[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126231-en":3,"doc-seo-126231-105":30,"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":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126231,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Unveiling psychobiological correlates in primary Sjögren’s syndrome - a machine learning approach to determinants of disease burden","Primary Sjögren’s syndrome (pSS) is typically evaluated using biological markers, yet psychological and social factors are increasingly implicated in disease burden. This study compares the relative predictive weight of biological, psychological (depression, anxiety, personality traits, self-esteem), and social measures. Machine learning models were trained to predict autoantibodies and the ESSPRI burden index, using permutation feature importance to rank predictors. Trait anxiety emerged as a key negative predictor, while immune markers such as IgG and RF showed distinct associations across outcomes.","TYPE Original Research PUBLISHED 03 June 2025  \nDOI 10.3389/fpsyt.2025.1549756  \nOPEN ACCESS  \nEDITED BY  \nMassimo Tusconi,  \nUniversity of Cagliari, Italy  \nREVIEWED BY  \nPaolo Meneguzzo, University of Padua, Italy Emanuele Maria Merlo, University of Messina, Italy  \n*CORRESPONDENCE  \nLa´ szlo´ V. Mo´ dis  \n [modis.laszlo6@med.unideb.hu](modis.laszlo6@med.unideb.hu)  \n†These authors have contributed equally to this work  \nRECEIVED 04 January 2025  \nACCEPTED 16 May 2025  \nPUBLISHED 03 June 2025  \nCITATION  \nV. Mo´ dis L, Matuz A, Aradi Z, Horv´ath IF, Sz´anto´ A and Bug´an A (2025) Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden. Front. Psychiatry 16:1549756 .  \ndoi: 10.3389/fpsyt.2025.1549756  \nCOPYRIGHT  \n© 2025 V. Mo´ dis, Matuz, Aradi, Horva´th, Sz´anto´ and Bug´an. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nUnveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden  \nL´aszlo´ V. Mo´ dis1,2*†, Andr´as Matuz 3,4†, Zso´ fia Aradi 5,  \nIldiko´ Fanny Horv´ath 5, Anto´ nia Sz´anto´ 5 and Antal Bug´an 1  \n1 Department of Behavioural Sciences, Faculty of Medicine, University of Debrecen,  \nDebrecen, Hungary, 2Szabolcs-Szatm´ar-Bereg County Teaching Hospital, Nagyk´allo´ S´antha K´alm´an Member Hospital, Nagyk´allo´, Hungary, 3 Department of Behavioural Sciences, Medical School, University of Pe´ cs, Pe´cs, Hungary, 4Szent´agothai Research Centre, University of Pe´ cs, Pe´cs, Hungary, 5 Division of Clinical Immunology, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary  \nIntroduction: Besides primary Sjögren ’s syndrome (pSS) is generally assessed through biological markers, growing evidence suggests that psychological and social factors—such as anxiety, depression, personality traits, and social support—may also play a role in disease burden . Relative contribution of these biopsychosocial dimensions to disease activity in pSS, however, has not been quantitatively compared. This study aimed to evaluate the predictive weight of different factors in determining both objective and subjective disease burden using machine learning (ML) models.  \nMethods: 117 pSS patients, whose biological (blood cell counts, complement activity, IgG, RF, SSA, SSB), psychological (personality traits, depression, anxiety, basic self-esteem assessed via self-reported questionnaires), and social (socioeconomic status and social support) measures were collected in a composite database. Outcome variables were SSA/SSB autoantibodies and EULAR Sjögren Syndrome Patient Reported Index (ESSPRI), as indicators of biological and perceived disease burden, respectively. Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. Permutation feature importance was used to assess the importance of the predictors. The five most important predictors were selected for all target outcomes.  \nResults: Concerning autoantibodies, the model performed best with biological input only, in the case of ESSPRI, the complete dataset gave the best performance. Trait anxiety was selected as important negative predictor of both autoantibodies. Besides, biological measures (IgG, RF, platelet count) and age were among the five most important features . State anxiety and temperament trait ‘ Fatigability’ were important positive predictors of ESSPRI, while character trait ‘ Pure-hearted conscience’, Ig","cbCailH00ST6zY1f","https://ap.wps.com/l/cbCailH00ST6zY1f","pdf",729438,1,11,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What was the main goal of this machine learning study in primary Sjögren’s syndrome?\",\"answer\":\"To evaluate how much different biological, psychological, and social factors predict both objective and subjective disease burden in pSS.\"},{\"question\":\"Which measures were used as predictors and outcomes?\",\"answer\":\"Predictors included blood/immune markers, personality and affect measures, self-esteem, and social variables; outcomes were autoantibodies (SSA/SSB) and the EULAR Sjögren Syndrome Patient Reported Index (ESSPRI).\"},{\"question\":\"What key predictors were identified by the models?\",\"answer\":\"Trait anxiety was selected as an important negative predictor for autoantibodies, while state anxiety and temperament “Fatigability” were important positive predictors of ESSPRI; IgG and RF contributed negatively to ESSPRI predictions.\"}]","Unveiling psychobiological correlates in primary Sjögren’s syndrome - a machine learning approach to determinants of disease burden | PDF",1785903953,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"unveiling-psychobiological-correlates-in-primary-sjogrens-syndrome-a-machine-learning-approach-to-determinants-of-disease-burden","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/unveiling-psychobiological-correlates-in-primary-sjogrens-syndrome-a-machine-learning-approach-to-determinants-of-disease-burden/126231/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main goal of this machine learning study in primary Sjögren’s syndrome?","Question",{"text":76,"@type":77},"To evaluate how much different biological, psychological, and social factors predict both objective and subjective disease burden in pSS.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which measures were used as predictors and outcomes?",{"text":81,"@type":77},"Predictors included blood/immune markers, personality and affect measures, self-esteem, and social variables; outcomes were autoantibodies (SSA/SSB) and the EULAR Sjögren Syndrome Patient Reported Index (ESSPRI).",{"name":83,"@type":74,"acceptedAnswer":84},"What key predictors were identified by the models?",{"text":85,"@type":77},"Trait anxiety was selected as an important negative predictor for autoantibodies, while state anxiety and temperament “Fatigability” were important positive predictors of ESSPRI; IgG and RF contributed negatively to ESSPRI predictions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]