Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry
Abstract Background Rheumatoid arthritis (RA) presents as pain, swelling and leads to irreversible damage in joint, and adversely affects the quality of life of patients with RA. However, the etiology of RA is still unclear, and novel biomarkers are demanded for the early prediction and diagnosis of...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-12994-5 |
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| author | Xue Wu Qi Yang Shanshan Liu Peng Yang Zhengqi Liu Zhitu Zhu |
| author_facet | Xue Wu Qi Yang Shanshan Liu Peng Yang Zhengqi Liu Zhitu Zhu |
| author_sort | Xue Wu |
| collection | DOAJ |
| description | Abstract Background Rheumatoid arthritis (RA) presents as pain, swelling and leads to irreversible damage in joint, and adversely affects the quality of life of patients with RA. However, the etiology of RA is still unclear, and novel biomarkers are demanded for the early prediction and diagnosis of RA and dissecting disease mechanisms. Objective This study aimed at profiling the disordered metabolic pathways in RA and selecting potential biomarkers to distinguish RA patients from healthy individuals, and systematically investigated the associations between metabolites and the risk of RA. Methods A total of 533 participants, including 382 healthy individuals and 151 RA patients, were recruited to explore altered metabolic profiles through the analysis of dried blood spot samples by mass spectrometry. Multiple algorithms were applied to identify potential biomarkers. Dose-response relationships were investigated by binary logistic regression and restricted cubic spline (RCS) analysis. Results There were different metabolic profiles between RA and healthy individuals. After systematic selection, a metabolic panel consisting of C20, C5, Leu, C14:1/C16, Arg/(Orn + Cit), and C2/C0 was used to differentiate the two groups. Ten-fold cross-validation and test set were employed to evaluate prediction models. The receiver operating characteristic analysis demonstrated an area under the curve of 0.920(95%CI: 0.851–0.990) in test set to distinguish the two groups. The strong correlations between the 6 metabolites and RA were observed in RCS regression model. Conclusions The selected biomarkers have the potential to improve the detection of RA, and may offer insights into the intervention strategies to susceptible at-risk populations of developing RA. |
| format | Article |
| id | doaj-art-609864eb4daa473b9aab5891be45dd33 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-609864eb4daa473b9aab5891be45dd332025-08-20T03:04:38ZengNature PortfolioScientific Reports2045-23222025-08-0115111210.1038/s41598-025-12994-5Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometryXue Wu0Qi Yang1Shanshan Liu2Peng Yang3Zhengqi Liu4Zhitu Zhu5Guizhou University of Traditional Chinese MedicineGuizhou University of Traditional Chinese MedicineGuizhou University of Traditional Chinese MedicineGuizhou University of Traditional Chinese MedicineGuizhou University of Traditional Chinese MedicineLiaoning Provincial Key Laboratory of Clinical Oncology Metabonomics, Jinzhou Medical UniversityAbstract Background Rheumatoid arthritis (RA) presents as pain, swelling and leads to irreversible damage in joint, and adversely affects the quality of life of patients with RA. However, the etiology of RA is still unclear, and novel biomarkers are demanded for the early prediction and diagnosis of RA and dissecting disease mechanisms. Objective This study aimed at profiling the disordered metabolic pathways in RA and selecting potential biomarkers to distinguish RA patients from healthy individuals, and systematically investigated the associations between metabolites and the risk of RA. Methods A total of 533 participants, including 382 healthy individuals and 151 RA patients, were recruited to explore altered metabolic profiles through the analysis of dried blood spot samples by mass spectrometry. Multiple algorithms were applied to identify potential biomarkers. Dose-response relationships were investigated by binary logistic regression and restricted cubic spline (RCS) analysis. Results There were different metabolic profiles between RA and healthy individuals. After systematic selection, a metabolic panel consisting of C20, C5, Leu, C14:1/C16, Arg/(Orn + Cit), and C2/C0 was used to differentiate the two groups. Ten-fold cross-validation and test set were employed to evaluate prediction models. The receiver operating characteristic analysis demonstrated an area under the curve of 0.920(95%CI: 0.851–0.990) in test set to distinguish the two groups. The strong correlations between the 6 metabolites and RA were observed in RCS regression model. Conclusions The selected biomarkers have the potential to improve the detection of RA, and may offer insights into the intervention strategies to susceptible at-risk populations of developing RA.https://doi.org/10.1038/s41598-025-12994-5Rheumatoid arthritisMass spectrometryMetabolomicsBiomarkerDried blood spot |
| spellingShingle | Xue Wu Qi Yang Shanshan Liu Peng Yang Zhengqi Liu Zhitu Zhu Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry Scientific Reports Rheumatoid arthritis Mass spectrometry Metabolomics Biomarker Dried blood spot |
| title | Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry |
| title_full | Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry |
| title_fullStr | Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry |
| title_full_unstemmed | Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry |
| title_short | Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry |
| title_sort | metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry |
| topic | Rheumatoid arthritis Mass spectrometry Metabolomics Biomarker Dried blood spot |
| url | https://doi.org/10.1038/s41598-025-12994-5 |
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