Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept
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 fl...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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The British Editorial Society of Bone & Joint Surgery
2020-09-01
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| Series: | Bone & Joint Research |
| Subjects: | |
| Online Access: | https://online.boneandjoint.org.uk/doi/epdf/10.1302/2046-3758.99.BJR-2019-0192.R1 |
| _version_ | 1811285640559460352 |
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| author | Chethan Jayadev Philippa Hulley Catherine Swales Sarah Snelling Gary Collins Peter Taylor Andrew Price |
| author_facet | Chethan Jayadev Philippa Hulley Catherine Swales Sarah Snelling Gary Collins Peter Taylor Andrew Price |
| author_sort | Chethan Jayadev |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T02:48:04Z |
| format | Article |
| id | doaj.art-ce3cc22b5f6a48819efcbe5b547bc851 |
| institution | Directory Open Access Journal |
| issn | 2046-3758 |
| language | English |
| last_indexed | 2024-04-13T02:48:04Z |
| publishDate | 2020-09-01 |
| publisher | The British Editorial Society of Bone & Joint Surgery |
| record_format | Article |
| series | Bone & Joint Research |
| spelling | doaj.art-ce3cc22b5f6a48819efcbe5b547bc8512022-12-22T03:05:57ZengThe British Editorial Society of Bone & Joint SurgeryBone & Joint Research2046-37582020-09-019962363210.1302/2046-3758.99.BJR-2019-0192.R1Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker conceptChethan Jayadev0Philippa Hulley1Catherine Swales2Sarah Snelling3Gary Collins4Peter Taylor5Andrew Price6Royal National Orthopaedic Hospital NHS Trust, Stanmore, UKNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UKNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UKNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UKCentre for Statistics in Medicine, Botnar Research Centre, University of Oxford, Oxford, UKNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UKNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UKAims: 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.https://online.boneandjoint.org.uk/doi/epdf/10.1302/2046-3758.99.BJR-2019-0192.R1osteoarthritisbiomarkermachine learning |
| spellingShingle | Chethan Jayadev Philippa Hulley Catherine Swales Sarah Snelling Gary Collins Peter Taylor Andrew Price Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept Bone & Joint Research osteoarthritis biomarker machine learning |
| title | Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept |
| title_full | Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept |
| title_fullStr | Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept |
| title_full_unstemmed | Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept |
| title_short | Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept |
| title_sort | synovial fluid fingerprinting in end stage knee osteoarthritis a novel biomarker concept |
| topic | osteoarthritis biomarker machine learning |
| url | https://online.boneandjoint.org.uk/doi/epdf/10.1302/2046-3758.99.BJR-2019-0192.R1 |
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