#4277 Egf, GDF-15 and Neopterin as Predictors of Chronic Kidney Disease Progression in Children by Means of Artificial Neural Network Models

Abstract

Background and Aims Progression of chronic kidney disease (CKD) is a compound process, where activation of immunocompetent cells and subclinical inflammation play pivotal role. Enhanced atrophy of the tubular cells, and finally, gradual fibrosis of tubulointerstitial tissue, are responsible for irreversible character of the disease. Multiple molecules influence above-mentioned processes. Growth differentiation factor (GDF)15, a member of TGF-β cytokine superfamily, is a marker of inflammation and an integrative signal in both acute and chronic stress conditions. Elevated serum concentrations of GDF15 were associated with increased risk of development and progression of CKD in adults, as well as with mortality in this group of patients. Our previous investigation revealed increased serum GDF15 concentrations in children on chronic dialysis. Epidermal growth factor (EGF), a tubule-specific protein, promotes proliferation, differentiation and migration of epithelial cells, and therefore, modulates regeneration of injured renal tubules. Decreased concentrations of EGF in urine were observed in variety of kidney diseases, including diabetic nephropathy, lupus nephritis or CKD. Our previous analysis of EGF serum concentrations in CKD children confirmed their decreased values on chronic dialysis. Neopterin is a product of activated monocytes and macrophages and serves as a marker of cell-mediated immunity. Elevated serum concentrations of neopterin were observed in CKD adult patients, our investigation revealed its increased values in children on chronic dialysis. None of the above mentioned markers was analyzed in the population of CKD children treated conservatively. Therefore, the aim of study was to assess the serum concentrations of EGF, GDF-15 and neopterin in children with CKD on conservative treatment and verify the usefulness of these markers in predicting CKD progression by means of artificial intelligence tools. Method The study group consisted of 153 children with pre-dialysis CKD stages 1-5 (stage 1 – 27 patients, stage 2 – 26 patients, stage 3 – 51 patients, stage 4 - 28 patients, stage 5 – 21 patients). EGF, GDF-15 and neopterin serum concentrations were assessed by ELISA. The patient database was implemented into the artificial neural network. In detail, the recursively selected subsets of input variables constituted the input layer of an artificial neural network built of perceptrons (multi-layer perceptron). Anthropometric data, biochemical parameters, EGF, GDF15 and neopterin were included into the model, serum creatinine and eGFR, as direct classifiers of CKD stage, were excluded. Various models were tested, regarding their accuracy, AUROC and Matthews correlation coefficient (MCC) values. Results EGF serum concentrations decreased gradually, whereas GDF15 and neopterin values rose systematically with CKD progression, keeping statistically significant inter-stage differences. Moreover, the most precise ANN model, among the tested artificial neural networks, contained EGF, GDF15 and neopterin as input parameters and classified patients into either CKD 1-3 or CKD 4-5 groups. This model has put new patients into appropriate classes with excellent Accuracy of 96.77%, AUROC 0.9169 and Matthews correlation coefficient (MCC) of 0.9157. Conclusion The presented model of an artificial neural network, with serum concentrations of EGF, GDF15 and neopterin as input parameters, may serve as a useful predictor of CKD progression in the pediatric population. It suggests the essential role of inflammatory processes, defined by newly discovered markers, in the renal function decline towards advanced stages of CKD in children.