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Relationship between Investigative Biomarkers and Radiographic Grading in Patients with Knee Osteoarthritis
Author(s) -
Eduardo Anitua,
Mikel Sánchez,
María de la Fuente,
Juan Azofra,
Mar Zalduendo,
José Javier Aguirre,
Isabel Andı́a
Publication year - 2009
Publication title -
international journal of rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.8
H-Index - 33
eISSN - 1687-9279
pISSN - 1687-9260
DOI - 10.1155/2009/747432
Subject(s) - osteoarthritis , medicine , algorithm , biomarker , artificial intelligence , pathology , mathematics , chemistry , computer science , biochemistry , alternative medicine
Objective . To examine new investigative biomarkers and their relevance for radiographic severity in knee osteoarthritis. Methods . The group comprised 63 patients with 73 knees examined. Patients were divided according to radiographic severity to allow for comparison of biomarker levels. Hyaluronic acid (HA), matrix metalloproteases (MMP-1, MMP-3 and MMP-13), tissue inhibitors of metalloproteases (TIMP-1 and TIMP-2), platelet-derived growth factor (PDGF-AB), transformed growth factor (TGF- β ), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF) and insulin-like growth factor (IGF-I) were measured on synovial fluid and in plasma releasate at a single time point. Principal component analysis (PCA) followed by analysis of covariance were applied to evaluate data. Results . Four different groups of biomarker were identified in plasma releasates. The first (platelet number, PDGF-AB and TGF- β ) and second groups (HA and IGF-I) were related to radiographic severity, P = .005 and P = .022, respectively. The third (MMP-1 and TIMP-2) and fourth groups (MMP-3 and TIMP-1) represented the catabolic balance, but were not associated to radiographic grading. Three different clusters of biomarkers were found in synovial fluid but did not show any significant association to radiographic grading. Conclusions . New imaging approaches to assess structural deterioration and correlation with biomarker levels are warranted to advance in OA research.

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