Predictive Epitranscriptomics: Computational Identification of m6A Methylation Patterns Associated with Future β-Cell Dysfunction and Hyperglycemic Transition

Predictive Epitranscriptomics: Computational Identification of m6A Methylation Patterns Associated with Future β-Cell Dysfunction and Hyperglycemic Transition | 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 Short Report Predictive Epitranscriptomics: Computational Identification of m6A Methylation Patterns Associated with Future β-Cell Dysfunction and Hyperglycemic Transition Luís Jesuíno de Oliveira Andrade, Gabriela Correia Matos de Oliveira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8302280/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 Objective To develop a computational framework integrating m6A methylation profiles with machine learning to identify patterns predictive of future β-cell dysfunction and hyperglycemic transition. Methods We performed a multi-phase bioinformatics analysis of transcriptome-wide m6A and RNA-seq data from human pancreatic islets across normoglycemic, prediabetic, and T2DM states. Differential methylation and expression analyses were conducted using established pipelines. Machine learning models were trained and validated on m6A features, transcript expression, and clinical variables. Results m6A methylation patterns robustly distinguished disease states, outperforming transcriptomic profiles alone. Hypomethylation of key β-cell genes (PDX1, MAFA, INS) and insulin signaling pathway components was strongly associated with β-cell dysfunction. Machine learning models achieved high accuracy (AUC-ROC 0.94) in predicting T2DM risk, with m6A features being the most influential predictors. Longitudinal analysis revealed progressive m6A hypomethylation preceding clinical hyperglycemia. Conclusion m6A methylation signatures serve as powerful biomarkers for early detection of β-cell dysfunction and hyperglycemic transition, offering a novel avenue for predictive medicine in DM. Bioinformatics Endocrinology & Metabolism m6A methylation β-cell dysfunction Hyperglycemia Machine learning 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. 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Identification of m6A Methylation Patterns Associated with Future β-Cell Dysfunction and Hyperglycemic Transition\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"m6A methylation, β-cell dysfunction, Hyperglycemia, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8302280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8302280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo develop a computational framework integrating m6A methylation profiles with machine learning to identify patterns predictive of future β-cell dysfunction and hyperglycemic transition.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed a multi-phase bioinformatics analysis of transcriptome-wide m6A and RNA-seq data from human pancreatic islets across normoglycemic, prediabetic, and T2DM states. Differential methylation and expression analyses were conducted using established pipelines. Machine learning models were trained and validated on m6A features, transcript expression, and clinical variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003em6A methylation patterns robustly distinguished disease states, outperforming transcriptomic profiles alone. Hypomethylation of key β-cell genes (PDX1, MAFA, INS) and insulin signaling pathway components was strongly associated with β-cell dysfunction. Machine learning models achieved high accuracy (AUC-ROC 0.94) in predicting T2DM risk, with m6A features being the most influential predictors. Longitudinal analysis revealed progressive m6A hypomethylation preceding clinical hyperglycemia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003em6A methylation signatures serve as powerful biomarkers for early detection of β-cell dysfunction and hyperglycemic transition, offering a novel avenue for predictive medicine in DM.\u003c/p\u003e","manuscriptTitle":"Predictive Epitranscriptomics: Computational Identification of m6A Methylation Patterns Associated with Future β-Cell Dysfunction and Hyperglycemic Transition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 12:47:44","doi":"10.21203/rs.3.rs-8302280/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":"ab6e42e9-7d0a-4b1a-b6b5-00f76d505028","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59235616,"name":"Bioinformatics"},{"id":59235617,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2025-12-09T12:47:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 12:47:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8302280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8302280","identity":"rs-8302280","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}