1H NMR Metabolomics Identifies Underlying Inflammatory Pathology in Osteoarthritis and Rheumatoid Arthritis Synovial Joints

Abstract

Despite osteoarthritis (OA) and rheumatoid arthritis (RA) being typically age-related, their underlying etiologies are markedly different. We used ¹H nuclear magnetic resonance (NMR) spectroscopy to identify differences in metabolite profiles in low volumes of OA and RA synovial fluid (SF). SF was aspirated from knee joints of 10 OA and 14 RA patients. 100 μL SF was analyzed using a 700 MHz Avance IIIHD Bruker NMR spectrometer with a TCI cryoprobe. Spectra were analyzed by Chenomx, Bruker TopSpin and AMIX software. Statistical analysis was undertaken using Metaboanalyst. 50 metabolites were annotated, including amino acids, saccharides, nucleotides and soluble lipids. Discriminant analysis identified group separation between OA and RA cohorts, with 32 metabolites significantly different between OA and RA SF (false discovery rate (FDR) < 0.05). Metabolites of glycolysis and the tricarboxylic acid cycle were lower in RA compared to OA; these results concur with higher levels of inflammation, synovial proliferation and hypoxia found in RA compared to OA. Elevated taurine in OA may indicate increased subchondral bone sclerosis. We demonstrate that quantifiable differences in metabolite abundance can be measured in low volumes of SF by ¹H NMR spectroscopy, which may be clinically useful to aid diagnosis and improve understanding of disease pathogenesis.

Keywords: metabolomics; nuclear magnetic resonance; osteoarthritis; rheumatoid arthritis; synovial fluid.

Figure 1

Quantile Plots of OA and RA spectra depicting the median spectral plot (black line) and variation from the median within each cohort (yellow to red scale) for the full spectral range (8.5–0.5 ppm) and a more detailed region (4.15–3.55 ppm). Peaks of interest are annotated as examples. Note multiple peaks for some metabolites, e.g., glucose.

Figure 2

Boxplots of representative metabolites identified from univariate analysis as significantly different between OA and RA SF (*p < 0.05, **p < 0.01, ***p < 0.001). Y-axis represents normalized peak intensity following median normalization and Pareto scaling.

Figure 3

Comparison of NMR SF metabolome between OA and RA. (A) Heatmap showing metabolites significantly different in OA and RA SF (p < 0.05). Blue = low, white = medium, pink = high concentration. (B) Unsupervised PCA scores plot showing metabolite profile is more variable between RA (green) than OA SF (red). Shading represents 95% confidence region. (C) Supervised multivariate analysis by PLS-DA segregated RA (green) and OA (red) SF samples. Shading represents 95% confidence region. Scores plot is shown for components 1 and 2. (D) Pathway scheme depicting metabolite levels detected in RA and OA SF. Blue = higher in RA SF, Red = higher in OA SF.

Figure 4

Correlation of acetylated saccharide with CRP, ESR and RF titer in RA patients. Levels of acetylated saccharide (median normalized with Pareto scaling) in RA SF correlated positively with serum levels of CRP (Pearson R² = 0.78, p = 0.008, n = 10), ESR (Pearson R² = 0.62, p = 0.02, n = 14) and RF titer (Pearson R² = 0.618, p = 0.018, n = 14).