Optimizing Metabolic Syndrome Screening: A New Region-Specific Body Shape Index

Optimizing Metabolic Syndrome Screening: A New Region-Specific Body Shape Index | 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 Optimizing Metabolic Syndrome Screening: A New Region-Specific Body Shape Index Ekaterina D. Konstantinova, Tatiana A. Maslakova, Svetlana Yu. Ogorodnikova This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8201698/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract To propose a new anthropometric index (AI) for identifying groups at risk of metabolic syndrome (MetS). Overall, 4360 male workers in hazardous occupations were examined. To determine region-specific ABSI scaling exponents, a least-squares linear regression model was constructed for a subset of 4014 participants. AI independence was assessed by correlation analysis. To estimate combined impact on MetS-risk and control for age, AIs were transformed into z-scores. The prognostic values of AI and their combinations with regional ABSI U were estimated with ROC curves. Model sensitivity (Se) and specificity (Sp) were determined using a confusion matrix. Region-specific ABSI U , z-scores and their combinations were calculated for the main dataset of 346 subjects. Correlation analysis revealed a weak association between ABSI U and height (r = 0.044), weight (r = -0.083), HC (r =0.073), BMI (r = -0.116) and BRI (r = 0.365). BRI was best at predicting MetS-risk among individual AIs (AUC = 0.768, Se = 0.896, Sp = 0.579); the best combination was - ABSI U + BMI (AUC = 0.777, Se = 0.792, Sp = 0.683). Our z-score, based on ABSI U and BMI, demonstrates a higher prognostic ability to identify groups at risk of MetS against known AIs, opening new prospects for early diagnosis and screening of MetS. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Anthropometric index Body shape index Metabolic syndrome ROC analyses Multiple linear regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 28 Apr, 2026 Editor invited by journal 08 Dec, 2025 Editor assigned by journal 26 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 25 Nov, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8201698","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":631025444,"identity":"906257d6-8eec-49b1-9ba7-b3026a7f12ae","order_by":0,"name":"Ekaterina D. 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Overall, 4360 male workers in hazardous occupations were examined. To determine region-specific ABSI scaling exponents, a least-squares linear regression model was constructed for a subset of 4014 participants. AI independence was assessed by correlation analysis. To estimate combined impact on MetS-risk and control for age, AIs were transformed into z-scores. The prognostic values of AI and their combinations with regional ABSI\u003csub\u003eU\u003c/sub\u003e were estimated with ROC curves. Model sensitivity (Se) and specificity (Sp) were determined using a confusion matrix.\u003cstrong\u003e \u003c/strong\u003eRegion-specific ABSI\u003csub\u003eU\u003c/sub\u003e, z-scores and their combinations were calculated for the main dataset of 346 subjects. Correlation analysis revealed a weak association between ABSI\u003csub\u003eU\u003c/sub\u003e and height (r = 0.044), weight (r = -0.083), HC (r =0.073), BMI (r = -0.116) and BRI (r = 0.365). 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