Background In addition to synovitis, rheumatoid arthritis (RA) is characterised by widespread systemic changes. These changes are largely mediated by pro-inflammatory cytokines that impact on metabolism, particularly that of muscle and fat. Given these widespread metabolic effects we hypothesised that the level of current inflammation would be reflected in changes in the types and levels of metabolites found in the serum of patients with inflammatory arthritis.

Objectives We have used NMR-based metabolomic fingerprinting to analyse serum from patients with newly presenting established RA, early arthritis and healthy controls. We sought to assess whether the metabolite fingerprint in patients with established RA differed from that of healthy controls and whether this fingerprint differed in patients with early arthritis in relation to the extent of inflammation and final outcomes.

Methods Serum samples were collected from newly presenting, disease-modifying drug naive patients with established RA (16 patients), matched healthy controls (14 patients), and two groups of newly presenting patients with synovitis of ≤3 months’ symptom duration (89 patients and 127 patients) whose outcomes were determined at clinical follow-up. Serum metabolic profiles were assessed using 1D ¹H-NMR spectroscopy. Discriminating metabolites were identified, and the relationships between metabolic profiles and clinical variables including CRP and outcomes were examined.

Results The serum metabolite fingerprint in established RA was clearly distinct from that of healthy controls. In early arthritis, we were able to stratify the patients according to the level of current inflammation, with CRP correlating with metabolic differences in two separate groups (p<0.001). Lactate, glucose, cholesterol, fatty acids and lipids were important discriminators of inflammatory burden in both early arthritis patient groups.

Conclusions The metabolomic fingerprint reflects inflammatory disease activity in patients with synovitis, demonstrating that underlying inflammatory processes drive widespread changes in metabolism that can be measured in the peripheral blood. The identification of novel metabolic alterations may provide insights into disease mechanisms operating in patients with inflammatory arthritis and provide novel variables that may add discriminating value to existing predictive algorithms.

Disclosure of Interest None Declared