Risk of Amyotrophic Lateral Sclerosis and Exposure to Particulate Matter from Vehicular Traffic: A Case-Control Study

Abstract

(1) Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown etiology. Some occupational and environmental risk factors have been suggested, including long-term air pollutant exposure. We carried out a pilot case-control study in order to evaluate ALS risk due to particulate matter with a diameter of ≤10 µm (PM10) as a proxy of vehicular traffic exposure. (2) Methods: We recruited ALS patients and controls referred to the Modena Neurology ALS Care Center between 1994 and 2015. Using a geographical information system, we modeled PM10 concentrations due to traffic emissions at the geocoded residence address at the date of case diagnosis. We computed the odds ratio (OR) and 95% confidence interval (CI) of ALS according to increasing PM10 exposure, using an unconditional logistic regression model adjusted for age and sex. (3) Results: For the 132 study participants (52 cases and 80 controls), the average of annual median and maximum PM10 concentrations were 5.2 and 38.6 µg/m3, respectively. Using fixed cutpoints at 5, 10, and 20 of the annual median PM10 levels, and compared with exposure <5 µg/m3, we found no excess ALS risk at 5–10 µg/m3 (OR 0.87, 95% CI 0.39–1.96), 10–20 µg/m3 (0.94, 95% CI 0.24–3.70), and ≥20 µg/m3 (0.87, 95% CI 0.05–15.01). Based on maximum PM10 concentrations, we found a statistically unstable excess ALS risk for subjects exposed at 10–20 µg/m3 (OR 4.27, 95% CI 0.69–26.51) compared with those exposed <10 µg/m3. However, risk decreased at 20–50 µg/m3 (OR 1.49, 95% CI 0.39–5.75) and ≥50 µg/m3 (1.16, 95% CI 0.28–4.82). ALS risk in increasing tertiles of exposure showed a similar null association, while comparison between the highest and the three lowest quartiles lumped together showed little evidence for an excess risk at PM10 concentrations (OR 1.13, 95% CI 0.50–2.55). After restricting the analysis to subjects with stable residence, we found substantially similar results. (4) Conclusions: In this pilot study, we found limited evidence of an increased ALS risk due to long-term exposure at high PM10 concentration, though the high statistical imprecision of the risk estimates, due to the small sample size, particularly in some exposure categories, limited our capacity to detect small increases in risk, and further larger studies are needed to assess this relation.

Residential exposure to electromagnetic fields and risk of amyotrophic lateral sclerosis: a dose–response meta-analysis

“…Similarly, high levels of residential exposure to traffic-derived aromatic solvents has been associated with increased risk of ALS in a U.S. study 36 . However, in a recent case–control we carried out in Italy, we did not find a positive association between PM 10 exposure and ALS, except for a very imprecise increase in risk between 10 and 20 µg/m 3 of annual maximum PM 10 levels 11 . In addition, an interaction between magnetic fields and air pollutants has been proposed due to formation of charged corona ions produced in the vicinity of power lines 37 , 38 .…”

Neurology in a Changing Climate: A Scoping Review

“…One study involving 917 ALS patients from a Dutch national registry showed an increased risk of ALS in patients with long-term exposure to PM2.5 and NO2, but another series of 52 patients examining PM10 exposure did not replicate these findings. 125,126 Among patients with ALS and Parkinson’s disease, less controversial is the risk of disease aggravation in association with shorter-term exposure to PM2.5, which may lead to first-time admission and diagnosis of the underlying neurological condition. 127…”

The association between first and second wave COVID-19 mortality in Italy

“…We examined the relation between first and second wave province-specific crude mortality rates from COVID-19 by modeling the mean mortality rate using a restricted cubic spline regression [21], a model that fits a curvilinear pattern to the data as previously reported [22][23][24]. In particular, we fitted a restricted cubic spline model that weighted provincial data by population size, performing both a crude analysis and adjusting for potential confounders.…”