Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) phenotypes 
are used to describe disease progression in affected individuals. However, 
considerable heterogeneity has been observed across and within these two 
phenotypes, suggesting a spectrum of severity rather than distinct conditions. 
Characterizing the phenotypes and subphenotypes aids researchers in the design 
of clinical studies and clinicians in providing anticipatory guidance to 
affected individuals and their families. Using data from the Muscular Dystrophy 
Surveillance, Tracking, and Research Network (MD STARnet), we used K-means 
cluster analysis to group phenotypically similar males with pediatric-onset 
dystrophinopathy. We identified four dystrophinopathy clusters: Classical BMD, 
Classical DMD, late ambulatory DMD, and severe DMD. The clusters that we 
identified align with both 'classical' and 'non-classical' dystrophinopathy 
described in the literature. Individuals with dystrophinopathies have 
heterogenous clinical presentations that cluster into phenotypically similar 
groups. Use of clinically-derived phenotyping may provide a clearer 
understanding of disease trajectories, reduce variability in study results, and 
prevent exclusion of certain cohorts from analysis. Findings from studying 
subphenotypes may ultimately improve our ability to predict disease progression.