Critical Transitions in Health-Disease Dynamics: A Complex Systems Approach for Early Warning Detection

Critical Transitions in Health-Disease Dynamics: A Complex Systems Approach for Early Warning Detection | 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 Critical Transitions in Health-Disease Dynamics: A Complex Systems Approach for Early Warning Detection Zhen Ma, Yang Zhang, Tao Feng, Xiao Liu, Jingliang Gu, Xinggang Xiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7631845/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 Traditional medical paradigms view disease progression as gradual processes, yet mounting evidence suggests that health-to-disease transitions may exhibit critical transition phenomena characterized by sudden shifts between stable states. Understanding the dynamical signatures preceding these transitions could revolutionize early disease detection and intervention strategies. Methods We conducted a comprehensive dynamical systems analysis of longitudinal clinical data from 360 patients across three distinct disease phenotypes: thyroid nodules, abdominal aortic aneurysms, and brain gliomas, spanning 120 months of follow-up. We computed multiple early warning indicators including variance, autocorrelation, recovery time, state flickering, and critical slowing down. Phase space reconstruction was performed using time-delay embedding to characterize system dynamics through Lyapunov exponents, correlation dimension, and phase space entropy. Dynamic network analysis captured time-varying correlations among physiological indicators. Machine learning models were developed using random forest algorithms to predict health state transitions. Results Our analysis identified 5,385 critical transition points across all patients, averaging 44.88 transitions per individual. Variance (importance score: 0.2263) and autocorrelation (0.1405) emerged as the most predictive early warning indicators. Lyapunov exponent analysis revealed chaotic dynamics in 97.2% of cases (mean: 0.6298), indicating inherent system instability near transition points. Dynamic network analysis demonstrated non-linear evolution patterns with network density fluctuating between 0.2000-1.0000 (mean: 0.8018) during disease progression. The integrated prediction model achieved 77.41% accuracy, with 83% precision for healthy state identification. Critical transition detection provided early warning signals 9.3 ± 8.6 time points before actual health state changes. Conclusions Critical transition theory effectively identifies early signatures of health-disease state transitions, with variance increase and autocorrelation enhancement serving as the most sensitive indicators. This approach provides a theoretical foundation for developing personalized disease risk assessment and early intervention strategies, potentially transforming precision medicine approaches to disease prevention and management. Critical transitions Complex systems Health dynamics Early warning indicators Phase space analysis Precision medicine 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-7631845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516026769,"identity":"6a3c5456-6de9-4eb0-a6b3-74a47e124b86","order_by":0,"name":"Zhen Ma","email":"","orcid":"https://orcid.org/0009-0006-7020-4473","institution":"Taylor`s university","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Ma","suffix":""},{"id":516026770,"identity":"b79b109a-dbeb-46f7-a72a-0a27bf41e0e2","order_by":1,"name":"Yang Zhang","email":"","orcid":"","institution":"Department of Neurology, the Fourth Affiliated 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Understanding the dynamical signatures preceding these transitions could revolutionize early disease detection and intervention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a comprehensive dynamical systems analysis of longitudinal clinical data from 360 patients across three distinct disease phenotypes: thyroid nodules, abdominal aortic aneurysms, and brain gliomas, spanning 120 months of follow-up. We computed multiple early warning indicators including variance, autocorrelation, recovery time, state flickering, and critical slowing down. Phase space reconstruction was performed using time-delay embedding to characterize system dynamics through Lyapunov exponents, correlation dimension, and phase space entropy. Dynamic network analysis captured time-varying correlations among physiological indicators. Machine learning models were developed using random forest algorithms to predict health state transitions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur analysis identified 5,385 critical transition points across all patients, averaging 44.88 transitions per individual. Variance (importance score: 0.2263) and autocorrelation (0.1405) emerged as the most predictive early warning indicators. Lyapunov exponent analysis revealed chaotic dynamics in 97.2% of cases (mean: 0.6298), indicating inherent system instability near transition points. Dynamic network analysis demonstrated non-linear evolution patterns with network density fluctuating between 0.2000-1.0000 (mean: 0.8018) during disease progression. The integrated prediction model achieved 77.41% accuracy, with 83% precision for healthy state identification. Critical transition detection provided early warning signals 9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 time points before actual health state changes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCritical transition theory effectively identifies early signatures of health-disease state transitions, with variance increase and autocorrelation enhancement serving as the most sensitive indicators. This approach provides a theoretical foundation for developing personalized disease risk assessment and early intervention strategies, potentially transforming precision medicine approaches to disease prevention and management.\u003c/p\u003e","manuscriptTitle":"Critical Transitions in Health-Disease Dynamics: A Complex Systems Approach for Early Warning Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 06:24:19","doi":"10.21203/rs.3.rs-7631845/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":"07049175-747e-4e0a-85bb-c4c7eba5c04a","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T06:24:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 06:24:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7631845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7631845","identity":"rs-7631845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}