Correlation Between the Arteriosclerosis Index of Plasma and Major Adverse Long-Term Prognosis in Patients with Coronary Artery Disease Combined with Stage 2–5 Chronic Kidney Disease

Correlation Between the Arteriosclerosis Index of Plasma and Major Adverse Long-Term Prognosis in Patients with Coronary Artery Disease Combined with Stage 2–5 Chronic Kidney 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 Research Article Correlation Between the Arteriosclerosis Index of Plasma and Major Adverse Long-Term Prognosis in Patients with Coronary Artery Disease Combined with Stage 2–5 Chronic Kidney Disease Hongya Liu, JiaYi Ren, Chao Jiang, Xian Shao, JinHua Zhao, KangYin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6800911/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 The prognostic utility of the atherogenic index of plasma (AIP) in patients with coronary artery disease (CAD) and chronic kidney disease (CKD) remains unclear. This study aimed to evaluate the association between AIP and long-term outcomes in patients with CAD and Stage 2–5 CKD. Methods This retrospective study included 1,816 patients with angiographically confirmed CAD and renal insufficiency (Stage 2–5 CKD) from a tertiary center between January 2019 and June 2023. AIP was defined as the logarithmic ratio of plasma triglycerides to HDL-C. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE), including cardiac death, non-fatal myocardial infarction (MI), non-fatal stroke, and ischemia-driven revascularization. Patients were stratified into high and low AIP groups based on the optimal ROC cut-off (0.148). Propensity score matching (PSM) yielded 589 well-balanced pairs. Multivariable Cox regression, Kaplan–Meier survival analysis, and model performance metrics (C-statistic, NRI, IDI) were used to assess prognostic value. Results MACCE occurred in 379 patients (20.9%). High AIP independently predicted increased MACCE risk (adjusted HR: 1.95; 95% CI: 1.45–2.63; P < 0.001). Specifically, the high-AIP group showed significantly elevated risks of cardiac death (HR: 2.07; 95% CI: 1.13–3.80; P = 0.019), non-fatal MI (HR: 1.97; 95% CI: 1.02–3.78; P = 0.043), and ischemia-driven revascularization (HR: 2.97; 95% CI: 1.76–5.02; P < 0.001). Integration of AIP improved the predictive accuracy of the GRACE risk model (C-statistic: 0.624 to 0.679; NRI = 0.139, P = 0.01; IDI = 0.037, P < 0.001). Optimal AIP cutoff for predicting MACCE was 0.148. Conclusions AIP is an independent predictor of adverse outcomes in patients with CAD and Stage 2–5 CKD. Its integration into existing risk models significantly enhances risk stratification and facilitates identification of high-risk individuals for personalized management. Coronary artery disease chronic kidney disease arteriosclerosis index of plasma prognosis Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.doc 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. 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-6800911","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491664318,"identity":"9e3b529a-aca1-423f-839f-e12128362144","order_by":0,"name":"Hongya Liu","email":"","orcid":"","institution":"Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongya","middleName":"","lastName":"Liu","suffix":""},{"id":491664319,"identity":"3b76b42e-4148-4055-b980-fb45c1e5370e","order_by":1,"name":"JiaYi Ren","email":"","orcid":"","institution":"Tianjin Institute 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Disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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},"keywords":"Coronary artery disease, chronic kidney disease, arteriosclerosis index of plasma, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6800911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6800911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe prognostic utility of the atherogenic index of plasma (AIP) in patients with coronary artery disease (CAD) and chronic kidney disease (CKD) remains unclear. This study aimed to evaluate the association between AIP and long-term outcomes in patients with CAD and Stage 2\u0026ndash;5 CKD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective study included 1,816 patients with angiographically confirmed CAD and renal insufficiency (Stage 2\u0026ndash;5 CKD) from a tertiary center between January 2019 and June 2023. AIP was defined as the logarithmic ratio of plasma triglycerides to HDL-C. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE), including cardiac death, non-fatal myocardial infarction (MI), non-fatal stroke, and ischemia-driven revascularization. Patients were stratified into high and low AIP groups based on the optimal ROC cut-off (0.148). Propensity score matching (PSM) yielded 589 well-balanced pairs. Multivariable Cox regression, Kaplan\u0026ndash;Meier survival analysis, and model performance metrics (C-statistic, NRI, IDI) were used to assess prognostic value.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMACCE occurred in 379 patients (20.9%). High AIP independently predicted increased MACCE risk (adjusted HR: 1.95; 95% CI: 1.45\u0026ndash;2.63; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, the high-AIP group showed significantly elevated risks of cardiac death (HR: 2.07; 95% CI: 1.13\u0026ndash;3.80; P\u0026thinsp;=\u0026thinsp;0.019), non-fatal MI (HR: 1.97; 95% CI: 1.02\u0026ndash;3.78; P\u0026thinsp;=\u0026thinsp;0.043), and ischemia-driven revascularization (HR: 2.97; 95% CI: 1.76\u0026ndash;5.02; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Integration of AIP improved the predictive accuracy of the GRACE risk model (C-statistic: 0.624 to 0.679; NRI\u0026thinsp;=\u0026thinsp;0.139, P\u0026thinsp;=\u0026thinsp;0.01; IDI\u0026thinsp;=\u0026thinsp;0.037, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Optimal AIP cutoff for predicting MACCE was 0.148.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAIP is an independent predictor of adverse outcomes in patients with CAD and Stage 2\u0026ndash;5 CKD. Its integration into existing risk models significantly enhances risk stratification and facilitates identification of high-risk individuals for personalized management.\u003c/p\u003e","manuscriptTitle":"Correlation Between the Arteriosclerosis Index of Plasma and Major Adverse Long-Term Prognosis in Patients with Coronary Artery Disease Combined with Stage 2–5 Chronic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 17:27:02","doi":"10.21203/rs.3.rs-6800911/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":"ec9afc4b-61d8-4b02-8c8f-6d59afbbceb0","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T08:25:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 17:27:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6800911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6800911","identity":"rs-6800911","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}