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BMJ Open. 2014 Nov 19;4(11):e006286. doi: 10.1136/bmjopen-2014-006286.

Classification of osteoarthritis phenotypes by metabolomics analysis.

Author information

1
Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
2
Department of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
3
Department of Surgery, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
4
Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada.
5
Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada Department of Twin Research & Genetic Epidemiology, King's College London, London, UK.

Abstract

OBJECTIVES:

To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups.

DESIGN:

A case-only study design was utilised.

PARTICIPANTS:

Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 and December 2013 in St. Clare's Mercy Hospital and Health Science Centre General Hospital in St. John's, capital of Newfoundland and Labrador (NL), Canada. 38 men and 42 women were included in the study. The mean age was 65.2±8.7 years.

OUTCOME MEASURES:

Synovial fluid samples were collected at the time of their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach, and various analytic methods were utilised to identify metabolic markers for classifying subgroups of patients with OA. Potential confounders such as age, sex, body mass index (BMI) and comorbidities were considered in the analysis.

RESULTS:

Two distinct patient groups, A and B, were clearly identified in the 80 patients with OA. Patients in group A had a significantly higher concentration on 37 of 39 acylcarnitines, but the free carnitine was significantly lower in their synovial fluids than in those of patients in group B. The latter group was further subdivided into two subgroups, that is, B1 and B2. The corresponding metabolites that contributed to the grouping were 86 metabolites including 75 glycerophospholipids (6 lysophosphatidylcholines, 69 phosphatidylcholines), 9 sphingolipids, 1 biogenic amine and 1 acylcarnitine. 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.

CONCLUSIONS:

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.

KEYWORDS:

EPIDEMIOLOGY; RHEUMATOLOGY

PMID:
25410606
PMCID:
PMC4244434
DOI:
10.1136/bmjopen-2014-006286
[Indexed for MEDLINE]
Free PMC Article

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