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Variation in the synovial fluid metabolome according to disease activity of rheumatoid arthritis
J.K. Ahn1, J. Kim2, Y.E. Cheong3, K.H. Kim4, H.-S. Cha5
- Division of Rheumatology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Biotechnology, Graduate School, Korea University, Seoul, Republic of Korea.
- Department of Biotechnology, Graduate School, Korea University, Seoul, Republic of Korea.
- Department of Biotechnology, Graduate School, Korea University, Seoul, Republic of Korea. khekim@korea.ac.kr
- Division of Rheumatology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. hoonsuk.cha@samsung.com
CER12439
2020 Vol.38, N°3
PI 0500, PF 0507
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PMID: 31498072 [PubMed]
Received: 22/05/2019
Accepted : 15/07/2019
In Press: 27/08/2019
Published: 26/05/2020
Abstract
OBJECTIVES:
Because genetic and environmental factors both contribute to rheumatoid arthritis (RA), metabolomics could be a very useful tool to elucidate the pathophysiology of RA, and to predict response to treatment. This study was carried out to investigate synovial fluid (SF) metabolic perturbation in RA patients according to the degree of disease activity using gas chromatography/time-of-flight mass spectrometry (GC/TOF MS).
METHODS:
SF samples were obtained from 48 RA patients. Disease activity was assessed using DAS28-ESR(3). SF metabolomics profiling was performed using GC/TOF-MS, in conjunction with multivariate statistical analyses and pathway analyses.
RESULTS:
Significant discrimination of metabolite profiles between moderate and high disease activity groups was shown by PLS-DA, which provided evidence that SF metabolic profiles predicted disease activity. We found the significant correlation between DAS28-ESR(3) value and the intensities of 12 metabolites. The intensities of glycocyamine and indol-3-lactate positively correlated with DAS28-ESR(3) value. On the other hand, β-alanine, asparagine, citrate, cyano-L-alanine, leucine, nicotinamide, citrulline, methionine, oxoproline, and salicylaldehyde negatively correlated with DAS28-ESR(3) value. We found fifteen pathways that were significantly associated with disease activity in RA and that the higher the disease activity, the more amino acid metabolic processes were affected.
CONCLUSIONS:
We found the SF metabolic alterations in RA patients according to disease activity by using GC/TOF MS and identified 12 candidate metabolic biomarkers that may well reflect the disease activity of RA. SF metabolomic approaches based on GC/TOF MS might provide additional information relating to monitoring disease activity in RA.
