Literature DB >> 26476882

Application of (1)H NMR-based serum metabolomic studies for monitoring female patients with rheumatoid arthritis.

Adam Zabek1, Jerzy Swierkot2, Anna Malak3, Iga Zawadzka1, Stanisław Deja4, Katarzyna Bogunia-Kubik5, Piotr Mlynarz6.   

Abstract

Rheumatoid arthritis is a chronic autoimmune-based inflammatory disease that leads to progressive joint degeneration, disability, and an increased risk of cardiovascular complications, which is the main cause of mortality in this population of patients. Although several biomarkers are routinely used in the management of rheumatoid arthritis, there is a high demand for novel biomarkers to further improve the early diagnosis of rheumatoid arthritis, stratification of patients, and the prediction of a better response to a specific therapy. In this study, the metabolomics approach was used to provide relevant biomarkers to improve diagnostic accuracy, define prognosis and predict and monitor treatment efficacy. The results indicated that twelve metabolites were important for the discrimination of healthy control and rheumatoid arthritis. Notably, valine, isoleucine, lactate, alanine, creatinine, GPC  APC and histidine relative levels were lower in rheumatoid arthritis, whereas 3-hydroxyisobutyrate, acetate, NAC, acetoacetate and acetone relative levels were higher. Simultaneously, the analysis of the concentration of metabolites in rheumatoid arthritis and 3 months after induction treatment revealed that L1, 3-hydroxyisobutyrate, lysine, L5, acetoacetate, creatine, GPC+APC, histidine and phenylalanine were elevated in RA, whereas leucine, acetate, betaine and formate were lower. Additionally, metabolomics tools were employed to discriminate between patients with different IL-17A genotypes. Metabolomics may provide relevant biomarkers to improve diagnostic accuracy, define prognosis and predict and monitor treatment efficacy in rheumatoid arthritis.
Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  (1)H NMR spectroscopy; Metabolomics; Rheumatoid arthritis

Mesh:

Substances:

Year:  2015        PMID: 26476882     DOI: 10.1016/j.jpba.2015.10.007

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  18 in total

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Authors:  Guangju Zhai; Edward W Randell; Proton Rahman
Journal:  Rheumatology (Oxford)       Date:  2018-12-01       Impact factor: 7.580

Review 2.  Immunometabolism in early and late stages of rheumatoid arthritis.

Authors:  Cornelia M Weyand; Jörg J Goronzy
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Review 3.  Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis.

Authors:  Xi Xie; Fen Li; Shu Li; Jing Tian; Jin-Wei Chen; Jin-Feng Du; Ni Mao; Jian Chen
Journal:  Clin Rheumatol       Date:  2017-06-10       Impact factor: 2.980

Review 4.  Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity.

Authors:  Erika Dorochow; Michaela Köhm; Lisa Hahnefeld; Robert Gurke
Journal:  J Pers Med       Date:  2022-06-02

5.  Protein oxidation, nitration and glycation biomarkers for early-stage diagnosis of osteoarthritis of the knee and typing and progression of arthritic disease.

Authors:  Usman Ahmed; Attia Anwar; Richard S Savage; Paul J Thornalley; Naila Rabbani
Journal:  Arthritis Res Ther       Date:  2016-10-27       Impact factor: 5.156

6.  MicroRNA-126 affects rheumatoid arthritis synovial fibroblast proliferation and apoptosis by targeting PIK3R2 and regulating PI3K-AKT signal pathway.

Authors:  Yuan Qu; Jing Wu; Jia-Xin Deng; Yu-Ping Zhang; Wan-Yi Liang; Zhen-Lan Jiang; Qing-Hong Yu; Juan Li
Journal:  Oncotarget       Date:  2016-11-08

7.  Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

Authors:  Bart V J Cuppen; Junzeng Fu; Herman A van Wietmarschen; Amy C Harms; Slavik Koval; Anne C A Marijnissen; Judith J W Peeters; Johannes W J Bijlsma; Janneke Tekstra; Jacob M van Laar; Thomas Hankemeier; Floris P J G Lafeber; Jan van der Greef
Journal:  PLoS One       Date:  2016-09-15       Impact factor: 3.240

Review 8.  Metabolic Adaptations of CD4+ T Cells in Inflammatory Disease.

Authors:  Cristina Dumitru; Agnieszka M Kabat; Kevin J Maloy
Journal:  Front Immunol       Date:  2018-03-15       Impact factor: 7.561

9.  GC/MS-Based Metabolomics Reveals Biomarkers in Asthma Murine Model Modulated by Opuntia humifusa.

Authors:  Seung-Ho Seo; Eun-Ju Kim; Seong-Eun Park; Sung-Hoon Byun; Soon-Young Lee; So-Hyeon Bok; Dae-Hun Park; Hong-Seok Son
Journal:  Evid Based Complement Alternat Med       Date:  2018-11-01       Impact factor: 2.629

Review 10.  Mixing omics: combining genetics and metabolomics to study rheumatic diseases.

Authors:  Cristina Menni; Jonas Zierer; Ana M Valdes; Tim D Spector
Journal:  Nat Rev Rheumatol       Date:  2017-02-02       Impact factor: 20.543

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