The Mechanistic Role of Different Mediators in the Pathophysiology of Nephropathy: A Review

Construction and diagnostic efficacy assessment of the urinary exosomal miRNA-mRNA network in children with IgA vasculitis nephritis

This study aimed to comprehensively evaluate the diagnostic potential of urinary exosomal microRNA (miRNA) in IgA vasculitis (IgAV) kidney injury by meticulously comparing the miRNA expression profiles in urine exosomes between children diagnosed with IgAV and those with IgA vasculitis nephritis (IgAVN). Urine samples were obtained from children with IgAV who were treated at our hospital from October 2022 to October 2023. These samples were then categorized into the IgAV group and the IgAVN group. High-throughput sequencing and bioinformatics analysis techniques were employed to conduct a thorough analysis of the differentially expressed miRNAs between the two groups. Additionally, the correlation between urinary exosomal miRNA and clinical parameters was evaluated. A total of 57 urinary exosomal miRNAs exhibited differential expression between the IgAV and IgAVN groups. Specifically, in the IgAVN group, 42 miRNAs were upregulated, while 15 were downregulated. Lasso regression analysis and ROC analysis identified five candidate urinary exosomal miRNAs with high diagnostic accuracy. A prediction of 95 target genes related to the candidate miRNAs led to the construction of an exosomal miRNA-mRNA regulatory network consisting of four key miRNAs and ten hub genes. Gene function and metabolic pathway analyses indicated that these ten hub genes were predominantly enriched in pro-fibrotic and inflammatory pathways. The analysis incorporating clinical parameters demonstrated a significant correlation between hsa-miR-383-5p and urinary protein levels. This research identified exosomal miRNAs and mRNAs with differential expression patterns associated with IgAVN and constructed the corresponding exosomal miRNA-mRNA network. It was determined that hsa-miR-3065-5p, hsa-miR-383-5p, hsa-miR-25-3p, and hsa-miR-450b-5p might mediate the pathogenesis of IgAVN by targeting pro-fibrotic and inflammatory pathways. Among them, exosomal hsa-miR-383-5p is highly likely to serve as a novel non-invasive biomarker for assessing the disease status of IgAVN, thereby offering new perspectives on the non-invasive diagnosis and treatment of IgAVN.

The Impact of Glomerular Disease on Dyslipidemia in Pediatric Patients Treated with Dialysis

BACKGROUND/OBJECTIVES

Children on dialysis have a 10-fold increase in cardiovascular disease (CVD)-related mortality when compared to the general population. The development of CVD in dialysis patients is attributed to Chronic Kidney Disease-Mineral Bone Disorder (CKD-MBD) and dyslipidemia. While the prevalence of dyslipidemia in adult dialysis patients has been described, there are limited data on prevalence, severity, and risk factors for pediatric dyslipidemia.

METHODS

Data from 1730 pediatric patients ≤ 21 years receiving maintenance hemodialysis or peritoneal dialysis with at least one lipid panel measurement were obtained from USRDS between 2001 and 2016. Disease etiology was classified as being glomerular (n = 1029) or non-glomerular (n = 701). Comparisons were made across etiologies using both linear and logistic regression models to determine the relationship between disease etiology and lipid levels.

RESULTS

The cohort had a mean age of 15.2 years and were 54.5% female. Adjusting for age, sex, race/ethnicity, modality, time with End Stage Kidney Disease (ESKD), and body mass index (BMI) and using non-glomerular etiology as the reference, glomerular disease [mean (95% CI)] was associated with +19% (+14.7%, +23.8%) higher total cholesterol level (183 mg/dL vs. 162 mg/dL), +21% (+14.8%, +26.6%) higher low density lipoprotein cholesterol level (108 mg/dL vs. 87 mg/dL), and +22.3% (+15.5%, +29.5%) higher triglyceride level (169 mg/dL vs. 147 mg/dL). Glomerular disease [OR (95% CI)] was associated with 3.0-fold [2.4, 3.9] higher odds of having an abnormal total cholesterol level, 3.8-fold [2.8, 5.0] higher odds of having an abnormal LDL-C level, and 1.9-fold [1.5, 2.4] higher odds of having an abnormal triglyceride level when compared to non-glomerular disease.

CONCLUSIONS

Pediatric dialysis patients have a high prevalence of dyslipidemia, particularly from elevated triglyceride levels. Specifically, patients with glomerular disease have an even higher risk of dyslipidemia from elevated non-HDL cholesterol and triglyceride levels than patients with non-glomerular disease. The long-term impact of this unfavorable lipid profile requires further investigation.

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors

BACKGROUND

Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making.

METHODS

This single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods.

RESULTS

The study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P < 0.05). Based on these six features, the random forest model (mtry = 5, 75%p) exhibited the best predictive performance among 97 machine learning models, with the area under the curve values reaching 0.983, 1, and 0.905 for the entire cohort, training set, and validation set, respectively. This model significantly outperformed single indicators. The DCA curve confirmed the clinical utility of this model.

CONCLUSIONS

In this study, we developed a machine learning-based predictive model for DGF following pediatric kidney transplantation, termed DGF-RS, which integrates both donor and recipient characteristics. The model demonstrated excellent predictive accuracy and provides essential guidance for clinical decision-making. These findings contribute to our understanding of the pathogenesis of DGF.

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

BACKGROUND

Because spontaneous remission is common in IMN, and there are adverse effects of immunosuppressive therapy, it is important to assess the risk of progressive loss of renal function before deciding whether and when to initiate immunosuppressive therapy. Therefore, this study aimed to establish a risk prediction model to predict patient prognosis and treatment response to help clinicians evaluate patient prognosis and decide on the best treatment regimen.

METHODS

From September 2019 to December 2020, a total of 232 newly diagnosed IMN patients from three hospitals in Liaoning Province were enrolled. Logistic regression analysis selected the risk factors affecting the prognosis, and a dynamic online nomogram prognostic model was constructed based on extreme gradient boost, random forest, logistic regression machine learning algorithms. Receiver operating characteristic and calibration curves and decision curve analysis were utilized to assess the performance and clinical utility of the developed model.

RESULTS

A total of 130 patients were in the training cohort and 102 patients in the validation cohort. Logistic regression analysis identified four risk factors: course ≥ 6 months, UTP, D-dimer and sPLA2R-Ab. The random forest algorithm showed the best performance with the highest AUROC (0.869). The nomogram had excellent discrimination ability, calibration ability and clinical practicability in both the training cohort and the validation cohort.

CONCLUSIONS

The dynamic online nomogram model can effectively assess the prognosis and treatment response of IMN patients. This will help clinicians assess the patient's prognosis more accurately, communicate with the patient in advance, and jointly select the most appropriate treatment plan.

Interleukin-6, tumor necrosis factor-α, and high-sensitivity C-reactive protein for optimal immunometabolic profiling of the lifestyle-related cardiorenal risk

OBJECTIVES

The present study aimed to identify optimal inflammatory biomarkers involved in cardiorenal risk in response to major lifestyle factors.

METHODS

One hundred and twenty-nine adults aged 35-77 years participated voluntarily from 2017 to 2019 (Córdoba, Argentina) in a cross-sectional study to collect sociodemographic, clinical, and lifestyle data. Blood biomarkers (different cytokines, monocyte chemoattractant protein-1 [MCP-1], and high-sensitivity C-reactive protein [hs-CRP]) were measured using standard methods and then evaluated by principal component analysis and structural equation modeling (SEM) according to Mediterranean diet adherence, physical activity level, and waist circumference, while cardiorenal risk involved blood diastolic pressure, HDL-cholesterol, triacylglycerols, creatinine, and glycosylated hemoglobin.

RESULTS

A principal component included TNF-α (tumor necrosis factor-alpha), IL-8 (interleukin-8), IL-6 (interleukin-6), hs-CRP, and MCP-1, with absolute rotated factor loadings >0.10. SEM showed that IL-6 (β=0.38, 95 % IC=0.08-0.68), hs-CRP (β=0.33, 95 % IC=0.17-0.48), and TNF-α (β=0.22, 95 % IC=0.11-0.32) were the mediators that better explained an inflammatory profile positively related to waist circumference (β=0.77, 95 % IC=0.61-0.94). Moreover, this profile was associated with an increased cardiorenal risk (β=0.78, 95 % IC=0.61-0.94), which was well-defined by the variable used.

CONCLUSIONS

Immune mediators are key elements in profiling the cardiorenal risk associated with lifestyle factors, for which the combination of hs-CRP, IL-6, and TNF-α has emerged as a robust indicator. This work reaffirms the need for biomarker optimization for early diagnosis and risk assessment.

A Mendelian randomization study to assess the genetic liability of type 1 diabetes mellitus for IgA nephropathy

Background

The prevalence of immunoglobulin A nephropathy (IgAN) seems to be higher in patients with type 1 diabetes mellitus (T1DM) than that in the general population. However, whether there exists a causal relationship between T1DM and IgAN remains unknown.

Methods

This study conducted a standard two-sample Mendelian randomization (MR) analysis to assess the causal inference by four MR methods, and the inverse variance-weighted (IVW) approach was selected as the primary method. To further test the independent causal effect of T1DM on IgAN, multivariable MR (MVMR) analysis was undertaken. Sensitivity analyses incorporating multiple complementary MR methods were applied to evaluate how strong the association was and identify potential pleiotropy.

Results

MR analyses utilized 81 single-nucleotide polymorphisms (SNPs) for T1DM. The evidence supports a significant causal relationship between T1DM and increased risk of IgAN [odds ratio (OR): 1.39, 95% confidence interval (CI): 1.10-1.74 for IVW, p < 0.05]. The association still exists after adjusting for triglyceride (TG), fasting insulin (FI), fasting blood glucose (FBG), homeostasis model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR), and glycated hemoglobin (HbA1c). MVMR analysis indicated that the effect of T1DM on IgAN vanished upon accounting for low-density lipoprotein cholesterol (LDL-c; OR: 0.97, 95% CI: 0.90-1.05, p > 0.05).

Conclusions

This MR study provided evidence that T1DM may be a risk factor for the onset of IgAN, which might be driven by LDL-c. Lipid-lowering strategies targeting LDL-c should be enhanced in patients with T1DM to prevent IgAN.