Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.

Background: Mounting studies have shown that hyperuricemia is related to kidney diseases through multiple ways. However, the application of urinary uric acid indicators in patients with reduced renal function is not clear. In this study, we aim to determine the effects of renal function on various indicators reflecting uric acid levels in patients with chronic kidney disease (CKD). Methods: Anthropometric and biochemical examinations were performed in 625 patients with CKD recruited from Dept of Nephrology of Huadong hospital affiliated to Fudan University. Multiple regression analyses were used to study correlations of the estimated glomerular filtration rate (eGFR) with serum uric acid (SUA) and renal handling of uric acid. For further study, smooth curve plots and threshold effect analyses were applied to clarify associations between renal function and uric acid levels. Results: The nonlinear relationships were observed between eGFR and urinary uric acid indicators. The obvious inflection points were observed in smooth curve fitting of eGFR and fractional excretion of uric acid (FEur), excretion of uric acid per volume of glomerular filtration (EurGF). In subsequent analyses where levels of eGFR< 15 mL/min/1.73m² were dichotomized (CKD5a/CKD5b), patients in the CKD5a showed higher levels of FEur and EurGF while lower levels of urinary uric acid excretion (UUA), clearance of uric acid (Cur) and glomerular filtration load of uric acid (FLur) compared with CKD5b group (all P < 0.05). And there was no significant difference of SUA levels between two groups. On the other hand, when eGFR< 109.9 ml/min/1.73 m² and 89.1 ml/min/1.73 m², the resultant curves exhibited approximately linear associations of eGFR with Cur and FLur respectively. Conclusion: FEur and EurGF showed significantly compensatory increases with decreased renal function. And extra-renal uric acid excretion may play a compensatory role in patients with severe renal impairment to maintain SUA levels. Moreover, Cur and FLur may be more reliable indicators of classification for hyperuricemia in CKD patients.

Background: Aberrant DNA methylation patterns, including hypermethylation of key genes that inhibit fibrosis and inflammation, have been described in human kidney diseases. However, the role of DNA methyltransferase 1 (DNMT1) in hepatitis B virus-associated glomerulonephritis (HBV-GN) remains unclear. Methods: We explored the underlying mechanism by establishing HBV X protein (HBx) overexpressing renal tubular epithelial (HK-2) cells and human podocytes with DNMT1 knockdown. Using RNA-sequencing to determine the downstream targets of DNMT1 and evaluate its levels of promoter methylation. HBV transgenic mice were used to examine the effects of DNMT1 inhibitor on renal in vivo. Results: DNMT1 was significantly upregulated in the renal tissue of HBV-GN patients, accompanied by injuries of HK-2 cells and podocytes. HBx markedly upregulated DNMT1 and induced epithelial-mesenchymal transition (EMT) and inflammation in HK-2 cells and human podocytes. This increased DNMT1 expression was attenuated after DNMT1 knockdown, accompanied by restored HK-2 cells and podocyte injuries resulting from the activation of PI3K/Akt/mTOR and nuclear factor-kappa B (NF-κB) pathways. Hypermethylation of the phosphatase and tensin homolog (PTEN) promoter and vitamin D receptor (VDR) was induced in HBx-overexpressing HK-2 cells and podocytes, respectively, whereas DNMT1 knockdown effectively corrected these alterations. Furthermore, PTEN and VDR ablation resulted in marked EMT and inflammation induction in HBx-overexpressing HK-2 cells and human podocytes even with DNMT1 knockdown. Downregulation of the PI3K/Akt/mTOR-related pathway attenuated HBx-induced EMT and inflammation in HK-2 cells. Luciferase reporter assay revealed VDR as a direct target of the Snail family transcriptional repressor 1 (SNAI1) in HBx-overexpressing podocytes. DNA methylation inhibitor 5-azacytidine alleviated urinary protein and renal inflammation in HBV transgenic mice via PTEN-PI3K/Akt signaling and VDR signaling axis. Conclusions: Our study clarifies the potential epigenetic mechanisms underlying HBx-induced renal injuries in HBV-GN and the renoprotective effects of inhibiting DNMT1, which can provide important insights into the development of treatments for HBV-GN.

This study aims to investigate the relationship between urinary creatinine (UCr) and the risk and severity of Diabetic Kidney Disease (DKD) in patients with Type 2 Diabetes Mellitus (T2DM). The goal is to establish UCr as a potential biomarker for early DKD detection and severity assessment. A retrospective cross-sectional analysis was conducted using medical records of T2DM patients. Patients were classified into groups with and without DKD, and relevant clinical data, including demographic, blood, and urine parameters, were collected. Logistic regression and receiver operating characteristic (ROC) curve analysis evaluated the association between UCr levels and DKD. Curve fitting and threshold effect model were used to further evaluate the relationship between UCr and the incidence and severity of DKD. A total of 302 T2DM patients were analyzed, with 137 diagnosed with DKD. Significant differences in clinical parameters were observed between the DKD and non-DKD groups, particularly in UCr, urine albumin levels, and eGFR. UCr levels demonstrated a strong association with DKD. Moreover, a non-linear relationship was identified, with specific inflection points indicating different correlation patterns of UCr with DKD occurrence and progression. The findings of this study highlight the potential of UCr as a valuable biomarker for early detection and assessment of DKD in T2DM patients. Incorporating UCr measurements into routine clinical practice could enhance early identification of patients at risk for kidney complications, leading to timely intervention and improved patient outcomes.