ArticleMetabolomics : Official journal of the Metabolomic Society2025
A diagnostic algorithm for inherited metabolic disorders using untargeted metabolomics.
Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Decoding rare inherited metabolic disorders: advancing precision in screening and diagnosis.Orphanet journal of rare diseases · 2026Review
- Insights from Metabolomics Profiling of MSUD in Pediatrics Toward Disease Progression.Metabolites · 2025Article
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9 authors.
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Abstract
introductionUntargeted metabolomics is a powerful tool for detecting perturbations in biological systems, offering significant potential for screening for rare inherited metabolic disorders (IMDs). However, the rarity and vast diversity of these diseases, results in limited availability of samples and incomplete metabolic pathway knowledge for each condition. Current diagnostic procedures rely heavily on manual interpretation, which is time-consuming, and data driven approaches are insufficient for small sample sizes.
objectivesTo develop a diagnostic algorithm for IMDs addressing the challenges posed by small sample sizes and continuously evolving datasets.
methods77 IMD patients (35 different IMDs) and 136 control samples were collected from Copenhagen University Hospital, Rigshospitalet. The metabolome was analyzed using liquid chromatography-mass spectrometry. An algorithm partially based on sparse hierarchical clustering was designed to generate IMD-specific metabolic signatures from metabolomics data, enabling comparison with undiagnosed patient samples to provide diagnostic predictions. An iterative improvement strategy was employed, where new data are continuously integrated to refine the IMD-specific signatures. The algorithm's performance was evaluated through both the current study and a case study using literature-derived data.
resultsThe algorithm demonstrated iterative improvement with each training round, correctly identifying the diagnosis within top 3 potential IMDs in 60% of samples (top 1 in 42%). The case study applied the method to literature-based data comprising 95 IMD samples (11 different IMDs) and 68 controls, yielding a correct diagnosis in 73.5% of cases.
conclusionThese results demonstrate that the algorithm provides a flexible, data-driven framework for continuous improvement in IMD diagnosis, even with limited number of samples.
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