Background: Pathogenic variants in the FKRP gene cause impaired glycosylation of α-dystroglycan in muscle, producing a limb-girdle muscular dystrophy with cardiomyopathy. Despite advances in understanding the pathophysiology of FKRP-associated myopathies, clinical research in the limb-girdle muscular dystrophies has been limited by the lack of normative biomarker data to gauge disease progression.
Methods: Participants in a phase 2 clinical trial were evaluated over a 4-month, untreated lead-in period to evaluate repeatability and to obtain normative data for timed function tests, strength tests, pulmonary function, and body composition using DEXA and whole-body MRI. Novel deep learning algorithms were used to analyze MRI scans and quantify muscle, fat, and intramuscular fat infiltration in the thighs. T-tests and signed rank tests were used to assess changes in these outcome measures.
Results: Nineteen participants were observed during the lead-in period for this trial. No significant changes were noted in the strength, pulmonary function, or body composition outcome measures over the 4-month observation period. One timed function measure, the 4-stair climb, showed a statistically significant difference over the observation period. Quantitative estimates of muscle, fat, and intramuscular fat infiltration from whole-body MRI corresponded significantly with DEXA estimates of body composition, strength, and timed function measures.
Conclusions: We describe normative data and repeatability performance for multiple physical function measures in an adult FKRP muscular dystrophy population. Our analysis indicates that deep learning algorithms can be used to quantify healthy and dystrophic muscle seen on whole-body imaging.
Trial registration: This study was retrospectively registered in clinicaltrials.gov (NCT02841267) on July 22, 2016 and data supporting this study has been submitted to this registry.
Keywords: Biomarkers; Convolutional neural network; Deep learning; FKRP; Limb-girdle muscular dystrophy; Tissue signatures; Whole-body MRI.