Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis

Autor: Stéphane Blot, Jean-Yves Hogrel, El Mostafa Qannari, Chantal Thorin, Caroline Le Guiner, Inès Barthélémy, Yan Cherel, Karl Rouger, Bodvael Fraysse
Přispěvatelé: UMR 1089, Atlantic Gene Therapies, Institut National de la Santé et de la Recherche Médicale (INSERM), UPR de neurobiologie, École nationale vétérinaire d'Alfort (ENVA), Chercheur indépendant, Physiopathologie Animale et bioThérapie du muscle et du système nerveux (PAnTher), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), UMR 1089, Atlantic Gene Therapies & Genethon, Neuromuscular Physiology and Evaluation Laboratory, Institut de Myologie, Laboratoire de Thérapie Génique Translationnelle des Maladies Génétiques (Inserm UMR 1089), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), Développement et Pathologie du Tissu Musculaire (DPTM), Ecole Nationale Vétérinaire de Nantes-Institut National de la Recherche Agronomique (INRA), Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Nantes
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Male
0301 basic medicine
lcsh:Diseases of the musculoskeletal system
[SDV]Life Sciences [q-bio]
0302 clinical medicine
Gait (human)
Accelerometry
Treatment evaluation
Orthopedics and Sports Medicine
Statistical analysis
Muscular dystrophy
Gait
ComputingMilieux_MISCELLANEOUS
Age Factors
Discriminant analysis
Phenotype
Treatment Outcome
Technical Advance
Disease Progression
GRMD
Immunosuppressive Agents
Golden retriever muscular dystrophy
medicine.medical_specialty
Genotype
Clinical Decision-Making
Sensitivity and Specificity
Gait assessment
03 medical and health sciences
Dogs
Animal model
Physical medicine and rehabilitation
Rheumatology
medicine
Animals
[SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health
business.industry
Disease progression
Muscular Dystrophy
Animal
medicine.disease
Linear discriminant analysis
Muscular Dystrophy
Duchenne
Disease Models
Animal
030104 developmental biology
Gait analysis
Linear Models
[SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology
lcsh:RC925-935
business
[SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
030217 neurology & neurosurgery
Zdroj: BMC Musculoskeletal Disorders
BMC Musculoskeletal Disorders, BioMed Central, 2017, 18 (1), ⟨10.1186/s12891-017-1494-4⟩
BMC Musculoskeletal Disorders, Vol 18, Iss 1, Pp 1-9 (2017)
BMC Musculoskeletal Disorders 1 (18), . (2017)
ISSN: 1471-2474
Popis: 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.
Databáze: OpenAIRE
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