Metabolomic profiling in patients with rheumatoid arthritis (a pilot study)

Abstract

Background. Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by progressive joint damage and leading to early disability in patients. The main purpose of its treatment is to alleviate the pain caused by the disease, delay the development of the disease, reduce the morbidity rate, and improve the quality of life of patients. At present the specificity and sensitivity of RA diagnosis methods are not convincing. In recent decades, research has focused on pathogenesis and the discovery of potential biomarkers using new and high-precision methods, in particular metabolomics. Metabolomics is an science studying low molecular weight compounds (metabolites) involved in biochemical processes and being the end products of metabolism. Metabolites reflect the current state of the humans body and can therefore serve as perspective biomarkers. Aim. To study the metabolomic profile of patients with RA without therapy in order to search for potential biomarkers for diagnosis and evaluation of therapy. Material and methods. The main study group consisted of 14 patients with a newly diagnosed RA – de novo RA. 16 healthy volunteers formed the control group. Metabolites in blood plasma were studied using ultra-performance liquid chromatography in combination with a triple quadrupole analyzer. A total of 93 metabolites were analyzed in de novo RA patients and healthy controls. In the group of RA patients the relationship of these metabolites with DAS28 activity, CRP, ESR, the presence of RF and ACCP were analyzed. Results. The most significant metabolites that play an important role in the pathogenesis of RA were identified: leucine/isoleucine, tyrosine, lysine, valine, phenylalanine, proline, ornithine, glutamine aspartate and long-chain acylcarnitines (C14, C14-OH, C16-1, C18). We found a correlation between DAS28 and leucine (p=0.03) and proline (p=0.05) levels. An inverse correlation was established between ACCP and glutamine (p=0,041) and a direct correlation between ACCP and proline (p=0.039), an inverse correlation between RF and phenylalanine (p=0.0491) and histidine (p=0.04). Conclusion. Metabolomics provides promising opportunities for further research in RA as it allows to identify metabolites most associated with disease, which can improve the accuracy of RA diagnosis and serve as targets for therapy

Keywords

• rheumatoid arthritis
• metabolomic profiling
• metabolomics