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Background <p>The 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.</p> Methods <p>This 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.</p> Results <p>Ultimately, 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.</p> Conclusions <p>Our 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.</p>

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A diagnostic model for assessing the risk of osteoporosis in patients with rheumatoid arthritis based on bone turnover markers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月1日

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  • Yubo Shao,
  • Yazhu Yang,
  • XiaoYu Yang,
  • Zihang Xu,
  • Hong Zhang,
  • Ning Li,
  • Hao Xu,
  • Yongjian Zhao,
  • Yongjun Wang,
  • Qi Shi,
  • Qianqian Liang

摘要

Background

The 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.

Methods

This 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.

Results

Ultimately, 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.

Conclusions

Our 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.