Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept. Issue 9 (5th September 2020)

• Record Type: Journal Article
• Authors: Jayadev, Chethan; Hulley, Philippa; Swales, Catherine; Snelling, Sarah; Collins, Gary; Taylor, Peter; Price, Andrew
• Is Part Of: Bone & joint research. Volume 9:Issue 9(2020)
• Page Start: 623
• Page End: 632
• Publication Date: 2020-09-05
• Subjects: Osteoarthritis -- Biomarker -- Machine learning; Musculoskeletal system -- Periodicals; 573.705
• DOI: 10.1302/2046-3758.99.BJR-2019-0192.R1
• Languages: English
• ISSNs: 2046-3758

Abstract

Abstract : Aims: The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Methods: Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA. Results: PLS-DA produced a streamlined biomarker model with excellent sensitivity (95%), specificity (98.4%), and reliability (97.4%). The eight-biomarker model produced a fingerprint for esOA comprising type IIA procollagen N-terminal propeptide (PIIANP), tissue inhibitor of metalloproteinase (TIMP)-1, a disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4), monocyte chemoattractant protein (MCP)-1, interferon-γ-inducible protein-10 (IP-10), and transforming growth factor (TGF)-β3. Receiver operating characteristic (ROC) analysis demonstrated excellent discriminatory accuracy: area under the curve (AUC) being 0.970 for esOA, 0.957 for knee injury, and 1 for inflammatory arthritis. All ten validation test patients were classified correctly as esOA (accuracy 100%; reliability 100%) by the biomarker model. Conclusion: SF analysis coupled with machine learning produced a partially validated biomarker model with cohort-specific fingerprints that accurately and reliably discriminated esOA from knee injury and inflammatory arthritis with almost 100% efficacy. The presented findings and approach represent a new biomarker concept and potential diagnostic tool to stage disease in therapy trials and monitor the efficacy of such interventions. Cite this article: Bone Joint Res 2020;9(9):623–632.