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<p>Osteoporosis (OP) is a prevalent and serious comorbidity in rheumatoid arthritis (RA), significantly increasing the fracture burden. Although early detection is crucial, OP screening in RA is often delayed, representing an unmet clinical need. Clinical prediction models (CPMs) offer a solution for early risk stratification, yet a synthesis that integrates RA-OP risk factors and evaluates existing CPMs is lacking. This review addresses this gap by delineating the multidimensional risk network for RA-OP and evaluating the development, performance, and applicability of current CPMs. We find that despite evolution from basic to sophisticated biomarker-integrated algorithms, most CPMs are constrained by modest sample sizes, insufficient external validation, and suboptimal clinical translatability. The current paradigm focuses predominantly on diagnosis rather than prognosis. To address these limitations, future research should aim to enhance the methodological rigor and generalizability of models, expand their predictive scope to encompass future risk of OP and fractures, and develop RA-specific fracture prediction tools that incorporate disease-specific pathophysiology, with performance rigorously benchmarked against established tools such as the fracture risk assessment tool (FRAX). Progress along these lines may help shift the management paradigm toward more proactive and personalized prevention, with the potential to improve long-term skeletal health outcomes in RA.</p>

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Osteoporosis in rheumatoid arthritis: a new perspective on risk factors and clinical prediction models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Yubo Shao,
  • XiaoYu Yang,
  • Qi Shi,
  • Zihang Xu,
  • Qianqian Liang

摘要

Osteoporosis (OP) is a prevalent and serious comorbidity in rheumatoid arthritis (RA), significantly increasing the fracture burden. Although early detection is crucial, OP screening in RA is often delayed, representing an unmet clinical need. Clinical prediction models (CPMs) offer a solution for early risk stratification, yet a synthesis that integrates RA-OP risk factors and evaluates existing CPMs is lacking. This review addresses this gap by delineating the multidimensional risk network for RA-OP and evaluating the development, performance, and applicability of current CPMs. We find that despite evolution from basic to sophisticated biomarker-integrated algorithms, most CPMs are constrained by modest sample sizes, insufficient external validation, and suboptimal clinical translatability. The current paradigm focuses predominantly on diagnosis rather than prognosis. To address these limitations, future research should aim to enhance the methodological rigor and generalizability of models, expand their predictive scope to encompass future risk of OP and fractures, and develop RA-specific fracture prediction tools that incorporate disease-specific pathophysiology, with performance rigorously benchmarked against established tools such as the fracture risk assessment tool (FRAX). Progress along these lines may help shift the management paradigm toward more proactive and personalized prevention, with the potential to improve long-term skeletal health outcomes in RA.