Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression

TL;DR

This study assesses how environmental pollutants like NO2 and PM2.5 influence relapse risk in Multiple Sclerosis patients, using predictive models to identify key environmental factors affecting disease progression.

Contribution

It introduces a predictive modeling approach combining clinical and environmental data to understand MS relapse triggers, highlighting the impact of specific pollutants.

Findings

Random Forest achieved an AUC-ROC of 0.713 in relapse prediction.

Environmental variables such as precipitation, NO2, and PM2.5 are significant predictors.

Pollutants are relevant factors influencing MS relapse occurrence.

Abstract

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction.