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Gene-environment interactions linking air pollution and inflammation in Parkinson's disease

Pei Chen Lee*, Ole Raaschou-Nielsen, Christina M. Lill, Lars Bertram, Janet S. Sinsheimer, Johnni Hansen, Beate Ritz

*Corresponding author for this work

Abstract

Both air pollution exposure and systemic inflammation have been linked to Parkinson's disease (PD). In the PASIDA study, 408 incident cases of PD diagnosed in 2006–2009 and their 495 population controls were interviewed and provided DNA samples. Markers of long term traffic related air pollution measures were derived from geographic information systems (GIS)-based modeling. Furthermore, we genotyped functional polymorphisms in genes encoding proinflammatory cytokines, namely rs1800629 in TNFα (tumor necrosis factor alpha) and rs16944 in IL1B (interleukin-1β). In logistic regression models, long-term exposure to NO2 increased PD risk overall (odds ratio (OR)=1.06 per 2.94 μg/m3 increase, 95% CI=1.00–1.13). The OR for PD in individuals with high NO2 exposure (≧75th percentile) and the AA genotype of IL1B rs16944 was 3.10 (95% CI=1.14–8.38) compared with individuals with lower NO2 exposure (<75th percentile) and the GG genotype. The interaction term was nominally significant on the multiplicative scale (p=0.01). We did not find significant gene-environment interactions with TNF rs1800629. Our finds may provide suggestive evidence that a combination of traffic-related air pollution and genetic variation in the proinflammatory cytokine gene IL1B contribute to risk of developing PD. However, as statistical evidence was only modest in this large sample we cannot rule out that these results represent a chance finding, and additional replication efforts are warranted.

Original languageEnglish
JournalEnvironmental Research
Volume151
Pages (from-to)713-720
Number of pages8
ISSN0013-9351
DOIs
Publication statusPublished - 01.11.2016

Funding

This work was supported by the National Institute of Environmental Health Science (NIEHS) [grant numbers R01ES013717 and R21ES022391 ]; PCL is supported in part by the Taiwan Ministry of Science and Technology (NSC103-2314-B-227-002-MY2) ; JSS is supported in part by NIH grant GM053275 .

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

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