Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites
Abstract Background To develop a predictive model for bone destruction in patients with rheumatoid arthritis (RA), based on the characteristics of plasma metabolites and common clinical indicators. Methods The cohort comprised 60 patients with RA, with baseline metabolite features identified using t...
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BMC
2025-05-01
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| Series: | Arthritis Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13075-025-03576-x |
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| author | Zihan Wang Tianyi Lan Yi Jiao Xing Wang Hongwei Yu Qishun Geng Jiahe Xu Cheng Xiao Qingwen Tao Yuan Xu |
| author_facet | Zihan Wang Tianyi Lan Yi Jiao Xing Wang Hongwei Yu Qishun Geng Jiahe Xu Cheng Xiao Qingwen Tao Yuan Xu |
| author_sort | Zihan Wang |
| collection | DOAJ |
| description | Abstract Background To develop a predictive model for bone destruction in patients with rheumatoid arthritis (RA), based on the characteristics of plasma metabolites and common clinical indicators. Methods The cohort comprised 60 patients with RA, with baseline metabolite features identified using the liquid chromatograph-mass spectrometer system. Radiographic outcomes were assessed using the van der Heijde-modified total Sharp score (mTSS) following a one-year follow-up period to quantify bone destruction. The longitudinal association between metabolites and radiographic progression was analyzed using several machine learning algorithms, and the significance of core metabolites was calculated. A new model incorporating metabolites and clinical indicators was created to evaluate its predictive performance for radiographic progression; the model was compared with other prediction models. Results The median increase in mTSS was 3.50. Of the 774 detected metabolites, 77 differed between patients with different outcomes. Core metabolites identified using the Gaussian Naive Bayes algorithm included mangiferic acid, O-acetyl-L-carnitine, 5,8,11-eicosatrienoic acid, and 16-methylheptadecanoic acid. A standardized bone erosion risk score (BERS) was developed based on these core metabolite features for assessing the radiographic progression outcome. Individuals with a high BERS exhibited a lower risk of rapid radiographic progression than those with a lower score (OR = 0.01, 95% CI = 0.01–0.03, P = 0.003). The “China-Japan Friendship Hospital-BERS Model” (CjBM), combining BERS with clinical features (methotrexate and C-reactive protein), produced an area under the receiver operating characteristic curve of 0.800. Moreover, compared with the reported models, the CjBM showed near statistical significance in identifying rapid radiographic progression; adding BERS can improve the discrimination of the original reported model (P DeLong=0.035). Conclusions The CjBM was developed for early prediction of bone destruction in patients with RA, and the evaluation of BERS emphasizes the significance of metabolite features. |
| format | Article |
| id | doaj-art-63ea0d78efbd4d22b13d0174a7e1a17d |
| institution | OA Journals |
| issn | 1478-6362 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | Arthritis Research & Therapy |
| spelling | doaj-art-63ea0d78efbd4d22b13d0174a7e1a17d2025-08-20T01:53:14ZengBMCArthritis Research & Therapy1478-63622025-05-0127111310.1186/s13075-025-03576-xEarly prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolitesZihan Wang0Tianyi Lan1Yi Jiao2Xing Wang3Hongwei Yu4Qishun Geng5Jiahe Xu6Cheng Xiao7Qingwen Tao8Yuan Xu9Department of Traditional Chinese Medicine Rheumatism, China-Japan Friendship HospitalDepartment of Traditional Chinese Medicine Rheumatism, China-Japan Friendship HospitalGraduate School, Beijing University of Chinese MedicineGraduate School, Beijing University of Chinese MedicineDepartment of Radiology, China-Japan Friendship HospitalChina-Japan Friendship Clinical Medical College, Chinese Academy of Medical Sciences & Peking Union Medical CollegePeking University China-Japan Friendship School of Clinical MedicineInstitute of Clinical Medicine, China-Japan Friendship HospitalDepartment of Traditional Chinese Medicine Rheumatism, China-Japan Friendship HospitalDepartment of Traditional Chinese Medicine Rheumatism, China-Japan Friendship HospitalAbstract Background To develop a predictive model for bone destruction in patients with rheumatoid arthritis (RA), based on the characteristics of plasma metabolites and common clinical indicators. Methods The cohort comprised 60 patients with RA, with baseline metabolite features identified using the liquid chromatograph-mass spectrometer system. Radiographic outcomes were assessed using the van der Heijde-modified total Sharp score (mTSS) following a one-year follow-up period to quantify bone destruction. The longitudinal association between metabolites and radiographic progression was analyzed using several machine learning algorithms, and the significance of core metabolites was calculated. A new model incorporating metabolites and clinical indicators was created to evaluate its predictive performance for radiographic progression; the model was compared with other prediction models. Results The median increase in mTSS was 3.50. Of the 774 detected metabolites, 77 differed between patients with different outcomes. Core metabolites identified using the Gaussian Naive Bayes algorithm included mangiferic acid, O-acetyl-L-carnitine, 5,8,11-eicosatrienoic acid, and 16-methylheptadecanoic acid. A standardized bone erosion risk score (BERS) was developed based on these core metabolite features for assessing the radiographic progression outcome. Individuals with a high BERS exhibited a lower risk of rapid radiographic progression than those with a lower score (OR = 0.01, 95% CI = 0.01–0.03, P = 0.003). The “China-Japan Friendship Hospital-BERS Model” (CjBM), combining BERS with clinical features (methotrexate and C-reactive protein), produced an area under the receiver operating characteristic curve of 0.800. Moreover, compared with the reported models, the CjBM showed near statistical significance in identifying rapid radiographic progression; adding BERS can improve the discrimination of the original reported model (P DeLong=0.035). Conclusions The CjBM was developed for early prediction of bone destruction in patients with RA, and the evaluation of BERS emphasizes the significance of metabolite features.https://doi.org/10.1186/s13075-025-03576-xRheumatoid arthritisBone destructionMetaboliteMachine learningPrediction model |
| spellingShingle | Zihan Wang Tianyi Lan Yi Jiao Xing Wang Hongwei Yu Qishun Geng Jiahe Xu Cheng Xiao Qingwen Tao Yuan Xu Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites Arthritis Research & Therapy Rheumatoid arthritis Bone destruction Metabolite Machine learning Prediction model |
| title | Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites |
| title_full | Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites |
| title_fullStr | Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites |
| title_full_unstemmed | Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites |
| title_short | Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites |
| title_sort | early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites |
| topic | Rheumatoid arthritis Bone destruction Metabolite Machine learning Prediction model |
| url | https://doi.org/10.1186/s13075-025-03576-x |
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