ACR Meeting Abstracts

ACR Meeting Abstracts

Abstract Number: 1289

Classification of Osteoarthritis Phenotypes By Metabolomics Analysis

Weidong Zhang1, Sergei Likhodii2, Yuhua Zhang1, Erfan Aref-Eshghi1, Patricia E. Harper1, Edward Randell2, Roger Green1, Glynn Martin3, Andrew Furey3, Guang Sun4, Proton Rahman5,6 and Guangju Zhai1, 1Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NF, Canada, 2Department of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NF, Canada, 3Department of Surgery, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NF, Canada, 4Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NF, Canada, 5Faculty of Medicine, Memorial University of Newfoundland, St. John's, NF, Canada, 6Rheumatology, St. Claires Mercy Hospital, St. Johns, NF, Canada

Meeting: 2014 ACR/ARHP Annual Meeting

Keywords: ,

Session Information

Title: Osteoarthritis - Clinical Aspects: Epidemiology and Pathogenesis

Session Type: Abstract Submissions (ACR)

Background/Purpose: Osteoarthritis (OA) is a group of heterogeneous conditions consisting of different subgroups or phenotypes that continuously evolve, eventually leading to common clinical manifestations. Identifying OA subphenotypes and uncovering their different mechanisms of pathogenesis is of fundamental importance for the development of appropriate therapies and diagnostic tools. The aim of the study was to identify metabolic markers that can classify OA patients into subgroups.

Methods: A case-only study design was utilized in this study. Patients undergoing total hip/knee joint replacements due to primary OA were recruited and their synovial fluid samples were collected during their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach using the Biocrates AbsoluteIDQ p180 kit. Various analytic methods including principal component analysis (PCA), hierarchical clustering (HCL) method, and partial least squares discriminant analysis (PLS-DA) were utilized to identify metabolic markers for classifying subgroups of OA patients. Potential confounders such as age, sex, body mass index (BMI), and comorbidities were considered in the analysis.

Results: A total of 80 OA patients were included in the study. 38 were males and 42 were females with an average age of 65.2 ± 8.7 years. Two distinct patient groups, A and B, were clearly identified. Patients in group A had significant higher concentration on 39 acylcarnitines in their synovial fluids than the patients in group B. Patients in group B were further subdivided into five subgroups, i.e., B1-1, B1-2-1, B1-2-2, B2-1 and B2-2. The corresponding metabolites that contribute to the grouping were 14 amino acids, 24 glycerophospholipids, 12 acylcarnitines and 1 biogenic amine. The grouping was not associated with any known confounders including age, sex, BMI, and comorbidities. The possible biological processes involved in these clusters are carnitine, lipid, and collagen metabolism, respectively.

Conclusion: The study demonstrated that OA consists of metabolically distinct subgroups. Identification of these distinct subgroups will help to unravel the pathogenesis and develop targeted therapies for OA.

Disclosure:

W. Zhang,
None;

S. Likhodii,
None;

Y. Zhang,
None;

E. Aref-Eshghi,
None;

P. E. Harper,
None;

E. Randell,
None;

R. Green,
None;

G. Martin,
None;

A. Furey,
None;

G. Sun,
None;

P. Rahman,
None;

G. Zhai,
None.

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