An Explainable Machine Learning Model that Uses Continuous Glucose Monitoring to Screen for Hypertension in Patients with Type 1 Diabetes

An Explainable Machine Learning Model that Uses Continuous Glucose Monitoring to Screen for Hypertension in Patients with Type 1 Diabetes | 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 An Explainable Machine Learning Model that Uses Continuous Glucose Monitoring to Screen for Hypertension in Patients with Type 1 Diabetes Vincent Liu, Laura Sue, Thomas Byrd IV, Oscar Madrid Padilla, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9067817/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Patients with Type 1 diabetes (T1D) routinely wear continuous glucose monitoring (CGM) devices for glycemic management. Beyond glucose tracking, CGM data may contain patterns associated with comorbidity risk. Objective We hypothesized that CGM metrics could enhance classification of hypertension when added to standard biochemical and demographic data. Methods We analyzed data from 689 T1D patients with a median of 286 days of CGM monitoring. We engineered 36 CGM-derived features including time in range quartiles, glycemic variability metrics, and temporal patterns. Using an XGBoost model with 10-fold cross-validation and SMOTE for class imbalance, we compared biochemical-and-demographic versus combined (biochemical + demographic + CGM) models for classifying seven diagnosed comorbidities: hypertension, hypothyroidism, retinopathy, lipid metabolism disorders, airway disease, nephropathy, and neuropathy. Results CGM data significantly improved classification of diagnosed hypertension (biochemical-and-demographic ROC-AUC: 0.748 ± 0.092; combined ROC-AUC: 0.770 ± 0.088; ΔROC-AUC = + 0.022, P = 0.0191 via DeLong test). CGM features such as TBR, CV, rolling mean, and slope of glucose emerged among the top 15 discriminators in the SHAP analysis. No significant improvements were observed for other comorbidities in terms of ROC-AUC. Decision curve analysis confirmed the net clinical benefit of the combined model across threshold probabilities consistent with the population prevalence. Conclusions CGM data provides significant added value for hypertension risk classification in T1D patients without additional patient burden. This represents an opportunistic screening approach leveraging existing monitoring infrastructure. Endocrinology & Metabolism Artificial Intelligence and Machine Learning continuous glucose monitoring hypertension machine learning opportunistic screening classification type 1 diabetes Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-9067817","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606529227,"identity":"00475100-7105-4b01-9e99-a3b8aa547e84","order_by":0,"name":"Vincent Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACZiBmbLDgYWBvYGB4ABI5QJwWCR4GHqDSBKK0MEC0MDBIJBCpxeA478EPH3dIyBjcfGP4IaGGQY7vRgIBLYf5kiVnnpHgMbidYyyRcIzBWJKQFslmHjNm3jawFjOGBDaGxA1EafkL0nLzDFDLP4Z6glr4mYFaGEFabvCYMSS2MSQYEKHFWLIX6BfJM2nFEol9EoYzzzzAr4WN/4zhh587bOz5jh/e+OHDNxt5vuMEbEEHEqQpHwWjYBSMglGAHQAAcAM+hfbHEQ8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6887-9910","institution":"UCLA","correspondingAuthor":true,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Liu","suffix":""},{"id":606529228,"identity":"47997a00-6e00-44a6-b53a-beeef0f7bc69","order_by":1,"name":"Laura Sue","email":"","orcid":"","institution":"UCLA David Geffen School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Sue","suffix":""},{"id":606529229,"identity":"cff7484e-99c5-4f65-a031-d02eb9b330cc","order_by":2,"name":"Thomas Byrd IV","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"Byrd","lastName":"IV","suffix":""},{"id":606529230,"identity":"83593d4b-20cb-4a45-9acd-8f51d667c17b","order_by":3,"name":"Oscar Madrid Padilla","email":"","orcid":"","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"Madrid","lastName":"Padilla","suffix":""},{"id":606529231,"identity":"1a95b127-9a2b-4190-bff0-3346c0b825bc","order_by":4,"name":"Yingnian Wu","email":"","orcid":"","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Yingnian","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-09 03:16:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9067817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9067817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104768175,"identity":"1f41d1a0-65b3-4b06-8553-d2fcd17d93ea","added_by":"auto","created_at":"2026-03-17 04:11:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2511705,"visible":true,"origin":"","legend":"","description":"","filename":"cgmhtnscreening03112026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9067817/v1_covered_63afb8a4-1cae-4b2e-ab2a-6ff05627d562.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAn Explainable Machine Learning Model that Uses Continuous Glucose Monitoring to Screen for Hypertension in Patients with Type 1 Diabetes\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of California, Los Angeles","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"continuous glucose monitoring, hypertension, machine learning, opportunistic screening, classification, type 1 diabetes","lastPublishedDoi":"10.21203/rs.3.rs-9067817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9067817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with Type 1 diabetes (T1D) routinely wear continuous glucose monitoring (CGM) devices for glycemic management. Beyond glucose tracking, CGM data may contain patterns associated with comorbidity risk.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eWe hypothesized that CGM metrics could enhance classification of hypertension when added to standard biochemical and demographic data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from 689 T1D patients with a median of 286 days of CGM monitoring. We engineered 36 CGM-derived features including time in range quartiles, glycemic variability metrics, and temporal patterns. Using an XGBoost model with 10-fold cross-validation and SMOTE for class imbalance, we compared biochemical-and-demographic versus combined (biochemical\u0026thinsp;+\u0026thinsp;demographic\u0026thinsp;+\u0026thinsp;CGM) models for classifying seven diagnosed comorbidities: hypertension, hypothyroidism, retinopathy, lipid metabolism disorders, airway disease, nephropathy, and neuropathy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCGM data significantly improved classification of diagnosed hypertension (biochemical-and-demographic ROC-AUC: 0.748\u0026thinsp;\u0026plusmn;\u0026thinsp;0.092; combined ROC-AUC: 0.770\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088; ΔROC-AUC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.022, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0191 via DeLong test). CGM features such as TBR, CV, rolling mean, and slope of glucose emerged among the top 15 discriminators in the SHAP analysis. No significant improvements were observed for other comorbidities in terms of ROC-AUC. Decision curve analysis confirmed the net clinical benefit of the combined model across threshold probabilities consistent with the population prevalence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCGM data provides significant added value for hypertension risk classification in T1D patients without additional patient burden. This represents an opportunistic screening approach leveraging existing monitoring infrastructure.\u003c/p\u003e","manuscriptTitle":"An Explainable Machine Learning Model that Uses Continuous Glucose Monitoring to Screen for Hypertension in Patients with Type 1 Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 04:10:51","doi":"10.21203/rs.3.rs-9067817/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bd1d7ba-7f9a-4c29-8b88-380a1c8569ea","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64539841,"name":"Endocrinology \u0026 Metabolism"},{"id":64539842,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-03-17T04:10:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 04:10:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9067817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9067817","identity":"rs-9067817","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}