Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis.

Sooah Kim,Jinhua Xuan,Jiwon Hwang,Hoon-Suk Cha,Young Hoon Jung,Kyoung Heon Kim,Yong-Sun Bahn

doi:10.1371/journal.pone.0097501

Abstract

Currently, reliable biomarkers that can be used to distinguish rheumatoid arthritis (RA) from other inflammatory diseases are unavailable. To find possible distinctive metabolic patterns and biomarker candidates for RA, we performed global metabolite profiling of synovial fluid samples. Synovial fluid samples from 38 patients with RA, ankylosing spondylitis, Behçet's disease, and gout were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). Orthogonal partial least-squares discriminant and hierarchical clustering analyses were performed for the discrimination of RA and non-RA groups. Variable importance for projection values were determined, and the Wilcoxon-Mann-Whitney test and the breakdown and one-way analysis of variance were conducted to identify potential biomarkers for RA. A total of 105 metabolites were identified from synovial fluid samples. The score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between the RA and non-RA groups. The 20 metabolites, including citrulline, succinate, glutamine, octadecanol, isopalmitic acid, and glycerol, were identified as potential biomarkers for RA. These metabolites were found to be associated with the urea and TCA cycles as well as fatty acid and amino acid metabolism. The metabolomic analysis results demonstrated that global metabolite profiling by GC/TOF MS might be a useful tool for the effective diagnosis and further understanding of RA.

Highlights

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial proliferation and damage of the affected joints
Patients with osteoarthritis or a septic condition were excluded from the screening, and 38 patients who were diagnosed with RA, ankylosing spondylitis (AS), Behcet’s disease (BD), and gout were enrolled in our study
orthogonal partial least squares discriminant analysis (OPLS-DA) successfully minimized the possible contribution of intergroup variability and further increased the discrimination between the RA and non-RA groups compared to the results obtained by the principal component analysis (PCA)

Summary

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial proliferation and damage of the affected joints. Rheumatoid factor (RF), a well-known biomarker for RA, is not useful for specific diagnosis of RA because RF is detected in various other rheumatic (other than RA) and nonrheumatic disorders such as infection and malignancy, and even in normal individuals [1,2]. Anti-citrullinated protein antibodies (ACPA) have recently received much attention as a valuable tool to differentiate RA from other kinds of arthritis in the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) classification criteria [3,4]. Not all RA patients are seropositive for ACPA, and the 2010 ACR/EULAR classification criteria does not satisfactorily rule in RA for patients with seronegative arthritis, especially involving only one joint. More reliable biomarkers with diagnostic capabilities are still needed for RA

Methods

Results

Conclusion