A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.

Journal: Orphanet journal of rare diseases
PMID: 39789579

Abstract

BACKGROUND: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on a combination of different biomarkers, mostly routine clinical chemical markers and subsequent genetic confirmation. However, a specific and reliable biomarker is lacking. As GSD Ia patients demonstrate altered lipid metabolism and mitochondrial fatty acid oxidation, we built a machine learning model to identify GSD Ia patients based on plasma acylcarnitine profiles.

Authors

  • Joost Groen
    Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus, Groningen, 30001 - 9700 RB, the Netherlands. j.groen@umcg.nl.
  • Bas M de Haan
    Laboratory of Special Chemistry, Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands.
  • Ruben J Overduin
    Division of Metabolic Diseases, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands.
  • Andrea B Haijer-Schreuder
    Division of Metabolic Diseases, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands.
  • Terry Gj Derks
    Division of Metabolic Diseases, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands.
  • M Rebecca Heiner-Fokkema
    Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus, Groningen, 30001 - 9700 RB, the Netherlands.

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