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Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis

Objective: To determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were...

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Autores principales: Sweeney, Shannon R, Kavanaugh, Arthur, Lodi, Alessia, Wang, Bo, Boyle, David, Tiziani, Stefano, Guma, Monica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013418/
https://www.ncbi.nlm.nih.gov/pubmed/27651926
http://dx.doi.org/10.1136/rmdopen-2016-000289
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author Sweeney, Shannon R
Kavanaugh, Arthur
Lodi, Alessia
Wang, Bo
Boyle, David
Tiziani, Stefano
Guma, Monica
author_facet Sweeney, Shannon R
Kavanaugh, Arthur
Lodi, Alessia
Wang, Bo
Boyle, David
Tiziani, Stefano
Guma, Monica
author_sort Sweeney, Shannon R
collection PubMed
description Objective: To determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were treated with rituximab. Patients were grouped into responders and non-responders according to the American College of Rheumatology improvement criteria, at a 20% level at 6 months. A Bruker Avance 700 MHz spectrometer and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer were used to acquire (1)H-NMR and ultra high pressure liquid chromatography (UPLC)–MS/MS spectra, respectively, of serum samples before and after rituximab therapy. Data processing and statistical analysis were performed in MATLAB. 14 patients were characterised as responders, and 9 patients were considered non-responders. 7 polar metabolites (phenylalanine, 2-hydroxyvalerate, succinate, choline, glycine, acetoacetate and tyrosine) and 15 lipid species were different between responders and non-responders at baseline. Phosphatidylethanolamines, phosphatidyserines and phosphatidylglycerols were downregulated in responders. An opposite trend was observed in phosphatidylinositols. At 6 months, 5 polar metabolites (succinate, taurine, lactate, pyruvate and aspartate) and 37 lipids were different between groups. The relationship between serum metabolic profiles and clinical response to rituximab suggests that (1)H-NMR and UPLC–MS/MS may be promising tools for predicting response to rituximab.
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spelling pubmed-50134182016-09-20 Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis Sweeney, Shannon R Kavanaugh, Arthur Lodi, Alessia Wang, Bo Boyle, David Tiziani, Stefano Guma, Monica RMD Open Rheumatoid Arthritis Objective: To determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were treated with rituximab. Patients were grouped into responders and non-responders according to the American College of Rheumatology improvement criteria, at a 20% level at 6 months. A Bruker Avance 700 MHz spectrometer and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer were used to acquire (1)H-NMR and ultra high pressure liquid chromatography (UPLC)–MS/MS spectra, respectively, of serum samples before and after rituximab therapy. Data processing and statistical analysis were performed in MATLAB. 14 patients were characterised as responders, and 9 patients were considered non-responders. 7 polar metabolites (phenylalanine, 2-hydroxyvalerate, succinate, choline, glycine, acetoacetate and tyrosine) and 15 lipid species were different between responders and non-responders at baseline. Phosphatidylethanolamines, phosphatidyserines and phosphatidylglycerols were downregulated in responders. An opposite trend was observed in phosphatidylinositols. At 6 months, 5 polar metabolites (succinate, taurine, lactate, pyruvate and aspartate) and 37 lipids were different between groups. The relationship between serum metabolic profiles and clinical response to rituximab suggests that (1)H-NMR and UPLC–MS/MS may be promising tools for predicting response to rituximab. BMJ Publishing Group 2016-08-16 /pmc/articles/PMC5013418/ /pubmed/27651926 http://dx.doi.org/10.1136/rmdopen-2016-000289 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Rheumatoid Arthritis
Sweeney, Shannon R
Kavanaugh, Arthur
Lodi, Alessia
Wang, Bo
Boyle, David
Tiziani, Stefano
Guma, Monica
Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
title Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
title_full Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
title_fullStr Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
title_full_unstemmed Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
title_short Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
title_sort metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis
topic Rheumatoid Arthritis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013418/
https://www.ncbi.nlm.nih.gov/pubmed/27651926
http://dx.doi.org/10.1136/rmdopen-2016-000289
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