Summary
Behavioral assays of animal pain and disability can increase the clinical relevance
of a preclinical study. However, pain and symptoms are difficult to measure in preclinical
models. Because animals often alter their movement patterns to reduce or avoid joint
pain, gait analysis can be an important tool for quantifying OA-related symptoms in
rodents. Technologies to measure rodent gait continue to advance and have been the
focus of prior reviews. Regardless of the techniques used, the analysis of rodent
gait data can be complex due to multiple confounding variables. The goal of this review
is to discuss recent advances in the understanding of OA-related gait changes and
provide recommendations on the analysis of gait data. Recent studies suggest OA-affected
animals reduce vertical loading through their injured limb while walking, indicating
dynamic ground reaction forces are important data to collect when possible. Moreover,
gait data analysis depends on accurately measuring and accounting for the confounding
effects of velocity and other covariates (such as animal size) when interpreting shifts
in various gait parameters. Herein, we discuss different statistical techniques to
account for covariates and interpret gait shifts. In particular, this review will
discuss residualization and linear mixed effects models, including how both techniques
can account for inter- and intra-animal variability and the effects of velocity. Furthermore,
this review discusses future considerations for using rodent gait analysis, while
highlighting the intricacies of gait analysis as a tool to measure joint function
and behavioral outcomes.
Keywords
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Article info
Publication history
Published online: November 23, 2022
Accepted:
November 10,
2022
Received:
July 13,
2022
Identification
Copyright
© 2022 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.
