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The study proposes a physiologically informed mathematical model that links nocturnal glucose levels with resting heart rate dynamics. A coupled ordinary differential equation system is fitted to wearable sensor data via particle swarm optimization. Sensitivity analysis identifies parameters driving metabolic dysregulation, enabling a personalized risk score. Validation with synthetic and cancer-survivor data supports non-invasive, interpretable early T2DM risk stratification based on nocturnal patterns.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/mathematical-modeling-of-glucose-regulation-and-sleep-resting-heart-rate-to-predict-risk-of-metabolic-dysregulation-in-cancer-survivors/419604/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/mathematical-modeling-of-glucose-regulation-and-sleep-resting-heart-rate-to-predict-risk-of-metabolic-dysregulation-in-cancer-survivors/419604.png","ImageObject",300,407,{"name":92,"@type":93},"Jasmine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What biological link does the model target between glucose and heart rate?","Question",{"text":112,"@type":113},"The model connects nocturnal glucose levels with resting heart rate dynamics, reflecting interactions between sleep-related glucose metabolism and autonomic regulation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the mathematical model fitted to data?",{"text":117,"@type":113},"It uses a coupled system of ordinary differential equations fitted to wearable sensor data through particle swarm optimization.",{"name":119,"@type":110,"acceptedAnswer":120},"How are key model parameters used to estimate risk?",{"text":121,"@type":113},"Sensitivity analysis identifies parameters driving metabolic dysregulation, which supports formulation of a personalized risk score for early T2DM risk stratification.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},419604,1790824209,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336478487870,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","OPEN ACCESS  \nCitation: Liao Y, Spadola C, Roy S (2026) Mathematical modeling of glucose regulation and sleep resting heart rate to predict risk of metabolic dysregulation in cancer survivors. PLoS One 21(1): e0339461 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0339461  \nEditor: Suyan Tian, The First Hospital of Jilin University, CHINA  \nReceived: June 22, 2025  \nAccepted: December 8, 2025  \nPublished: January 2, 2026  \nCopyright: © 2026 Liao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: The data underlying the results presented in the study are available from [https://github](https://github). com/roysouvik2/Diabetes-code.git.  \nFunding: This study was supported by the following funders: National Science Foundation 2212938 and 2309491,  \nRESEARCH ARTICLE  \nMathematical modeling of glucose regulation and sleep resting heart rate to predict risk of metabolic dysregulation in cancer survivors  \nYue Liao1 ☯ , Christine Spadola2 ☯ , Souvik Roy3 ☯ *  \n1 Public Health Program, Department of Kinesiology, The University of Texas at Arlington, Arlington, Texas, United States of America, 2 School of Social Work, The University of Texas at Arlington, Arlington, Texas, United States of America, 3 Department of Mathematics, The University of Texas at Arlington, Arlington, Texas, United States of America  \n☯ All authors contributed equally to this work.  \n* [souvik.roy@uta.edu](souvik.roy@uta.edu)  \nAbstract  \nSleep-related alterations in glucose metabolism and autonomic regulation play a critical role in the early development of type 2 diabetes mellitus (T2DM), especially among cancer survivors. Motivated by this interplay, we develop a physiologically informed mathematical model linking nocturnal glucose levels with resting heart rate dynamics. The model comprises of a coupled system of ordinary differential equations, and is fitted to wearable sensor data using particle swarm optimization. Sensitivity analysis is carried out to obtain the key parameters driving metabolic dysregulation, enabling the formulation of a personalized risk score. Successful validation using synthetic patient data and cancer survivor patient data suggests that this framework provides a non-invasive, interpretable tool for early T2DM risk stratification based on nocturnal physiological patterns.  \nIntroduction  \nThere are more than 18 million cancer survivors in the US, and this number is expected to grow with advances in diagnostic and treatment capabilities [1] . Improved survival rates have highlighted the need to optimize the long-term health and well-being of cancer patients after treatment as part of their standard cancer care. Notably, the number of adult cancer survivors with comorbid diseases has increased substantially over the past two decades. Comorbidities can affect the life expectancy and survival of cancer patients, regardless of cancer stage and disease status. Therefore, optimal management of comorbid conditions and aggressive interventions for risk reduction can benefit the cancer survivor population.  \nType 2 diabetes mellitus (T2DM) with hyperglycemia (high blood sugar) is a chronic metabolic disorder characterized by persistent high blood glucose levels, often due to insulin resistance and/or inadequate insulin production. T2DM is oneof the most common comorbidities in cancer survivors and has been linked with  \nAmerican Institute for Cancer Research (IIG-849385), Texas Higher Education Coordinating Board (02459), American Heart Association (23CDA1050513) .  \nCompeting interests: The authors have declared that no competing interests exist.  \nincreased cancer mortality [2 ,3] . Prediabetes is a critical stage in the progression towards T2DM, as individuals with prediabetes can effectively ","cbCaik3ElS4TOhTR","https://ap.wps.com/l/cbCaik3ElS4TOhTR","pdf",1894023,22,"English","# Abstract\n# Introduction","[{\"question\":\"What biological link does the model target between glucose and heart rate?\",\"answer\":\"The model connects nocturnal glucose levels with resting heart rate dynamics, reflecting interactions between sleep-related glucose metabolism and autonomic regulation.\"},{\"question\":\"How is the mathematical model fitted to data?\",\"answer\":\"It uses a coupled system of ordinary differential equations fitted to wearable sensor data through particle swarm optimization.\"},{\"question\":\"How are key model parameters used to estimate risk?\",\"answer\":\"Sensitivity analysis identifies parameters driving metabolic dysregulation, which supports formulation of a personalized risk score for early T2DM risk stratification.\"}]","Mathematical modeling of glucose regulation and sleep resting heart rate to predict risk of metabolic dysregulation in cancer survivors | PDF",1790617690,55]