Jessica D Murillo-Saich1, Cesar Diaz-Torne2, M Angeles Ortiz2, Roxana Coras1,3, Paulo Gil-Alabarse4, Anders Pedersen5, Hector Corominas2, Silvia Vidal6, Monica Guma7,8. 1. Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA. 2. Group of Inflammatory Diseases, Institute Rec. Hospital de la Santa Creu I Sant Pau, Carrer de Sant Quintí, 89, 08041, Barcelona, Spain. 3. Department of Medicine, Autonomous University of Barcelona, Plaça Cívica, 08193, Bellaterra, Barcelona, Spain. 4. VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA. 5. Swedish NMR Centre, University of Gothenburg, Medicinaregatan 5C, 413 90, Gothenburg, Sweden. 6. Group of Inflammatory Diseases, Institute Rec. Hospital de la Santa Creu I Sant Pau, Carrer de Sant Quintí, 89, 08041, Barcelona, Spain. SVidal@santpau.cat. 7. Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA. mguma@health.ucsd.edu. 8. Department of Medicine, Autonomous University of Barcelona, Plaça Cívica, 08193, Bellaterra, Barcelona, Spain. mguma@health.ucsd.edu.
Abstract
INTRODUCTION: To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response. OBJECTIVES: 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). METHODS: 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 12 months 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. RESULTS: 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 6 months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders. CONCLUSION: 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.
INTRODUCTION: To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response. OBJECTIVES: 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). METHODS: 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 12 months 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. RESULTS: 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 6 months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders. CONCLUSION: 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.
Authors: Cesar Diaz-Torne; Maria Dels Angels Ortiz; Patricia Moya; Maria Victoria Hernandez; Delia Reina; Ivan Castellvi; Juan Jose De Agustin; Diana de la Fuente; Hector Corominas; Raimon Sanmarti; Carlos Zamora; Elisabet Cantó; Silvia Vidal Journal: Semin Arthritis Rheum Date: 2017-11-20 Impact factor: 5.532
Authors: Unnikrishnan M Chandrasekharan; Zeneng Wang; Yuping Wu; W H Wilson Tang; Stanley L Hazen; Sihe Wang; M Elaine Husni Journal: Arthritis Res Ther Date: 2018-06-08 Impact factor: 5.156
Authors: Theodoros Dimitroulas; James Hodson; Aamer Sandoo; Jacqueline Smith; George D Kitas Journal: Arthritis Res Ther Date: 2017-02-10 Impact factor: 5.156