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Predicting Multiple Sclerosis: Challenges and Opportunities.

Luke Hone¹, Gavin Giovannoni^1,2, Ruth Dobson^1,2

¹Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and Queen Mary University of London, London, United Kingdom.

Frontiers in Neurology

|February 25, 2022

Summary

This summary is machine-generated.

Developing predictive risk scores using genetic and environmental data can help identify individuals at high risk for Multiple Sclerosis (MS) for prevention trials. These scores aid trial design but are not yet suitable for individual clinical prediction.

Keywords:

Multiple Sclerosis environmental risk score genetics polygenic risk score prediction

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Area of Science:

Neuroimmunology
Clinical Trial Design
Biostatistics

Background:

Multiple Sclerosis (MS) prevention research requires large or enriched populations due to low disease incidence.
Identifying high-risk individuals is crucial for efficient clinical trials of preventive strategies.

Purpose of the Study:

To explore the development and application of predictive risk scores for identifying individuals at high risk of Multiple Sclerosis (MS).
To assess the potential of risk scores in stratifying populations for MS prevention trials.

Main Methods:

Review of concepts for developing predictive scores integrating genetic and environmental factors.
Analysis of empirical efforts using real-world cohorts to build and validate risk scores.
Discussion of theoretical and practical challenges in predictive score development.

Main Results:

Predictive scores can theoretically identify individuals at higher risk for MS.
Empirical studies show promise but face significant theoretical and practical limitations.
Risk stratification for trials is a feasible application.

Conclusions:

Predictive risk scores show potential for enhancing Multiple Sclerosis (MS) prevention trial design through risk stratification.
Current scores are unlikely to be clinically useful for predicting MS in individual patients.
Further research is needed to overcome challenges in developing robust predictive models.