[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120249-en":3,"doc-seo-120249-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120249,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles","Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism that often presents in early infancy with fasting hypoketotic hypoglycemia and hepatomegaly. Diagnosis depends on multiple biomarkers followed by genetic confirmation, yet a specific reliable biomarker has been lacking. By leveraging altered lipid metabolism and mitochondrial fatty acid oxidation, a gradient-boosted-tree machine learning approach was developed to identify GSD Ia from plasma acylcarnitine profiles despite class imbalance.","University of Groningen  \nA machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles  \nGroen, Joost; de Haan, Bas M; Overduin, Ruben J; Haijer-Schreuder, Andrea B; Derks, Terry  \nGj; Heiner-Fokkema, M Rebecca Published in:  \nOrphanet journal of rare diseases  \nDOI:  \n10.1186/s13023-025-03537-2  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nGroen, J. , de Haan, B. M. , Overduin, R. J. , Haijer-Schreuder, A. B. , Derks, T. G. , & Heiner-Fokkema, M. R.(2025) . A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles. Orphanet journal of rare diseases, 20, Article 15.  \n[https://doi.org/10.1186/s13023-025-03537-2](https://doi.org/10.1186/s13023-025-03537-2)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nGroen etal. Orphanet Journal of Rare Diseases (2025) 20:15  \n[https://doi.org/10.1186/s13023-025-03537-2](https://doi.org/10.1186/s13023-025-03537-2)  \nOrphanet Journal of Rare Diseases  \nRESEARCH Open Access  \nA machine learning model accurately  identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles  \nJoost Groen 1*, Bas M. de Haan2, Ruben J. Overduin3, Andrea B. Haijer-Schreuder3, Terry GJ Derks3 and  \nM. Rebecca Heiner-Fokkema 1  \nAbstract  \nBackground Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on a combination of different biomarkers, mostly routine clinical chemical markers and subsequent genetic confirmation. However, a specific and reliable biomarker is lacking. As GSD Ia patients demonstrate altered lipid metabolism and mitochondrial fatty acid oxidation, we built a machine learning model to identify GSD Ia patients based on plasma acylcarnitine profiles.  \nMethods We collected plasma acylcarnitine profiles from 3958 patients, of whom 31 have GSD Ia. Synthetic samples were generated to address the problem of class imbalance in the dataset. We built several machine learning models based on gradient-boosted trees. Our approach included hyperparameter tuning and feature selection and generalization was checked using both nested cross-validation and a held-out test set.  \nResults The binary classifier was able to correctly identify 5/6 GSD Ia patients in a held-out test set ","cbCaiptHCYU2oya0","https://ap.wps.com/l/cbCaiptHCYU2oya0","pdf",2589142,1,11,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What is the main diagnostic problem addressed for GSD Ia?\",\"answer\":\"GSD Ia diagnosis relies on multiple biomarkers and genetic confirmation, but no specific and reliable biomarker is available, motivating the search for an accurate alternative approach.\"},{\"question\":\"How was the machine learning model built and validated?\",\"answer\":\"Plasma acylcarnitine profiles from 3958 patients (31 with GSD Ia) were used, synthetic samples addressed class imbalance, and gradient-boosted-tree models were tuned. Performance was assessed with nested cross-validation and a held-out test set.\"},{\"question\":\"What biomarkers/features were most predictive in the best model?\",\"answer\":\"Strong predictors included C16-carnitine, C14OH-carnitine, total carnitine, and acetylcarnitine, enabling high sensitivity and specificity with interpretable selected parameters.\"}]","A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles | PDF",1785728993,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-model-accurately-identifies-glycogen-storage-disease-ia-patients-based-on-plasma-acylcarnitine-profiles","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-model-accurately-identifies-glycogen-storage-disease-ia-patients-based-on-plasma-acylcarnitine-profiles/120249/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main diagnostic problem addressed for GSD Ia?","Question",{"text":75,"@type":76},"GSD Ia diagnosis relies on multiple biomarkers and genetic confirmation, but no specific and reliable biomarker is available, motivating the search for an accurate alternative approach.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model built and validated?",{"text":80,"@type":76},"Plasma acylcarnitine profiles from 3958 patients (31 with GSD Ia) were used, synthetic samples addressed class imbalance, and gradient-boosted-tree models were tuned. Performance was assessed with nested cross-validation and a held-out test set.",{"name":82,"@type":73,"acceptedAnswer":83},"What biomarkers/features were most predictive in the best model?",{"text":84,"@type":76},"Strong predictors included C16-carnitine, C14OH-carnitine, total carnitine, and acetylcarnitine, enabling high sensitivity and specificity with interpretable selected parameters.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]