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<p>Osteoarthritis (OA) is a common degenerative whole joint disorder characterized by articular cartilage breakdown, mostly often in the knee’s medial compartment. Traditional attempts to predict structural progression have focused on biochemical markers from blood, synovia, or urine; however, these do not capture internal mechanical environments that drives cartilage degeneration. In silico modelling enables non-invasive, patient-specific estimation of internal joint loading, including contact pressure, compressive strain, fibril strain and maximum shear strain, as potential mechanical biomarkers for OA progression. This study developed a novel MSK-FE workflow linking gait kinematics to contact pressures and cartilage tissue strains. Subjects showing medial OA progression over 2 years exhibited elevated medial compartment contact pressures and a posterolateral location shift at baseline. Unsupervised k-means clustering of cartilage tissue strain histograms further distinguished progressors from non-progressors and controls. These results demonstrate the potential of computationally efficient mechanical biomarkers to identify patients at risk of OA progression, supporting early and targeted preventative treatment strategies.</p>

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Cartilage mechanical responses during gait as in silico biomarkers for medial knee OA progression

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月9日

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  • Yixuan Zhang,
  • Bryce A. Killen,
  • Ikram Mohout,
  • Miel Willems,
  • Frank P. Luyten,
  • Sabine Verschueren,
  • Seyed Ali Elahi,
  • Ilse Jonkers

摘要

Osteoarthritis (OA) is a common degenerative whole joint disorder characterized by articular cartilage breakdown, mostly often in the knee’s medial compartment. Traditional attempts to predict structural progression have focused on biochemical markers from blood, synovia, or urine; however, these do not capture internal mechanical environments that drives cartilage degeneration. In silico modelling enables non-invasive, patient-specific estimation of internal joint loading, including contact pressure, compressive strain, fibril strain and maximum shear strain, as potential mechanical biomarkers for OA progression. This study developed a novel MSK-FE workflow linking gait kinematics to contact pressures and cartilage tissue strains. Subjects showing medial OA progression over 2 years exhibited elevated medial compartment contact pressures and a posterolateral location shift at baseline. Unsupervised k-means clustering of cartilage tissue strain histograms further distinguished progressors from non-progressors and controls. These results demonstrate the potential of computationally efficient mechanical biomarkers to identify patients at risk of OA progression, supporting early and targeted preventative treatment strategies.