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FRI0515 Discriminative metabolite profiling of synovial fluid in rheumatoid arthritis compared to osteoarthritis
  1. H. Kim2,
  2. J. K. Ahn1,
  3. J. Hwang2,
  4. E.-J. Park2,
  5. J. Lee2,
  6. H.-S. Cha2,
  7. E.-M. Koh2
  1. 1Department of Medicine, Kangbuk Samsung hospital, Sungkyunkwan University School of Medicine
  2. 2Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea, Republic Of

Abstract

Background Metabolomics is the study of unique chemical imprints that represent specific cellular processes in a cell, tissue, organ or organism. Synovial fluid in pathologic conditions reflects the diseased process and its distinctive metabolite profiles could facilitate the diagnostic ability and the understanding of disease state.

Objectives The aim is to investigate the metabolites of synovial fluid in patients with RA and OA and to identify characteristic biomarkers to differentiate two diseases.

Methods Synovial fluid samples were obtained from patients with RA (n = 18, 17 females, mean age 50.3 ± 13.9 yr, disease duration 7.9 ± 6.8 yr) and OA (n = 11, 10 females, mean age 60.9 ± 8.4 yr, disease duration 2.8 ± 4.7 yr). The extracted metabolites from synovial fluid were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). The identified metabolites from synovial fluid extracts of RA and OA were then subjected to multivariate statistical analysis by orthogonal partial least squares discriminant analysis (OPLS-DA): R2 indicates the fitting ability of total variation and Q2designates the validity of discrimination. Both have range from 0 to 1, where the higher R2 or Q2connotes a model with the higher predictive and discriminative value. Values of variable importance for projection (VIP) greater than 1 from OPLS-DA were used to identify potential biomarkers and the significance was defined by Welch’s t-test with level of p < 0.01.

Results Sixty-three metabolites were identified; 20 amino acids, 14 fatty acids, 10 sugars, 7 organic acids, 5 amines and 7 others. The OPLS-DA demonstrated a distinctive metabolite profile of synovial fluid between RA and OA (Figure 1), with the variation value (R2) of 0.97 and the predictive capability value (Q2) of 0.75. Twenty four metabolites were obtained by VIP values of greater than 1 and 17 of them were selected as specific biomarkers by Welch’s t-test. Of 17 metabolites, 6 were up-regulated in RA (maltose, lignoceric acid, uracil, mannitol, pyrophosphate and phosphoric acid) and 11 in OA (lysine, tyrosine, valine, glyceric acid, alanine, asparagines, hydroxylamine, tryptophan, glycerol, glutamine and citrulline).

Conclusions Our results demonstrated that metabolite profiling of synovial fluid clearly separates RA from OA. This study suggests that metabolomics could be a useful diagnostic tool by identifying discriminative biomarkers.

Disclosure of Interest None Declared

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