AI driven analysis of MRI to measure health and disease progression in FSHD.

Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers.

Lean tissue mass measurements by dual-energy X-ray absorptiometry and associations with strength and functional outcome measures in facioscapulohumeral muscular dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is a slowly progressive disease of skeletal muscle. Dual energy X-ray absorptiometry (DEXA) is a widely available, cost-effective and sensitive technique for measuring whole body and regional lean tissue mass and has been used in prior clinical trials in neuromuscular diseases. The Clinical Trial Readiness to Solve Barriers to Drug Development in FSHD (ReSolve) study is a prospective, longitudinal, observational multisite study. We obtained concurrent DEXA scans and functional outcome measurements in 185 patients with FSHD at the baseline visit. We determined the associations between lean tissue mass in the upper and lower extremities and corresponding clinical outcome measures. There were moderate correlations between upper and lower extremity lean tissue mass and their corresponding strengths and function. Lean tissue mass obtained by DEXA scan may be useful as a biomarker in future clinical trials in FSHD.

Intron mutations and early transcription termination in Duchenne and Becker muscular dystrophy.

DMD pathogenic variants for Duchenne and Becker muscular dystrophy are detectable with high sensitivity by standard clinical exome analyses of genomic DNA. However, up to 7% of DMD mutations are deep intronic and analysis of muscle-derived RNA is an important diagnostic step for patients who have negative genomic testing but abnormal dystrophin expression in muscle. In this study, muscle biopsies were evaluated from 19 patients with clinical features of a dystrophinopathy, but negative clinical DMD mutation analysis. Reverse transcription-polymerase chain reaction or high-throughput RNA sequencing methods identified 19 mutations with one of three pathogenic pseudoexon types: deep intronic point mutations, deletions or insertions, and translocations. In association with point mutations creating intronic splice acceptor sites, we observed the first examples of DMD pseudo 3'-terminal exon mutations causing high efficiency transcription termination within introns. This connection between splicing and premature transcription termination is reminiscent of U1 snRNP-mediating telescripting in sustaining RNA polymerase II elongation across large genes, such as DMD. We propose a novel classification of three distinct types of mutations identifiable by muscle RNA analysis, each of which differ in potential treatment approaches. Recognition and appropriate characterization may lead to therapies directed toward full-length dystrophin expression for some patients.