Bibliographic Details
| Title: |
Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept |
| Authors: |
Chethan Jayadev, Philippa Hulley, Catherine Swales, Sarah Snelling, Gary Collins, Peter Taylor, Andrew Price |
| Source: |
Bone & Joint Research, Vol 9, Iss 9, Pp 623-632 (2020) |
| Publisher Information: |
The British Editorial Society of Bone & Joint Surgery, 2020. |
| Publication Year: |
2020 |
| Collection: |
LCC:Diseases of the musculoskeletal system |
| Subject Terms: |
osteoarthritis, biomarker, machine learning, Diseases of the musculoskeletal system, RC925-935 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2046-3758 |
| Relation: |
https://doaj.org/toc/2046-3758 |
| DOI: |
10.1302/2046-3758.99.BJR-2019-0192.R1 |
| Access URL: |
https://doaj.org/article/ce3cc22b5f6a48819efcbe5b547bc851 |
| Accession Number: |
edsdoj.3cc22b5f6a48819efcbe5b547bc851 |
| Database: |
Directory of Open Access Journals |
