ArticleArthritis research & therapy2025
A diagnostic model for assessing the risk of osteoporosis in patients with rheumatoid arthritis based on bone turnover markers.
Article in Arthritis research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Bone Health in Rheumatoid Arthritis: An Update.Current rheumatology reports · 2026Review
- Osteoporosis in rheumatoid arthritis: a new perspective on risk factors and clinical prediction models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Review
- Biomarkers and ImmuneScores in lung cancer: predictive insights for immunotherapy and combination treatment strategies.Biological procedures online · 2025Review
- Towards Precision Diagnosis: Thoughts and Suggestions on Enhancing the Nomogram for Ventilator-Associated Pneumonia [Letter].Journal of inflammation research · 2025Article
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11 authors.
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Abstract
backgroundThe risk of developing osteoporosis (OP) is increased in patients with rheumatoid arthritis (RA), which is associated with poorer prognosis and higher mortality. Many patients with RA may experience bone loss early in the disease course. Therefore, timely assessment of the risk of OP in RA patients is essential.
methodsThis is a retrospective study in which we collected information from 500 RA patients who underwent bone mineral density assessments at Longhua Hospital, Shanghai University of Traditional Chinese Medicine, from January 2018 to December 2022. Based on the data collection timeline, the first 70% of patients were assigned to the training set, while the remaining 30% were included in the validation set. The model was established using the training set and evaluated through plotting of the receiver operating characteristic curves, calibration curves, and clinical decision curves. Internal validation was performed by resampling the training set data 1,000 times using the bootstrap method, while internal hold-out validation was conducted using the validation dataset.
resultsUltimately, six variables were identified as independently associated with RA combined with OP (RA-OP): female sex, age, beta C-terminal cross-linked peptide (β-CTX), anti-cyclic citrullinated peptide antibody (ACPA), triglycerides (TG), and N-terminal propeptide of type I procollagen (PINP). The regression equation for the model is as follows: Logistic (RA-OP) = -8.703 + 0.946*female + 0.053*age + 0.004*β-CTX + 0.001*ACPA + 0.6*TG-0.008*PINP. The model demonstrated good discrimination (AUC = 0.819, 95% CI: 0.775-0.863) and calibration. In both internal and internal hold-out validation, the model also performed well, with AUC values of 0.814 (95% CI: 0.772-0.864) and 0.772 (95% CI: 0.697-0.847), respectively. Clinical decision curves indicated that the model outperformed both extreme curves, suggesting good clinical utility.
conclusionsOur model is user-friendly and has shown good predictive performance in both internal and internal hold-out validation, offering new insights for the early screening and treatment of OP risk in RA patients.
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