Ozone, NO 2 and PM 10 are associated with the occurrence of multiple sclerosis relapses. Evidence from seasonal multi-pollutant analyses.

Background: Triggers of multiple sclerosis (MS) relapses are essentially unknown. PM ₁₀ exposure has recently been associated with an increased risk of relapses.
Objectives: We further explore the short-term associations between PM ₁₀ , NO ₂ , benzene (C ₆ H ₆ ), O ₃ , and CO exposures, and the odds of MS relapses' occurrence.
Methods: Using a case-crossover design, we studied 424 MS patients living in the Strasbourg area, France between 2000 and 2009 (1783 relapses in total). Control days were chosen to be ± 35 days relative to the case (relapse) day. Exposure was modeled through ADMS-Urban software at the census block scale. We consider single-pollutant and multi-pollutant conditional logistic regression models coupled with a distributed-lag linear structure, stratified by season ("hot" vs. "cold"), and adjusted for meteorological parameters, pollen count, influenza-like epidemics, and holidays.
Results: The single-pollutant analyses indicated: 1) significant associations between MS relapse incidence and exposures to NO ₂ , PM ₁₀ , and O ₃ , and 2) seasonality in these associations. For instance, an interquartile range increase in NO ₂ (lags 0-3) and PM ₁₀ exposure were associated with MS relapse incidence (OR = 1.08; 95%CI: [1.03-1.14] and OR = 1.06; 95%CI: [1.01-1.11], respectively) during the "cold" season (i.e., October-March). We also observed an association with O ₃ and MS relapse incidence during "hot" season (OR = 1.16; 95%CI: [1.07-1.25]). C ₆ H ₆ and CO were not significantly related to MS relapse incidence. However, using multi-pollutant models, only O ₃ remained significantly associated with the odds of relapse triggering during "hot" season.
Conclusion: We observed significant single-pollution associations between the occurrence of MS relapses and exposures to NO ₂ , O ₃ and PM ₁₀ , only O ₃ remained significantly associated with occurrence of MS relapses in the multi-pollutant model.
(Copyright © 2018. Published by Elsevier Inc.)