Predicting Knee Osteoarthritis

B.S. Gardiner, Francis Woodhouse, T.F. Besier, A.J. Grodzinsky, D.G. Lloyd, L. Zhang, David Smith

    Research output: Contribution to journalArticlepeer-review

    31 Citations (Scopus)
    132 Downloads (Pure)

    Abstract

    © 2015, The Author(s). Treatment options for osteoarthritis (OA) beyond pain relief or total knee replacement are very limited. Because of this, attention has shifted to identifying which factors increase the risk of OA in vulnerable populations in order to be able to give recommendations to delay disease onset or to slow disease progression. The gold standard is then to use principles of risk management, first to provide subject-specific estimates of risk and then to find ways of reducing that risk. Population studies of OA risk based on statistical associations do not provide such individually tailored information. Here we argue that mechanistic models of cartilage tissue maintenance and damage coupled to statistical models incorporating model uncertainty, united within the framework of structural reliability analysis, provide an avenue for bridging the disciplines of epidemiology, cell biology, genetics and biomechanics. Such models promise subject-specific OA risk assessment and personalized strategies for mitigating or even avoiding OA. We illustrate the proposed approach with a simple model of cartilage extracellular matrix synthesis and loss regulated by daily physical activity.
    Original languageEnglish
    Pages (from-to)222-233
    Number of pages12
    JournalAnnals of Biomedical Engineering
    Volume44
    Issue number1
    DOIs
    Publication statusPublished - Jan 2016

    Fingerprint

    Dive into the research topics of 'Predicting Knee Osteoarthritis'. Together they form a unique fingerprint.

    Cite this