ArticleInternational journal of molecular sciences2020
Identification of a Reliable Biomarker Profile for the Diagnosis of Gaucher Disease Type 1 Patients Using a Mass Spectrometry-Based Metabolomic Approach.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Review
- Contradictory Effects on Hepatocytes in ASMD.International journal of molecular sciences · 2026Review
- Constraint-based modelling of metabolic dysregulation in Gaucher disease: mitochondrial dysfunction and disrupted cholesterol homeostasis.Orphanet journal of rare diseases · 2026Article
- Decoding rare inherited metabolic disorders: advancing precision in screening and diagnosis.Orphanet journal of rare diseases · 2026Review
- Obstacles to Early Diagnosis of Gaucher Disease.Therapeutics and clinical risk management · 2025Review
- UPLC-MS/MS High-Risk Screening for Sphingolipidoses Using Dried Urine Spots.Biomolecules · 2024Article
- An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism.Molecular genetics and metabolism · 2024Review
- Molecular biomarkers, network biomarkers, and dynamic network biomarkers for diagnosis and prediction of rare diseases.Fundamental research · 2022Review
- Quantitation of a Urinary Profile of Biomarkers in Gaucher Disease Type 1 Patients Using Tandem Mass Spectrometry.Diagnostics (Basel, Switzerland) · 2022Article
- Searching, Structural Determination, and Diagnostic Performance Evaluation of Biomarker Molecules for Niemann-Pick Disease Type C Using Liquid Chromatography/Tandem Mass Spectrometry.Mass spectrometry (Tokyo, Japan) · 2022Review
- Current and emerging pharmacotherapy for Gaucher disease in pediatric populations.Expert opinion on pharmacotherapy · 2021Article
- Biomarkers in Rare Diseases.International journal of molecular sciences · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
Gaucher disease (GD) is a rare autosomal recessive multisystemic lysosomal storage disorder presenting a marked phenotypic and genotypic variability. GD is caused by a deficiency in the glucocerebrosidase enzyme. The diagnosis of GD remains challenging because of the large clinical spectrum associated with the disease. Moreover, GD biomarkers are often not sensitive enough and can be subject to polymorphic variations. The main objective of this study was to perform a metabolomic study using an ultra-performance liquid chromatography system coupled to a time-of-flight mass spectrometer to identify novel GD biomarkers. Following the analysis of plasma samples from patients with GD, and age- and gender-matched control samples, supervised statistical analyses were used to find the best molecules to differentiate the two groups. Targeted biomarkers were structurally elucidated using accurate mass measurements and tandem mass spectrometry. This metabolomic study was successful in highlighting seven biomarkers associated with GD. Fragmentation tests revealed that these latter biomarkers were lyso-Gb
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