Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Tytuł pozycji:

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Metabolomics

Tytuł:
Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Metabolomics
Autorzy:
Jessica R. Gooding
Shipra Agrawal
Susan McRitchie
Zach Acuff
Michael L. Merchant
Jon B. Klein
William E. Smoyer
Susan J. Sumner
John Mahan
Hiren Patel
Richard F. Ransom
Cynthia Pan
Denis F. Geary
Myra L. Chang
Keisha L. Gibson
Franca M. Iorember
Patrick D. Brophy
Tarak Srivastava
Larry A. Greenbaum
Temat:
Diseases of the genitourinary system. Urology
RC870-923
Źródło:
Kidney International Reports, Vol 5, Iss 1, Pp 81-93 (2020)
Wydawca:
Elsevier, 2020.
Rok publikacji:
2020
Kolekcja:
LCC:Diseases of the genitourinary system. Urology
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2468-0249
Relacje:
http://www.sciencedirect.com/science/article/pii/S2468024919315001; https://doaj.org/toc/2468-0249
DOI:
10.1016/j.ekir.2019.09.010
Dostęp URL:
https://doaj.org/article/1b5280e5d877451bafee09b406b2ee8f  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.1b5280e5d877451bafee09b406b2ee8f
Czasopismo naukowe
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. Keywords: biomarkers, metabolomics, nephrotic syndrome, steroid resistance

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies