Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data

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• dc.contributor.author Segarra-Queralt, Maria
• dc.contributor.author Galofré, Mar
• dc.contributor.author Tío, Laura
• dc.contributor.author Monfort, Jordi
• dc.contributor.author Monllau García, Juan Carlos
• dc.contributor.author Piella Fenoy, Gemma
• dc.contributor.author Noailly, Jérôme
• dc.date.accessioned 2025-07-17T06:08:43Z
• dc.date.available 2025-07-17T06:08:43Z
• dc.date.issued 2024
• dc.description.abstract Knee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren-Lawrence grade 2-3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden's test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.
• dc.description.sponsorship Catalan and Spanish Governments (2020FI b00680; STRATO PID2021126469ob-C21-2), European Commission (MSCA-TN-ETN-2020-Disc4All-955735, ERC-2021-CoG-O-Health-101044828). G. Piella is supported by the ICREA Academia programme.
• dc.format.mimetype application/pdf
• dc.identifier.citation Segarra-Queralt M, Galofré M, Tio L, Monfort J, Monllau JC, Piella G, et al. Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data. Sci Rep. 2024 May 23;14(1):11797. DOI: 10.1038/s41598-024-62212-x
• dc.identifier.doi http://dx.doi.org/10.1038/s41598-024-62212-x
• dc.identifier.issn 2045-2322
• dc.identifier.uri http://hdl.handle.net/10230/70941
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Sci Rep. 2024 May 23;14(1):11797
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/955735
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101044828
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-126469OB-C21
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-126469OB-C22
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Biochemical networks
• dc.subject.keyword Biomarkers
• dc.subject.keyword Computational science
• dc.subject.keyword Molecular medicine
• dc.subject.keyword Rheumatology
• dc.title Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion