Evidence map›Paper›PMID 34776418›Full record

ArticleJournal of neuromuscular diseases2022

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

Abstract read
In one paragraph

Article in Journal of neuromuscular diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jennifer G AndrewsDepartment of Pediatrics, University of Arizona, Tucson, USA.
Molly M LambDepartment of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, USA.
Kristin M ConwayDepartment of Epidemiology, College of Public Health, The University of Iowa, Iowa City, USA.
Natalie StreetNational Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, USA.
Christina WestfieldNew York State Department of Health, Western Regional Office, Buffalo, USA.
Emma CiafaloniDepartment of Neurology, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, USA.
Dennis MatthewsPhysical Medicine and Rehabilitation, School of Medicine, University of Colorado, Aurora, USA.
Shree PandyaDepartment of Neurology, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, USA.
MD STAR

Funding

MD STARnet: Feasibility of Expansion to other Muscular DystrophiesU01DD000831 · DD · UNIVERSITY OF IOWA · PI ROMITTI, PAUL A · 2011 to 2013
$2.2M
Component A: Iowa MD STARnet Core SiteU01DD001248 · DD · UNIVERSITY OF IOWA · PI MATHEWS, KATHERINE DIANNE, ROMITTI, PAUL A · 2019 to 2023
$2.0M
Arizona Muscular Dystrophy Surveillance and Research Tracking Network (MD STARnetU01DD000830 · DD · UNIVERSITY OF ARIZONA · PI CUNNIFF, CHRISTOPHER MCCORD · 2011 to 2013
$1.6M
New York MDSTARnet Feasibility of Expansion to other Muscular DystrophiesU01DD000836 · DD · CENTER OF ENVIRONMENTAL HEALTH · PI FOX, DEBORAH · 2011 to 2013
$1.6M
Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet): FeasiU01DD000835 · DD · COLORADO STATE DEPT/PUB HLTH & ENVIRONMT · PI HERLIHY, RACHEL WIERZBA · 2011 to 2013
$1.4M
Component C: Iowa MD STARnet Site Data DisseminationU01DD001247 · DD · UNIVERSITY OF IOWA · PI ROMITTI, PAUL A · 2019 to 2023
$400k
The Muscular Dystrophy Surveillance, Tracking and Research Network (MD STARnet) Component C-Enhanced-DisseminationU01DD001250 · DD · CENTER OF ENVIRONMENTAL HEALTH · PI SOIM, AIDA · 2019 to 2023
$400k
Intramural CDC HHS CC999999NCBDD CDC HHS U01 DD000830NCBDD CDC HHS U01 DD000831NCBDD CDC HHS U01 DD000835NCBDD CDC HHS U01 DD000836NCBDD CDC HHS U01 DD001247NCBDD CDC HHS U01 DD001248NCBDD CDC HHS U01 DD001250
6 · The paper itself

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.

Indexed as

Age of OnsetChildChild, PreschoolCluster AnalysisHumansMaleMuscular Dystrophy, DuchennePhenotypebeckercluster analysisDuchennedystrophinopathyMD STARnetphenotypesubphenotype

Identifiers

PMID34776418
PMCPMC9059491

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.