Next Article in Journal
Modulation of AMPA Receptors by Nitric Oxide in Nerve Cells
Next Article in Special Issue
Fractionation of Enriched Phosphopeptides Using pH/Acetonitrile-Gradient-Reversed-Phase Microcolumn Separation in Combination with LC–MS/MS Analysis
Previous Article in Journal
The Epigenetic Landscape of Vascular Calcification: An Integrative Perspective
Previous Article in Special Issue
Metabolic Profile, Bioavailability and Toxicokinetics of Zearalenone-14-Glucoside in Rats after Oral and Intravenous Administration by Liquid Chromatography High-Resolution Mass Spectrometry and Tandem Mass Spectrometry
Open AccessArticle

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm

1
Section Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands
2
Section Metabolic Diagnostics, Department of Child Health, Wilhelmina Children’s Hospital, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(3), 979; https://doi.org/10.3390/ijms21030979
Received: 13 November 2019 / Revised: 26 January 2020 / Accepted: 29 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue High Resolution Mass Spectrometry in Molecular Sciences)
Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automated data interpretation that preselects the most likely inborn errors of metabolism (IEM). The input parameters of the knowledge-based algorithm were (1) weight scores assigned to 268 unique metabolites for 119 different IEM based on literature and expert opinion, and (2) metabolite Z-scores and ranks based on direct-infusion high resolution mass spectrometry. The output was a ranked list of differential diagnoses (DD) per sample. The algorithm was first optimized using a training set of 110 dried blood spots (DBS) comprising 23 different IEM and 86 plasma samples comprising 21 different IEM. Further optimization was performed using a set of 96 DBS consisting of 53 different IEM. The diagnostic value was validated in a set of 115 plasma samples, which included 58 different IEM and resulted in the correct diagnosis being included in the DD of 72% of the samples, comprising 44 different IEM. The median length of the DD was 10 IEM, and the correct diagnosis ranked first in 37% of the samples. Here, we demonstrate the accuracy of the diagnostic algorithm in preselecting the most likely IEM, based on the untargeted metabolomics of a single sample. We show, as a proof of principle, that automated data interpretation has the potential to facilitate the implementation of untargeted metabolomics for metabolic diagnostic screening, and we provide suggestions for further optimization of the algorithm to improve diagnostic accuracy. View Full-Text
Keywords: untargeted metabolomics; inborn errors of metabolism; IEM; direct-infusion high-resolution mass spectrometry; automated data interpretation; next generation metabolic screening; diagnostics untargeted metabolomics; inborn errors of metabolism; IEM; direct-infusion high-resolution mass spectrometry; automated data interpretation; next generation metabolic screening; diagnostics
Show Figures

Figure 1

MDPI and ACS Style

Haijes, H.A.; van der Ham, M.; Prinsen, H.C.M.T.; Broeks, M.H.; van Hasselt, P.M.; de Sain-van der Velden, M.G.M.; Verhoeven-Duif, N.M.; Jans, J.J.M. Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm. Int. J. Mol. Sci. 2020, 21, 979. https://doi.org/10.3390/ijms21030979

AMA Style

Haijes HA, van der Ham M, Prinsen HCMT, Broeks MH, van Hasselt PM, de Sain-van der Velden MGM, Verhoeven-Duif NM, Jans JJM. Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm. International Journal of Molecular Sciences. 2020; 21(3):979. https://doi.org/10.3390/ijms21030979

Chicago/Turabian Style

Haijes, Hanneke A.; van der Ham, Maria; Prinsen, Hubertus C.M.T.; Broeks, Melissa H.; van Hasselt, Peter M.; de Sain-van der Velden, Monique G.M.; Verhoeven-Duif, Nanda M.; Jans, Judith J.M. 2020. "Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm" Int. J. Mol. Sci. 21, no. 3: 979. https://doi.org/10.3390/ijms21030979

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop