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dc.contributor.authorGardiner, Bruce S
dc.contributor.authorWoodhouse, Francis G
dc.contributor.authorBesier, Thor F
dc.contributor.authorGrodzinsky, Alan J
dc.contributor.authorLloyd, David G
dc.contributor.authorZhang, Lihai
dc.contributor.authorSmith, David W
dc.date.accessioned2017-07-10T04:06:48Z
dc.date.available2017-07-10T04:06:48Z
dc.date.issued2016
dc.identifier.issn0090-6964
dc.identifier.doi10.1007/s10439-015-1393-5
dc.identifier.urihttp://hdl.handle.net/10072/141174
dc.description.abstractTreatment 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto12
dc.relation.ispartofjournalAnnals of Biomedical Engineering
dc.subject.fieldofresearchMedical and Health Sciences not elsewhere classified
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchcode119999
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode11
dc.titlePredicting Knee Osteoarthritis
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2015. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorLloyd, David


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