Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Jul 11;14(7):e0219400.
doi: 10.1371/journal.pone.0219400. eCollection 2019.

Metabolomic approach to the exploration of biomarkers associated with disease activity in rheumatoid arthritis

Chiyomi Sasaki  1   2 , Tomoko Hiraishi  3 , Takuma Oku  1   2 , Kenji Okuma  1   2 , Kenichi Suzumura  3 , Motomu Hashimoto  4 , Hiromu Ito  5 , Ichiro Aramori  1   2 , Yoshitaka Hirayama  1   2
Affiliations

Metabolomic approach to the exploration of biomarkers associated with disease activity in rheumatoid arthritis

Chiyomi Sasaki et al. PLoS One. .

Abstract

We aimed to investigate metabolites associated with the 28-joint disease activity score based on erythrocyte sedimentation rate (DAS28-ESR) in patients with rheumatoid arthritis (RA) using capillary electrophoresis quadrupole time-of-flight mass spectrometry. Plasma and urine samples were collected from 32 patients with active RA (DAS28-ESR≥3.2) and 17 with inactive RA (DAS28-ESR<3.2). We found 15 metabolites in plasma and 20 metabolites in urine which showed a significant but weak positive or negative correlation with DAS28-ESR. When metabolites between active and inactive patients were compared, 9 metabolites in plasma and 15 in urine were found to be significantly different. Consequently, we selected 11 metabolites in plasma and urine as biomarker candidates which significantly correlated positively or negatively with DAS28-ESR, and significantly differed between active and inactive patients. When a multiple logistic regression model was built to discriminate active and inactive cohorts, three variables-histidine and guanidoacetic acid from plasma and hypotaurine from urine-generated a high area under the receiver operating characteristic (ROC) curve value (AUC = 0.8934). Thus, this metabolomics approach appeared to be useful for investigating biomarkers of RA. Combination of plasma and urine analysis may lead to more precise and reliable understanding of the disease condition. We also considered the pathophysiological significance of the found biomarker candidates.

PubMed Disclaimer

Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: CS, TH, TO, KO, KS, IA and YH are employees of Astellas Pharma Inc. MH received research grant and/or speaker fee from Astellas Pharma Inc, Eisai Co. Ltd., Mitsubishi Tanabe Pharma Co., and Brystol-Meyers Squibb Co., Ltd., for other unrelated work. HI received a grant from Bristol-Myers Squibb Co., Ltd., and Asahi-kasei for other unrelated work. Department of Advanced Medicine for Rheumatic Diseases, Graduate School of Medicine, Kyoto University is supported by Nagahama City, Shiga, Japan and four pharmaceutical companies (Mitsubishi Tanabe Pharma Co., Chugai Pharmaceutical Co. Ltd, UCB Japan Co. Ltd, and AYUMI Pharmaceutical Co.). KURAMA cohort study is supported by grant from Daiichi Sankyo Co., Ltd. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. PLS-DA score plot between RA patients (n = 49) and control subjects (n = 10) based on metabolic profiles in plasma.
The green and red dots represent RA patient and control samples, respectively.
Fig 2
Fig 2. ROC curve of the metabolites that correlated with DAS28-ESR and significantly differed between active and inactive patients.
The selected metabolites in this model were histidine and guanidoacetic acid in plasma and hypotaurine in urine.
See this image and copyright information in PMC

References

    1. Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO 3rd, et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010;62(9):2569–2581. 10.1002/art.27584 - DOI - PubMed
    1. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 2016;388(10055):2023–2038. 10.1016/S0140-6736(16)30173-8 - DOI - PubMed
    1. Deane KD, Demoruelle MK, Kelmenson LB, Kuhn KA, Norris JM, Holers VM. Genetic and environmental risk factors for rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2017;31(1):3–18. 10.1016/j.berh.2017.08.003 - DOI - PMC - PubMed
    1. Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142–147. 10.1038/nbt.2487 - DOI - PMC - PubMed
    1. Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet. 2010;42(6):508–514. 10.1038/ng.582 - DOI - PMC - PubMed

Publication types