ABSTRACT
BACKGROUND: Growth retardation is a common complication of chronic kidney disease in children, which can be partially relieved after renal transplantation. This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease (ESRD) after kidney transplantation using machine learning algorithms based on genomic and clinical variables. METHODS: A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing (WES), and another 39 children who underwent transplant from September 2021 to March 2022 were enrolled for external validation. Based on previous studies, we comprehensively collected 729 height-related single-nucleotide polymorphisms (SNPs) in exon regions. Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction. RESULTS: The 110 children were divided into two groups according to change in height-for-age Z-score. After univariate analysis, age and 19 SNPs were incorporated into the model and validated. The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve (AUC) of 0.924, and also performed well in the external validation cohort (accuracy, 0.7949; AUC, 0.796). CONCLUSIONS: A model with good performance for predicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms. The model is expected to guide clinicians in the management of children after renal transplantation, including the use of growth hormone, glucocorticoid withdrawal, and nutritional supplementation, to alleviate growth retardation in children with ESRD.
ABSTRACT
Ischemia/reperfusion- (I/R-) induced injury is unavoidable and a major risk factor for graft failure and acute rejection following kidney transplantation. However, few effective interventions are available to improve the outcome due to the complicated mechanisms and lack of appropriate therapeutic targets. Hence, this research aimed to explore the effect of the thiazolidinedione (TZD) compounds on I/R-induced kidney damage. One of the main causes of renal I/R injury is the ferroptosis of renal tubular cells. In this study, compared with the antidiabetic TZD pioglitazone (PGZ), we found its derivative mitoglitazone (MGZ) exerted significantly inhibitory effects on erastin-induced ferroptosis by suppressing mitochondrial membrane potential hyperpolarization and lipid ROS production in HEK293 cells. Moreover, MGZ pretreatment remarkably alleviated I/R-induced renal damages by inhibiting cell death and inflammation, upregulating the expression of glutathione peroxidase 4 (GPX4), and reducing iron-related lipid peroxidation in C57BL/6 N mice. Additionally, MGZ exhibited excellent protection against I/R-induced mitochondrial dysfunction by restoring ATP production, mitochondrial DNA copy numbers, and mitochondrial morphology in kidney tissues. Mechanistically, molecular docking and surface plasmon resonance experiments demonstrated that MGZ exhibited a high binding affinity with the mitochondrial outer membrane protein mitoNEET. Collectively, our findings indicated the renal protective effect of MGZ was closely linked to regulating the mitoNEET-mediated ferroptosis pathway, thus offering potential therapeutic strategies for ameliorating I/R injuries.
