Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept.
Biomarker
Machine learning
Osteoarthritis
Journal
Bone & joint research
ISSN: 2046-3758
Titre abrégé: Bone Joint Res
Pays: England
ID NLM: 101586057
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
entrez:
26
10
2020
pubmed:
27
10
2020
medline:
27
10
2020
Statut:
epublish
Résumé
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). 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. 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. 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:
Identifiants
pubmed: 33101658
doi: 10.1302/2046-3758.99.BJR-2019-0192.R1
pii: BJR-9-623
pmc: PMC7548522
doi:
Types de publication
Journal Article
Langues
eng
Pagination
623-632Informations de copyright
© 2020 Author(s) et al.
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