Prediction of steroid resistance and steroid dependence in nephrotic syndrome children

Abstract

Abstract Background Steroid resistant (SR) nephrotic syndrome (NS) affects up to 30% of children and is responsible for fast progression to end stage renal disease. Currently there is no early prognostic marker of SR and studied candidate variants and parameters differ highly between distinct ethnic cohorts. Methods Here, we analyzed 11polymorphic variants, 6 mutations, SOCS3 promoter methylation and biochemical parameters as prognostic markers in a group of 124 Polish NS children (53 steroid resistant, 71 steroid sensitive including 31 steroid dependent) and 55 controls. We used single marker and multiple logistic regression analysis, accompanied by prediction modeling using neural network approach. Results We achieved 92% (AUC = 0.778) SR prediction for binomial and 63% for multinomial calculations, with the strongest predictors ABCB1 rs1922240, rs1045642 and rs2235048, CD73 rs9444348 and rs4431401, serum creatinine and unmethylated SOCS3 promoter region. Next, we achieved 80% (AUC = 0.720) in binomial and 63% in multinomial prediction of SD, with the strongest predictors ABCB1 rs1045642 and rs2235048. Haplotype analysis revealed CD73_AG to be associated with SR while ABCB1_AGT was associated with SR, SD and membranoproliferative pattern of kidney injury regardless the steroid response. Conclusions We achieved prediction of steroid resistance and, as a novelty, steroid dependence, based on early markers in NS children. Such predictions, prior to drug administration, could facilitate decision on a proper treatment and avoid diverse effects of high steroid doses.

Keywords

• Nephrotic syndrome
• Prediction modeling
• Single nucleotide polymorphisms
• Steroid dependence
• Steroid resistance