[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128242-en":3,"doc-seo-128242-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128242,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","The Association of Periodontal Inflammation and Systemic Health Indicators - A Machine Learning Approach","This study examines how periodontal inflammation relates to systemic inflammation and metabolic health markers, and evaluates whether systemic health indicators can support prediction of periodontal status using machine learning. Using a cross-sectional cohort (N=667), multiple regression and classification models assess links between periodontal inflamed surface area (PISA) and serum C-reactive protein (CRP) as well as demographic and anthropometric factors. Models are validated with NHANES datasets (2001–2002, 2003–2004, and 2009–2010).","University of Birmingham  \nThe Association of Periodontal Inflammation and Systemic Health Indicators  \nYan, Yumeng; Sharma, Praveen; Suvan, Jeanie; D'Aiuto, Francesco  \nDOI:  \n10.1111/jcpe.70000  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nYan, Y, Sharma, P, Suvan, J & D'Aiuto, F 2025, 'The Association of Periodontal Inflammation and Systemic Health Indicators: A Machine Learning Approach', Journal of Clinical Periodontology, vol. 52, no. 10, pp. 1466-  \n1477. [https://doi.org/10.1111/jcpe.70000](https://doi.org/10.1111/jcpe.70000)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Aug. 2026  \nJournal of Clinical Periodontology  \nORIGINAL ARTICLE  OPEN ACCESS   \nThe Association of Periodontal Inflammation and Systemic Health Indicators: A Machine Learning Approach  \nYumeng Yan1  | Praveen Sharma2,3,4  | Jeanie Suvan5  | Francesco D'Aiuto1   \n1Periodontology Unit, UCL Eastman Dental Institute, London, UK | 2Periodontal Research Group, School of Dentistry, Institute of Clinical Sciences, University of Birmingham, Birmingham, UK | 3National Institute for Health Research, Birmingham Biomedical Research Centre, Birmingham,  \nUK | 4Birmingham Dental Hospital, Birmingham Community Healthcare NHS Foundation Trust, Birmingham, UK | 5Oral Sciences, University of Glasgow Dental School, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK Correspondence: Francesco D'Aiuto ([f.daiuto@ucl.ac.uk](f.daiuto@ucl.ac.uk))  \nReceived: 27 February 2025 | Revised: 21 May 2025 | Accepted: 3 July 2025  \nFunding: This study was completed at University College London (UCL) and the University of Birmingham Biomedical Research Centre which receives funding from the National Institute for Health Research (NIHR) .  \nKeywords: C-reacive protein | inflammation | periodontal inflamed surface area | periodontitis | systemic diseases  \nABSTRACT  \nAim: The relationship between oral and systemic inflammation has profound implications for understanding the broader health impacts of periodontitis. The aim of this study was to (a) explore the association between periodontal inflammation and markers of systemic inflammation and metabolic health, and (b) preliminarily assess periodontal status based on systemic health indicators using machine learning techniques.  \nMethods: Data from a cross-sectional cohort (N = 667) were modelle","cbCaigF5GbTHSo6l","https://ap.wps.com/l/cbCaigF5GbTHSo6l","pdf",1278194,2,1,13,"English","en",105,"# Abstract\n## Aim\n## Methods\n## Results","[{\"question\":\"What is the study aiming to evaluate regarding periodontal inflammation and systemic health?\",\"answer\":\"The study explores associations between periodontal inflammation and systemic inflammation/metabolic markers, and preliminarily assesses whether periodontal status can be predicted from systemic health indicators using machine learning.\"},{\"question\":\"How were the models built and what inputs were used?\",\"answer\":\"Cross-sectional cohort data (N=667) were modeled using regression techniques and classifiers, with PISA plus demographic and anthropometric variables (age, gender, ethnicity, BMI, smoking) to predict systemic inflammation defined by serum CRP.\"},{\"question\":\"How were the machine learning models validated and what were the key findings?\",\"answer\":\"The best models were validated using combined NHANES 2001–2002 and 2003–2004 datasets, then further validated with NHANES 2009–2010. CRP and PISA showed a nonlinear trend, and SVM performed strongly in distinguishing CRP categories and predicting periodontitis status.\"}]","The Association of Periodontal Inflammation and Systemic Health Indicators - A Machine Learning Approach | PDF",1785946044,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"the-association-of-periodontal-inflammation-and-systemic-health-indicators-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/the-association-of-periodontal-inflammation-and-systemic-health-indicators-a-machine-learning-approach/128242/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study aiming to evaluate regarding periodontal inflammation and systemic health?","Question",{"text":76,"@type":77},"The study explores associations between periodontal inflammation and systemic inflammation/metabolic markers, and preliminarily assesses whether periodontal status can be predicted from systemic health indicators using machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the models built and what inputs were used?",{"text":81,"@type":77},"Cross-sectional cohort data (N=667) were modeled using regression techniques and classifiers, with PISA plus demographic and anthropometric variables (age, gender, ethnicity, BMI, smoking) to predict systemic inflammation defined by serum CRP.",{"name":83,"@type":74,"acceptedAnswer":84},"How were the machine learning models validated and what were the key findings?",{"text":85,"@type":77},"The best models were validated using combined NHANES 2001–2002 and 2003–2004 datasets, then further validated with NHANES 2009–2010. 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