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

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.

Figure 1. OPLS-DA of the metabolite profiles of RA and non-RA groups.

(a) Score plot of the OPLS-DA model for RA and non-RA groups (tP, score of the non-orthogonal component; tO, 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.

Figure 2. HCA of 105 metabolites from synovial fluid samples of RA and non-RA patients.

Each column and row represents a disease and an individual metabolite, respectively.

Figure 3. ROC analysis of the predictive power of the 20 combined biomarkers for distinguishing RA and non-RA groups.

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

Figure 4. Schematic comparison of the primary metabolisms of RA vs. non-RA groups (AS, BD, and GO).

The box and whisker plots indicate the intracellular metabolite levels for each disease group (red, increased in RA; green, increased in non-RA). AcCOA, acetyl-CoA; ALA, alanine; ARG, arginine; ARG-SUC, arginine-succinate; ASN, asparagine; ASP, aspartate; CIT, citrate; CITR, citrulline; CMP, carbamoyl phosphate; FAs, fatty acids; FUM, fumarate; GLC, glucose; GLN, glutamine; GLU, glutamate; αKG, α-ketoglutarate; LYS, lysine; MAL, malate; OA, oxalate; ORNT, ornithine; PEP, phosphoenolpyruvate; PHA, phenylalanine; PRO, proline; SER, serine; SUCC, succinate; TRP, tryptophan; TYR, tyrosine.