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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2016
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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. |
format | Online Article Text |
id | pubmed-5013418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
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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