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Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Metabolomics.
Gooding, Jessica R; Agrawal, Shipra; McRitchie, Susan; Acuff, Zach; Merchant, Michael L; Klein, Jon B; Smoyer, William E; Sumner, Susan J.
Afiliação
  • Gooding JR; National Institutes of Health Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC) at University of North Carolina, Chapel Hill, North Carolina, USA.
  • Agrawal S; Discovery, Science and Technology, RTI International, Research Triangle Park, North Carolina, USA.
  • McRitchie S; Department of Pediatrics, The Ohio State University, Columbus, Ohio, USA.
  • Acuff Z; Center for Clinical and Translational Research, Nationwide Children's Hospital, Columbus, Ohio, USA.
  • Merchant ML; National Institutes of Health Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC) at University of North Carolina, Chapel Hill, North Carolina, USA.
  • Klein JB; Nutrition Research Institute, University of North Carolina, Chapel Hill, North Carolina, USA.
  • Smoyer WE; National Institutes of Health Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC) at University of North Carolina, Chapel Hill, North Carolina, USA.
  • Sumner SJ; Discovery, Science and Technology, RTI International, Research Triangle Park, North Carolina, USA.
Kidney Int Rep ; 5(1): 81-93, 2020 Jan.
Article em En | MEDLINE | ID: mdl-31922063
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Kidney Int Rep Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Kidney Int Rep Ano de publicação: 2020 Tipo de documento: Article