Differentiation of Pediatric-Onset Duchenne and Becker Muscular Dystrophy Subphenotypes Using Data from the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet)

Jennifer G Andrews; Molly M Lamb; Kristin M Conway; Natalie Street; Christina Westfield; Emma Ciafaloni; Dennis Matthews; Shree Pandya; MD STAR

doi:10.3233/JND-210739

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Differentiation of Pediatric-Onset Duchenne and Becker Muscular Dystrophy Subphenotypes Using Data from the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet)

Journal article

Peer reviewed

Differentiation of Pediatric-Onset Duchenne and Becker Muscular Dystrophy Subphenotypes Using Data from the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet)

Jennifer G Andrews, Molly M Lamb, Kristin M Conway, Natalie Street, Christina Westfield, Emma Ciafaloni, Dennis Matthews, Shree Pandya and MD STAR

Journal of neuromuscular diseases, Vol.9(1), pp.171-178

01/01/2022

DOI: 10.3233/JND-210739

PMCID: PMC9059491

PMID: 34776418

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Abstract

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.

Age of Onset

Child

Child, Preschool

Cluster Analysis

Humans

Male

Muscular Dystrophy, Duchenne - classification

Muscular Dystrophy, Duchenne - physiopathology

Phenotype

Details

Title: Subtitle: Differentiation of Pediatric-Onset Duchenne and Becker Muscular Dystrophy Subphenotypes Using Data from the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet)
Creators: Jennifer G Andrews - University of Arizona
Molly M Lamb - Colorado School of Public Health
Kristin M Conway - University of Iowa
Natalie Street - National Center on Birth Defects and Developmental Disabilities
Christina Westfield - New York State Department of Health
Emma Ciafaloni - University of Rochester Medical Center
Dennis Matthews - University of Colorado Denver
Shree Pandya - University of Rochester Medical Center
MD STAR
Resource Type: Journal article
Publication Details: Journal of neuromuscular diseases, Vol.9(1), pp.171-178
DOI: 10.3233/JND-210739
PMID: 34776418
PMCID: PMC9059491
NLM abbreviation: J Neuromuscul Dis
ISSN: 2214-3599
eISSN: 2214-3602
Grant note: U01 DD000830 / NCBDD CDC HHS U01 DD001247 / NCBDD CDC HHS CC999999 / Intramural CDC HHS U01 DD000835 / NCBDD CDC HHS U01 DD001248 / NCBDD CDC HHS U01 DD000831 / NCBDD CDC HHS U01 DD000836 / NCBDD CDC HHS U01 DD001250 / NCBDD CDC HHS
Language: English
Date published: 01/01/2022
Academic Unit: Epidemiology
Record Identifier: 9984627303702771

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