Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept
| Autor: | Philippa A. Hulley, Peter C. Taylor, Catherine Swales, Sarah J. B. Snelling, Gary S. Collins, Chethan Jayadev, Andrew Price |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
030203 arthritis & rheumatology
Oncology medicine.medical_specialty lcsh:Diseases of the musculoskeletal system business.industry Economic shortage Osteoarthritis medicine.disease 030218 nuclear medicine & medical imaging 03 medical and health sciences osteoarthritis 0302 clinical medicine machine learning Internal medicine medicine Biomarker (medicine) Synovial fluid biomarker Orthopedics and Sports Medicine Surgery Synovial fluid analysis Stage (cooking) lcsh:RC925-935 business |
| Zdroj: | Bone & Joint Research, Vol 9, Iss 9, Pp 623-632 (2020) |
| ISSN: | 2046-3758 |
| DOI: | 10.1302/2046-3758.99.BJR-2019-0192.R1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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