Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy

Doris G Leung, Alex E Bocchieri, Shivani Ahlawat, Michael A Jacobs, Vishwa S Parekh, Vladimir Braverman, Katherine Summerton, Jennifer Mansour, Genila Bibat, Carl Morris, Shannon Marraffino, Kathryn R Wagner, Doris G Leung, Alex E Bocchieri, Shivani Ahlawat, Michael A Jacobs, Vishwa S Parekh, Vladimir Braverman, Katherine Summerton, Jennifer Mansour, Genila Bibat, Carl Morris, Shannon Marraffino, Kathryn R Wagner

Abstract

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.

Conflict of interest statement

Funding for the overall study was provided by Pfizer, Inc. The authors declare that they have no financial or non-financial competing interests.

Figures

Fig. 1
Fig. 1
Demonstration of the MPDL tissue signature model and CNN segmentation mapping in participants with (a) severe fat infiltration and (b) moderate fat infiltration at the level of the mid-thigh. An image of a healthy volunteer (c) is provided for comparison. The color scale is coded as follows: healthy muscle is blue, bone is yellow, fat is orange, and fat infiltrated muscle is red. The number of voxels and the fraction of total voxels corresponding to each tissue type are shown in the table
Fig. 2
Fig. 2
Bland-Altman plots of five timed function tests that underwent repeatability testing on two consecutive days. Blue dashed lines represent the mean of the difference between consecutive tests. Red lines show the 95% limits of agreement for the differences between consecutive tests
Fig. 3
Fig. 3
Body composition measures on MRI and DEXA scanning. The number of voxels identified as muscle strongly correlate with estimates of lean body mass on DEXA (3A). The number of voxels corresponding to body fat on MRI strongly correlate to DEXA estimates of total body fat (3B). Estimates of the intramuscular fat fraction are strongly correlated when comparing baseline to follow-up measurements
Fig. 4
Fig. 4
Timed function testing (2MWD, 10MWR, 4SC, TUG), manual muscle testing, and forced vital capacity as a function of the intramuscular fat fraction derived from MRI

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