A type 1 diabetes prediction model has utility across multiple screening settings with recalibration

A type 1 diabetes prediction model has utility across multiple screening settings with recalibration | 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 A type 1 diabetes prediction model has utility across multiple screening settings with recalibration Erin L. Templeman, Lauric A. Ferrat, Hemang M. Parikh, Lu You, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5773430/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in BMC Medicine → Version 1 posted 11 You are reading this latest preprint version Abstract Background Accurate type 1 diabetes prediction is important to facilitate screening for pre-clinical type 1 diabetes to enable potential early disease-modifying interventions and to reduce the risk of severe presentation with diabetic ketoacidosis. We aimed to assess the generalisability of a prediction model developed in children followed from birth. Additionally, we sought to create an application for easy calculation and visualization of individualized risk prediction. Methods We developed and refined a stratified prediction model combining a genetic risk score, age, islet autoantibodies, and family history using data from children followed since birth by The Environmental Determinants of Diabetes in the Young (TEDDY) study. We tested the validity of the model through external validation in the Type 1 Diabetes TrialNet Pathway to Prevention study, which conducts cross-sectional screening in relatives of people with type 1 diabetes. We recalibrated the model by adjusting for baseline risk and selection criteria in TrialNet using logistic recalibration to improve calibration across all ages. Results The study included 7,798 TEDDY and 4,068 TrialNet participants, with 305 (4%) and 1,373 (34%) developing type 1 diabetes, respectively. The combined model showed similar discriminative ability in autoantibody-positive individuals across TEDDY and TrialNet (p = 0.14), but inferior calibration in TrialNet (Brier score 0.40 [0.38,0.43]). Adjustment for baseline risk and selection criteria in TrialNet using logistic recalibration improved calibration across all ages (Brier score 0.16 [0.14,0.17]; p < 0.001). A web calculator was developed to visualise individual risk estimates ( https://t1dpredictor.diabetesgenes.org ). Conclusions A stratified model of type 1 diabetes genetic risk score, family history, age, and autoantibody status accurately predicts type 1 diabetes risk, but may need recalibration according to screening stategy. type 1 diabetes prediction recalibration adjustment genetics autoantibody FDR Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable3.docx SupplementaryMethods.docx SupplementaryFigures.docx SupplementaryMethods.docx SupplementaryMethods.docx Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2025 Read the published version in BMC Medicine → Version 1 posted Editorial decision: Revision requested 06 Mar, 2025 Reviews received at journal 06 Mar, 2025 Reviews received at journal 25 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviews received at journal 10 Feb, 2025 Reviewers agreed at journal 30 Jan, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviewers invited by journal 20 Jan, 2025 Editor assigned by journal 07 Jan, 2025 Submission checks completed at journal 07 Jan, 2025 First submitted to journal 06 Jan, 2025 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-5773430","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":410685759,"identity":"51061917-97f6-4217-b32f-bbc8b09e17fe","order_by":0,"name":"Erin L. 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recalibration","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type 1 diabetes, prediction, recalibration, adjustment, genetics, autoantibody, FDR","lastPublishedDoi":"10.21203/rs.3.rs-5773430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5773430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate type 1 diabetes prediction is important to facilitate screening for pre-clinical type 1 diabetes to enable potential early disease-modifying interventions and to reduce the risk of severe presentation with diabetic ketoacidosis. We aimed to assess the generalisability of a prediction model developed in children followed from birth. Additionally, we sought to create an application for easy calculation and visualization of individualized risk prediction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We developed and refined a stratified prediction model combining a genetic risk score, age, islet autoantibodies, and family history using data from children followed since birth by The Environmental Determinants of Diabetes in the Young (TEDDY) study. We tested the validity of the model through external validation in the Type 1 Diabetes TrialNet Pathway to Prevention study, which conducts cross-sectional screening in relatives of people with type 1 diabetes. We recalibrated the model by adjusting for baseline risk and selection criteria in TrialNet using logistic recalibration to improve calibration across all ages.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 7,798 TEDDY and 4,068 TrialNet participants, with 305 (4%) and 1,373 (34%) developing type 1 diabetes, respectively. The combined model showed similar discriminative ability in autoantibody-positive individuals across TEDDY and TrialNet (p\u0026thinsp;=\u0026thinsp;0.14), but inferior calibration in TrialNet (Brier score 0.40 [0.38,0.43]). Adjustment for baseline risk and selection criteria in TrialNet using logistic recalibration improved calibration across all ages (Brier score 0.16 [0.14,0.17]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A web calculator was developed to visualise individual risk estimates (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://t1dpredictor.diabetesgenes.org\u003c/span\u003e\u003cspan address=\"https://t1dpredictor.diabetesgenes.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA stratified model of type 1 diabetes genetic risk score, family history, age, and autoantibody status accurately predicts type 1 diabetes risk, but may need recalibration according to screening stategy.\u003c/p\u003e","manuscriptTitle":"A type 1 diabetes prediction model has utility across multiple screening settings with recalibration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 20:17:45","doi":"10.21203/rs.3.rs-5773430/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-06T11:23:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-06T08:34:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-25T07:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143933554165078037886518042478566606772","date":"2025-02-24T20:00:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-10T22:30:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98401851300844971368111933581001347445","date":"2025-01-31T00:15:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85316397822007000527344290142742668677","date":"2025-01-29T16:15:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-20T10:18:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-07T09:30:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-07T08:40:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2025-01-06T11:27:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35907d89-536b-44a0-86d7-3e252faee530","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T15:59:02+00:00","versionOfRecord":{"articleIdentity":"rs-5773430","link":"https://doi.org/10.1186/s12916-025-04225-5","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2025-07-21 15:56:57","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2025-02-06 20:17:45","video":"","vorDoi":"10.1186/s12916-025-04225-5","vorDoiUrl":"https://doi.org/10.1186/s12916-025-04225-5","workflowStages":[]},"version":"v1","identity":"rs-5773430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5773430","identity":"rs-5773430","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}