Dataset Information

Discovery and initial validation of ? 1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome.

ABSTRACT: PURPOSE:In this cross-sectional pilot study we set out to discover a non-invasive biomarker that could distinguish steroid-resistant nephrotic syndrome (SRNS) from steroid-sensitive nephrotic syndrome (SSNS). EXPERIMENTAL DESIGN:Urine and clinical data were collected from patients with idiopathic nephrotic syndrome and healthy controls. Using SELDI-TOF-MS, we identified an 11-fold upregulated 13.8?kDa fragment of ? 1-B glycoprotein (A1BG) in urine in SRNS. To validate our findings, A1BG was detected by Western blot. Creatinine was measured and transformed to glomerular filtration rate (GFR) by the new Schwartz formula and classified to chronic kidney disease (CKD) stage. p-Values were determined by unpaired t-test and Mann-Whitney rank sum test. Microalbumin was also measured to determine albumin/creatinine ratios. RESULTS:The 13.8?kDa A1BG was present in 7 of 19 patients with SRNS; but absent in all SSNS (n=15) and controls (n=10). The A1BG(+) patients had lower GFR than A1BG(-) patients (p<0.009) and tended to have higher CKD stage. CONCLUSION AND CLINICAL RELEVANCE:The 13.8?kDa A1BG fragment had a high discriminatory power for steroid resistance in pediatric nephrotic syndrome, but is only present in a subset of patients. Additional longitudinal studies are required to determine the usefulness of this biomarker as a non-invasive predictive marker of therapeutic response.

SUBMITTER: Piyaphanee N

PROVIDER: S-EPMC7039306 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Discovery and initial validation of α 1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome.

Piyaphanee Nuntawan N Ma Qing Q Kremen Oran O Czech Kimberly K Greis Kenneth K Mitsnefes Mark M Devarajan Prasad P Bennett Michael R MR

Proteomics. Clinical applications 20110518 5-6

<h4>Purpose</h4>In this cross-sectional pilot study we set out to discover a non-invasive biomarker that could distinguish steroid-resistant nephrotic syndrome (SRNS) from steroid-sensitive nephrotic syndrome (SSNS).<h4>Experimental design</h4>Urine and clinical data were collected from patients with idiopathic nephrotic syndrome and healthy controls. Using SELDI-TOF-MS, we identified an 11-fold upregulated 13.8 kDa fragment of α 1-B glycoprotein (A1BG) in urine in SRNS. To validate our findings ...[more]

PMID: 21591266

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Project description:Introduction:Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS. Methods:We performed proteomic analyses on paired pediatric NS patient plasma samples obtained both at disease presentation before glucocorticoid initiation and after approximately 7 weeks of GC therapy to identify candidate biomarkers able to either predict steroid resistance before treatment or define critical molecular pathways/targets regulating steroid resistance. Results:Proteomic analyses of 15 paired NS patient samples identified 215 prevalent proteins, including 13 candidate biomarkers that predicted SRNS before GC treatment, and 66 candidate biomarkers that mechanistically differentiated steroid-sensitive NS (SSNS) from SRNS. Ingenuity Pathway Analyses and protein networking pathways approaches further identified proteins and pathways associated with SRNS. Validation using 37 NS patient samples (24 SSNS/13 SRNS) confirmed vitamin D binding protein (VDB) and APOL1 as strong predictive candidate biomarkers for SRNS, and VDB, hemopexin (HPX), adiponectin (ADIPOQ), sex hormone-binding globulin (SHBG), and APOL1 as strong candidate biomarkers to mechanistically distinguish SRNS from SSNS. Logistic regression analysis identified a candidate biomarker panel (VDB, ADIPOQ, and matrix metalloproteinase 2 [MMP-2]) with significant ability to predict SRNS at disease presentation (P = 0.003; area under the receiver operating characteristic curve = 0.78). Conclusion:Plasma proteomic analyses and immunoblotting of serial samples in childhood NS identified a candidate biomarker panel able to predict SRNS at disease presentation, as well as candidate molecular targets/pathways associated with clinical steroid resistance.

Project description:Introduction:Nephrotic syndrome (NS) is a kidney disease that affects both children and adults. Glucocorticoids have been the primary therapy for >60 years but are ineffective in approximately 20% of children and approximately 50% of adult patients. Unfortunately, patients with steroid-resistant NS (SRNS; vs. steroid-sensitive NS [SSNS]) are at high risk for both glucocorticoid-induced side effects and disease progression. Methods:We performed proton nuclear magnetic resonance (1H NMR) metabolomic analyses on plasma samples (n = 86) from 45 patients with NS (30 SSNS and 15 SRNS) obtained at initial disease presentation before glucocorticoid initiation and after approximately 7 weeks of glucocorticoid therapy to identify candidate biomarkers able to either predict SRNS before treatment or define critical molecular pathways/targets regulating steroid resistance. Results:Stepwise logistic regression models identified creatinine concentration and glutamine concentration (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.99-1.02) as 2 candidate biomarkers predictive of SRNS, and malonate concentration (OR: 0.94; 95% CI: 0.89-1.00) as a third candidate predictive biomarker using a similar model (only in children >3 years). In addition, paired-sample analyses identified several candidate biomarkers with the potential to identify mechanistic molecular pathways/targets that regulate clinical steroid resistance, including lipoproteins, adipate, pyruvate, creatine, glucose, tyrosine, valine, glutamine, and sn-glycero-3-phosphcholine. Conclusion:Metabolomic analyses of serial plasma samples from children with SSNS and SRNS identified elevated creatinine and glutamine concentrations, and reduced malonate concentrations, as auspicious candidate biomarkers to predict SRNS at disease onset in pediatric NS, as well as additional candidate biomarkers with the potential to identify mechanistic molecular pathways that may regulate clinical steroid resistance.

Dataset Information

Discovery and initial validation of ? 1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome.

Publications

Discovery and initial validation of α 1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome.

Similar Datasets

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