Robbie M Parks1,2, Yanelli Nunez1, Arin A Balalian3, Elizabeth A Gibson1,4, Johnni Hansen5, Ole Raaschou-Nielsen5,6, Matthias Ketzel6,7, Jibran Khan6, Jørgen Brandt6,8, Roel Vermeulen9, Susan Peters9, Jeff Goldsmith10, Diane B Re1, Marc G Weisskopf11, Marianthi-Anna Kioumourtzoglou1. 1. From the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY. 2. The Earth Institute, Columbia University, New York, NY. 3. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. 4. Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA. 5. Danish Cancer Society Research Center, Copenhagen, Denmark. 6. Department of Environmental Science, Aarhus University, Roskilde, Denmark. 7. Global Centre for Clean Air Research (GCARE), University of Surrey, Guildford, United Kingdom. 8. iClimate-interdisciplinary Center for Climate Change, Aarhus University, Aarhus, Denmark. 9. Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands. 10. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY. 11. Departments of Environmental Health and Epidemiology, T. H. Chan School of Public Health, Harvard University, Boston, MA.
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants. METHODS: In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NO x ), carbon monoxide (CO), and fine particles (PM 2.5 ) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NO x , CO). RESULTS: For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m 3 ) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NO x (SD = 20 µg/m 3 ) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m 3 ) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM 2.5 ) (SD = 2.37 µg/m 3 ) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations. CONCLUSIONS: This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis.
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants. METHODS: In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NO x ), carbon monoxide (CO), and fine particles (PM 2.5 ) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NO x , CO). RESULTS: For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m 3 ) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NO x (SD = 20 µg/m 3 ) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m 3 ) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM 2.5 ) (SD = 2.37 µg/m 3 ) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations. CONCLUSIONS: This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis.
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