Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis
Résumé
Background: Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs is
of limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler and
more sensitive evaluation of treatment benefits in this important preclinical model.
Methods: Accelerometry was performed twice monthly between the ages of 2 and 12 months on 8 healthy and
20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminant analysis (LDA). Manipulation
of the dependent and independent variables produced three distinct models. The ability of each model to detect gait
alterations and their pattern change with age was tested using a leave-one-out cross-validation approach.
Results: Selecting genotype (healthy or GRMD) as the dependent variable resulted in a model (Model 1) allowing a
good discrimination between the gait phenotype of GRMD and healthy dogs. However, this model was not sufficiently
representative of the disease progression. In Model 2, age in months was added as a supplementary dependent
variable (GRMD_2 to GRMD_12 and Healthy_2 to Healthy_9.5), resulting in a high overall misclassification rate (83.2%).
To improve accuracy, a third model (Model 3) was created in which age was also included as an explanatory variable.
This resulted in an overall misclassification rate lower than 12%. Model 3 was evaluated using blinded data pertaining
to 81 healthy and GRMD dogs. In all but one case, the model correctly matched gait phenotype to the actual
genotype. Finally, we used Model 3 to reanalyse data from a previous study regarding the effects of
immunosuppressive treatments on muscular dystrophy in GRMD dogs. Our model identified significant effect of
immunosuppressive treatments on gait quality, corroborating the original findings, with the added advantages of
direct statistical analysis with greater sensitivity and more comprehensible data representation.
Conclusions: Gait analysis using LDA allows for improved analysis of accelerometry data by applying a
decision-making analysis approach to the evaluation of preclinical treatment benefits in GRMD dogs.
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