Global Metabolite Profiling of Synovial Fluid for the Specific Diagnosis of Rheumatoid Arthritis from Other Inflammatory Arthritis

<div><p>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.</p></div

Identification of 123 metabolites from synovial fluid samples of 24 patients with Behcet’s disease with arthritis and seronegative arthritis using BinBase.

<p>Identification of 123 metabolites from synovial fluid samples of 24 patients with Behcet’s disease with arthritis and seronegative arthritis using BinBase.</p

Receiver operating characteristic (ROC) curve of 3 combined biomarkers for distinguishing Behcet’s disease (BD) with arthritis from seronegative arthritis (SNA) groups.

<p>Glutamate, citramalate, and valine were selected and validated as putative biomarkers for BD with arthritis for distinguishing BD with arthritis from SNA groups by ROC curve analysis. The sensitivity and specificity were 100% and 61.1%, respectively, and the value of the area under curve (AUC) was 0.870.</p

OPLS-DA of the metabolite profiles of RA and non-RA groups.

<p>(a) Score plot of the OPLS-DA model for RA and non-RA groups (t<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097501#pone.0097501-Cammarata1" target="_blank">[1]</a>P, score of the non-orthogonal component; t<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097501#pone.0097501-Litwin1" target="_blank">[2]</a>O, score of the orthogonal component). (b) V-plot with p(corr) and VIP values of 105 metabolites. The metabolites with p(corr) <0 were those decreased in RA groups while the metabolites with p(corr) >0 were those increased in RA groups.</p

HCA of 105 metabolites from synovial fluid samples of RA and non-RA patients.

<p>Each column and row represents a disease and an individual metabolite, respectively.</p

ROC analysis of the predictive power of the 20 combined biomarkers for distinguishing RA and non-RA groups.

<p>A sensitivity and specificity were 92.3% and 68.0%, respectively, and the value of AUC was 0.812.</p

The orthogonal least square-discriminative analysis (OPLS-DA) of metabolomic profiles of synovial fluids of Behcet’s disease (BD) with arthritis and seronegative arthritis (SNA).

<p>(A) The score plot of the OPLS-DA model for the BD with arthritis and SNA groups (t[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135856#pone.0135856.ref001" target="_blank">1</a>], score of the non-orthogonal component; to[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135856#pone.0135856.ref002" target="_blank">2</a>], score of the orthogonal component). The generated explained variation values, 0.45 of <i>R</i>^<i>2</i><i>X</i> and 0.91 of <i>R</i>^<i>2</i><i>Y</i>, and the predictive capability, 0.64 of <i>Q</i>^<i>2</i> indicated the excellence in modeling and prediction of the OPLS-DA model, respectively, with clear discrimination between BD with arthritis and SNA groups. (B) V-plot with p(corr) and VIP values of 123 different metabolites in OPLS-DA. The metabolites with p(corr) < 0 were those decreased in the BD with arthritis group while the metabolites with p(corr) > 0 were those increased in the BD with arthritis group. The metabolites with VIP > 1 were represented in Fig 1B.</p

Baseline characteristics of RA and non-RA groups.

<p>ACPA, anti-CCP antibody; ACR, The American College of Rheumatology classification criteria of RA; ACR/EULAR, The American College of Rheumatology/European League Against Rheumatism classification criteria for RA; AS, ankylosing spondylitis; ASAS axial, Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis; BD, Behçet's disease; FANA, fluorescent anti-nuclear antibody; HLA-B27, human leukocyte antigen B27; modified NY, Modified New York criteria for the diagnosis of AS; n.a, not applicable; non-RA, non-rheumatoid arthritis including ankylosing spondylitis, Behçet's disease, and gout; Previous NSAID, previously use of non-steroidal anti-inflammatory drug; RA, rheumatoid arthritis; RF, rheumatoid factor.</p

VIP and AUC values of the metabolites that significantly contribute to the discrimination between the RA and non-RA groups.

a<p><i>p</i>-values were determined using the Wilcoxon-Mann-Whitney test.</p>b<p>Area under the receiver operator characteristics curve.</p

The intensities of metabolites related to amino acid metabolism (A) and fatty acid metabolism (B).

<p>The intensities of metabolites related to amino acid metabolism (A) and fatty acid metabolism (B).</p