Mapping the Landscape of Treatable Inborn Errors of Metabolism: A Systematic Gene-Level Evaluation Based on the ICIMD Classification

Mapping the Landscape of Treatable Inborn Errors of Metabolism: A Systematic Gene-Level Evaluation Based on the ICIMD Classification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping the Landscape of Treatable Inborn Errors of Metabolism: A Systematic Gene-Level Evaluation Based on the ICIMD Classification Tabeer Fatima, Fatma Al-Jasmi, Hiba Alblooshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9355661/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Inborn 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. Methods: In 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. Results: Among 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 genes remain unrepresented in structured databases. Conclusions: Our 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. Inborn errors of metabolism Inherited Metabolic Disorders ICIMD Targeted therapy Treatable IEMs Rare diseases Metabolic Treatabolome Treatable ID DDIEM IEMbase Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Rare genetic diseases collectively impact millions globally, but only 5% have available treatment. 1–4 Among these diseases, Inborn Errors of Metabolism (IEMs) are one of the most clinically actionable groups, as treatment may directly modulate the underlying biochemical pathway rather than merely alleviating symptoms. Consequently, early diagnosis is crucial, as it has immediate implications for clinical management and is essential for improving clinical outcomes. For many IEMs, delayed treatment may result in preventable morbidity/mortality or irreversible complications. 5–12 Therefore, to consistently define IEMs, the International Classification of Inherited Metabolic Disorders (ICIMD) provides a mechanism-oriented classification that delineates disease within the IEM spectrum. 13 In parallel, distinguishing the nature of therapeutic interventions is essential for understanding the overall treatment landscape of these diseases. Within IEMs, therapeutic interventions differ substantially in clinical relevance. Some treatments target the underlying biocehemical defect and may alter the disease course, either by reducing toxic substrate accumulation, replacing deficient productss, replacing the missing enzyme. Strategies include dietary restriction, prevention of catabolism, alternative pathway therapy, dialysis, enzyme replacement, cofactor supplementation, substrate reduction therapy, chaperone therapy, and in selected case, stem cell transplantation, liver trapnsplantation or gene therapy, whereas others symptomatically address downstream manifestations. 14 To formalize this distinction, a recent GeneReviews-based study defined targeted therapy as an intervention that acts on the underlying molecular mechanism of a disease and has the potential to modify disease course, including curative outcomes in some instances. Such therapies may be recognized only after genetic diagnosis. This definition enables systematic differentiation between mechanism-based therapies and symptomatic management. 15 Despite the clinical importance of this distinction, treatment-related information remains distributed across multiple databases, making it difficult to consistently determine which IEMs have mechanism-based therapeutic options. As a result, the presence or absence of a targeted treatment for a given IEM may depend on the database consulted. This complicates the interpretation of genetic findings and the identification of treatable IEMs. Several structured databases catalog therapeutic information for IEMs, including Treatable ID, Metabolic Treatabolome, and the Drug Database for Inborn Errors of Metabolism (DDIEM). These databases compile treatment data using different scopes, curation strategies, and data structures. In addition to structural heterogeneity, the level of therapeutic evidence differs across databases, ranging from experimental or literature-reported interventions to clinically established treatments. A consistent reference grounded on established therapeutic practices is therefore required to interpret treatment information across databases. Furthermore, different databases capture overlapping but distinct subsets of IEMs. Integrating information across databases is therefore necessary to obtain a comprehensive view of treatment availability. 14,16–18 To address the need for a consistent clinical reference, GeneReviews was used as the anchor resource because its management recommendations primarily reflect established therapeutic practice. Based on the definition of targeted therapy above, IEMs with mechanism-based therapies were identified in GeneReviews and used to interpret treatment information in other databases. 15,19 We therefore mapped IEM treatment landscape across four databases, GeneReviews, Metabolic Treatabolome, Treatable ID, and DDIEM, using GeneReviews as the reference. Our work enabled systematic evaluation of treatment coverage and identification of gaps across databases. Methods An overview of the study workflow, including data integration, gene symbol harmonization, ICIMD-based IEM genes classification, and treatment categorization, is provided in Figure 1. The complete HGNC-harmonized gene lists and unified IEM gene sets are provided in Supplementary Tables 1–3. Gene Symbol Input from Databases Gene symbols were extracted from publicly available datasets and primary publications of Metabolic Treatabolome, Treatable ID, DDIEM, and GeneReviews. In addition, a previously published GeneReviews-curated targeted therapy list was obtained and used as a benchmark reference set for categorizing IEM genes by treatment. 14,15,17–19 All analyses were conducted at the gene-symbol level. HGNC Gene Harmonization All extracted gene symbols were harmonized to the current HUGO Gene Nomenclature Committee (HGNC) approved nomenclature (last accessed 24 September 2025). Historical symbols and aliases were mapped to their corresponding approved HGNC symbols. 20,21 Duplicate entries arising from cross-database overlap or symbol updates were removed. Following harmonization, the HGNC-approved gene list was generated, representing all genes from all databases. These harmonized lists were used for all downstream mapping processes. ICIMD-Based IEM Classification To restrict the analysis to Inborn Errors of Metabolism (IEMs), harmonized gene symbols from all databases were mapped to the ICIMD classification dataset. 13 Genes successfully mapped to ICIMD were defined as IEM genes. Genes not corresponding to ICIMD-defined IEMs were excluded from further analysis. The same filtering procedure was applied to the GeneReviews curated targeted therapy reference set to exclude entries other than IEMs. IEM Gene Set Consolidation A unified IEM gene set was generated by taking the union of all ICIMD-mapped genes across all databases. This unified IEM gene set served as the basis for the IEM gene landscape for treatment coverage analysis. Treatment coverage was identified by performing a set intersection between the unified IEM gene set and the GeneReviews curated targeted therapy reference set for IEM genes. Genes present in both sets were considered to have targeted therapy. Treatment Coverage Categorization Based on the GeneReviews curated targeted therapy list, IEM genes from all databases were categorized into two groups: IEM genes with targeted therapy coverage , defined as genes present in the GeneReviews curated targeted therapy reference set. IEM genes without targeted therapy coverage , operationally classified as supportive treatment genes, defined as ICIMD-mapped IEM genes not identified in the targeted therapy reference set. Coverage Assessment and Comparative Analysis Comparative analyses were performed across all databases, and identified IEM genes based on ICIMD, followed by treatment categorization using a targeted therapy reference set from GeneReviews. A unified IEM gene set was mapped across all 24 ICIMD disease categories. The final classified IEM gene dataset was used to evaluate database-level coverage of targeted therapy versus supportive treatment information. Statistical Analysis and Visualization All data processing, set operations, and visualizations were performed in Python using the pandas and matplotlib libraries. Results ICIMD-Based Filtering of IEM Genes Across All Databases Figure 2 summarizes the gene overlap among Metabolic Treatabolome, Treatable ID, DDIEM, GeneReviews, and ICIMD following gene symbol harmonization. The number of curated genes varied substantially across databases, ranging from 134 genes in Treatable ID and 266 in Metabolic Treatabolome to 287 in DDIEM and 1,893 in GeneReviews, while ICIMD contained 1,396 genes (Detailed gene lists are provided in Supplementary Table 1). Pairwise comparisons revealed varying degrees of agreement among databases. Metabolic Treatabolome shared 120 genes with Treatable ID and 110 genes with DDIEM and shared a larger set of genes with GeneReviews (185 genes). Notably, all 266 Metabolic Treatabolome genes were represented within ICIMD, indicating that this database is entirely confined to ICIMD-defined IEMs. Treatable ID exhibited a smaller but consistent overlap pattern, sharing 64 genes with DDIEM and 105 genes with GeneReviews, while its entire gene set (134 genes) was also contained within ICIMD. Similarly, a substantial proportion of DDIEM genes aligned with ICIMD (254 of 287 genes), whereas only 185 overlapped with GeneReviews. Furthermore, GeneReviews showed the broadest coverage with 656 genes overlapping ICIMD. IEMs accounted for a major proportion of the GeneReviews rare-disease genes, representing 34.7% of the total of 1,893 genes. Collectively, these results demonstrate heterogeneous coverage of ICIMD-defined IEM genes across databases. To further assess heterogeneity among IEM-focused databases, concordance of ICIMD-defined IEM genes across the Metabolic Treatabolome, Treatable ID, and DDIEM was examined (Fig. 3). Despite their shared objective of cataloging treatable metabolic disorders, the extent of shared gene representation varied across databases. Only 52 genes were common to all three databases, while many genes were unique to individual databases or shared only between two databases. This shows that the databases not only partially overlap but also include different sets of IEM genes. To facilitate systematic comparison despite this discordance, a standardized clinical reference framework based on curated management recommendations was required; therefore, the GeneReviews database was used as the primary source, as it consists of the largest set of IEM genes among all databases (Fig 2). GeneReviews-Based Categorization of IEM Genes Across IEM-focused Databases Among the 656 ICIMD-defined IEM genes identified in GeneReviews, 155 were associated with targeted treatment based on our reference set and the remaining 501 with supportive treatment, indicating that supportive management accounted for the majority of documented interventions within GeneReviews (Figure 4, Supplementary Table 3). Using this GeneReviews-based categorization, genes present in other databases were evaluated according to whether they corresponded to targeted treatment, supportive treatment, or were absent from GeneReviews. Metabolic Treatabolome contained 110 genes aligned with targeted treatment and 75 aligned with supportive treatment, while 81 genes in this database were not represented in GeneReviews. Treatable ID showed a similar but smaller distribution, with 63 targeted and 42 supportive genes, and 29 genes absent from GeneReviews. In contrast, DDIEM included 73 targeted-treatment genes and 93 supportive-treatment genes, alongside 88 genes not captured in GeneReviews. Overall, the three IEM-focused databases differed in how their gene content aligned with the GeneReviews as the primary source: Metabolic Treatabolome showed the highest number of IEM genes with targeted therapy, Treatable ID represented a more restricted subset, and DDIEM contained a comparatively larger proportion of IEM genes with supportive treatment as well as the greatest number of genes not covered by GeneReviews. Cross-Database Interpretation of Targeted and Supportive Treatments Following the GeneReviews-based categorization, we examined how IEM genes with targeted or supportive treatments were represented across the Metabolic Treatabolome, Treatable ID, and DDIEM (Figure 5). For targeted therapy, 36 genes were shared across all three databases. A further 50 genes were shared between two databases, including 27 shared between Metabolic Treatabolome and Treatable ID and 23 shared between Metabolic Treatabolome and DDIEM. Thirty-one genes were unique to GeneReviews, while 24 and 14 genes were unique to Metabolic Treatabolome and DDIEM, respectively. For supportive treatment, 9 genes were shared across all three databases. Forty-four genes were shared between two databases, including 19 shared between Metabolic Treatabolome and Treatable ID, 17 shared between Metabolic Treatabolome and DDIEM, and 8 shared between Treatable ID and DDIEM. GeneReviews additionally contained 369 supportive-treatment genes not captured in the other databases. Furthermore, certain genes were shared exclusively between GeneReviews and individual databases: 30 with Metabolic Treatabolome, 6 with Treatable ID, and 43 with DDIEM. Treatment Landscape Across ICIMD Categories The distribution of IEM genes with targeted therapy, supportive treatment, and those without documented management was evaluated across 24 ICIMD categories (Figure 6). Genes associated with targeted therapy were most frequent in disorders of fatty acid and ketone body metabolism (48%), tetrapyrrole metabolism (44%), vitamin and cofactor metabolism (40%), and miscellaneous intermediary metabolism (40%). Intermediate proportions (20–30%) were observed in amino acid metabolism (28%), trace element and metal disorders (26%), lipoprotein metabolism (24%), and carbohydrate metabolism (22%). Lower proportions (10–20%) were identified in complex molecule degradation (18%), energy substrate metabolism (16%), and peptide and amine metabolism (16%). Very low representation (<10%) occurred in neurotransmitter disorders (7%), endocrine metabolic disorders (7%), lipid metabolism (5%), nucleobase, nucleotide and nucleic acid metabolism (4%), organelle biogenesis and dynamics (4%), and mitochondrial cofactor biosynthesis (4%). Minimal representation was observed in mitochondrial gene expression (2%) and congenital disorders of glycosylation (1%), while no genes with targeted therapy were identified in mtDNA-related disorders, nuclear-encoded oxidative phosphorylation disorders, metabolite repair/proofreading disorders, or mitochondrial DNA maintenance and replication. IEM genes with supportive treatment were distributed across most ICIMD categories and commonly represented the predominant management approach when targeted therapies were limited. This pattern was particularly evident in disorders involving complex cellular and mitochondrial processes, where interventions were largely supportive rather than mechanism-directed. In several mitochondrial-related categories, including oxidative phosphorylation, metabolite repair/proofreading, and mitochondrial DNA maintenance, IEM genes were almost exclusively associated with supportive treatment when present. Discussion Our study evaluated treatment representation for inborn errors of metabolism (IEMs) across major IEM-focused databases and GeneReviews to identify substantial gene-level discordance among them. To enable reliable cross-database comparison, we first harmonized gene symbols to the current HGNC-approved nomenclature. Without this harmonization, comparisons across all databases could retrieve partial, duplicated, or misaligned gene entries due to previous symbols and aliases, which are known to hinder consistent data mining across biomedical resources. 22 Using the ICIMD classification as a mechanism-based framework , 656 of the 1,893 curated GeneReviews genes were identified as IEM genes, indicating that approximately one-third of GeneReviews entries correspond to metabolic disorders. Given that Metabolic Treatabolome and Treatable ID have already adopted the ICIMD classification, our mapping of DDIEM to ICIMD showed that 254 of 287 genes aligned with ICIMD-defined IEMs, demonstrating substantial overlap with the metabolic disease spectrum. After we have established a unified IEM gene set, treatment categorization analysis revealed that only 155 of 656 IEM genes in GeneReviews were associated with targeted therapies, whereas the majority (501 genes) were linked to supportive management. This imbalance indicates that most metabolic diseases are still managed through symptomatic or downstream interventions rather than by correcting the primary molecular defect. 23 Cross-database comparison demonstrated heterogeneous representation of IEM genes. Metabolic Treatabolome contained a higher proportion of IEM genes for which targeted therapies are available; Treatable ID represented a selective subset focused on IEMs with intellectual disability; and DDIEM included a broader range of genes for which supportive treatments are available. Only 36 genes with targeted therapies were shared across all databases, whereas many were unique to individual databases. Notably, 31 genes with targeted therapies were identified exclusively in GeneReviews and were absent from the IEM-focused databases, indicating that clinically actionable information may reside outside specialized metabolic knowledgebases. Conversely, 147 genes were present in IEM-focused databases but not captured in GeneReviews, many of which reflect conditions managed primarily through symptomatic or literature-reported interventions; detailed clinical evaluation of these cases was beyond the scope of the present analysis. Together, these observations emphasize fragmentation and incomplete coverage of therapeutic knowledge across IEM-focused databases. 18 To understand whether treatability reflects biological properties of metabolic pathways, treatment distribution was examined across ICIMD categories. Categories with the highest proportions of targeted therapies, fatty acid and ketone body metabolism, tetrapyrrole metabolism, and vitamin/cofactor metabolism correspond to disorders in which the biochemical defect can be directly modified by substrate restriction, metabolite supplementation, or enzyme replacement. This distribution indicates that diseases involving defined metabolic blocks are particularly amenable to mechanism-directed treatment strategies. 6,24 The scarcity of targeted therapies in mitochondrial and organelle-related categories suggests reduced pharmacologic tractability of disorders involving multi-component cellular systems. Rather than the accumulation of a single measurable metabolite, these conditions frequently involve impaired energy production or structural cellular dysfunction, and consequently, current treatment approaches remain predominantly supportive rather than disease-modifying. 25–27 Overall, this pattern indicates that therapeutic tractability is closely linked to pathway architecture: disorders of intermediary metabolism are more likely to be directly treatable, whereas diseases affecting complex cellular systems remain less amenable to targeted therapies, with emerging treatments currently limited to a small subset of conditions within this group. The treatment categorization used in this study was derived from the GeneReviews-based definition of targeted therapy and was applied as a comparative framework rather than a clinical classification. Individual interventions may reasonably be interpreted differently by experts, particularly when partial biochemical improvement does not translate into uniform clinical benefit. Moreover, in many IEMs, therapeutic response varies according to phenotype severity and clinical presentation: patients with severe disease may show limited response, whereas those diagnosed earlier or with milder phenotypes may benefit from the same intervention. This consideration applies to both targeted and supportive management; therefore, gene-level categorization reflects the availability of a therapeutic strategy rather than expected effectiveness in every patient. Supportive treatment was documented across nearly all ICIMD categories, yet a substantial proportion of IEM genes lacked documented management in IEM-focused databases; specifically, 53% of IEM genes were not represented in any database, including GeneReviews. This indicates that symptomatic care is frequently applied in clinical practice but is not consistently captured in structured databases, reflecting both limited disease-modifying options and incomplete representation of routine clinical management. 18 Evaluating treatment availability at the gene level aligns with emerging diagnostic and newborn-screening frameworks based on genomic testing. A population study from the Qatar Genome Program reported that approximately 1 in 13 individuals carries a pathogenic variant in a treatable IEM gene set derived from Treatable ID. 28 By providing a set of 155 IEM genes with targeted therapies, the present analysis expands the reference framework and may support carrier-burden estimation and prioritization of disorders for genomic screening strategies. Importantly, the study also serves as a practical guide: when information on targeted therapy is absent, supportive management strategies for a given IEM can often be identified across multiple IEM-focused databases. The observed discordance across databases highlights the practical necessity of integrating treatment knowledge to improve clinical actionability. This study has limitations. The analysis was gene-based and did not consider variant-specific or phenotype-dependent treatment responses. Evidence strength across databases was also not evaluated. Future work should incorporate graded levels of evidence and phenotype-specific treatment annotations to better reflect clinical decision-making. The observed fragmentation of therapeutic knowledge further highlights the need for harmonized annotation frameworks and scalable curation strategies. Automated approaches, including artificial-intelligence assisted text mining combined with expert review, may enable continuous updating of therapeutic knowledge and reduce discrepancies between clinical practice and structured databases. 14 By defining a unified set of IEM genes with targeted therapy, the present analysis provides a framework that may directly inform prioritization of disorders in genomic screening programs. Conclusion This study presents an ICIMD-guided, gene-level comparison of treatment representation across major databases for inborn errors of metabolism. Treatment information was inconsistently represented across databases. Although a subset of IEM genes was associated with targeted therapies, supportive treatment remained the predominant documented approach, and a substantial proportion of ICIMD-defined IEM genes were not represented in existing databases. The identified set of IEM genes with targeted therapies may assist genomic interpretation and prioritization of treatable disorders in diagnostic and screening contexts. Continued harmonization and systematic curation of therapeutic information will be important to improve the consistency and clinical utility of IEM-focused databases. Declarations Ethics approval and consent to participate Not applicable. Consent for publication All authors give consent for the publication of this article. Data Availability Statement The data that support the findings of this study are available in the supplementary material of this article. Competing Interests The authors have no competing interests to declare. Funding This work is funded by Khalifa University and the United Arab Emirates University (UAEU) under Award No. KU-UAEU-2023-068. Author Contributions TF designed the study, curated the data, performed the formal analysis, and contributed to methodology, visualization, writing, and editing. FAJ contributed to clinical interpretation, supervision, validation, and review and editing. HA contributed to study design, supervision, validation, writing, review, and editing. All authors reviewed and approved the final manuscript. Acknowledgements This work was supported by the College of Graduate Studies through a PhD fellowship awarded to TF. References The Lancet Global Health. The landscape for rare diseases in 2024. Lancet Glob Health . 2024;12(3):e341. doi:10.1016/S2214-109X(24)00056-1 Kruse J, Mueller R, Aghdassi AA, Lerch MM, Salloch S. Genetic Testing for Rare Diseases: A Systematic Review of Ethical Aspects. 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Curr Treat Options Neurol . 2014;16(6):292. doi:10.1007/s11940-014-0292-7 Khan NA, Govindaraj P, Meena AK, Thangaraj K. Mitochondrial disorders: Challenges in diagnosis & treatment. Indian J Med Res . 2015;141(1):13. doi:10.4103/0971-5916.154489 Gandhi G, Aliyev E, Syed N, Vempalli F, … CSG in, 2024 undefined. Mapping the genetic landscape of treatable inherited metabolic disorders in a large Middle Eastern biobank. Elsevier . Accessed December 8, 2025. https://www.sciencedirect.com/science/article/pii/S1098360024002028 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.xlsx Supplementary Table Legends Supplementary Table 1. HGNC-harmonized gene lists compiled from ICIMD, Treatable ID, Metabolic Treatabolome, DDIEM, and GeneReviews. Gene symbols were standardized according to HGNC-approved nomenclature to ensure consistency across datasets prior to cross-database comparison. Supplementary Table 2. ICIMD-defined IEM genes set absent from GeneReviews but identified in IEM-focused databases, including Metabolic Treatabolome, Treatable ID, and DDIEM. As GeneReviews served as the primary source for treatment information in this study, these genes were excluded from subsequent comparative analyses. Supplementary Table 3. Unified set of ICIMD-defined Inborn Errors of Metabolism (IEM) genes represented in GeneReviews (n = 565). Genes were classified based on treatment information reported in GeneReviews as targeted therapy (n = 155) or managed primarily with supportive treatment (n = 501). The table also indicates the presence of these genes across three other databases, including Metabolic Treatabolome, Treatable ID, and DDIEM, allowing assessment of concordance across resources. Based on this comparison, genes are classified as unique to GeneReviews, unique to other databases, or supported by consensus across two or three databases. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9355661","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641475373,"identity":"6d72cf42-ece5-4616-b1b4-de8142d5a7b4","order_by":0,"name":"Tabeer Fatima","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Tabeer","middleName":"","lastName":"Fatima","suffix":""},{"id":641475377,"identity":"d5fca6da-bc86-44b8-a61c-a62c363b7543","order_by":1,"name":"Fatma Al-Jasmi","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"","lastName":"Al-Jasmi","suffix":""},{"id":641475383,"identity":"53f52a56-d29d-40ea-9e28-246287eca513","order_by":2,"name":"Hiba Alblooshi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACA2YeEGWRwAckmRkMbIjWIpHABtGSRoQWBhQtDIcJazFn5z34gaFGIo+Nvfnw54KC84n97T0GDD9qGOT5cWixbOZLlmA4JlHMxnMsTXqGwe3EGWfOGDD2HGMwnNmAw2GHeQwkGNgkEtskcsyYeYBaGm7kGDDwNjAwbjiAU4vxD4Z/YC3Gn3kMziXOv//GgPFvA4P9ftxazCQY28BaDKR5DA4kbrjBY8AMtCVxAw6/GBzmS7NI7IP6hccg2XjjmbSCwzLHJJJn4LLl/NnDNz58s8njB4UYzx872XnHD298+KbGxrYfh/fBIAFdAGi+BB71o2AUjIJRMAoIAQC6ElRmrm7IJgAAAABJRU5ErkJggg==","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":true,"prefix":"","firstName":"Hiba","middleName":"","lastName":"Alblooshi","suffix":""}],"badges":[],"createdAt":"2026-04-08 10:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9355661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9355661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109480648,"identity":"d1f8ff7f-3faf-45bf-b650-9c3a901f176f","added_by":"auto","created_at":"2026-05-18 15:00:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":467141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic workflow for HGNC harmonization, ICIMD-based IEM genes classification, and treatment categorization.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/219e6fb143eef7ca4154cec2.jpeg"},{"id":109760731,"identity":"bd647906-f072-4513-ad73-78e1501cb715","added_by":"auto","created_at":"2026-05-22 07:29:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise overlap of genes across rare disease databases.\u003c/strong\u003e\u003cbr\u003e\nHeatmap showing the number of shared genes between Metabolic Treatabolome, Treatable ID, DDIEM, GeneReviews, and ICIMD. Diagonal cells indicate the total number of genes curated within each database, while off-diagonal cells represent pairwise gene overlap. Color intensity reflects the magnitude of shared gene counts, as indicated by the color scale. Gene symbols were harmonized prior to comparison to ensure consistency across databases.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/bcf68912a6d801b23af79295.jpeg"},{"id":109480650,"identity":"06fa5222-a3d0-4480-9cd3-fd8ecee8440a","added_by":"auto","created_at":"2026-05-18 15:00:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":266582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverlap of ICIMD-defined IEM genes across Metabolic Treatabolome, Treatable ID, and DDIEM after HGNC harmonization and ICIMD filtering, showing limited concordance among IEM-focused databases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/98ddf039b73f4e7a5b2a1c8d.jpeg"},{"id":109760474,"identity":"6f44fe41-95f5-4eac-95f9-84bc203a2f83","added_by":"auto","created_at":"2026-05-22 07:28:44","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169763,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneReviews-based treatment categorization across IEM-focused databases.\u003c/strong\u003e\u003cbr\u003e\nStacked bar chart showing the number of ICIMD-defined IEM genes categorized according to GeneReviews as having targeted treatment, supportive treatment, or not present in GeneReviews across Metabolic Treatabolome, Treatable ID, DDIEM, and GeneReviews (Detailed gene list is provided in Supplementary Table 2 and 3). The x-axis represents the databases and the y-axis represents gene counts. Numbers within bars indicate the number of genes in each category.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/7ac65fb5713917f076249a53.jpeg"},{"id":109480652,"identity":"ed4ac1ae-16fe-4b46-8751-4c663a19ce30","added_by":"auto","created_at":"2026-05-18 15:00:51","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCross-database distribution of IEM genes with targeted and supportive treatments.\u003c/strong\u003e\u003cbr\u003e\nBar plots show the number of IEM genes categorized according to GeneReviews as having targeted treatment (left panel) or supportive treatment (right panel). The x-axis represents gene groups shared across three databases, shared between pairs of databases, or unique to individual databases (Metabolic Treatabolome, Treatable ID, DDIEM, and GeneReviews), and the y-axis represents gene counts. Numbers above bars indicate the number of genes. *Targeted therapy assigned based on the published GeneReviews-derived list; **Supportive treatment assigned to remaining GeneReviews IEM genes.\u003c/p\u003e","description":"","filename":"floatimage4Copy.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/bc8300f7f0c54b8ac8545ccf.jpeg"},{"id":109759965,"identity":"3bf8450c-629a-4380-9149-6b5c99c9a3bc","added_by":"auto","created_at":"2026-05-22 07:28:00","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":826664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment distribution for IEM genes across ICIMD categories.\u003c/strong\u003e\u003cbr\u003e\nStacked horizontal bars show the proportions of genes in each ICIMD category that are distributed as IEM genes with targeted treatment, supportive treatment, or with no treatment information across the surveyed databases. The x-axis represents the percentage of genes, and the y-axis lists 24 ICIMD categories. Percent labels indicate the fraction of genes with targeted treatment within each category, and the numbers on the right indicate counts of targeted/supportive/absent genes, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/9e0bee8f7a619a21d401ff1d.jpeg"},{"id":109765148,"identity":"fc96f5a0-7a80-4981-a76b-d0b60840cc28","added_by":"auto","created_at":"2026-05-22 07:39:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2401342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/98aca0a8-40f0-4996-9678-2f1f557889b9.pdf"},{"id":109480649,"identity":"9c017fa3-6a9a-4df7-8ddb-235239c19e98","added_by":"auto","created_at":"2026-05-18 15:00:51","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":81262,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table Legends\u003c/p\u003e\n\u003cp\u003eSupplementary Table 1. HGNC-harmonized gene lists compiled from ICIMD, Treatable ID, Metabolic Treatabolome, DDIEM, and GeneReviews. Gene symbols were standardized according to HGNC-approved nomenclature to ensure consistency across datasets prior to cross-database comparison.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2. ICIMD-defined IEM genes set absent from GeneReviews but identified in IEM-focused databases, including Metabolic Treatabolome, Treatable ID, and DDIEM. As GeneReviews served as the primary source for treatment information in this study, these genes were excluded from subsequent comparative analyses.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 3. Unified set of ICIMD-defined Inborn Errors of Metabolism (IEM) genes represented in GeneReviews (n = 565). Genes were classified based on treatment information reported in GeneReviews as targeted therapy (n = 155) or managed primarily with supportive treatment (n = 501). The table also indicates the presence of these genes across three other databases, including Metabolic Treatabolome, Treatable ID, and DDIEM, allowing assessment of concordance across resources. Based on this comparison, genes are classified as unique to GeneReviews, unique to other databases, or supported by consensus across two or three databases.\u003c/p\u003e","description":"","filename":"SupplementaryInformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9355661/v1/edb1453145bb15ada31e26cd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping the Landscape of Treatable Inborn Errors of Metabolism: A Systematic Gene-Level Evaluation Based on the ICIMD Classification","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRare genetic diseases collectively impact millions globally, but only 5% have available treatment.\u0026nbsp;\u003csup\u003e1\u0026ndash;4\u003c/sup\u003e Among these diseases, Inborn Errors of Metabolism (IEMs) are one of the most clinically actionable groups, as treatment may directly modulate the underlying biochemical pathway rather than merely alleviating symptoms.\u0026nbsp;Consequently, early diagnosis is crucial, as it has immediate implications for clinical management and is essential for improving clinical outcomes. For many IEMs, delayed treatment may result in preventable morbidity/mortality or irreversible complications.\u0026nbsp;\u003csup\u003e5\u0026ndash;12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTherefore, to consistently define IEMs, the International Classification of Inherited Metabolic Disorders (ICIMD) provides a mechanism-oriented classification that delineates disease within the IEM spectrum.\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e In parallel, distinguishing the nature of therapeutic interventions is essential for understanding the overall treatment landscape of these diseases. Within IEMs, therapeutic interventions differ substantially in clinical relevance. Some treatments target the underlying biocehemical defect and may alter the disease course, either by reducing toxic substrate accumulation, replacing deficient productss, replacing the missing enzyme. Strategies include dietary restriction, prevention of catabolism, alternative pathway therapy, dialysis, enzyme replacement, cofactor supplementation, substrate reduction therapy, chaperone therapy, and in selected case, stem cell transplantation, liver trapnsplantation or gene therapy, whereas others symptomatically address downstream manifestations.\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTo formalize this distinction, a recent GeneReviews-based study defined targeted therapy as an intervention that acts on the underlying molecular mechanism of a disease and has the potential to modify disease course, including curative outcomes in some instances. Such therapies may be recognized only after genetic diagnosis.\u0026nbsp;This definition enables systematic differentiation between mechanism-based therapies and symptomatic management.\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eDespite the clinical importance of this distinction, treatment-related information remains distributed across multiple databases, making it difficult to consistently determine which IEMs have mechanism-based therapeutic options. \u0026nbsp;As a result, the presence or absence of a targeted treatment for a given IEM may depend on the database consulted. This complicates the interpretation of genetic findings and the identification of treatable IEMs.\u0026nbsp;Several structured databases catalog therapeutic information for IEMs, including Treatable ID, Metabolic Treatabolome, and the Drug Database for Inborn Errors of Metabolism (DDIEM). These databases compile treatment data using different scopes, curation strategies, and data structures. In addition to structural heterogeneity, the level of therapeutic evidence differs across databases, ranging from experimental or literature-reported interventions to clinically established treatments. A consistent reference grounded on established therapeutic practices is therefore required to interpret treatment information across databases.\u0026nbsp;Furthermore, different databases capture overlapping but distinct subsets of IEMs.\u0026nbsp;Integrating information across databases is therefore necessary to obtain a comprehensive view of treatment availability.\u0026nbsp;\u003csup\u003e14,16\u0026ndash;18\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address the need for a consistent clinical reference, GeneReviews was used as the anchor resource because its management recommendations primarily reflect established therapeutic practice. Based on the definition of targeted therapy above, IEMs with mechanism-based therapies were identified in GeneReviews and used to interpret treatment information in other databases.\u0026nbsp;\u003csup\u003e15,19\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe therefore mapped IEM treatment landscape across four databases, GeneReviews, Metabolic Treatabolome, Treatable ID, and DDIEM, using GeneReviews as the reference. \u0026nbsp;Our work enabled systematic evaluation of treatment coverage and identification of gaps across databases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAn overview of the study workflow, including data integration, gene symbol harmonization, ICIMD-based IEM genes classification, and treatment categorization, is provided in \u003cstrong\u003eFigure 1.\u0026nbsp;\u003c/strong\u003eThe complete HGNC-harmonized gene lists and unified IEM gene sets are provided in Supplementary Tables 1\u0026ndash;3.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGene Symbol Input from Databases\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGene symbols were extracted from publicly available datasets and primary publications of Metabolic Treatabolome, Treatable ID, DDIEM, and GeneReviews. In addition, a previously published GeneReviews-curated targeted therapy list was obtained and used as a benchmark reference set for categorizing IEM genes by treatment.\u0026nbsp;\u003csup\u003e14,15,17\u0026ndash;19\u003c/sup\u003e All analyses were conducted at the gene-symbol level.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eHGNC Gene Harmonization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll extracted gene symbols were harmonized to the current HUGO Gene Nomenclature Committee (HGNC) approved nomenclature (last accessed 24 September 2025). Historical symbols and aliases were mapped to their corresponding approved HGNC symbols.\u0026nbsp;\u003csup\u003e20,21\u003c/sup\u003e Duplicate entries arising from cross-database overlap or symbol updates were removed.\u0026nbsp;Following harmonization, the HGNC-approved gene list was generated, representing all genes from all databases.\u0026nbsp;These harmonized lists were used for all downstream mapping processes.\u0026nbsp;\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eICIMD-Based IEM Classification\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo restrict the analysis to Inborn Errors of Metabolism (IEMs), harmonized gene symbols from all databases were mapped to the ICIMD classification dataset.\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e Genes successfully mapped to ICIMD were defined as IEM genes. Genes not corresponding to ICIMD-defined IEMs were excluded from further analysis. The same filtering procedure was applied to the GeneReviews curated targeted therapy reference set to exclude entries other than IEMs.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eIEM Gene Set Consolidation\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA unified IEM gene set was generated by taking the union of all ICIMD-mapped genes across all databases. This unified IEM gene set served as the basis for the IEM gene landscape for treatment coverage analysis. Treatment coverage was identified by performing a set intersection between the unified IEM gene set and the GeneReviews curated targeted therapy reference set for IEM genes. Genes present in both sets were considered to have targeted therapy.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003eTreatment Coverage Categorization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBased on the GeneReviews curated targeted therapy list, IEM genes from all databases were categorized into two groups:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eIEM genes with targeted therapy coverage\u003c/strong\u003e, defined as genes present in the GeneReviews curated targeted therapy reference set.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIEM genes without targeted therapy coverage\u003c/strong\u003e, operationally classified as supportive treatment genes, defined as ICIMD-mapped IEM genes not identified in the targeted therapy reference set.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"6\"\u003e\n \u003cli\u003e\u003cstrong\u003eCoverage Assessment and Comparative Analysis\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eComparative analyses were performed across all databases, and identified IEM genes based on ICIMD, followed by treatment categorization using a targeted therapy reference set from GeneReviews. A unified IEM gene set was mapped across all 24 ICIMD disease categories. The final classified IEM gene dataset was used to evaluate database-level coverage of targeted therapy versus supportive treatment information.\u0026nbsp;\u003c/p\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003e\u003cstrong\u003eStatistical Analysis and Visualization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll data processing, set operations, and visualizations were performed in Python using the pandas and matplotlib libraries.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eICIMD-Based Filtering of IEM Genes Across All Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 summarizes the gene overlap among Metabolic Treatabolome, Treatable ID, DDIEM, GeneReviews, and ICIMD following gene symbol harmonization. The number of curated genes varied substantially across databases, ranging from 134 genes in Treatable ID and 266 in Metabolic Treatabolome to 287 in DDIEM and 1,893 in GeneReviews, while ICIMD contained 1,396 genes (Detailed gene lists are provided in Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003ePairwise comparisons revealed varying degrees of agreement among databases. Metabolic Treatabolome shared 120 genes with Treatable ID and 110 genes with DDIEM and shared a larger set of genes with GeneReviews (185 genes). Notably, all 266 Metabolic Treatabolome genes were represented within ICIMD, indicating that this database is entirely confined to ICIMD-defined IEMs.\u003c/p\u003e\n\u003cp\u003eTreatable ID exhibited a smaller but consistent overlap pattern, sharing 64 genes with DDIEM and 105 genes with GeneReviews, while its entire gene set (134 genes) was also contained within ICIMD. Similarly, a substantial proportion of DDIEM genes aligned with ICIMD (254 of 287 genes), whereas only 185 overlapped with GeneReviews. Furthermore, GeneReviews showed the broadest coverage with 656 genes overlapping ICIMD. IEMs accounted for a major proportion of the GeneReviews rare-disease genes, representing 34.7% of the total of 1,893 genes. Collectively, these results demonstrate heterogeneous coverage of ICIMD-defined IEM genes across databases.\u003c/p\u003e\n\u003cp\u003eTo further assess heterogeneity among IEM-focused databases, concordance of ICIMD-defined IEM genes across the Metabolic Treatabolome, Treatable ID, and DDIEM was examined (Fig. 3). Despite their shared objective of cataloging treatable metabolic disorders, the extent of shared gene representation varied across databases. Only 52 genes were common to all three databases, while many genes were unique to individual databases or shared only between two databases. This shows that the databases not only partially overlap but also include different sets of IEM genes. To facilitate systematic comparison despite this discordance, a standardized clinical reference framework based on curated management recommendations was required; therefore, the GeneReviews database was used as the primary source, as it consists of the largest set of IEM genes among all databases (Fig 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneReviews-Based Categorization of IEM Genes Across IEM-focused Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 656 ICIMD-defined IEM genes identified in GeneReviews, 155 were associated with targeted treatment based on our reference set and the remaining 501 with supportive treatment, indicating that supportive management accounted for the majority of documented interventions within GeneReviews (Figure 4, Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003eUsing this GeneReviews-based categorization, genes present in other databases were evaluated according to whether they corresponded to targeted treatment, supportive treatment, or were absent from GeneReviews. Metabolic Treatabolome contained 110 genes aligned with targeted treatment and 75 aligned with supportive treatment, while 81 genes in this database were not represented in GeneReviews. Treatable ID showed a similar but smaller distribution, with 63 targeted and 42 supportive genes, and 29 genes absent from GeneReviews. In contrast, DDIEM included 73 targeted-treatment genes and 93 supportive-treatment genes, alongside 88 genes not captured in GeneReviews.\u003c/p\u003e\n\u003cp\u003eOverall, the three IEM-focused databases differed in how their gene content aligned with the GeneReviews as the primary source: Metabolic Treatabolome showed the highest number of IEM genes with targeted therapy, Treatable ID represented a more restricted subset, and DDIEM contained a comparatively larger proportion of IEM genes with supportive treatment as well as the greatest number of genes not covered by GeneReviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-Database Interpretation of Targeted and Supportive Treatments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the GeneReviews-based categorization, we examined how IEM genes with targeted or supportive treatments were represented across the Metabolic Treatabolome, Treatable ID, and DDIEM\u0026nbsp;(Figure 5).\u003c/p\u003e\n\u003cp\u003eFor targeted therapy, 36 genes were shared across all three databases. A further 50 genes were shared between two databases, including 27 shared between Metabolic Treatabolome and Treatable ID and 23 shared between Metabolic Treatabolome and DDIEM. Thirty-one genes were unique to GeneReviews, while 24 and 14 genes were unique to Metabolic Treatabolome and DDIEM, respectively.\u003c/p\u003e\n\u003cp\u003eFor supportive treatment, 9 genes were shared across all three databases. Forty-four genes were shared between two databases, including 19 shared between Metabolic Treatabolome and Treatable ID, 17 shared between Metabolic Treatabolome and DDIEM, and 8 shared between Treatable ID and DDIEM. GeneReviews additionally contained 369 supportive-treatment genes not captured in the other databases.\u0026nbsp;Furthermore, certain genes were shared exclusively between GeneReviews and individual databases: 30 with Metabolic Treatabolome, 6 with Treatable ID, and 43 with DDIEM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment Landscape Across ICIMD Categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of IEM genes with targeted therapy, supportive treatment, and those without documented management was evaluated across 24 ICIMD categories (Figure 6).\u003c/p\u003e\n\u003cp\u003eGenes associated with targeted therapy were most frequent in disorders of fatty acid and ketone body metabolism (48%), tetrapyrrole metabolism (44%), vitamin and cofactor metabolism (40%), and miscellaneous intermediary metabolism (40%). Intermediate proportions (20\u0026ndash;30%) were observed in amino acid metabolism (28%), trace element and metal disorders (26%), lipoprotein metabolism (24%), and carbohydrate metabolism (22%). Lower proportions (10\u0026ndash;20%) were identified in complex molecule degradation (18%), energy substrate metabolism (16%), and peptide and amine metabolism (16%). Very low representation (\u0026lt;10%) occurred in neurotransmitter disorders (7%), endocrine metabolic disorders (7%), lipid metabolism (5%), nucleobase, nucleotide and nucleic acid metabolism (4%), organelle biogenesis and dynamics (4%), and mitochondrial cofactor biosynthesis (4%). Minimal representation was observed in mitochondrial gene expression (2%) and congenital disorders of glycosylation (1%), while no genes with targeted therapy were identified in mtDNA-related disorders, nuclear-encoded oxidative phosphorylation disorders, metabolite repair/proofreading disorders, or mitochondrial DNA maintenance and replication.\u003c/p\u003e\n\u003cp\u003eIEM genes with supportive treatment were distributed across most ICIMD categories and commonly represented the predominant management approach when targeted therapies were limited. This pattern was particularly evident in disorders involving complex cellular and mitochondrial processes, where interventions were largely supportive rather than mechanism-directed. In several mitochondrial-related categories, including oxidative phosphorylation, metabolite repair/proofreading, and mitochondrial DNA maintenance, IEM genes were almost exclusively associated with supportive treatment when present.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study evaluated treatment representation for inborn errors of metabolism (IEMs) across major IEM-focused databases and GeneReviews to identify substantial gene-level discordance among them. To enable reliable cross-database comparison, we first harmonized gene symbols to the current HGNC-approved nomenclature.\u0026nbsp;Without this harmonization, comparisons across\u0026nbsp;all\u0026nbsp;databases could retrieve partial, duplicated, or misaligned gene entries due to previous symbols and aliases, which are known to hinder consistent data mining across biomedical resources.\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eUsing the ICIMD classification as a\u0026nbsp;\u003cstrong\u003emechanism-based framework\u003c/strong\u003e, 656 of the 1,893 curated GeneReviews genes were identified as IEM genes, indicating that approximately one-third of GeneReviews entries correspond to metabolic disorders.\u0026nbsp;Given that Metabolic Treatabolome and Treatable ID have already adopted the ICIMD classification, our mapping\u0026nbsp;of DDIEM to ICIMD showed that 254 of 287 genes aligned with ICIMD-defined IEMs, demonstrating substantial overlap with the metabolic disease spectrum.\u003c/p\u003e\n\u003cp\u003eAfter we have established a unified IEM gene set, treatment categorization analysis revealed that only 155 of 656 IEM genes in GeneReviews were associated with targeted therapies, whereas the majority (501 genes) were linked to supportive management. This imbalance indicates that most metabolic diseases are still managed through symptomatic or downstream interventions rather than by correcting the primary molecular defect.\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCross-database comparison demonstrated heterogeneous representation of IEM genes. Metabolic Treatabolome contained a higher proportion of IEM genes for which targeted therapies are available; Treatable ID represented a selective subset focused on IEMs with intellectual disability; and DDIEM included a broader range of genes for which supportive treatments are available. Only 36 genes with targeted therapies were shared across all databases, whereas many were unique to individual databases. Notably, 31 genes with targeted therapies were identified exclusively in GeneReviews and were absent from the IEM-focused databases, indicating that clinically actionable information may reside outside specialized metabolic knowledgebases. Conversely, 147 genes were present in IEM-focused databases but not captured in GeneReviews, many of which reflect conditions managed primarily through symptomatic or literature-reported interventions; detailed clinical evaluation of these cases was beyond the scope of the present analysis. Together, these observations emphasize fragmentation and incomplete coverage of therapeutic knowledge across IEM-focused databases.\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo understand whether treatability reflects biological properties of metabolic pathways, treatment distribution was examined across ICIMD categories. Categories with the highest proportions of targeted therapies, fatty acid and ketone body metabolism, tetrapyrrole metabolism, and vitamin/cofactor metabolism correspond to disorders in which the biochemical defect can be directly modified by substrate restriction, metabolite supplementation, or enzyme replacement. This distribution indicates that diseases involving defined metabolic blocks are particularly amenable to mechanism-directed treatment strategies.\u0026nbsp;\u003csup\u003e6,24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe scarcity of targeted therapies in mitochondrial and organelle-related categories suggests reduced pharmacologic tractability of disorders involving multi-component cellular systems. Rather than the accumulation of a single measurable metabolite, these conditions frequently involve impaired energy production or structural cellular dysfunction, and consequently, current treatment approaches remain predominantly supportive rather than disease-modifying.\u0026nbsp;\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOverall, this pattern indicates that therapeutic tractability is closely linked to pathway architecture: disorders of intermediary metabolism are more likely to be directly treatable, whereas diseases affecting complex cellular systems remain less amenable to targeted therapies, with emerging treatments currently limited to a small subset of conditions within this group.\u003c/p\u003e\n\u003cp\u003eThe treatment categorization used in this study was derived from the GeneReviews-based definition of targeted therapy and was applied as a comparative framework rather than a clinical classification. Individual interventions may reasonably be interpreted differently by experts, particularly when partial biochemical improvement does not translate into uniform clinical benefit. Moreover, in many IEMs, therapeutic response varies according to phenotype severity and clinical presentation: patients with severe disease may show limited response, whereas those diagnosed earlier or with milder phenotypes may benefit from the same intervention. This consideration applies to both targeted and supportive management; therefore, gene-level categorization reflects the availability of a therapeutic strategy rather than expected effectiveness in every patient.\u003c/p\u003e\n\u003cp\u003eSupportive treatment was documented across nearly all ICIMD categories, yet a substantial proportion of IEM genes lacked documented management in IEM-focused databases; specifically, 53% of IEM genes were not represented in any database, including GeneReviews. This indicates that symptomatic care is frequently applied in clinical practice but is not consistently captured in structured databases, reflecting both limited disease-modifying options and incomplete representation of routine clinical management.\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eEvaluating treatment availability at the gene level aligns with emerging diagnostic and newborn-screening frameworks based on genomic testing. A population study from the Qatar Genome Program reported that approximately 1 in 13 individuals carries a pathogenic variant in a treatable IEM gene set derived from Treatable ID.\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e By providing a set of 155 IEM genes with targeted therapies, the present analysis expands the reference framework and may support carrier-burden estimation and prioritization of disorders for genomic screening strategies.\u003c/p\u003e\n\u003cp\u003eImportantly, the study also serves as a practical guide: when information on targeted therapy is absent, supportive management strategies for a given IEM can often be identified across multiple IEM-focused databases. The observed discordance across databases highlights the practical necessity of integrating treatment knowledge to improve clinical actionability.\u003c/p\u003e\n\u003cp\u003eThis study has limitations. The analysis was gene-based and did not consider variant-specific or phenotype-dependent treatment responses. Evidence strength across databases was also not evaluated. Future work should incorporate graded levels of evidence and phenotype-specific treatment annotations to better reflect clinical decision-making.\u003c/p\u003e\n\u003cp\u003eThe observed fragmentation of therapeutic knowledge further highlights the need for harmonized annotation frameworks and scalable curation strategies. Automated approaches, including artificial-intelligence assisted text mining combined with expert review, may enable continuous updating of therapeutic knowledge and reduce discrepancies between clinical practice and structured databases.\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e By defining a unified set of IEM genes with targeted therapy, the present analysis provides a framework that may directly inform prioritization of disorders in genomic screening programs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents an ICIMD-guided, gene-level comparison of treatment representation across major databases for inborn errors of metabolism. Treatment information was inconsistently represented across databases. Although a subset of IEM genes was associated with targeted therapies, supportive treatment remained the predominant documented approach, and a substantial proportion of ICIMD-defined IEM genes were not represented in existing databases.\u003c/p\u003e\n\u003cp\u003eThe identified set of IEM genes with targeted therapies may assist genomic interpretation and prioritization of treatable disorders in diagnostic and screening contexts. Continued harmonization and systematic curation of therapeutic information will be important to improve the consistency and clinical utility of IEM-focused databases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors give consent for the publication of this article.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in the supplementary material of this article.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work is funded by Khalifa University and the United Arab Emirates University (UAEU) under Award No. KU-UAEU-2023-068.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eTF designed the study, curated the data, performed the formal analysis, and contributed to methodology, visualization, writing, and editing. FAJ contributed to clinical interpretation, supervision, validation, and review and editing. HA contributed to study design, supervision, validation, writing, review, and editing. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis work was supported by the College of Graduate Studies through a PhD fellowship awarded to TF.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThe Lancet Global Health. The landscape for rare diseases in 2024. \u003cem\u003eLancet Glob Health\u003c/em\u003e. 2024;12(3):e341. doi:10.1016/S2214-109X(24)00056-1\u003c/li\u003e\n\u003cli\u003eKruse J, Mueller R, Aghdassi AA, Lerch MM, Salloch S. Genetic Testing for Rare Diseases: A Systematic Review of Ethical Aspects. \u003cem\u003eFront Genet\u003c/em\u003e. 2022;12:701988. doi:10.3389/fgene.2021.701988\u003c/li\u003e\n\u003cli\u003eRARE Disease Facts - Global Genes. 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Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app. \u003cem\u003eOrphanet J Rare Dis\u003c/em\u003e. \u003cem\u003eBioMed Central Ltd\u003c/em\u003e. 2021;16(1). doi:10.1186/s13023-021-01727-2\u003c/li\u003e\n\u003cli\u003eAbdelhakim M, Abdelhakim M, McMurray E, et al. DDIEM: Drug database for inborn errors of metabolism. \u003cem\u003eOrphanet J Rare Dis\u003c/em\u003e. 2020;15(1). doi:10.1186/s13023-020-01428-2\u003c/li\u003e\n\u003cli\u003eR\u0026eacute;dei GP. GeneReviews. In: \u003cem\u003eEncyclopedia of Genetics, Genomics, Proteomics and Informatics\u003c/em\u003e. 2008. doi:10.1007/978-1-4020-6754-9_6624\u003c/li\u003e\n\u003cli\u003eHome | HUGO Gene Nomenclature Committee. Accessed February 18, 2026. https://www.genenames.org/\u003c/li\u003e\n\u003cli\u003eSeal RL, Braschi B, Gray K, et al. Genenames.org: the HGNC resources in 2023. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2023;51(D1). doi:10.1093/nar/gkac888\u003c/li\u003e\n\u003cli\u003eBruford EA, Braschi B, Haim-Vilmovsky L, Jones TEM, Seal RL, Tweedie S. The importance of being the HGNC. \u003cem\u003eHum Genomics\u003c/em\u003e. 2022;16(1). doi:10.1186/s40246-022-00432-w\u003c/li\u003e\n\u003cli\u003eFerreira CR. The burden of rare diseases. \u003cem\u003eAm J Med Genet A\u003c/em\u003e. 2019;179(6):885-892. doi:10.1002/AJMG.A.61124;JOURNAL:JOURNAL:10968628;PAGE:STRING:ARTICLE/CHAPTER\u003c/li\u003e\n\u003cli\u003eStockler-Ipsiroglu S, Potter BK, Yuskiv N, Tingley K, Patterson M, van Karnebeek C. Developments in evidence creation for treatments of inborn errors of metabolism. \u003cem\u003eJ Inherit Metab Dis\u003c/em\u003e. 2021;44(1):88-98. doi:10.1002/jimd.12315\u003c/li\u003e\n\u003cli\u003eKanabus M, Heales SJ, Rahman S. 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Accessed December 8, 2025. https://www.sciencedirect.com/science/article/pii/S1098360024002028 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Inborn errors of metabolism, Inherited Metabolic Disorders, ICIMD, Targeted therapy, Treatable IEMs, Rare diseases, Metabolic Treatabolome, Treatable ID, DDIEM, IEMbase","lastPublishedDoi":"10.21203/rs.3.rs-9355661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9355661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Inborn 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 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 genes remain unrepresented in structured databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our 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.\u003c/p\u003e","manuscriptTitle":"Mapping the Landscape of Treatable Inborn Errors of Metabolism: A Systematic Gene-Level Evaluation Based on the ICIMD Classification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 15:00:43","doi":"10.21203/rs.3.rs-9355661/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32527315901467717969861742085592960901","date":"2026-05-10T10:32:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T09:07:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T10:30:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-10T10:29:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2026-04-08T10:08:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d7ab684-885d-448a-a3ce-5d5e4d522c15","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:58:59+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"32527315901467717969861742085592960901","date":"2026-05-10T10:32:49+00:00","index":33,"fulltext":""},{"type":"reviewersInvited","content":"20","date":"2026-05-07T09:07:51+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T15:00:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 15:00:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9355661","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9355661","identity":"rs-9355661","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}