Fast and accurate quantitative organic acid analysis with LC-QTOF/MS facilitates screening of patients for inborn errors of metabolism

• Verfasst von: Körver‐Keularts, Irene [VerfasserIn]
• Verfasst von: Langhans, Claus-Dieter [VerfasserIn]
• Titel: Fast and accurate quantitative organic acid analysis with LC-QTOF/MS facilitates screening of patients for inborn errors of metabolism
• Verf.angabe: Irene M.L.W. Körver‐Keularts, Ping Wang, Huub W.A.H. Waterval, Leo A.J. Kluijtmans, Ron A. Wevers, Claus-Dieter Langhans, Camilla Scott, Daphna D.J. Habets, Jörgen Bierau
• E-Jahr: 2018
• Jahr: 12 February 2018
• Umfang: 10 S.
• Fussnoten: Gesehen am 23.01.2020
• Titel Quelle: Enthalten in: Journal of inherited metabolic disease
• Ort Quelle: Hoboken, NJ : Wiley, 1978
• Jahr Quelle: 2018
• Band/Heft Quelle: 41(2018), 3, Seite 415-424
• ISSN Quelle: 1573-2665
• DOI: doi:10.1007/s10545-017-0129-0
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1688140107

Abstract

Since organic acid analysis in urine with gaschromatography-mass spectrometry (GC-MS) is a time-consuming technique, we developed a new liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) method to replace the classical analysis for diagnosis of inborn errors of metabolism (IEM). Sample preparation is simple and experimental time short. Targeted mass extraction and automatic calculation of z-scores generated profiles characteristic for the IEMs in our panel consisting of 71 biomarkers for defects in amino acids, neurotransmitters, fatty acids, purine, and pyrimidine metabolism as well as other disorders. In addition, four medication-related metabolites were included in the panel. The method was validated to meet Dutch NEN-EN-ISO 15189 standards. Cross validation of 24 organic acids from 28 urine samples of the ERNDIM scheme showed superiority of the UPLC-QTOF/MS method over the GC-MS method. We applied our method to 99 patient urine samples with 32 different IEMs, and 88 control samples. All IEMs were unambiguously established/diagnosed using this new QTOF method by evaluation of the panel of 71 biomarkers. In conclusion, we present a LC-QTOF/MS method for fast and accurate quantitative organic acid analysis which facilitates screening of patients for IEMs. Extension of the panel of metabolites is easy which makes this application a promising technique in metabolic diagnostics/laboratories.