{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T01:10:25Z","timestamp":1777857025099,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JND"],"published-print":{"date-parts":[[2022,1,4]]},"abstract":"<jats:p>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 \u2018classical\u2019 and \u2018non-classical\u2019 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.<\/jats:p>","DOI":"10.3233\/jnd-210739","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T13:28:31Z","timestamp":1636464511000},"page":"171-178","source":"Crossref","is-referenced-by-count":4,"title":["Differentiation of Pediatric-Onset Duchenne and Becker Muscular Dystrophy Subphenotypes Using Data from the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet)"],"prefix":"10.1177","volume":"9","author":[{"given":"Jennifer G.","family":"Andrews","sequence":"first","affiliation":[{"name":"Department of Pediatrics, University of Arizona, Tucson, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"the MD STARnet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Molly M.","family":"Lamb","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristin M.","family":"Conway","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalie","family":"Street","sequence":"additional","affiliation":[{"name":"National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina","family":"Westfield","sequence":"additional","affiliation":[{"name":"New York State Department of Health, Western Regional Office, Buffalo, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Ciafaloni","sequence":"additional","affiliation":[{"name":"Department of Neurology, School of Medicine and Dentistry, University of 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