[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123382-en":3,"doc-seo-123382-105":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":4,"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},123382,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Predicting non-responders to lifestyle intervention in prediabetes - a machine learning approach","Clinical care for prediabetes typically begins with lifestyle intervention, but many patients fail to normalize fasting glycemia, leading to escalation toward more intensive therapy. Clear guidance is lacking on which individuals would benefit from early intensification, motivating development of an early identification algorithm. The study screened statistical and machine learning methods using internal cross-validation to classify future non-responders based solely on baseline measurements, selecting a random forest model with validated predictive performance.","European Journal of Clinical Nutrition [www.nature.com/ejcn](www.nature.com/ejcn)  \nARTICLE OPEN   \nPredicting non-responders to lifestyle intervention in prediabetes: a machine learning approach  \nAndrea Foppiani 1,2 ✉, Ramona De Amicis 1,3, Alessandro Leone 1,2, Federica Sileo1,2, Sara Paola Mambrini 1,4, Francesca Menichetti 1, Giorgia Pozzi 1, Simona Bertoli 1,3 and Alberto Battezzati1,2  \n© The Author(s) 2024  \n\n|  | BACKGROUND: The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due to the low success rate of the first-line intervention. Clinicians lack clear guidelines on which patients would benefit from early treatment with more intensive therapeutic options, so we aimed to develop an algorithm to early identify nonresponders to lifestyle intervention for prediabetes.\u003Cbr>METHOD: Several statistical and machine learning algorithms were screened with internal cross-validation on the basis of accuracy and discrimination ability to correctly classify patients that would fail to normalize fasting glycemia within one year of being prescribed a lifestyle intervention, solely based on the first examination measurements.\u003Cbr>RESULT: Of the many screened algorithm, only a random forest model performed with sufficient accuracy to exceed the historical failure rate of patients within our center, with an accuracy of 0.689 (CI 0.669, 0.710) and an AUROC of 0.687 (CI 0.673, 0.701) . CONCLUSIONS: This study showcases the ability of machine learning models to provide useful insight in clinical practice leveraging |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| knowledge contained in routinely collected data. |  |  |\n|  | European Journal of Clinical Nutrition; [https://doi.org/10.1038/s41430-024-01495-9](https://doi.org/10.1038/s41430-024-01495-9) |  |\n|  |  |  |\n\nINTRODUCTION  \nPeople with prediabetes have abnormal glucose metabolism while not meeting the criteria for diabetes [1] . As they are often characterized by abdominal obesity, screening is recommended for all adults with a body mass index indicative of overweight or obesity and with one or more risk factors. Diagnosis is made with fasting glucose levels between 100 and 125 mg/dL and/or and 2 h glucose levels between 140 to 199 mg/dL during a 75 g oral glucose tolerance test [2] .  \nAfter diagnosis, therapy focuses on normalization of fasting glycemia through body weight management, physical activity, and/or hypoglycemic medications. There are no clear guidelines between choosing lifestyle behavior change or pharmacological interventions and the intensity of treatment is often escalated once a more conservative approach has proven to be not conclusive [3] .  \nBig data and artificial intelligence may provide insight in situations where guidelines lack a clear course of action. Leveraging data and outcomes collected in everyday clinical practice with traditional or more recent tools may inform the clinician of the probability of success of alternative treatments based on site-and population-specific historical success rate. Also, combining the formalized knowledge contained in guidelines with“learnt context-specific knowledge” may constitute a promising strategy to deal with transparency and explainability issues arising with the use of new artificial intelligence algorithms [4] .  \nConsidering the lack of clear guidelines and the high rate of failure of more conservative approaches, we aimed to develop an algorithm to early identify non-responders to lifestyle intervention for prediabetes.  \nMETHODS  \nSources of data and participants  \nThe database used for developing the predictive algorithm was the International Center for the Assessment of Nutritional Status (ICANS, University of Milan, Milan, Italy) database, which contains data of a large ongoing open-cohort nutritional study. As part of the protocol of the study, all patien","cbCaiqQ5i7yi7iYf","https://ap.wps.com/l/cbCaiqQ5i7yi7iYf","pdf",1479203,1,6,"English","en",105,"# Introduction\n## Prediabetes and treatment gap\n## Rationale for machine learning\n# Methods\n## Data sources and participants\n## Model development and evaluation","[{\"question\":\"Why is early identification of non-responders important in prediabetes care?\",\"answer\":\"Because lifestyle intervention often has a low success rate, treatment is commonly escalated, yet clinicians lack clear guidelines for selecting patients who may need more intensive options early.\"},{\"question\":\"How were non-responders defined for the prediction task?\",\"answer\":\"Non-responders were patients who would fail to normalize fasting glycemia within one year after being prescribed a lifestyle intervention.\"},{\"question\":\"Which modeling approach performed best in this study?\",\"answer\":\"A random forest model met the study’s accuracy requirement and exceeded the historical failure rate in the center, achieving the reported accuracy and AUROC values.\"}]","Predicting non-responders to lifestyle intervention in prediabetes - a machine learning approach | PDF",1785816212,15,{"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},"predicting-non-responders-to-lifestyle-intervention-in-prediabetes-a-machine-learning-approach","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-non-responders-to-lifestyle-intervention-in-prediabetes-a-machine-learning-approach/123382/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early identification of non-responders important in prediabetes care?","Question",{"text":75,"@type":76},"Because lifestyle intervention often has a low success rate, treatment is commonly escalated, yet clinicians lack clear guidelines for selecting patients who may need more intensive options early.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were non-responders defined for the prediction task?",{"text":80,"@type":76},"Non-responders were patients who would fail to normalize fasting glycemia within one year after being prescribed a lifestyle intervention.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach performed best in this study?",{"text":84,"@type":76},"A random forest model met the study’s accuracy requirement and exceeded the historical failure rate in the center, achieving the reported accuracy and AUROC values.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]