ArticleJournal of inherited metabolic disease2022
Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders.
Article in Journal of inherited metabolic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomic profiling in phenylketonuria: a systematic review of human studies.Metabolomics : Official journal of the Metabolomic Society · 2026Pooled it
- Decoding rare inherited metabolic disorders: advancing precision in screening and diagnosis.Orphanet journal of rare diseases · 2026Review
- Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders.Journal of inherited metabolic disease · 2026Review
- Disrupted fetal carbohydrate metabolism in children with autism spectrum disorder.Journal of neurodevelopmental disorders · 2025Article
- CRISPR/Cas9-mediated silencing of CD44: unveiling the role of hyaluronic acid-mediated interactions in cancer drug resistance.Naunyn-Schmiedeberg's archives of pharmacology · 2024Review
- A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.Translational pediatrics · 2022Review
- Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders.Journal of inherited metabolic disease · 2022Article
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Authors and funding
8 authors.
Funding
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Abstract
Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway-based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature-based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway-based interpretation of UM data in IMD diagnostics context.
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