Long-Term Exposure to Ultrafine Particles and Particulate Matter Constituents and the Risk of Amyotrophic Lateral Sclerosis

Zhebin Yu,1,2 Susan Peters,1 Loes van Boxmeer,3 George S. Downward,1,4 Gerard Hoek,1 Marianthi-Anna Kioumourtzoglou,5 Marc G. Weisskopf,6,7 Johnni Hansen,8 Leonard H. van den Berg,3,* and Roel C.H. Vermeulen1,4,* Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands Department of Epidemiology and Health Statistics, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, UMC Utrecht, Utrecht, Netherlands Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, Netherlands Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Danish Cancer Society Research Center, Copenhagen, Denmark


Introduction
The etiology of amyotrophic lateral sclerosis (ALS) remains unknown but is considered to be an interplay of environmental exposures and genetic predisposition (van Es et al. 2017). Few epidemiological studies have examined the association between ambient air pollution and ALS. We previously reported an increased risk of developing ALS for long-term exposure to traffic-related air pollution in a Dutch case-control study (917 cases and 2,662 controls) (Seelen et al. 2017). Increased knowledge about the possible associations between particulate matter (PM) and its constituents and ALS will provide additional insight into the potential pathophysiology of ALS. We aimed to extend on our previous analyses by including 2,081 more cases and controls and by extending the exposure assessment to a broader range of air pollutants [ultrafine particles (PM ≤ 0:1 lm in aerodynamic diameter or UFPs), PM elemental components, and oxidative potentials (OPs)].

Methods
Present analyses were based on ALS patients and controls enrolled in the Prospective ALS in the Netherlands (PAN) study (Huisman et al. 2011) from 1 January 2006 to 31 December 2018. All patients with a diagnosis of possible, probable, or definite ALS according to the revised El Escorial criteria (Brooks et al. 2000) were included. Population-based controls selected from the registers of the patients' general practitioners were frequency matched by sex and age ( ± 5 y). Information including sex, date of birth, education level, body mass index, smoking, alcohol consumption, residential history, and area-level socioeconomic status (SES) was collected. Annual concentrations of air pollution constituents were estimated at the geocoded residential addresses of each participant based on land-use regression (LUR) models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Exposomics projects de Hoogh et al. 2013;Eeftens et al. 2012;van Nunen et al. 2017) (see supporting information at https://github.com/ kevininef/Airpollution-ALS). We averaged the air pollutant concentrations from 1992 to the date of onset for cases or recruitment for controls as the main exposure.
Unconditional logistic regression models were used to estimate the association between exposure to air pollution and ALS in single-pollutant models. Two-pollutant models were performed for each air pollutant by additionally adjusting for the other pollutants one by one. All analyses were performed within R software (version 3.6.1; R Development Core Team). Supporting information is available at https://github.com/ kevininef/Airpollution-ALS. The PAN study was approved by the institutional review board of the University Medical Center Utrecht.

Results and Discussion
A total of 1,636 patients with ALS and 4,024 controls were included (see supporting information at https://github.com/ kevininef/Airpollution-ALS), covering all of the Netherlands. We observed increased odds ratios (ORs) for ALS in association with most air pollutants, with the strongest associations for ( (Table 1). For UFPs, an elevated OR of 1.11 (95% CI: 1.05, 1.16) was observed. For particle elements, road traffic non-tailpipe emissions of copper (Cu), iron (Fe), nickel (Ni), sulfur (S), silicon (Si), and vanadium (V) were associated with significantly higher ORs for ALS in both PM 2:5 and PM 10 fractions. Marginal effects for all air pollutants are presented in the supporting information at https://github.com/ kevininef/Airpollution-ALS.
In two-pollutant models adjusted for PM mass, the associations of most air pollutant elements with ALS remained positive, whereas the association of PM mass became null (Figure 1). In two-pollutant models corrected for NO 2 , the associations of most air pollutants were reduced toward the null, except for Si in the PM 10 Si fraction (PM 10 Si), whereas the estimated positive association for NO 2 remained, indicating independent associations between NO 2 , PM 10 Si, and the risk of ALS. Sensitivity analyses showed the associations of NO 2 and PM 10 Si with ALS were robust (see supporting information at https://github.com/kevininef/ Airpollution-ALS).
With an extended sample [nearly twice the size of the previous analyses by Seelen et al. (2017)], we here confirm the positive associations for NO 2 (see supporting information at https://github.com/ kevininef/Airpollution-ALS). Moreover, restricting the analysis to the participants who were recruited after the previous publication showed consistent associations for air pollution and ALS, speaking * These authors contributed equally to this work. The authors declare they have no actual or potential competing financial interests.
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to the robustness of the associations (see supporting information at https://github.com/kevininef/Airpollution-ALS). We also broadened our previously published analyses to a wider range of air pollutants and found that the association between long-term air pollution exposure and ALS, as previously hypothesized (Seelen et al. 2017), is mainly driven by local traffic-related constituents. NO 2 primarily comes from tailpipe emissions and predictors in the Si LUR models were also traffic variables. The NO 2 concentrations were already below the current World Health Organization air quality guidelines (40 lg=m 3 ), suggesting potential benefits of tightening the guidelines and regulatory limits of NO 2 (World Health Organisation Fact Sheet 2018).
A potential limitation might be that we used the disease onset date for cases in calculating the exposure period, subsequently resulting in a slightly different etiological time window for cases than controls. However, we reestimated the average concentrations for controls from 1992 to 1 y prior to the recruitment date (see supporting information at https://github.com/ kevininef/Airpollution-ALS) and generated essentially the same exposure values.
Using the air pollution models developed in 2010 for PM elements and in 2014 for UFPs to predict historical exposure might also be a concern, but this is supported by previous studies that reported that the spatial contrasts in measured and modeled annual average levels were stable over time (Eeftens et al. 2011;Downward et al. 2018). Sensitivity analysis of the present study using concentrations without back-extrapolation rendered essentially similar results (see supporting information at https://github.com/kevininef/Airpollution-ALS). Possible residual confounding cannot be excluded given that data regarding medical comorbidities, for example, were not included in the present analysis.
Overall, we found that long-term exposures to NO 2 and PM 10 Si were independently associated with ALS in a large population-based case-control study. These associations hint toward the potential health relevance of both tailpipe and nontailpipe emissions of motorized traffic contributing to ALS risk. Note: ALS, amyotrophic lateral sclerosis; CI, confidence interval; Cu, copper; Fe, iron; IQR, interquartile range; K, potassium; Ni, nickel; NO 2 , nitrogen dioxide; NO x , nitrogen oxides; PM 2:5 , particulate matter with aerodynamic diameter ≤2:5 lm; PM 10 , particulate matter with aerodynamic diameter ≤10 lm; PM 2:5 absorbance, PM ≤ 2:5 lm absorbance; PM coarse , particulate matter with aerodynamic diameter between 2:5 lm and 10 lm; OP DTT, oxidative potential metric with dithiothreitol; OP ESR, oxidative potential metric with electron spin resonance; OR, odds ratio; S, sulfur; SES, socioeconomic status; Si, silicon; UFPs, ultrafine particles; V, vanadium; Zn, zinc. a Units are lg=m 3 for PM 10 , PM 2:5 , PM coarse , NO 2 and NO x ; 10 −5 =m for PM 2:5 absorbance; particle numbers=cm 3 for UFPs; ng=m 3 for all PM elemental constituents; atomic units =m 3 for OP ESR; and mol DTT=min per meter cubed for OP DTT.