Dataset Information

Genetic testing in steroid-resistant nephrotic syndrome: when and how?

ABSTRACT: Steroid-resistant nephrotic syndrome (SRNS) represents the second most frequent cause of chronic kidney disease in the first three decades of life. It manifests histologically as focal segmental glomerulosclerosis (FSGS) and carries a 33% risk of relapse in a renal transplant. No efficient treatment exists. Identification of single-gene (monogenic) causes of SRNS has moved the glomerular epithelial cell (podocyte) to the center of its pathogenesis. Recently, mutations in >30 recessive or dominant genes were identified as causing monogenic forms of SRNS, thereby revealing the encoded proteins as essential for glomerular function. These findings helped define protein interaction complexes and functional pathways that could be targeted for treatment of SRNS. Very recently, it was discovered that in the surprisingly high fraction of ?30% of all individuals who manifest with SRNS before 25 years of age, a causative mutation can be detected in one of the ?30 different SRNS-causing genes. These findings revealed that SRNS and FSGS are not single disease entities but rather are part of a spectrum of distinct diseases with an identifiable genetic etiology. Mutation analysis should be offered to all individuals who manifest with SRNS before the age of 25 years, because (i) it will provide the patient and families with an unequivocal cause-based diagnosis, (ii) it may uncover a form of SRNS that is amenable to treatment (e.g. coenzyme Q10), (iii) it may allow avoidance of a renal biopsy procedure, (iv) it will further unravel the puzzle of pathogenic pathways of SRNS and (v) it will permit personalized treatment options for SRNS, based on genetic causation in way of 'precision medicine'.

SUBMITTER: Lovric S

PROVIDER: S-EPMC6367944 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Genetic testing in steroid-resistant nephrotic syndrome: when and how?

Lovric Svjetlana S Ashraf Shazia S Tan Weizhen W Hildebrandt Friedhelm F

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 20151027 11

Steroid-resistant nephrotic syndrome (SRNS) represents the second most frequent cause of chronic kidney disease in the first three decades of life. It manifests histologically as focal segmental glomerulosclerosis (FSGS) and carries a 33% risk of relapse in a renal transplant. No efficient treatment exists. Identification of single-gene (monogenic) causes of SRNS has moved the glomerular epithelial cell (podocyte) to the center of its pathogenesis. Recently, mutations in >30 recessive or dominan ...[more]

PMID: 26507970

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Project description:BackgroundIn the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify the steroid responsiveness of idiopathic nephrotic syndrome (INS) early.MethodsTo build a prediction model for SRNS, we collected 91 subjects; 57 of them had steroid-sensitive nephrotic syndrome, and the others had SRNS. For each subject, 87 clinical variables were measured. In general, only a small part of these variables is informative to SRNS. Thus, we proposed a new variable selection framework including a penalized regression approach (named MLR+TLP) to select variables having a linear effect on the SRNS and a nonparametric screening method (MAC) to select variables having a nonlinear marginal (joint) effect on the SRNS. Thereafter, considering the correlation between selected clinical variables, we used a stepwise method to build our final model for predicting SRNS. In addition, a statistical testing procedure is proposed to test the overfitting of the proposed model.ResultsTwenty-six clinical variables were selected to be informative to SRNS, and an SVM model was built to predict SRNS with a leave-one-out cross-validation (LOO-CV) accuracy of 95.2% (overfitting p value<0.005). To make the model more useful, we incorporate prior medical information into the model and consider the correlation between selected variables. Then, a reduced SVM model including only eight clinical variables (erythrocyte sedimentation rate, urine occult blood, percentage of neutrophils, immunoglobulin A, cholesterol, vinculin autoantibody, aspartate aminotransferase, and prolonged prothrombin time) was built to have a LOO-CV accuracy of 92.8% (overfitting p value<0.005). The validation cohort showed that the reduced model obtained an accuracy of 94.0% (overfitting p value<0.005), with a sensitivity of 90.0% and a specificity of 96.7%. Notably, vinculin autoantibody is the only podocyte autoantibody included in this model. It is linearly related to steroid responsiveness. Finally, our model is freely available as a user-friendly web tool at https://datalinkx.shinyapps.io/srns/.ConclusionThe SRNS prediction model constructed in this study comprehensively and objectively evaluates the internal conditions and disease status of INS patients and will provide scientific guidance for selecting treatment methods for children with nonhereditary SRNS.

Dataset Information

Genetic testing in steroid-resistant nephrotic syndrome: when and how?

Publications

Genetic testing in steroid-resistant nephrotic syndrome: when and how?

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