Nephronectin (NPNT) and the prediction of nephrotic syndrome response to steroid treatment

Watany, Mona Mohamed; El‐Horany, Hemat El‐Sayed

doi:10.1038/s41431-018-0182-7

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…The Npnt levels were significantly higher in SIL (mean ± standard deviation (SD): 12.77 ± 1.50) compared to HV (9.86 ± 3.16). The only report describing serum Npnt levels was that by Watany and El-Horany [62]. Although their values differed from this report, our ELISA was developed first.…”

Section: Npnt In Silicosismentioning

confidence: 96%

Role of Nephronectin in Pathophysiology of Silicosis

Lee

Honda

Yamamoto

et al. 2019

IJMS

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Silicosis is a typical form of pneumoconiosis and is characterized as a type of lung fibrosis. Silica particles are captured and recognized upon by alveolar macrophages via the macrophage receptor with collagenous structure (MARCO) scavenger receptor, and thereafter the inflammasome is activated. Thereafter, various chemokines/cytokines play their roles to eventually form fibrosis. Additionally, silica particles chronically activate T helper cells which sets the background for the formation of silicosis-associated autoimmune disturbances. The occurrence and progression of lung fibrosis, the extracellular matrix-related molecules such as integrins and their ligands including fibronectin, vitronectin, laminin, and collagens, all play important roles. Here, the roles of these molecules in silicosis-related lung fibrosis are reviewed from the literature. Additionally, the measurement of serum nephronectin (Npnt), a new member of the integrin family of ligands, is discussed, together with investigations attempting to delineate the role of Npnt in silica-induced lung fibrosis. Serum Npnt was found to be higher in silicosis patients compared to healthy volunteers and seems to play a role in the progression of fibrosis with other cytokines. Therefore, serum Npnt levels may be employed as a suitable marker to monitor the progression of fibrosis in silicosis patients.

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Section: Npnt In Silicosismentioning

confidence: 96%

Role of Nephronectin in Pathophysiology of Silicosis

Lee

Honda

Yamamoto

et al. 2019

IJMS

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“…By flow cytometry, P-glycoprotein expression was significantly higher in SRNS ( 29 ). By ELISA, serum nephronectin concentrations were significantly lower in patients with SRNS than in patients with SSNS and controls ( 30 ). Metabolomic profiling of plasma samples from children with INS suggested that creatinine concentration, glutamine concentration, and malonate concentration were three candidate biomarkers predictive of SRNS ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning models for predicting steroid-resistant of nephrotic syndrome

Li²,

Liu³

et al. 2023

Front. Immunol.

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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.

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“…(3) Other serum/plasma markers Molecules related to steroid metabolism have been studied as biomarkers 60,70) . The level of a downstream sig naling molecule of glucocorticosteroids, histone deacety lase (HDAC)2, was lower in SRNS 60) , while MIF, the level 71) . The level of endothelin1, which is related to the pathogenesis of prote inuria, was higher in SRNS 72) .…”

Section: Peripheral Blood Markersmentioning

confidence: 99%

Biomarkers Predicting Treatment-Response in Nephrotic Syndrome of Children: A Systematic Review

et al. 2021

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Purpose: Nephrotic syndrome (NS) is the most common form of glomerulopathy in children. Most pediatric patients respond to glucocorticosteroid treatment (steroid-sensitive NS, SSNS), while approximately 10–15% will remain unresponsive or later become steroid-resistant. There has been a long-standing effort to find biomarkers that may predict steroid responsiveness.Methods: We systematically reviewed current studies which investigated clinically relevant biomarkers for predicting steroid responsiveness in pediatric NS. We performed a PubMed and EMBASE search to identify eligible articles. We collected data on urinary markers, blood/serum markers (including cellular phenotypes and mRNA expression), genotypes and HLA allele frequency.Results: A total of 659 articles were identified following electronic and manual searches. After reviewing the titles, abstracts, and full texts, 72 eligible articles were finally included. Vitamin D-binding protein (VDBP) seemed to be significantly elevated in SRNS than in SSNS, in both serum and urine specimen, although further validation is required.Conclusions: The present paper narratively illustrates current understandings of potential biomarkers that may help predict steroid responsiveness. Further investigation and collaboration involving a larger number of patients are necessary.

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Nephronectin (NPNT) and the prediction of nephrotic syndrome response to steroid treatment

Cited by 10 publications

References 30 publications

Role of Nephronectin in Pathophysiology of Silicosis

Role of Nephronectin in Pathophysiology of Silicosis

Machine learning models for predicting steroid-resistant of nephrotic syndrome

Biomarkers Predicting Treatment-Response in Nephrotic Syndrome of Children: A Systematic Review

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