Variations in the metabolome in response to disease activity of rheumatoid arthritis

Abstract

BackgroundAnti-Tumor Necrosis Factor (TNF) therapies are able to control rheumatoid arthritis (RA) disease activity and limit structural damage. Yet no predictive factor of response to anti-TNF has been identified. Metabolomic profile is known to vary in response to different inflammatory rheumatisms so determining it could substantially improve diagnosis and, consequently, prognosis.The aim of this study was to use mass spectrometry to determine whether there is variation in the metabolome in patients treated with anti-TNF and whether any particular metabolomic profile can serve as a predictor of therapeutic response.MethodsBlood samples were analyzed in 140 patients with active RA before initiation of anti-TNF treatment and after 6 months of Anti-TNF treatment (100 good responders and 40 non-responders). Plasma was deproteinized, extracted and analyzed by reverse-phase chromatography–QToF mass spectrometry. Extracted and normalized ions were tested by univariate and ANOVA analysis followed by partial least-squares regression-discriminant analysis (PLS-DA). Orthogonal Signal Correction (OSC) was also used to filter data from unwanted non-related effects. Disease activity scores (DAS 28) obtained at 6 months were correlated with metabolome variation findings to identify a metabolite that is predictive of therapeutic response to anti-TNF.ResultsAfter 6 months of anti-TNF therapy, 100 patients rated as good responders and 40 patients as non-responders according to EULAR criteria. Metabolomic investigations suggested two different metabolic fingerprints splitting the good-responders group and the non-responders group, without differences in anti-TNF therapies. Univariate analysis revealed 24 significant ions in positive mode (p < 0.05) and 31 significant ions in negative mode (p < 0.05). Once intersected with PLS results, only 35 ions remained. Carbohydrate derivates emerged as strong candidate determinants of therapeutic response.ConclusionsThis is the first study describing metabolic profiling in response to anti-TNF treatments using plasma samples. The study highlighted two different metabolic profiles splitting good responders from non-responders.

Review: Synovial Cell Metabolism and Chronic Inflammation in Rheumatoid Arthritis

“…Serum (17) and synovial fluid (SF) (18, 19) metabolomic profiles have also demonstrated the potential to distinguish RA from psoriatic arthritis and other diseases (14). Furthermore, our data and that of others show that patient responses to biological therapies including etanercept and rituximab can be predicted from urine, serum and plasma metabolic profiles, highlighting the power of metabolomics in stratifying patients and directing RA treatment (20–22).…”

Plasma Itaconate elevation following successful cDMARD treatment in early rheumatoid arthritis patients elucidates disease activity associated macrophage activation

“…Metabolomics is emerging as a tool to identify biomarkers for disease, response to treatment and also indicators of pathogenesis that may offer routes for novel interventions. In the past few years a number of studies using both NMR and mass spectrometry based approaches have been applied to study RA (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(25)(26)(27). The use of an untargeted LC-MS platform has several benefits over the use of other platforms, such as NMR.…”

PREDIRA (PRediction mEdical DevIce for Rheumatoid Arthritis): Scale-up of unique predictive online platform highly improving the quality of life of Rheumatoid Arthritis’ patient by personalized and efficient biotherapies prescription

“…Similarly, epigenetics has provided very preliminary data for the time being (13,14). Finally, the study of the transcriptome makes it possible to identify a certain number of genes but its use is difficult on a daily basis (15) and the study of the serum metabolome remains complicated (16).…”