[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127762-en":3,"doc-seo-127762-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127762,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","An omics-based machine learning approach to predict diabetes progression - a RHAPSODY study","Type 2 diabetes shows heterogeneous progression, with some individuals initiating insulin sooner than others. Although clinical biomarkers such as age, HbA1c, and diabetes duration relate to glycaemic progression, their ability to predict insulin initiation or insulin requirement remains limited, and the added value of newly identified molecular markers is uncertain. Using two prospective cohorts within IMI-RHAPSODY, machine-learning models were trained and validated to predict time to insulin requirement from clinical variables and metabolite, lipid, and protein markers.","Diabetologia (2024) 67:885–894  \n[https://doi.org/10.1007/s00125-024-06105-8](https://doi.org/10.1007/s00125-024-06105-8)  \nAn omics‑based machine learning approach to predict diabetes progression: a RHAPSODY study  \nRoderick C. Slieker1,2,3,4 · Magnus Münch1 · Louise A. Donnelly5 · Gerard A. Bouland4,6 · Iulian Dragan7 · Dmitry Kuznetsov7 · Petra J. M. Elders2,3,8 · Guy A. Rutter9,10,11 · Mark Ibberson7 · Ewan R. Pearson5 · Leen M. ’t Hart1,4,12 · Mark A. van de Wiel1,2 · Joline W. J. Beulens1,2,3,13  \nReceived: 3 July 2023 / Accepted: 5 January 2024 / Published online: 19 February 2024 © The Author(s) 2024  \nAbstract  \nAims/hypothesis People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value.  \nMethods In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest) . Clinical variables included age, sex, HbA 1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic.  \nResults Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%) . Base models (age, sex, BMI, HbA 1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]) . A more extensive model including HDLcholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance.  \nConclusions/interpretation Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5% . Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification.  \nData availability Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at [https://rhapdata-app.vital-it.ch](https://rhapdata-app.vital-it.ch).  \nKeywords Machine learning · Prediction model · Progression · Type 2 diabetes  \nAbbreviations  \nApoM Apolipoprotein M  \nCCL14/HCC-1 C-C motif chemokine 14  \nCoRF Co-data regularised random forest  \nDCS Hoorn Diabetes Care System  \nExtended author information available on the last page of the article  \nGoDARTS  \nGRridge  \nGRridgesel  \nGRlasso IL18Ra  \nGenetics of Diabetes Audit and Research in Tayside Scotland  \nEmpirical Bayes group-regularised ridge  \nEmpirical Bayes group-regularised ridge with se","cbCaiiaqeoYeXh96","https://ap.wps.com/l/cbCaiiaqeoYeXh96","pdf",1237261,3,1,10,"English","en",105,"# Abstract\n## Aims/hypothesis\n## Methods\n## Results\n## Conclusions/interpretation","[{\"question\":\"What outcome does this RHAPSODY study predict in type 2 diabetes?\",\"answer\":\"It predicts time to insulin requirement, focusing on when participants progress to needing insulin during follow-up.\"},{\"question\":\"Which types of biomarkers are used to build the prediction models?\",\"answer\":\"The models use clinical variables and three molecular marker types: metabolites, lipids, and proteins.\"},{\"question\":\"Do molecular markers improve prediction beyond clinical variables?\",\"answer\":\"Yes. Adding molecular markers improves prognostic performance by up to about 5% compared with clinical-variable-only base models.\"}]","An omics-based machine learning approach to predict diabetes progression - a RHAPSODY study | PDF",1785941474,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"an-omics-based-machine-learning-approach-to-predict-diabetes-progression-a-rhapsody-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/an-omics-based-machine-learning-approach-to-predict-diabetes-progression-a-rhapsody-study/127762/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What outcome does this RHAPSODY study predict in type 2 diabetes?","Question",{"text":76,"@type":77},"It predicts time to insulin requirement, focusing on when participants progress to needing insulin during follow-up.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which types of biomarkers are used to build the prediction models?",{"text":81,"@type":77},"The models use clinical variables and three molecular marker types: metabolites, lipids, and proteins.",{"name":83,"@type":74,"acceptedAnswer":84},"Do molecular markers improve prediction beyond clinical variables?",{"text":85,"@type":77},"Yes. Adding molecular markers improves prognostic performance by up to about 5% compared with clinical-variable-only base models.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]