. Four studies met the inclusion criteria. Heterogeneity was discussed through subgroup analysis of dialysis method, the addition of ketoacid and other relevant factors. We included four prospective studies of low-protein diets among dialysis patients, each of which included at least 40 participants. Individuals receiving a 12-months low-protein diet had a higher GFR (MD = 1.37 ml/min; 95

Objectives: Previous research has demonstrated associations between various inflammatory cytokines and IgA nephropathy (IgAN). However, the causal relationships between them remain unclear. The purpose of this study is to extensively analyze the causal links between 91 circulating cytokines and IgAN. Methods: This study commenced with a two-sample bidirectional Mendelian randomization analysis. Genetic variations associated with 91 circulating inflammatory cytokines were extracted from genome-wide association study (GWAS) data involving individuals of European ancestry (n = 14824). In the corresponding GWAS dataset, the genetic variations for IgAN were obtained from a Finnish cohort of European ancestry, consisting of a case group (n = 653) and a control group (n = 411528). The findings from the Mendelian randomization analysis were subsequently subjected to preliminary validation using the GSE116626 dataset from the GEO database. Results: Our MR analysis indicates that transforming growth factor-alpha (TGF-alpha), leukemia inhibitory factor (LIF), and C-C motif chemokine 19 (CCL19) are linked to an increased risk of IgAN. There were no causal connections found when IgAN was used as an exposure and the 91 circulating inflammatory cytokines as outcomes. In addition, the GSE116626 dataset from the GEO database revealed significant upregulation of CCL19 in renal tissues from patients diagnosed with IgAN. Conclusions: This study shows a causal link between inflammatory cytokines and IgAN, suggesting that TGF-alpha, LIF, and CCL19 may act as upstream mediators in the pathogenic pathways of IgAN. The critical role of CCL19 in the pathogenesis of IgAN was further validated using data from the GEO database. However, whether these cytokines can be used to predict or ameliorate the progression of IgAN requires further investigation.

BACKGROUND:Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. METHODS:In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD; and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. FINDINGS:For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838-0·846) on the internal validation dataset and AUCs of 0·791-0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825-0·966) on the internal validation dataset and AUCs of 0·733-0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD with higher sensitivity (89·8% vs 66·3%, p<0·0001). In the longitudinal study, participants with isolated diabetic nephropathy and participants with NDKD identified by DeepDKD had a significant difference in renal function outcomes (proportion of estimated glomerular filtration rate decline: 27·45% vs 52·56%, p=0·0010). INTERPRETATION:Among diverse multi-ethnic populations with diabetes, a retinal image-based AI-deep learning system showed its potential for detecting DKD and differentiating isolated diabetic nephropathy from NDKD in clinical practice. FUNDING:National Key R & D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Shanghai Municipal Key Clinical Specialty, Shanghai Research Centre for Endocrine and Metabolic Diseases, Innovative research team of high-level local universities in Shanghai, Noncommunicable Chronic Diseases-National Science and Technology Major Project, Clinical Special Program of Shanghai Municipal Health Commission, and the three-year action plan to strengthen the construction of public health system in Shanghai.

Objectives:To investigate the longitudinal D-dimer trajectories in hospitalized acute kidney injury (AKI) patients and analyze their association with in-hospital mortality risk. Methods:A retrospective study was conducted using data from AKI patients admitted to Tongji Hospital (July 2012-April 2024). General information, laboratory results, and outcomes were extracted from the medical record system. Patients with at least three D-dimer measurements within 30 days after AKI onset were included. Several latent class trajectory models (LCTMs) were constructed to identify distinct longitudinal dynamic trajectories of D-dimer. Model fit was assessed using Akaike Information Criterion, Bayesian information criterion, entropy, category probability and the optimal model was selected. Logistic regression and Kaplan-Meier survival analysis were employed to evaluate the relationship between D-dimer trajectories and in-hospital mortality. Subgroup analyses were performed to explore potential interactions between D-dimer trajectories and other variables. Results:Based on LCTMs evaluation, the model fitting indices were comprehensively analyzed, and a two-class model was identified as the optimal LCTM. The dynamic trajectories revealed two distinct patterns: an early peak followed by a gradual decline and a low-level continuous stability after AKI onset. Accordingly, patients were categorized into the high-peak decline group and the sustained low-level group. Logistic regression analysis demonstrated that AKI patients in the high-peak decline group had a significantly increased risk of in-hospital mortality (OR 2.27, 95% CI: 1.94-2.65). Kaplan-Meier survival curves indicated a reduced in-hospital survival rate in the high-peak decline group (p < 0.05). Subgroup analyses showed that, across age, gender, chronic kidney disease, cancer, surgery, myocardial infarction, and cerebral infarction subgroups, the high-peak decline group exhibited a significantly elevated risk of in-hospital mortality (p < 0.05), with no significant interaction effects observed among subgroups (p > 0.05). Conclusion:Using LCTM analysis, it was determined that D-dimer exhibits two characteristic longitudinal dynamic trajectories following AKI onset: an early peak followed by a gradual decline and a continuous low-level stability. Among these, the trajectory characterized by an early peak followed by a decline in AKI patients was associated with an increased risk of in-hospital mortality and reduced in-hospital survival, independent of age, gender, chronic kidney disease, cancer, surgery, myocardial infarction, or cerebral infarction.

ObjectiveTo establish a predictive model for acute kidney injury （AKI） in elderly inpatients utilizing machine learning algorithms， and to evaluate the predictive efficacy of various models， thus identifying an optimal predictive model of AKI in elderly hospitalized patients.MethodsElderly inpatients aged ≥65 years with AKI who were admitted to Tongji Hospital， Tongji Medical College， Huazhong University of Science and Technology from January 2018 to December 2022 were retrospectively selected. Then， they were randomly assigned to the training set and test set at a ratio of 7∶3 by utilizing the cerateDataPartition function in the caret package of R. Characteristic variables， such as demographic data and comorbidity were extracted， and AKI was taken as the outcome variable. Seven machine learning algorithms， including Logistic Regression， Random Forest， Light Gradient Boosting Machine （LightGBM） and Adaptive Boosting， were used to develop establish a binary classification prediction model. Then， the model performance was assessed by various evaluation indicators. The optimal model was interpreted and simplified.ResultsA total of 15，134 elderly patients were included for the model construction into training sets and test sets， of which 6，879 patients developed AKI. Sixty-five characteristic variables were included in the prediction model after data cleaning. Among the seven machine learning algorithms， the LightGBM algorithm exhibited the best predictive performance， with the area under the receiver operating characteristic curve （AUROC）， F1 index， and Brier score of 0.896， 0.822， and 0.127， respectively. In addition， the Gain Importance （GI） index of LightGBM algorithm showed that the use of diuretics， neutrophil percentage and D-dimer were closely related to the occurrence of AKI. Finally， LightGBM （AUROC=0.884） was the optimal model by selecting 10 features with greater contribution.ConclusionMachine learning models represented by LightGBM can better predict the occurrence of AKI in elderly hospitalized patients.

Background:: Tertiary lymphoid structure (TLS) is an ectopic lymphoid structure that develops in non-lymphoid structures. Some studies have shown that the TLS formed in autoimmune diseases, such as lupus nephropathy (LN), can cause damage to normal tissues and continuous disease progression. Nevertheless, there is still a lack of efficient treatments for TLS in LN. Thus, the study aims to identify potential targets for therapy of TLS in LN. Methods:: Mice datasets relative to TLS were obtained from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were identified from mice datasets. Then, the Genetic Ontological (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. The Protein-Protein Interaction (PPI) network was constructed. Additionally, the hub genes were selected by Cytoscape and verified by human databases from GEO. The relationships between the immune cells with hub genes were explored. Finally, the two genes PSMB9 and STAT1 were validated in the kidney tissues of LN patients and mice. Results:: 443 DEGs and 178 DEGs relative to TLS were filtered from GSE160488 and GSE155405, respectively. The enrichment results of these genes mostly focused on inflammatory response, cytokine-cytokine receptor interaction, and immune system process. Six genes were recognized by Cytoscape. According to the validation of six genes in human databases, the two hub genes (PSMB9 and STAT1) were also significantly expressed in LN patients. Immune infiltration analysis of hub genes shows immune cells are significantly crucial in LN patients with TLS. Conclusion:: PSMB9 and STAT1 may be identified as possible targets for the treatment of TLS in LN. According to the analysis of the interaction between these genes and immune cells, the immune process mediated by these signature targets takes part in the advancement and formation of TLS.

Abstract Background Recent data has shown a considerable advancement in understanding the role of lymphotoxin-β receptor (LTβR) in inflammation. However, the functions and underlying mechanisms of LTβR in acute kidney injury (AKI) remain largely unknown. Methods AKI was induced in mice by renal ischemia-reperfusion (I/R). HK-2 cells and primary renal tubular epithelial cells (RTECs) were subjected to hypoxia/reoxygenation (H/R) injury. The effects of LTβR depletion were examined in mice, as well as primary RTECs. Bone marrow chimeric mice was generated to determine whether the involvement of LTβR expression by parenchymal cells or bone marrow derived cells contributes to renal injury during AKI. RNA sequencing techniques were employed to investigate the mechanism via which LTβR signaling provides protection against I/R-induced AKI Results LTβR expression was downregulated both in vivo and in vitro models of AKI. Moreover, depletion of LTβR decreased renal damage and inflammation in I/R-induced AKI. We also found that LTβR deficient mice engrafted with wild type bone marrow had significantly less tubular damage, implying that LTβR in renal parenchymal cells may play dominant role in I/R-induced AKI. RNA sequencing indicated that the protective effect of LTβR deletion was associated with activation of PPARα signaling. Furthermore, upregulation of PPARα was observed upon depletion of LTβR. PPARα inhibitor, GW6471, aggravated the tubular damage and inflammation in LTβR−/− mice following I/R injury. Then we further demonstrated that LTβR depletion down-regulated non-canonical NF-κB and Bax/Bcl-2 apoptosis pathway through PPARα. Conclusions Our results suggested that the LTβR/PPARα axis may be a potential therapeutic target for the treatment of AKI.

AimThis study aimed to establish a prediction model in peritoneal dialysis patients to estimate the risk of technique failure and guide clinical practice.MethodsClinical and laboratory data of 424 adult peritoneal dialysis patients were retrospectively collected. The risk prediction models were built using univariate Cox regression, best subsets approach and LASSO Cox regression. Final nomogram was constructed based on the best model selected by the area under the curve.ResultsAfter comparing three models, the nomogram was built using the LASSO Cox regression model. This model included variables consisting of hypertension and peritonitis, serum creatinine, low-density lipoprotein, fibrinogen and thrombin time, and low red blood cell count, serum albumin, triglyceride and prothrombin activity. The predictive model constructed performed well using receiver operating characteristic curve and area under the curve value, C-index and calibration curve.ConclusionThis study developed and verified a new prediction instrument for the risk of technique failure among peritoneal dialysis patients. imageConclusionThis study developed and verified a new prediction instrument for the risk of technique failure among peritoneal dialysis patients. image Technique failure is a significant obstacle for peritoneal dialysis (PD), which may be the main reason for relatively poor PD retention rates worldwide. This study aimed to establish a prediction model in PD patients to estimate the risk of technique failure and guide clinical practice. image

Introduction: Peripheral neuropathy (PN), one of the commonest neurological complications of chronic kidney disease (CKD), was associated with physical limitation. Studies showed that a decrease in physical capability in patients with CKD is related with an increased risk of mortality. The objective of our research was to directly explore the relationship between PN and risk of mortality in patients with CKD. Method: 1,836 participants with CKD and 6,036 participants without CKD, which were classified by PN based on monofilament examination in National Health and Nutrition Examination Survey (NHANES), were collected from the 1999 to 2004 National Health and Nutrition Examination Surveys. Multivariable Cox proportional hazard models were conducted to assess the relationships of PN and deaths in patients with CKD and non-CKD. Results: During 14 years of a median follow-up from 1999 to 2015 and 2004 to 2015, 1,072 (58.4%) and 1,389 (23.0%) deaths were recorded in participants with CKD and without CKD, respectively. PN was related with increased all-cause mortality even after adjusting possible confounding factors in population with CKD (hazard ratio [HR] 1.34, 95% confidence interval [CI] 1.17-1.53) and without CKD (HR 1.27, 95% CI 1.12-1.43). And the adjusted HRs (95% CI) for cardiovascular mortality of the people with CKD and without CKD who suffered from PN were 1.42 (1.07, 1.90) and 1.23 (0.91, 1.67), respectively, versus those without PN. Conclusion: PN was related with a higher risk of all-cause and cardiovascular death in people with CKD, which clinically suggests that the adverse prognostic impact of PN in the CKD population deserves attention and is an important target for intervention.