Multiscale Theoretical Foundations of DiabeticNeuropathy and AI-Driven Diagnosis

Multiscale Theoretical Foundations of DiabeticNeuropathy and AI-Driven Diagnosis | 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 Article Multiscale Theoretical Foundations of DiabeticNeuropathy and AI-Driven Diagnosis Hossein Sadeghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6799303/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 This study establishes an integrated theoretical framework for understanding and diagnosing diabetic neuropathy through combined metabolic, electrophysiological, and artificial intelligence (AI) approaches. We derive mathematical models characterizing polyol pathway kinetics (kdep = 0.18 month−1), sodium channel decay (τ = 8.2 months), and conduction velocity reduction (Δθ = 28.4%). The machine learning (ML) implementation achieves 92.5% diagnostic accuracy with optimal decision threshold at θ∗ = 0.63. Our analysis reveals three critical pathophysiological phases: initial NADPH depletion exceeding 0.4 mM triggers oxidative stress, followed by progressive ion channel dysfunction, and ultimately leads to measurable conduction deficits. The framework bridges molecular mechanisms to clinical manifestations, demonstrating a strong correlation between metabolic markers (NADPH, AGEs) and electrophysiological parameters (conduction velocity, propagation failure). Validation against clinical datasets confirms model robustness across disease stages, with the staging system showing 89% concordance with expert assessments(κ = 0.81). Our SVM-CNN hybrid model demonstrates superior performance (AUC=0.94, ΔAUC=+0.12 vs. conventional methods), enabling detection 6 months earlier than current standards. SHAP analysis identifies NADPH depletion rate (importance weight=0.41) as the top predictive biomarker. These results provide quantitative biomarkers for early detection and a foundation for AI-enhanced personalized management of diabetic neuropathy. Biological sciences/Neuroscience Biological sciences/Neuroscience/Neural circuit Diabetic Neuropathy Nerve Conduction Studies Artificial In- telligence Mathematical Modeling Diagnostic Thresholds Full Text Additional Declarations No competing interests reported. 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. 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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-6799303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470757089,"identity":"7c1cc9a1-32fc-4022-8ce6-e467bbfdd2b2","order_by":0,"name":"Hossein Sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACgwPsDxh4GBgS2NgbgNwDENED+LXwGEC08BwgXgsDWAuDRAIhtQgtjB/eVNTm8Uk+fvbhx5nDDPztBxgPV+DRYn+A/bHknDPHi9mk04xn9tw4zCBxJoHh4Bl8ttx/YCDN23YssU06wZiB58NhBoYbDAwHG/A6jMH4N+8/oBbJ458Z/wC1yBOhxUyat6EmsU2Cx5iZB+gwA8JaeMws5xw7kNjGk1PMLHMmncfwTGIDAS3sj2+8qalLnN9+fDPjm2PWcnLHDx/+iE8LFByGs4BxxEiEBgaGOmIUjYJRMApGwUgFALkNVzYUpbuDAAAAAElFTkSuQmCC","orcid":"","institution":"Arak University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Sadeghi","suffix":""}],"badges":[],"createdAt":"2025-06-02 06:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6799303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6799303/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88471293,"identity":"20e14a14-0541-416a-b815-9a2f64b16b66","added_by":"auto","created_at":"2025-08-06 19:08:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508635,"visible":true,"origin":"","legend":"","description":"","filename":"PaperNCVMN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6799303/v1_covered_1145ff47-193c-4af2-a07b-e6ce75f38bb4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiscale Theoretical Foundations of DiabeticNeuropathy and AI-Driven Diagnosis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Diabetic Neuropathy, Nerve Conduction Studies, Artificial In- telligence, Mathematical Modeling, Diagnostic Thresholds","lastPublishedDoi":"10.21203/rs.3.rs-6799303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6799303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study establishes an integrated theoretical framework for understanding and diagnosing diabetic neuropathy through combined metabolic, electrophysiological, and artificial intelligence (AI) approaches. 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