A data-driven multiple linear regression model for cardiovascular homeostatic regulation in health, ageing, and disease

A data-driven multiple linear regression model for cardiovascular homeostatic regulation in health, ageing, and disease | 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 A data-driven multiple linear regression model for cardiovascular homeostatic regulation in health, ageing, and disease Luis Ruelas, Claudia Lerma, Jesus Espinal, Mireya Osorio-Palacios, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8735767/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Heart rate variability (HRV) is a widely used noninvasive marker of cardiovascular and general health risk, whereas blood pressure variability (BPV) remains comparatively underexplored. Under adverse conditions such as aging or disease, HRV typically decreases while BPV increases. However, most studies assess these metrics independently, neglecting their potential multivariate interactions. In this work, we analyze two datasets: the autonomic aging (AA) dataset (n= 1,121 healthy individuals stratified into age groups) and the type-2 diabetes (T2D) dataset (n = 75, including controls and short- and long-term T2D patients). We examine variations in mean heart rate (HR), HRV, mean blood pressure (BP), and BPV across health states. Univariate analyses showed that HRV (sdIBI) was the most sensitive metric, consistently decreasing with aging and disease, whereas BPV (sdSBP) differed mainly in older individuals or long-term T2D. Bivariate and multivariate models were constructed using correlation matrices and linear regression, revealing two distinct correlation patterns: one characteristic of youth and health, dominated by interactions between HRV and mean HR, and another associated with aging and disease, characterized by absent correlations or a dependence of BPV on HRV. These findings align with principles of homeostatic regulation, where BP acts as a regulated variable and HR as an effector. Overall, the results indicate that HRV and BPV cannot be fully understood in isolation; their joint analysis reveals shared regulatory dynamics across health states and supports the use of multivariate physiological models in clinical practice. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Health sciences/Risk factors Full Text Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.zip Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 07 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 06 Feb, 2026 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-8735767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618708605,"identity":"b3808bd8-63bb-4fe4-9971-d43f558399be","order_by":0,"name":"Luis Ruelas","email":"","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Ruelas","suffix":""},{"id":618708608,"identity":"dd0cdcef-bf09-4b54-823e-4f68c8c8eef1","order_by":1,"name":"Claudia Lerma","email":"","orcid":"","institution":"Instituto Nacional de 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