A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait

Gustavo A. Orozco*, Kalle Karjalainen, Eng Kuan Moo, Lauri Stenroth, Petri Tanska, Jaqueline Lourdes Rios, Teemu V. Tuomainen, Mikko J. Nissi, Hanna Isaksson, Walter Herzog, Rami K. Korhonen

*Corresponding author af dette arbejde

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Abstract

Abnormal loading of the knee due to injuries or obesity is thought to contribute to the development of osteoarthritis (OA). Small animal models have been used for studying OA progression mechanisms. However, numerical models to study cartilage responses under dynamic loading in preclinical animal models have not been developed. Here we present a musculoskeletal finite element model of a rat knee joint to evaluate cartilage biomechanical responses during a gait cycle. The rat knee joint geometries were obtained from a 3-D MRI dataset and the boundary conditions regarding loading in the joint were extracted from a musculoskeletal model of the rat hindlimb. The fibril-reinforced poroelastic (FRPE) properties of the rat cartilage were derived from data of mechanical indentation tests. Our numerical results showed the relevance of simulating anatomical and locomotion characteristics in the rat knee joint for estimating tissue responses such as contact pressures, stresses, strains, and fluid pressures. We found that the contact pressure and maximum principal strain were virtually constant in the medial compartment whereas they showed the highest values at the beginning of the gait cycle in the lateral compartment. Furthermore, we found that the maximum principal stress increased during the stance phase of gait, with the greatest values at midstance. We anticipate that our approach serves as a first step towards investigating the effects of gait abnormalities on the adaptation and degeneration of rat knee joint tissues and could be used to evaluate biomechanically-driven mechanisms of the progression of OA as a consequence of joint injury or obesity.

OriginalsprogEngelsk
Artikelnummere1009398
TidsskriftPLOS Computational Biology
Vol/bind18
Udgave nummer6
Sider (fra-til)1-23
ISSN1553-734X
DOI
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
Academy of Finland (https://www.aka.fi/ en; grant nos. 285909 [MJN], 324529 [RKK], 332915 [LS], 334773 - under the frame of ERA PerMed [RKK]), Strategic Funding of the University of Eastern Finland (PT), Swedish Research Council (2019-00953 - under the frame of ERA PerMed; [HI]), European Regional Development Fund [MJN], Innovation Fund Denmark (9088-00006B - under the frame of ERA PerMed; [LS]), Finnish Cultural Foundation (grant #191044 and #200059; [PT]), Maire Lisko Foundation [PT], Sigrid Juselius Foundation [RKK], Päivikki ja Sakari Sohlberg Foundation [GAO], Maud Kuistila Memorial Foundation [GAO], Saastamoinen Foundation [GAO], The Killam Foundation [WH], The Canada Research Chair Programme [WH] and The Canadian Institutes for Health Research [WH]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors appreciate the support of the University of Eastern Finland, Lund University, and the University of Calgary to undertake this study.

Publisher Copyright:
© 2022 Orozco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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