ALS risk factors: Industrial airborne chemical releases

Abstract

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

Keywords: Airborne; Amyotrophic lateral sclerosis; Residential history; Risk factors; Solvents.

Fig. 1.

ALS rate in 863 zip3 polygons calculated in the SYMPHONY dataset. Within each of the 863 zip3 regions nationwide, we calculated the logit of the proportion of ALS cases to SYMPHONY network controls, r, in each region, i.e., $Y=\mathrm{l}\mathrm{o}\mathrm{g}\frac{r}{1-r}$.

Fig. 2.

US EPA TRI reported contaminant levels are zero-inflated and right-skewed. A) Average dichloromethane release concentrations by zip3 region over the period 2002–2012, as an example contaminant. B) Example showing that the distribution of contaminant levels is skewed and shows a high degree of ‘zero-inflation’. We used an algorithm to bin the continuous values into four categories, as shown by the colored symbols. The purple ‘+’ represents the bin with the highest contaminant level.

Fig. 3.

Volcano plot showing top-ranking contaminants selected from SYMPHONY by the lasso algorithm. We performed weighted univariate regression of airborne contaminants using the logit of the ALS rate in the SYMPHONY dataset as the outcome. The y-axis shows increasing statistical significance, while the x-axis reflects the size of the effect. Contaminants with increasing ALS risk are shown in the top right portion of the plot.

Fig. 4.

Top-ranking contaminants validated in NH/VT/OH residential history studies of ALS. We used residential history data for the epochs prior to the index date to estimate exposure at the geospatial coordinates of each residence. Bars depict the Odds Ratio (OR) and 95% confidence interval by quartile, using exposure below the median as the reference. We show the epochs with the largest magnitude effect size, which was 15-years for styrene, and 5-years for the other contaminants.

Fig. 5.

Volcano plot showing combinations of contaminants in the SYMPHONY dataset. We assessed a pair-wise interaction-effects model of airborne contaminants using the logit of the ALS rate as the outcome. Higher vertical points depict stronger statistical significance, however none of the pairs of contaminants met our FDR significance threshold. Positive interaction coefficients indicating synergistic combinations are shown to the right of the plot.