Metabolomic Profiling Reveals Interindividual Metabolic Variability and Its Association with Cardiovascular-Kidney-Metabolic Syndrome Risk

Metabolomic Profiling Reveals Interindividual Metabolic Variability and Its Association with Cardiovascular-Kidney-Metabolic Syndrome Risk | 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 Metabolomic Profiling Reveals Interindividual Metabolic Variability and Its Association with Cardiovascular-Kidney-Metabolic Syndrome Risk Meng Zhou, Wenxiu Sun, Yuhan Gao, Bei Jiang, Tianwei Sun, Rui Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7120089/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 and Objective: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the interrelated pathophysiology of obesity, insulin resistance, type 2 diabetes, chronic kidney disease, and cardiovascular disease. Conventional CKM staging often detects risk only after substantial organ dysfunction and may overlook early metabolic heterogeneity. This study aimed to employ plasma metabolomics to identify metabolic subtypes linked to CKM severity and explore early biomarkers for high-risk individuals. Methods: A cross-sectional study was conducted involving 163 adults, which included 86 individuals clinically staged as CKM 0–3 according to the criteria proposed by the American Heart Association (AHA). Plasma samples underwent untargeted metabolomic and lipidomic profiling using liquid chromatography–mass spectrometry (LC-MS). Unsupervised clustering identified metabolic subtypes, with validation via random forest analysis. Group differences were assessed using orthogonal partial least squares–discriminant analysis (OPLS-DA) and logistic regression classifiers. Results: A total of 390 metabolites, categorized into 9 superclasses and 30 subclasses, were identified. Three distinct metabolic clusters emerged: Cluster 1 (glycerophospholipid-enriched), Cluster 2 (fatty acyl–dominant), and Cluster 3 (glycolipid-enriched). At the individual differential metabolite level, Cluster 1 exhibited a generally low metabolic status, Cluster 2 demonstrated an intermediate metabolic profile, and Cluster 3 showed a high metabolic status. High-risk CKM individuals were predominantly assigned to Cluster 3 (p < 0.001). Within each cluster, OPLS-DA effectively differentiated high- and low-risk individuals based on lipid profiles, highlighting triglycerides, fatty acids, phosphatidylcholines, sphingolipids, and acylcarnitines as key discriminators. Secondary clustering among stage 3 of CKM patients revealed substantial metabolic heterogeneity. A panel of 20 metabolites achieved high diagnostic performance for stage 3 of CKM individual (AUC = 0.875). Conclusions: Untargeted plasma metabolomic profiling reveals distinct metabolic subtypes corresponding to CKM severity and uncovers marked heterogeneity within the high-risk group. Key metabolite signatures may enhance early risk stratification and support more personalized management strategies beyond conventional CKM staging. Cardiac & Cardiovascular Systems Cardiovascular-Kidney-Metabolic (CKM) Syndrome Liquid Chromatography-Mass Spectrometry (LC–MS) Metabolomics Lipidomics Unsupervised Clustering Metabolic Endotypes Full Text Additional Declarations The authors declare no competing interests. Supplementary Material are not available with this version. 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. 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interests.\u003c/p\u003e\n\u003cp\u003eSupplementary Material are not available with this version.\u003c/p\u003e","formattedTitle":"\u003cp\u003eMetabolomic Profiling Reveals Interindividual Metabolic Variability and Its Association with Cardiovascular-Kidney-Metabolic Syndrome Risk\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Shandong Provincial 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4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objective:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular-Kidney-Metabolic (CKM) syndrome reflects the interrelated pathophysiology of obesity, insulin resistance, type 2 diabetes, chronic kidney disease, and cardiovascular disease. Conventional CKM staging often detects risk only after substantial organ dysfunction and may overlook early metabolic heterogeneity. This study aimed to employ plasma metabolomics to identify metabolic subtypes linked to CKM severity and explore early biomarkers for high-risk individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted involving 163 adults, which included 86 individuals clinically staged as CKM 0–3 according to the criteria proposed by the American Heart Association (AHA). Plasma samples underwent untargeted metabolomic and lipidomic profiling using liquid chromatography–mass spectrometry (LC-MS). Unsupervised clustering identified metabolic subtypes, with validation via random forest analysis. Group differences were assessed using orthogonal partial least squares–discriminant analysis (OPLS-DA) and logistic regression classifiers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 390 metabolites, categorized into 9 superclasses and 30 subclasses, were identified. Three distinct metabolic clusters emerged: Cluster 1 (glycerophospholipid-enriched), Cluster 2 (fatty acyl–dominant), and Cluster 3 (glycolipid-enriched). At the individual differential metabolite level, Cluster 1 exhibited a generally low metabolic status, Cluster 2 demonstrated an intermediate metabolic profile, and Cluster 3 showed a high metabolic status. High-risk CKM individuals were predominantly assigned to Cluster 3 (p \u0026lt; 0.001). Within each cluster, OPLS-DA effectively differentiated high- and low-risk individuals based on lipid profiles, highlighting triglycerides, fatty acids, phosphatidylcholines, sphingolipids, and acylcarnitines as key discriminators. Secondary clustering among stage 3 of CKM patients revealed substantial metabolic heterogeneity. A panel of 20 metabolites achieved high diagnostic performance for stage 3 of CKM individual (AUC = 0.875).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUntargeted plasma metabolomic profiling reveals distinct metabolic subtypes corresponding to CKM severity and uncovers marked heterogeneity within the high-risk group. Key metabolite signatures may enhance early risk stratification and support more personalized management strategies beyond conventional CKM staging.\u003c/p\u003e","manuscriptTitle":"Metabolomic Profiling Reveals Interindividual Metabolic Variability and Its Association with Cardiovascular-Kidney-Metabolic Syndrome Risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 13:58:34","doi":"10.21203/rs.3.rs-7120089/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":"76ebff14-8c1a-4bbb-b7f8-429db386651d","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51547501,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2025-07-16T13:58:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-16 13:58:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7120089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7120089","identity":"rs-7120089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}