The association between insulin resistance and the occurrence of metabolic syndrome in Chinese patients with type 2 diabetes, and the application of Age–Sex–Ethnicity–Specific Severity scoring

The association between insulin resistance and the occurrence of metabolic syndrome in Chinese patients with type 2 diabetes, and the application of Age–Sex–Ethnicity–Specific Severity scoring | 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 The association between insulin resistance and the occurrence of metabolic syndrome in Chinese patients with type 2 diabetes, and the application of Age–Sex–Ethnicity–Specific Severity scoring Jiying Zhu, Tao Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8366375/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 27 You are reading this latest preprint version Abstract Background Metabolic syndrome (MetS) is commonly diagnosed using a binary classification approach, which does not adequately capture the substantial heterogeneity in metabolic risk among patients with type 2 diabetes mellitus (T2DM). Transforming MetS into a continuous severity score allows for a more precise quantification of the overall metabolic burden. This study aimed to investigate the associations of the metabolic score for insulin resistance (METS-IR), homeostasis model assessment for insulin resistance (HOMA-IR), and serum uric acid to high density lipoprotein-cholesterol ratio (UHR) with the presence and severity of MetS among patients with T2DM. Methods This retrospective study included 726 patients with confirmed T2DM hospitalized at Longyan First Hospital, Fujian Medical University, from June 2022 to May 2025. Correlation analysis, multivariable regression analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were performed to evaluate the relationships between insulin resistance (IR) indicators and MetS and its severity. Results After multivariable adjustment, METS-IR, HOMA-IR, and UHR were all identified as independent risk factors for MetS (P < 0.001) and were positively associated with MetS severity (METS-IR: β = 0.12, 95% CI 0.11–0.12; HOMA-IR: β = 0.14, 95% CI 0.12–0.17; UHR: β = 0.14, 95% CI 0.13–0.15; P < 0.001). Among these, METS-IR demonstrated the strongest predictive performance (AUC = 0.893). The DCA curve shows that METS-IR has the highest clinical net benefit across a wide range of risk thresholds, confirming its practical application value as a screening tool that is superior to the other two. Conclusion The assessment of METS-IR is of great importance for the early identification and intervention of metabolic syndrome and its progression. metabolic syndrome metabolic syndrome score insulin resistance METS-IR HOMA-IR UHR Full Text Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviews received at journal 17 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers invited by journal 16 Jan, 2026 Editor invited by journal 19 Dec, 2025 Editor assigned by journal 17 Dec, 2025 Submission checks completed at journal 17 Dec, 2025 First submitted to journal 15 Dec, 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-8366375","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576975090,"identity":"b2814d26-bbb1-4c84-a058-56a6eaa0c0ba","order_by":0,"name":"Jiying Zhu","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiying","middleName":"","lastName":"Zhu","suffix":""},{"id":576975091,"identity":"23a8c137-10b2-4621-bb8a-0e76a7848375","order_by":1,"name":"Tao 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scoring","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"metabolic syndrome, metabolic syndrome score, insulin resistance, METS-IR, HOMA-IR, UHR","lastPublishedDoi":"10.21203/rs.3.rs-8366375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8366375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic syndrome (MetS) is commonly diagnosed using a binary classification approach, which does not adequately capture the substantial heterogeneity in metabolic risk among patients with type 2 diabetes mellitus (T2DM). Transforming MetS into a continuous severity score allows for a more precise quantification of the overall metabolic burden. This study aimed to investigate the associations of the metabolic score for insulin resistance (METS-IR), homeostasis model assessment for insulin resistance (HOMA-IR), and serum uric acid to high density lipoprotein-cholesterol ratio (UHR) with the presence and severity of MetS among patients with T2DM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 726 patients with confirmed T2DM hospitalized at Longyan First Hospital, Fujian Medical University, from June 2022 to May 2025. Correlation analysis, multivariable regression analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were performed to evaluate the relationships between insulin resistance (IR) indicators and MetS and its severity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter multivariable adjustment, METS-IR, HOMA-IR, and UHR were all identified as independent risk factors for MetS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and were positively associated with MetS severity (METS-IR: β\u0026thinsp;=\u0026thinsp;0.12, 95% CI 0.11\u0026ndash;0.12; HOMA-IR: β\u0026thinsp;=\u0026thinsp;0.14, 95% CI 0.12\u0026ndash;0.17; UHR: β\u0026thinsp;=\u0026thinsp;0.14, 95% CI 0.13\u0026ndash;0.15; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among these, METS-IR demonstrated the strongest predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.893). The DCA curve shows that METS-IR has the highest clinical net benefit across a wide range of risk thresholds, confirming its practical application value as a screening tool that is superior to the other two.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe assessment of METS-IR is of great importance for the early identification and intervention of metabolic syndrome and its progression.\u003c/p\u003e","manuscriptTitle":"The association between insulin resistance and the occurrence of metabolic syndrome in Chinese patients with type 2 diabetes, and the application of Age–Sex–Ethnicity–Specific Severity scoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 10:31:22","doi":"10.21203/rs.3.rs-8366375/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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