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Related Experiment Video

Updated: Dec 29, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based

Hanneke A Haijes1,2, Maria van der Ham1, Hubertus C M T Prinsen1

  • 1Section Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands.

International Journal of Molecular Sciences
|February 7, 2020
PubMed
Summary

Automated data interpretation aids in diagnosing inborn errors of metabolism (IEM) using untargeted metabolomics. This knowledge-based algorithm preselects likely IEM diagnoses from mass spectrometry data, improving diagnostic efficiency.

Keywords:
IEMautomated data interpretationdiagnosticsdirect-infusion high-resolution mass spectrometryinborn errors of metabolismnext generation metabolic screeninguntargeted metabolomics

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Clinical Diagnostics

Background:

  • Untargeted metabolomics offers a powerful tool for diagnostic requests.
  • Current data interpretation for metabolomics is labor-intensive and hinders widespread clinical use.
  • Automated interpretation methods are needed to facilitate the implementation of metabolomics in metabolic diagnostic screening.

Purpose of the Study:

  • To develop and validate an automated data interpretation method for preselecting inborn errors of metabolism (IEM).
  • To assess the diagnostic accuracy of a knowledge-based algorithm utilizing untargeted metabolomics data.
  • To demonstrate the potential of automated interpretation for metabolic diagnostic screening.

Main Methods:

  • A knowledge-based algorithm was developed using metabolite weight scores and Z-scores/ranks from high-resolution mass spectrometry.
  • The algorithm was trained and optimized on dried blood spot (DBS) and plasma samples with known IEM.
  • Diagnostic performance was validated using a distinct set of plasma samples with confirmed IEM.

Main Results:

  • The algorithm successfully included the correct IEM diagnosis in the differential diagnosis list for 72% of validation samples.
  • The correct diagnosis was ranked first in 37% of samples, with a median differential diagnosis list length of 10 IEM.
  • The method demonstrated accuracy in preselecting likely IEM based on single-sample untargeted metabolomics data.

Conclusions:

  • Automated data interpretation can significantly streamline the diagnostic process for inborn errors of metabolism.
  • The developed algorithm shows promise for facilitating the clinical implementation of untargeted metabolomics.
  • Further optimization of the algorithm is suggested to enhance diagnostic accuracy and broaden its application.