Metabolomics profiling predicts outcome of tocilizumab in rheumatoid arthritis: an exploratory study

Abstract

To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response. We hypothesized that the characterization of a patients' metabolic profile, utilizing one-dimensional nuclear magnetic resonance (1H-NMR), may predict a response to tocilizumab in patients with rheumatoid arthritis (RA). 40 active RA patients meeting the 2010 ACR/EULAR classification criteria initiating treatment with tocilizumab were recruited. Clinical outcomes were determined at baseline, and after six and twelve months of treatment. EULAR response criteria at 6 and 12months to categorize patients as responders and non-responders. Blood was collected at baseline and after six months of tocilizumab therapy. 1H-NMR was used to acquire a spectra of plasma samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. SPSS v.27 and MetaboAnalyst 4.0 were used for statistical and pathway analysis. Isobutyrate, 3-hydroxybutyrate, lysine, phenylalanine, sn-glycero-3-phosphocholine, tryptophan and tyrosine were significantly elevated in responders at the baseline. OPLS-DA at baseline partially discriminated between RA responders and non-responders. A multivariate diagnostic model showed that concentrations of 3-hydroxybutyrate and phenylalanine improved the ability to specifically predict responders classifying 77.1% of the patients correctly. At 6months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders. The relationship between plasma metabolic profiles and the clinical response to tocilizumab suggests that 1H-NMR may be a promising tool for RA therapy optimization. More studies are needed to determine if metabolic profiling can predict the response to biological therapies in RA patients.