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Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning

Faraz Faghri1, Fabian Brunn2, Anant Dadu3

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Summary

Machine learning identified distinct subtypes of Amyotrophic Lateral Sclerosis (ALS), confirming the Chiò classification system. This data-driven approach enhances understanding of ALS heterogeneity and aids clinical care.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Genetics

Background:

  • Amyotrophic Lateral Sclerosis (ALS) presents as a complex syndrome with overlapping clinical features.
  • Existing classification systems for ALS heterogeneity are based on empirical observations and their population substructure reflection is unclear.
  • Understanding ALS subtypes is crucial for improving disease comprehension and patient care.

Purpose of the Study:

  • To employ machine learning techniques to identify and characterize distinct subtypes within the Amyotrophic Lateral Sclerosis (ALS) population.
  • To determine the number and nature of ALS subtypes for a better understanding of disease heterogeneity.
  • To enhance clinical care and management strategies for ALS patients through data-driven insights.

Main Methods:

  • Retrospective analysis using unsupervised (UMAP), semi-supervised (neural network UMAP), and supervised (LightGBM) machine learning models.
  • Application to a population-based discovery cohort from Piedmont and Valle d'Aosta, Italy, with replication in a cohort from Emilia Romagna.
  • Exclusion of patients with missing ALSFRS-R data for unsupervised and semi-supervised analyses.

Main Results:

  • Semi-supervised machine learning optimally clustered ALS patients, aligning with the six established Chiò clinical subtypes (bulbar, respiratory, flail arm, classical, pyramidal, flail leg).
  • These clusters were consistently identified in both the discovery and replication cohorts.
  • Supervised learning accurately predicted ALS clinical subtypes using 11 clinical parameters (AUC 0.982), outperforming other classification schemes like El Escorial and Milano-Torino.

Conclusions:

  • The study validates the Chiò classification system through a data-driven, machine learning approach, confirming its ability to identify ALS subtypes.
  • The developed algorithms provide significant insight into ALS clinical heterogeneity, aiding in the identification of distinct disease subtypes.
  • Further validation is needed, but these findings promise to improve clinical care and optimize clinical trial design for Amyotrophic Lateral Sclerosis.