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Related Experiment Video

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Measuring Progressive Neurological Disability in a Mouse Model of Multiple Sclerosis
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A data-driven model of disability progression in progressive multiple sclerosis.

Sara Garbarino1,2, Carmen Tur3, Marco Lorenzi4

  • 1Life Science Computational laboratory, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy.

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|January 8, 2025
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Summary
This summary is machine-generated.

This study reveals distinct primary progressive multiple sclerosis (PPMS) progression rates using a Bayesian model. It identifies three patient subgroups: normative, accelerated, and decelerated, aiding personalized treatment strategies.

Keywords:
Bayesian learningPPMS sub-groupsdata-driven disease progression modellingmultimodal dataprimary progressive multiple sclerosis

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Primary progressive multiple sclerosis (PPMS) is characterized by continuous neurological decline.
  • Understanding individual progression variability is crucial for effective management and clinical trials.
  • Existing models may not fully capture the heterogeneity of PPMS progression.

Purpose of the Study:

  • To apply a Bayesian data-driven disease progression model (Gaussian process progression model) to analyze PPMS evolution.
  • To identify distinct patient subgroups based on individual progression rates.
  • To investigate the prognostic implications of these subgroups.

Main Methods:

  • Utilized data from 1521 PPMS participants (International Progressive Multiple Sclerosis Alliance Project) with longitudinal disability and MRI metrics.
  • Applied the Gaussian process progression model to infer population-level progression and individual rates.
  • Employed Cox proportional hazard modeling for prognostic analysis and external validation on the SPI2 trial data.

Main Results:

  • Identified three PPMS subgroups: normative (76%), accelerated (13%), and decelerated (11%) progression.
  • Fast progressors showed earlier symptom onset, higher male prevalence, and greater lesion volumes.
  • Fast progressors had a worse prognosis, with doubled risk of confirmed disability progression and shorter time to reach Expanded Disability Status Scale 6.

Conclusions:

  • The Gaussian process progression model effectively characterizes PPMS heterogeneity.
  • Distinct subgroups (fast, normative, slow progressors) have significant prognostic differences.
  • Findings support patient stratification in clinical trials and personalized intervention strategies for PPMS.