Emodin improves renal fibrosis in chronic kidney disease by regulating mitochondrial homeostasis through the mediation of peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α).

Chronic kidney disease (CKD) is a leading public health issue associated with high morbidity worldwide. However, there are only a few effective therapeutic strategies for CKD. Emodin, an anthraquinone compound from rhubarb, can inhibit fibrosis in tissues and cells. Our study aims to investigate the antifibrotic effect of emodin and the underlying molecular mechanism. A unilateral ureteral obstruction (UUO)-induced rat model was established to evaluate the effect of emodin on renal fibrosis development. Hematoxylin and eosin staining, Masson's trichrome staining, and immunohistochemistry staining were performed to analyze histopathological changes and fibrotic features after emodin treatment. Subsequently, a transforming growth factor-beta 1 (TGF-ß1)-induced cell model was used to assess the inhibition of emodin on cell fibrosis in vitro. Furthermore, Western blot analysis and real-time quantitative reverse transcription-polymerase chain reaction were performed to validate the regulatory mechanism of emodin on renal fibrosis progression. As a result, emodin significantly improved histopathological abnormalities in rats with UUO. The expression of fibrosis biomarkers and mitochondrial biogenesis-related proteins also decreased after emodin treatment. Moreover, emodin blocked TGF-ß1-induced fibrotic phenotype, lipid accumulation, and mitochondrial homeostasis in NRK-52E cells. Conversely, peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α) silencing significantly reversed these features in emodin-treated cells. Collectively, emodin plays an important role in regulating PGC-1α-mediated mitochondria function and energy homeostasis. This indicates that emodin exhibits great inhibition against renal fibrosis and acts as a promising inhibitor of CKD.

Nomogram for the prediction of crescent formation in IgA nephropathy patients: a retrospective study.

BACKGROUND: The 2017 Oxford classification of immunoglobulin A nephropathy (IgAN) recently reported that crescents could predict a worse renal outcome. Early prediction of crescent formation can help physicians determine the appropriate intervention, and thus, improve the outcomes. Therefore, we aimed to establish a nomogram model for the prediction of crescent formation in IgA nephropathy patients. METHODS: We retrospectively analyzed 200 cases of biopsy-proven IgAN patients. Least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic regression was applied to screen for influencing factors of crescent formation in IgAN patients. The performance of the proposed nomogram was evaluated based on Harrell's concordance index (C-index), calibration plot, and decision curve analysis. RESULTS: Multivariate logistic analysis showed that urinary protein ≥ 1 g (OR = 3.129, 95%CI = 1.454-6.732), urinary red blood cell (URBC) counts ≥ 30/ul (OR = 3.190, 95%CI = 1.590-6.402), mALBU ≥ 1500 mg/L(OR = 2.330, 95%CI = 1.008-5.386), eGFR < 60ml/min/1.73m2(OR = 2.295, 95%CI = 1.016-5.187), Serum IgA/C3 ratio ≥ 2.59 (OR = 2.505, 95%CI = 1.241-5.057), were independent risk factors for crescent formation. Incorporating these factors, our model achieved well-fitted calibration curves and a good C-index of 0.776 (95%CI [0.711-0.840]) in predicting crescent formation. CONCLUSIONS: Our nomogram showed good calibration and was effective in predicting crescent formation risk in IgAN patients.

Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning.

BACKGROUND AND OBJECTIVES: Immunoglobulin a nephropathy (IgAN) is the most common primary glomerular disease in the world, with different clinical manifestations, varying severity of pathological changes, common complications of crescent formation in different proportions, and great individual heterogeneous in clinical outcomes. Therefore, we aim to develop a machine learning (ML) based predictive model for predicting the prognosis of IgAN with focal crescent formation and without obvious chronic renal lesions (glomerulosclerosis <25%). MATERIALS: We retrospectively reviewed biopsy-proven IgAN patients in our hospital and cooperative hospital from 2005 to 2017. The method of feature importance of random forest (RF) was applied to conduct feature exploration of feature variables to establish the characteristic variables that are closely related to the prognosis of focal crescent IgAN. Multiple ML algorithms were attempted to establish the prediction models. The area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) were applied to evaluate the predictive performance via three-fold cross validation (namely 2 training sets and 1 validation set). RESULTS: RF was used to screen the important features, the top three of which were baseline estimated glomerular filtration rate (eGFR), serum creatine and triglyceride. Ten important features were selected as important predictors for modeling on the basis of data-driven and medical selection, predictors include: age, baseline eGFR, serum creatine, serum triglycerides, complement 3(C3), proteinuria, mean arterial pressure (MAP) and Hematuria, crescents proportion of glomeruli, Global crescent proportion of glomeruli. In a variety of ML algorithms, the support vector machine (SVM) algorithm displayed better predictive performance, with Precision of 0.77, Recall of 0.77, F1-score of 0.73, accuracy of 0.77, AUROC of 79.57%, and AUPRC of 76.5%. CONCLUSIONS: The SVM model is potentially useful for predicting the prognosis of IgAN patients with focal crescent shape and without obvious chronic renal lesions.

Volume of Crescents Affects Prognosis of IgA Nephropathy in Patients without Obvious Chronic Renal Pathology.

INTRODUCTION: A working group on the Oxford classification of IgA nephropathy (IgAN) recently reported that crescents detected in the kidney tissue predicted a worse renal outcome. However, the effect of C1 lesion (crescents in <1/4th of all glomeruli) and their volume on the prognosis of IgAN is still unclear. We explored the association of C1 lesion with the renal prognosis in IgAN patients without obvious chronic renal lesions (glomerulosclerosis <25%, T score <2). METHODS: We investigated 305 biopsy-proven IgAN patients without obvious chronic renal lesions. Clinicopathologic features and treatment modalities were recorded. The patients were divided into several groups according to the presence or absence of a global crescent: no crescent (NC) group, only segmental crescent (SC) group, and global crescent (GC) group. The outcome was the survival from a combined event defined by a ≥15% decline in the estimated glomerular filtration rate (eGFR) after 1 year or ≥30% decline in the eGFR after 2 years. RESULTS: Among all patients, 75.7% were in the NC group, 14.8% were in the SC group, and 9.5% were in the GC group. Compared with the NC group, patients in the SC group and the GC group had more urine protein, lower eGFR, and presented with more severe pathological change. During a median follow-up of 34.8 (26.16-57.95) months, the combined event occurred in 34 individuals (11.1%). In a multivariate model, the GC group (HR = 2.756, 95% CI = 1.068-7.109) was associated with an increased risk of the combined event. CONCLUSIONS: In IgAN patients without obvious chronic renal lesions, the GC group had more severe clinical and pathological manifestations than in the NC group. GC is an independent risk factor for the progression of IgAN renal function.