Yoo-Jin Lee1, Sora Mun1, You-Rim Lee1, Seungyeon Lee1, Sohyen Kwon1, Doojin Kim2, Mi-Kyoung Lim3, Hee-Gyoo Kang1,2,4, Jiyeong Lee5. 1. Department of Senior Healthcare, BK21 Plus Program, Graduate School, Eulji University, Daejeon, Korea. 2. Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Seongnam, Korea. 3. Division of Rheumatology, Department of Medicine, Eulji University School of Medicine, Daejeon, Korea. 4. Seongnam Senior Industry Innovation Center, Eulji University, Seongnam, Korea. 5. Department of Biomedical Laboratory Science, School of Medicine, Eulji University, Daejeon, Korea.
Abstract
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.
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 RApatients 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 RApatients 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.