ArticleOrphanet journal of rare diseases2026
Mapping the landscape of treatable inborn errors of metabolism: a systematic gene-level evaluation based on the ICIMD classification.
Article in Orphanet journal of rare diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundInborn errors of metabolism (IEMs) are among the most clinically actionable groups of rare genetic diseases, yet therapeutic knowledge remains distributed across multiple databases, complicating consistent identification of treatable IEMs.
methodsIn this study, we performed a cross-database analysis to characterize the current therapeutic landscape of IEM genes using GeneReviews as a clinical reference. Gene symbols from GeneReviews, Metabolic Treatabolome, Treatable ID, and the Drug Database for Inborn Errors of Metabolism (DDIEM) were harmonized to HGNC nomenclature and mapped to the International Classification of Inherited Metabolic Disorders (ICIMD). IEM genes were categorized as associated with targeted or supportive treatment based on a GeneReviews-derived benchmark.
resultsAmong 656 ICIMD-defined IEM genes in GeneReviews, 155 (23.6%) were associated with targeted therapies and 501 (76.4%) with supportive treatment. Cross-database comparison demonstrated limited concordance, with only 36 genes associated with targeted therapy shared across Metabolic Treatabolome, Treatable ID, and DDIEM, reflecting heterogeneous representation of treatable IEMs across databases. Targeted therapies were more common in intermediary metabolic pathways, including fatty acid, tetrapyrrole, and vitamin/cofactor metabolism, whereas mitochondrial and complex cellular disorders were predominantly associated with supportive management. Moreover, 53% of the ICIMD-defined IEM genes remain unrepresented in structured databases.
conclusionsOur study defines a set of IEM genes for which targeted therapies are available and demonstrates that treatment information is largely fragmented across databases. This set of IEM genes may facilitate the interpretation of genomic findings and aid the prioritization of IEMs for genomic screening and clinical decision-making.
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