Objective:

This study aims to characterize the imaging phenotype and utility of imaging biomarkers in DM2 using artificial intelligence-based (AI) muscle segmentation algorithms to analyze whole-body MRI scans.

Background:

Myotonic Dystrophy Type 2 (DM2) is an adult-onset genetic condition characterized by myotonia, proximal muscle weakness and wasting, pain, and multi-systemic features. DM2 causes muscle degeneration resulting in the replacement of muscle by adipose tissue and fibrosis. The distribution of weakness in DM2 is distinct from that seen in other muscular dystrophies. Therefore, understanding the relationship of both muscle fat fraction (MFF) and contractile volume (CV) with physical functioning of DM2 patients is essential for understanding progression in DM2.

Design/Methods:

We performed a cross-sectional study to document the relationship between MRI-based muscle measurements and clinical endpoints in patients with genetically confirmed DM2. Each patient was evaluated by a neurologist to assess their strength, physical function, and patient reported outcomes. We utilized AI-based muscle segmentation to calculate MFF and CV in 38 muscles bilaterally from whole-body Dixon MRI scans. 

Results:

This cohort included 6 females and 6 males with a mean age of 52.5±15.4 years and mean disease duration of 12.5±7.8 years. The gluteus maximus and erector spinae muscles had the highest average MFF. Mean MFF in fully captured muscles ranged from 36.5-7.4%. Higher MFF was associated with shorter 6-minute walk distance (ρ=-0.65, p-value=0.02) and slower 10 m walk/run (ρ=0.74, p-value=0.01). Higher CV was associated with longer 6-minute walk distance (ρ=0.63, p-value=0.03) and faster 10 m walk/run (ρ=-0.73, p-value=0.01).

Conclusions:

This study is the first to use whole body MRI to characterize muscle involvement in DM2. The results identify a pattern of proximal muscle involvement in DM2 and an association between higher MFF and diminished physical performance. Larger, longitudinal studies are needed to validate the utility of imaging biomarkers in DM2.