Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index and its derivatives in the prediction of 10-year cardiovascular mortality: insights from MASHAD cohort study

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Abstract Background Cardiovascular diseases (CVDs) remain a major global health challenge, accounting for substantial illness and death worldwide. Growing evidence suggests that insulin resistance (IR) has a substantial role in their development and progression. A recently introduced IR surrogate marker, the Cholesterol-HDL-Glucose (CHG) index, has been suggested as an index for identifying metabolic disturbances, but its value in predicting cardiovascular mortality has not been clearly established. This study set out to examine how well the CHG index predicts cardiovascular mortality when compared with the Triglyceride-Glucose (TyG) index and its variants combined with obesity measures. Method Data from a total of 7467 adults aged 35 to 65 years were derived from the MASHAD study, with data collected from 2011 to 2020. Cardiovascular and all-cause mortalities were tracked over at least 10-year follow-up. The CHG, TyG and its derived substitutes were calculated, and their associations with mortality outcomes were assessed using univariate and multivariate Cox regression models. Additionally, receiver operating characteristic (ROC) analysis, Harrell’s C-index, restricted cubic spline (RCS), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), likelihood-based pseudo-R² and the share of explainable log-likelihood, decision curve analysis (DCA) for clinical utility, Kaplan-Meier survival curves, E-value for robustness of associations, and subgroup analysis were performed in statistical analysis. Results Higher CHG values were strongly associated with a greater risk of both cardiovascular and all-cause mortality. Each standard deviation (SD) increase in CHG value, raise the risk of cardiovascular mortality by 35.4%, which was higher than TyG and its related markers. For all-cause mortality, 1-SD increase in CHG was associated with 21.4% higher risk. In predicting cardiovascular mortality, CHG outperformed TyG and its derivatives on ROC, C-index, IDI, pseudo-R², share of log-likelihood and DCA measures. For all-cause mortality, certain TyG-based indices performed better. RCS modeling indicated a linear relationship between CHG and cardiovascular mortality, and a non-linear relationship with all-cause mortality. Survival analysis, robustness checks, and subgroup analyses supported these findings, with no evidence of effect modification for cardiovascular mortality. Conclusion Elevated CHG is independently associated with a higher risk of cardiovascular mortality and shows a consistent linear pattern. It provides stronger predictive value for cardiovascular death than TyG and its derivatives, supporting its role as a useful marker for assessing CVD risk.
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Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index and its derivatives in the prediction of 10-year cardiovascular mortality: insights from MASHAD cohort study | 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 Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index and its derivatives in the prediction of 10-year cardiovascular mortality: insights from MASHAD cohort study Ali Tajik, Majid Ghayour-Mobarhan, Susan Darroudi, Bahram Shahri, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797604/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Cardiovascular diseases (CVDs) remain a major global health challenge, accounting for substantial illness and death worldwide. Growing evidence suggests that insulin resistance (IR) has a substantial role in their development and progression. A recently introduced IR surrogate marker, the Cholesterol-HDL-Glucose (CHG) index, has been suggested as an index for identifying metabolic disturbances, but its value in predicting cardiovascular mortality has not been clearly established. This study set out to examine how well the CHG index predicts cardiovascular mortality when compared with the Triglyceride-Glucose (TyG) index and its variants combined with obesity measures. Method Data from a total of 7467 adults aged 35 to 65 years were derived from the MASHAD study, with data collected from 2011 to 2020. Cardiovascular and all-cause mortalities were tracked over at least 10-year follow-up. The CHG, TyG and its derived substitutes were calculated, and their associations with mortality outcomes were assessed using univariate and multivariate Cox regression models. Additionally, receiver operating characteristic (ROC) analysis, Harrell’s C-index, restricted cubic spline (RCS), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), likelihood-based pseudo-R² and the share of explainable log-likelihood, decision curve analysis (DCA) for clinical utility, Kaplan-Meier survival curves, E-value for robustness of associations, and subgroup analysis were performed in statistical analysis. Results Higher CHG values were strongly associated with a greater risk of both cardiovascular and all-cause mortality. Each standard deviation (SD) increase in CHG value, raise the risk of cardiovascular mortality by 35.4%, which was higher than TyG and its related markers. For all-cause mortality, 1-SD increase in CHG was associated with 21.4% higher risk. In predicting cardiovascular mortality, CHG outperformed TyG and its derivatives on ROC, C-index, IDI, pseudo-R², share of log-likelihood and DCA measures. For all-cause mortality, certain TyG-based indices performed better. RCS modeling indicated a linear relationship between CHG and cardiovascular mortality, and a non-linear relationship with all-cause mortality. Survival analysis, robustness checks, and subgroup analyses supported these findings, with no evidence of effect modification for cardiovascular mortality. Conclusion Elevated CHG is independently associated with a higher risk of cardiovascular mortality and shows a consistent linear pattern. It provides stronger predictive value for cardiovascular death than TyG and its derivatives, supporting its role as a useful marker for assessing CVD risk. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Cholesterol high-density lipoprotein glucose index Cardiovascular mortality Triglyceride-glucose index obesity marker prediction Full Text Additional Declarations No competing interests reported. Table 1 to 4 are available in the Supplementary Files section. Supplementary Files Supplementaryfiles.rar Table.docx Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 20 Oct, 2025 Editor invited by journal 10 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 Oct, 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-7797604","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535442891,"identity":"6d58bba3-7afa-4a9b-a000-a4fadcc105bd","order_by":0,"name":"Ali Tajik","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Tajik","suffix":""},{"id":535442893,"identity":"60a47a96-adba-431d-8299-006180aada4c","order_by":1,"name":"Majid Ghayour-Mobarhan","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Ghayour-Mobarhan","suffix":""},{"id":535442894,"identity":"7fb9ce07-0b61-4542-9500-2dd26f446fa3","order_by":2,"name":"Susan Darroudi","email":"","orcid":"","institution":"University of Modena and Reggio Emilia","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Darroudi","suffix":""},{"id":535442895,"identity":"b939cac2-9eb3-4619-bfbf-0eb7f173adb5","order_by":3,"name":"Bahram Shahri","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bahram","middleName":"","lastName":"Shahri","suffix":""},{"id":535442896,"identity":"58ebde46-f23d-4b16-b816-2e7cf3ad1573","order_by":4,"name":"Habibollah Esmaily","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Habibollah","middleName":"","lastName":"Esmaily","suffix":""},{"id":535442897,"identity":"10207ce5-d92d-428c-a6e6-cbe9cf4b2c49","order_by":5,"name":"Sara Saffar Soflaei","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Saffar","lastName":"Soflaei","suffix":""},{"id":535442898,"identity":"91556233-bd24-4bea-9eb1-d06411f4de33","order_by":6,"name":"Gordon A. 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study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cholesterol, high-density lipoprotein, glucose index, Cardiovascular mortality, Triglyceride-glucose index, obesity marker, prediction","lastPublishedDoi":"10.21203/rs.3.rs-7797604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCardiovascular diseases (CVDs) remain a major global health challenge, accounting for substantial illness and death worldwide. Growing evidence suggests that insulin resistance (IR) has a substantial role in their development and progression. A recently introduced IR surrogate marker, the Cholesterol-HDL-Glucose (CHG) index, has been suggested as an index for identifying metabolic disturbances, but its value in predicting cardiovascular mortality has not been clearly established. This study set out to examine how well the CHG index predicts cardiovascular mortality when compared with the Triglyceride-Glucose (TyG) index and its variants combined with obesity measures.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eData from a total of 7467 adults aged 35 to 65 years were derived from the MASHAD study, with data collected from 2011 to 2020. Cardiovascular and all-cause mortalities were tracked over at least 10-year follow-up. The CHG, TyG and its derived substitutes were calculated, and their associations with mortality outcomes were assessed using univariate and multivariate Cox regression models. Additionally, receiver operating characteristic (ROC) analysis, Harrell\u0026rsquo;s C-index, restricted cubic spline (RCS), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), likelihood-based pseudo-R\u0026sup2; and the share of explainable log-likelihood, decision curve analysis (DCA) for clinical utility, Kaplan-Meier survival curves, E-value for robustness of associations, and subgroup analysis were performed in statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher CHG values were strongly associated with a greater risk of both cardiovascular and all-cause mortality. Each standard deviation (SD) increase in CHG value, raise the risk of cardiovascular mortality by 35.4%, which was higher than TyG and its related markers. For all-cause mortality, 1-SD increase in CHG was associated with 21.4% higher risk. In predicting cardiovascular mortality, CHG outperformed TyG and its derivatives on ROC, C-index, IDI, pseudo-R\u0026sup2;, share of log-likelihood and DCA measures. For all-cause mortality, certain TyG-based indices performed better. RCS modeling indicated a linear relationship between CHG and cardiovascular mortality, and a non-linear relationship with all-cause mortality. Survival analysis, robustness checks, and subgroup analyses supported these findings, with no evidence of effect modification for cardiovascular mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated CHG is independently associated with a higher risk of cardiovascular mortality and shows a consistent linear pattern. It provides stronger predictive value for cardiovascular death than TyG and its derivatives, supporting its role as a useful marker for assessing CVD risk.\u003c/p\u003e","manuscriptTitle":"Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index and its derivatives in the prediction of 10-year cardiovascular mortality: insights from MASHAD cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 20:06:06","doi":"10.21203/rs.3.rs-7797604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T08:27:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T12:47:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T13:44:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172941968294417249180667219536156801091","date":"2025-11-13T13:19:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144587896071758496109375092627082849942","date":"2025-11-06T16:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338930755036338441530250703265246795180","date":"2025-10-27T09:24:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-20T14:35:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-20T13:42:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T15:25:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-09T07:56:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-09T07:24:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4171013a-f743-476f-8636-2ae3eaa91aec","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56918393,"name":"Health sciences/Biomarkers"},{"id":56918394,"name":"Health sciences/Cardiology"},{"id":56918395,"name":"Health sciences/Diseases"},{"id":56918396,"name":"Health sciences/Endocrinology"},{"id":56918397,"name":"Health sciences/Medical research"},{"id":56918398,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-02T16:02:56+00:00","versionOfRecord":{"articleIdentity":"rs-7797604","link":"https://doi.org/10.1038/s41598-026-41569-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-26 15:58:27","publishedOnDateReadable":"February 26th, 2026"},"versionCreatedAt":"2025-10-30 20:06:06","video":"","vorDoi":"10.1038/s41598-026-41569-1","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41569-1","workflowStages":[]},"version":"v1","identity":"rs-7797604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797604","identity":"rs-7797604","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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