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Peer-reviewedCC BY-NC-NDNeurology. Genetics

Clinical Heterogeneity in Myotonic Dystrophy Type 1: Identifying Phenotype Categories Using Unsupervised Clustering.

  • L A la Fontaine1,2
  • J W T M de Kok3,4
  • M Imkamp5
  • D van As6,7
  • J E Bruijnes1,2
  • P A C 't Hoen6
  • B A H Vosse2,8
  • S M J van Kuijk5
  • I S J Merkies2,9
  • C G Faber1,2
  • K Mul7
  • Myodraft Consortium and the Myotonic Dystrophy Clinical Research Network
9 affiliations
  1. 1Department of Neurology, Maastricht University Medical Center, the Netherlands.
  2. 2Mental Health and Neuroscience Research Institute (MHeNS), Maastricht University, the Netherlands.
  3. 3Department of Intensive Care Medicine, Maastricht University Medical Center, the Netherlands.
  4. 4Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.
  5. 5Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, the Netherlands.
  6. 6Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, the Netherlands.
  7. 7Department of Respiratory Medicine, Maastricht University Medical Center, the Netherlands.
  8. 8Department of Neurology, Curaçao Medical Center, Willemstad, Curaçao; and.
  9. 9Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
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Abstract

Myotonic dystrophy type 1 (DM1) is a genetic multisystem disorder with a wide variability in symptom severity and symptom co-occurrence. This study aimed to identify homogeneous patient subgroups in DM1 with distinct disease characteristics, determine key features underlying these subgroups, and evaluate their potential to inform clinical care and individualized therapy. Adult patients with genetically confirmed DM1 were assessed during annual visits to the Dutch DM1 expert center using standardized examinations and questionnaires to characterize disease burden. K-means cluster analysis was conducted on 10 baseline parameters that reflect the multisystemic disease impact of DM1. External validation was conducted using baseline data from the END-DM1 natural history study. Six hundred fifty-eight Dutch patients with DM1 were included, with a mean age of 45.33 ± 14.30 years and a mean disease duration of 13.24 ± 10.42 years. Four clusters were identified, including 166, 169, 149, and 174 patients. Cluster 1 included patients with severe myotonia, gastrointestinal complaints, fatigue and daytime sleepiness, and reduced ability to perform daily activities. Cluster 2 included patients severely affected across all domains except for myotonia. Cluster 3 showed reduced respiratory functioning and reduced ability to perform daily activities. Cluster 4 included patients with minimal symptoms. Clustering of 581 patients in the external data set resulted in similar cluster profiles. Four distinct clinical subgroups with characteristic multisystemic profiles were identified and independently validated. These findings demonstrate that DM1 heterogeneity reflects qualitatively different disease patterns rather than a simple severity continuum and provide foundation for more targeted patient stratification in clinical trials and health care.

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Cite

BibTeXRIS
BibTeX
@article{fontaine2026clinical,
  title = {{Clinical Heterogeneity in Myotonic Dystrophy Type 1: Identifying Phenotype Categories Using Unsupervised Clustering.}},
  author = {L A la Fontaine and J W T M de Kok and M Imkamp and D van As and J E Bruijnes and P A C 't Hoen and B A H Vosse and S M J van Kuijk and I S J Merkies and C G Faber and K Mul and Myodraft Consortium and the Myotonic Dystrophy Clinical Research Network},
  journal = {Neurology. Genetics},
  year = {2026},
  month = nov,
  doi = {10.1212/nxg.0000000000200454},
  url = {https://europepmc.org/article/MED/42997914},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Clinical Heterogeneity in Myotonic Dystrophy Type 1: Identifying Phenotype Categories Using Unsupervised Clustering.
AU  - L A la Fontaine
AU  - J W T M de Kok
AU  - M Imkamp
AU  - D van As
AU  - J E Bruijnes
AU  - P A C 't Hoen
AU  - B A H Vosse
AU  - S M J van Kuijk
AU  - I S J Merkies
AU  - C G Faber
AU  - K Mul
AU  - Myodraft Consortium and the Myotonic Dystrophy Clinical Research Network
PY  - 2026
DA  - 2026/11/05
JO  - Neurology. Genetics
DO  - 10.1212/nxg.0000000000200454
UR  - https://europepmc.org/article/MED/42997914
AB  - Myotonic dystrophy type 1 (DM1) is a genetic multisystem disorder with a wide variability in symptom severity and symptom co-occurrence. This study aimed to identify homogeneous patient subgroups in DM1 with distinct disease characteristics, determine key features underlying these subgroups, and evaluate their potential to inform clinical care and individualized therapy. Adult patients with genetically confirmed DM1 were assessed during annual visits to the Dutch DM1 expert center using standardized examinations and questionnaires to characterize disease burden. K-means cluster analysis was conducted on 10 baseline parameters that reflect the multisystemic disease impact of DM1. External validation was conducted using baseline data from the END-DM1 natural history study. Six hundred fifty-eight Dutch patients with DM1 were included, with a mean age of 45.33 ± 14.30 years and a mean disease duration of 13.24 ± 10.42 years. Four clusters were identified, including 166, 169, 149, and 174 patients. Cluster 1 included patients with severe myotonia, gastrointestinal complaints, fatigue and daytime sleepiness, and reduced ability to perform daily activities. Cluster 2 included patients severely affected across all domains except for myotonia. Cluster 3 showed reduced respiratory functioning and reduced ability to perform daily activities. Cluster 4 included patients with minimal symptoms. Clustering of 581 patients in the external data set resulted in similar cluster profiles. Four distinct clinical subgroups with characteristic multisystemic profiles were identified and independently validated. These findings demonstrate that DM1 heterogeneity reflects qualitatively different disease patterns rather than a simple severity continuum and provide foundation for more targeted patient stratification in clinical trials and health care.
SN  - 2376-7839
ER  -