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Background <p>Prediction of osteoporotic fractures remains suboptimal, leaving many high-risk individuals untreated while others fracture without prior diagnosis. Muscle characteristics are not part of prediction algorithms although studies showed that paraspinal muscle size, density, and fat infiltration are associated with vertebral fractures (VF). However, an analysis of the prospective AGES-Reykjavik study did not show improved prediction of the first incident VF when combining muscle parameters with BMD.</p> Objective <p>To determine whether paraspinal muscle characteristics are associated with prevalent fractures using the prospective AGES-Reykjavik study.</p> Methods <p>In the current study associations of muscle parameters with prevalent VF were analyzed in the same cohort of 486 women and 340 men with 96 and 78 prevalent VF, respectively. 50 parameters from CT scans of the L1 and L2 vertebrae, divided into a BMD, a trabecular texture and a muscle subset were used as discriminators. Each subset also included age and BMI. The number of parameters was reduced using stepwise logistic regression to create multivariable fracture discrimination models. Model accuracy was assessed using the likelihood ratio test (LRT) and the area under the curve (AUC). Bootstrap analyses were performed to assess stability of the model selection process.</p> Results <p>23 parameters significantly discriminated prevalent VFs univariately in women and 5 in men. In women multivariable bone and muscle models showed significantly better fracture discrimination (<i>p</i> &lt; 0.01) than the combination of age and BMI. Compared to the BMD model, LRT showed a significantly improved VF discrimination of the combinations of BMD with texture or with muscle models (<i>p</i> &lt; 0.001). In men the BMD model (AUC 0.64) did not significantly improve VF discrimination compared to age and BMI.</p> Conclusions <p>In older women, but not men, paraspinal muscle characteristics significantly enhance the discrimination of prevalent VFs beyond BMD alone. Muscle parameters were not predictive of incident VFs in prior analyses of the same cohort, suggesting that muscle deterioration may occur concurrently or follow vertebral fracture.</p>

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Association of trabecular texture and paraspinal muscle characteristics with prevalent vertebral fractures - QCT results from a subcohort of the AGES population

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Tobias Stumpf,
  • Oliver Chaudry,
  • Jana Hummel,
  • Sandra Freitag-Wolf,
  • Eren Yilmaz,
  • Stefan Bartenschlager,
  • Sigurdur Sigurdsson,
  • Vilmundur Gudnason,
  • Nicolai R. Krekiehn,
  • Claus C. Glüer,
  • Klaus Engelke

摘要

Background

Prediction of osteoporotic fractures remains suboptimal, leaving many high-risk individuals untreated while others fracture without prior diagnosis. Muscle characteristics are not part of prediction algorithms although studies showed that paraspinal muscle size, density, and fat infiltration are associated with vertebral fractures (VF). However, an analysis of the prospective AGES-Reykjavik study did not show improved prediction of the first incident VF when combining muscle parameters with BMD.

Objective

To determine whether paraspinal muscle characteristics are associated with prevalent fractures using the prospective AGES-Reykjavik study.

Methods

In the current study associations of muscle parameters with prevalent VF were analyzed in the same cohort of 486 women and 340 men with 96 and 78 prevalent VF, respectively. 50 parameters from CT scans of the L1 and L2 vertebrae, divided into a BMD, a trabecular texture and a muscle subset were used as discriminators. Each subset also included age and BMI. The number of parameters was reduced using stepwise logistic regression to create multivariable fracture discrimination models. Model accuracy was assessed using the likelihood ratio test (LRT) and the area under the curve (AUC). Bootstrap analyses were performed to assess stability of the model selection process.

Results

23 parameters significantly discriminated prevalent VFs univariately in women and 5 in men. In women multivariable bone and muscle models showed significantly better fracture discrimination (p < 0.01) than the combination of age and BMI. Compared to the BMD model, LRT showed a significantly improved VF discrimination of the combinations of BMD with texture or with muscle models (p < 0.001). In men the BMD model (AUC 0.64) did not significantly improve VF discrimination compared to age and BMI.

Conclusions

In older women, but not men, paraspinal muscle characteristics significantly enhance the discrimination of prevalent VFs beyond BMD alone. Muscle parameters were not predictive of incident VFs in prior analyses of the same cohort, suggesting that muscle deterioration may occur concurrently or follow vertebral fracture.