Epidemiological Association in Asthma Rhinitis Overlap: Unveiling Key Hematological Markers Using Machine Learning

Epidemiological Association in Asthma Rhinitis Overlap: Unveiling Key Hematological Markers Using Machine Learning | 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 Epidemiological Association in Asthma Rhinitis Overlap: Unveiling Key Hematological Markers Using Machine Learning Haiyang Li, Jing Yang, Ronghua Liao, Fei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4558734/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 Allergic rhinitis (AR) has been rising globally in recent years, now affecting between 10-30% of the population. The condition frequently coexists with asthma, with up to 40% of AR patients experiencing asthma symptoms and 80% of asthmatics reporting symptomatic AR. The presence of co-occurring AR exacerbates asthma, leading to increased risks of asthma attacks, emergency visits, and hospitalizations. This study aims to identify key biomarkers for assessing AR comorbidities in asthmatic patients to improve treatment and management strategies. We categorized subjects into three groups: healthy controls (n=4,032), asthma patients (n=21,506), and asthma patients with rhinitis (n=3,881). We applied a naive Bayesian machine learning algorithm to evaluate routine blood examinations and specific indicators. The ROC curve analysis was conducted to distinguish between normal, asthma, and asthma with AR states, revealing BAS%, ALB, NEUT%, and HB as key biomarkers with high diagnostic accuracy (AUCs of 0.722, 0.712, 0.680, and 0.711, respectively). Our findings indicate significant correlations between these biomarkers and disease states. In asthma with AR patients, lower levels of NEUT% and EOS% were observed compared to healthy and asthma-only groups. Gender differences were also noted, with a higher prevalence of asthma combined with AR in males. The analysis underscores the utility of BAS%, ALB, NEUT%, and HB in predictive modeling, providing a more accurate and early diagnosis crucial for effective disease management. This study highlights the potential of integrating biochemical indicators into advanced predictive models to enhance diagnostic precision and treatment outcomes for asthma and AR. Future research should validate these findings in larger cohorts and explore the integration of these biomarkers into clinical practice for better disease management and patient care. Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Respiratory tract diseases/Asthma Health sciences/Health care/Public health/Epidemiology Allergic rhinitis Predicted progression of asthma to rhinitis Asthma-rhinitis overlap Machine learning Full Text Additional Declarations No competing interests reported. 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-4558734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318924800,"identity":"0a7a9fb2-20e6-46d5-8381-7fa19bca0ff7","order_by":0,"name":"Haiyang Li","email":"data:image/png;base64,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","orcid":"","institution":"University of Cambridge","correspondingAuthor":true,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Li","suffix":""},{"id":318924802,"identity":"51ae2e04-98f6-4b46-9fc6-756175b3f3dc","order_by":1,"name":"Jing Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Yang","suffix":""},{"id":318924803,"identity":"3a503ea1-5bf6-48f1-b36f-50c8ec14a047","order_by":2,"name":"Ronghua Liao","email":"","orcid":"","institution":"Dazhou Dachuan District People’s Hospital (Dazhou Third People’s Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Ronghua","middleName":"","lastName":"Liao","suffix":""},{"id":318924805,"identity":"7c921b9d-a016-49ea-8cc2-600b47cb9f12","order_by":3,"name":"Fei Wang","email":"","orcid":"","institution":"Dazhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-10 14:46:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4558734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4558734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77024914,"identity":"a6d49a0e-61d9-4d37-acc9-bc8a76829cbf","added_by":"auto","created_at":"2025-02-24 11:17:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15573175,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptLatex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4558734/v1_covered_cef954fe-9769-4b60-b148-d603f1235aa8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiological Association in Asthma Rhinitis Overlap: Unveiling Key Hematological Markers Using Machine Learning","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":"Allergic rhinitis, Predicted progression of asthma to rhinitis, Asthma-rhinitis overlap, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4558734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4558734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Allergic rhinitis (AR) has been rising globally in recent years, now affecting between 10-30% of the population. The condition frequently coexists with asthma, with up to 40% of AR patients experiencing asthma symptoms and 80% of asthmatics reporting symptomatic AR. The presence of co-occurring AR exacerbates asthma, leading to increased risks of asthma attacks, emergency visits, and hospitalizations. This study aims to identify key biomarkers for assessing AR comorbidities in asthmatic patients to improve treatment and management strategies. We categorized subjects into three groups: healthy controls (n=4,032), asthma patients (n=21,506), and asthma patients with rhinitis (n=3,881). We applied a naive Bayesian machine learning algorithm to evaluate routine blood examinations and specific indicators. The ROC curve analysis was conducted to distinguish between normal, asthma, and asthma with AR states, revealing BAS%, ALB, NEUT%, and HB as key biomarkers with high diagnostic accuracy (AUCs of 0.722, 0.712, 0.680, and 0.711, respectively). Our findings indicate significant correlations between these biomarkers and disease states. In asthma with AR patients, lower levels of NEUT% and EOS% were observed compared to healthy and asthma-only groups. Gender differences were also noted, with a higher prevalence of asthma combined with AR in males. The analysis underscores the utility of BAS%, ALB, NEUT%, and HB in predictive modeling, providing a more accurate and early diagnosis crucial for effective disease management. This study highlights the potential of integrating biochemical indicators into advanced predictive models to enhance diagnostic precision and treatment outcomes for asthma and AR. Future research should validate these findings in larger cohorts and explore the integration of these biomarkers into clinical practice for better disease management and patient care.","manuscriptTitle":"Epidemiological Association in Asthma Rhinitis Overlap: Unveiling Key Hematological Markers Using Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 04:14:11","doi":"10.21203/rs.3.rs-4558734/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":"6a1fa94b-2118-4ae5-b9ba-d8d22910fa41","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33724075,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":33724076,"name":"Health sciences/Diseases/Respiratory tract diseases/Asthma"},{"id":33724077,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2025-02-24T11:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 04:14:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4558734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4558734","identity":"rs-4558734","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}