Mechanistic Modeling of Stress-Induced Glucose Dysregulation: Calibrating a Coupled HPA-Metabolic Framework to the UK Biobank

Mechanistic Modeling of Stress-Induced Glucose Dysregulation: Calibrating a Coupled HPA-Metabolic Framework to the UK Biobank | 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 Mechanistic Modeling of Stress-Induced Glucose Dysregulation: Calibrating a Coupled HPA-Metabolic Framework to the UK Biobank Rezo Getsadze, Qiziyi Cao, Shivam Kumar, Vivek M. Sheraton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9475176/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 Type 2 Diabetes (T2D) is a heterogeneous metabolic disorder driven by the interplay between endocrine stress and glucose regulation. We adapt an 18-variable ordinary differential equation (ODE) model of the hypothalamic-pituitary-adrenal (HPA) and metabolic systems to a UK Biobank pilot cohort ( N = 100 ) representing metabolic extremes (50 healthy, 50 T2D). Scaling mechanistic models is challenged by inherent population metabolic diversity. To address this, we implement an individualized scaling framework anchoring glucose uptake and hepatic production to baseline fasting glucose. Results demonstrate that this approach reproduces clinical bimodal distributions and phenotypic attractors without manual refitting. Validation against independently observed HbA1c ( r = 0.82 , p < 0.0001 , n = 95 ) confirms that the simulated steady states track a clinically meaningful glycemic marker not used during model initialization. By achieving statistical parity using only baseline glucose and mental health data, this framework enables scaling neuroendocrine-metabolic models to the full UK Biobank for virtual trials and personalized interventions. Endocrinology & Metabolism Computational Biology Mechanistic Modeling HPA Axis Type 2 Diabetes UK Biobank Individualized Calibration 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. 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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-9475176","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626514092,"identity":"5b6fa8d1-76be-47f2-90f7-94f3df59ca68","order_by":0,"name":"Rezo Getsadze","email":"","orcid":"https://orcid.org/0009-0008-1153-2455","institution":"University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Rezo","middleName":"","lastName":"Getsadze","suffix":""},{"id":626514093,"identity":"954d4292-5924-4f5c-ac64-627f44671f70","order_by":1,"name":"Qiziyi Cao","email":"","orcid":"https://orcid.org/0009-0006-8394-0277","institution":"University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Qiziyi","middleName":"","lastName":"Cao","suffix":""},{"id":626514094,"identity":"5106bbc5-83d1-4a8e-ac64-8520f4ac1014","order_by":2,"name":"Shivam Kumar","email":"","orcid":"https://orcid.org/0009-0005-6766-6565","institution":"Computational Science Lab, University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Shivam","middleName":"","lastName":"Kumar","suffix":""},{"id":626514095,"identity":"bb7cb285-5635-47a8-9365-595b5de9e69c","order_by":3,"name":"Vivek M. 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We adapt an 18-variable ordinary differential equation (ODE) model of the hypothalamic-pituitary-adrenal (HPA) and metabolic systems to a UK Biobank pilot cohort (\u003cem\u003eN = 100\u003c/em\u003e) representing metabolic extremes (50 healthy, 50 T2D). Scaling mechanistic models is challenged by inherent population metabolic diversity. To address this, we implement an individualized scaling framework anchoring glucose uptake and hepatic production to baseline fasting glucose. Results demonstrate that this approach reproduces clinical bimodal distributions and phenotypic attractors without manual refitting. Validation against independently observed HbA1c (\u003cem\u003er = 0.82\u003c/em\u003e, \u003cem\u003ep \u0026lt; 0.0001\u003c/em\u003e, \u003cem\u003en = 95\u003c/em\u003e) confirms that the simulated steady states track a clinically meaningful glycemic marker not used during model initialization. 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