Urinary Metabolic Distinction of Niemann--Pick Class 1 Disease through the Use of Subgroup Discovery

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dc.contributor.authorCarmona, C. J.
dc.contributor.authorGerman-Morales, M.
dc.contributor.authorElizondo, David
dc.contributor.authorRuiz-Rodado, V.
dc.contributor.authorGrootveld, Martin
dc.date.acceptance2023-10-03
dc.date.accessioned2023-12-15T14:33:48Z
dc.date.available2023-12-15T14:33:48Z
dc.date.issued2023-10-13
dc.descriptionopen access article
dc.description.abstractIn this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann–Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved ‘normal’ (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised ‘normal’ 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and ‘biomarkers’ featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with ‘normal’ levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan–nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.
dc.funderNo external funder
dc.identifier.citationCarmona, C.J., German-Morales, M., Elizondo, D., Ruiz-Rodado, V., Grootveld, M. (2023) Urinary Metabolic Distinction of Niemann–Pick Class 1 Disease through the Use of Subgroup Discovery. Metabolites, 13, 1079
dc.identifier.doihttps://doi.org/10.3390/metabo13101079
dc.identifier.urihttps://hdl.handle.net/2086/23402
dc.language.isoen
dc.peerreviewedYes
dc.publisherMDPI
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.titleUrinary Metabolic Distinction of Niemann--Pick Class 1 Disease through the Use of Subgroup Discovery
dc.typeArticle

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