Article Text

Classification of osteoarthritis phenotypes by metabolomics analysis
  1. Weidong Zhang1,
  2. Sergei Likhodii2,
  3. Yuhua Zhang1,
  4. Erfan Aref-Eshghi1,
  5. Patricia E Harper1,
  6. Edward Randell2,
  7. Roger Green1,
  8. Glynn Martin3,
  9. Andrew Furey3,
  10. Guang Sun4,
  11. Proton Rahman4,
  12. Guangju Zhai1,5
  1. 1Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada
  2. 2Department of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada
  3. 3Department of Surgery, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada
  4. 4Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, Newfoundland, Canada
  5. 5Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
  1. Correspondence to Dr Guangju Zhai; guangju.zhai{at}med.mun.ca

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.

  • RHEUMATOLOGY
  • EPIDEMIOLOGY

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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