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:: Volume 17, Issue 2 (Iranian South Medical journal 2014) ::
Iran South Med J 2014, 17(2): 141-149 Back to browse issues page
H Nuclear magnetic resonance based metabonomics data analysis in rheumatoid arthritis
Mohammad Arjmand1, Atoosa Golshahi1, Ali Movahed 2, Azam Amini3, Ziba Akbari1
1- Department of Biochemistry, Pasteur Institute of Iran, Tehran, IRAN
2- Department of Biochemistry, School of Medicine, Bushehr University of Medical Sciences, Bushehr, IRAN
Research Center for Nuclear Medicine, The Persian Gulf Biomedical Research Institute, Bushehr University of Medical Sciences, Bushehr, IRAN , amovahed58@gmail,com.
3- Department of Rehumathology, School Of Medicine, Bushehr University of Medical Sciences, Bushehr, IRAN
Abstract:   (6998 Views)

Background: Rheumatoid arthritis (RA) is a chronic, systematic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints and it is a common rheumatic disease with many subtypes. Nuclear Magnetic resonance (1H NMR) spectrometers with high sensitivity, resolution and dynamic range has permitted the rapid, simultaneous investigation of complex mixtures of endogenous or exogenous components present in biological materials. Metabonomics is the systematic study of chemical finger print resulted from cell reactions and could be used as a new biomarker for early disease diagnosis. In the present investigation, we studied serum metabolic profile in rheumatoid arthritis (RA) in order to find out the metabolic finger print pattern of the disease. Materials and methods: In our metabonomics study serum samples were collected from 16 patients with active RA, and from equal number of healthy subjects. They were evaluated during a one-year follow-up with the assessment of disease activity and 1H NMR spectroscopy of sera samples. In all the cases, the presence of active rheumatoid arthritis was shown by an increase in the T1 values of the synovium of the joints. We specified and classified all metabolites using PCA, PLSDA chemometrics methods. Chenomx (Trail Version) and ProMetab codes in Matlab software environments were used for our data analysis. Results were compared with the NMR metabolite data bank (www.metabolomics.ca). Anti-CCP, ANA and urea were also analyzed by ElISA and colorimetric methods respectively. Results: The most changes identified in this study were in the biosynthesis pathways of steroid hormones, biotin, fatty acids, amino acids (Leucine, Valin and isoleucine) and also linoleic acid. Conclusion: In rheumatoid arthritis disease, the activation of the immune system consumes larg amounts of energy. The main donor of free energy in cells is ATP, which is generated by both glycolysis and oxidative phosphorylation. Changes in amino acids and free fatty acids biosynthesis pathways confirm the high energy utilization. In this disease, the increase in free fatty acid metabolism leads to production of Acetyl CoA and ketone bodies. Since there are many diseases subtype in rheumatoid arthritis, more sensitive diagnostic method is required. The result of our investigation suggests that metabolome profiling method could be used as a new biomarker for early diagnosis of rheumatoid arthritis disease.

Keywords: Metabonomics, rheumatoid arthritis, NMR spectroscopy, PCA, PLSDA, Pattern recognition, metabolic finger print
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Type of Study: Original | Subject: General
Received: 2012/09/8 | Accepted: 2013/03/14 | Published: 2014/04/14
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Arjmand M, Golshahi A, Movahed A, Amini A, Akbari Z. H Nuclear magnetic resonance based metabonomics data analysis in rheumatoid arthritis. Iran South Med J. 2014; 17 (2) :141-149
URL: http://ismj.bpums.ac.ir/article-1-520-en.html


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Volume 17, Issue 2 (Iranian South Medical journal 2014) Back to browse issues page
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