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A discovery of screening markers for rheumatoid arthritis by liquid chromatography mass spectrometry: A metabolomic approach
Author(s) -
Lee YooJin,
Mun Sora,
Lee YouRim,
Lee Seungyeon,
Kwon Sohyen,
Kim Doojin,
Lim MiKyoung,
Kang HeeGyoo,
Lee Jiyeong
Publication year - 2020
Publication title -
international journal of rheumatic diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.795
H-Index - 41
eISSN - 1756-185X
pISSN - 1756-1841
DOI - 10.1111/1756-185x.13935
Subject(s) - receiver operating characteristic , metabolomics , medicine , rheumatoid arthritis , area under the curve , liquid chromatography–mass spectrometry , metabolite , chromatography , mass spectrometry , chemistry
Aim This study aimed to discover serum metabolite biomarkers for potential use in screening for rheumatoid arthritis (RA). Methods The sera from 43 healthy controls (HCs) and 49 RA patients were globally analyzed using high‐performance liquid chromatography‐ tandem mass spectrometry. Molecular features (MFs) from samples were analyzed using volcano plots, partial least squares discriminant analysis, and variable importance in projection scores to select candidates. The spectra of candidate MFs were matched with the METLIN database. We confirmed the association between candidates and RA and analyzed the receiver‐operating characteristic (ROC) curves. Results We selected a total of 57 candidate MFs that had a fold change ≥1.5, P value ≤.05, and over 80% of frequency. Among them, 18 MFs were identified as metabolites with the METLIN database. Six metabolites (dehydroepiandrosterone sulfate, androsterone sulfate, γ‐linolenic acid, 9[E],11[E]‐conjugated linoleic acid, docosahexaenoic acid, and docosapentaenoic acid [22n‐3]) out of the 18 were associated with mechanisms of RA and were selected as final candidates. ROC curve analysis revealed their area under the curve (AUC) values were all above 0.75 and the combined AUC of the six candidates was 0.89. Conclusion Using six candidates as a marker set showed potential in distinguishing RA patients from HCs, based on high AUC values. Therefore, we propose that a marker set of these six candidates has potential clinical application in RA screening.