Predicting Knee Osteoarthritis
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10439_2015_Article_1393.pdf
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2.79 MB
Format
Adobe PDF
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Author(s)
Zhang, Lihai
Gardiner, Bruce S.
Woodhouse, Francis G.
Besier, Thor F.
Lloyd, David G.
Smith, David W.
Grodzinsky, Alan J
Date Issued
July 2015
Journal
Annals of Biomedical Engineering
Publisher
Springer US
Citation
Gardiner, Bruce S. et al. “Predicting Knee Osteoarthritis.” Annals of Biomedical Engineering 44.1 (2016): 222–233.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10439-015-1393-5
