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Subtypes of relapsing-remitting multiple sclerosis identified by network analysis.

Quentin Howlett-Prieto1, Chelsea Oommen1, Michael D Carrithers1

  • 1Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States.

Frontiers in Digital Health
|January 30, 2023
PubMed
Summary
This summary is machine-generated.

Network analysis identified potential subtypes in relapsing-remitting multiple sclerosis (RRMS) patients based on symptoms. This approach grouped subjects into distinct communities, offering new insights into RRMS heterogeneity.

Keywords:
communitiesfeature reductionmodularitymultiple sclerosisnetwork analysisphenotypesubsumptionsubtype

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Informatics

Background:

  • Relapsing-remitting multiple sclerosis (RRMS) is characterized by diverse clinical presentations.
  • Identifying distinct patient subtypes is crucial for personalized treatment strategies.

Purpose of the Study:

  • To apply network analysis to electronic medical records for identifying potential subtypes of RRMS patients.
  • To explore the utility of network analysis in understanding symptom clusters within the RRMS population.

Main Methods:

  • Reviewed electronic medical records of 113 RRMS subjects.
  • Mapped signs and symptoms to a neuro-ontology, collapsing classes into superclasses.
  • Created and analyzed bipartite and unipartite network graphs using NetworkX, partitioned using modularity scores.

Main Results:

  • Unipartite network analysis yielded higher modularity (0.49) than bipartite (0.25).
  • Five distinct communities were identified in both network types, characterized by symptom clusters like fatigue, sensory, gait, and cognitive features.
  • Pure subtypes were not identified, but network analysis demonstrated partitioning capability.

Conclusions:

  • Network analysis can partition RRMS subjects into symptom-based communities, suggesting potential subtypes.
  • Further validation with larger datasets and diverse algorithms is needed to confirm findings and clinical significance.
  • This study integrates feature reduction with network analysis to advance the investigation of MS subtypes.