Air pollutant mixtures and autoimmune skin disease prevalence: findings from the Personalized Environment and Genes Study

Abstract

BACKGROUND AND AIM: Autoimmune (AI) diseases are thought to be a product of genetic predisposition and environmental triggers. Disruptions of the skin barrier cause exacerbations of psoriasis where oxidative stress represents a mechanistic pathway for the pathogenesis of the disease. Oxidative stress is also thought to be the primary mechanism for the detrimental effects of air pollution. METHODS: We evaluated the association between the prevalence of autoimmune skin diseases (psoriasis or eczema,) and mixtures of air pollutants including six criteria air pollutants and constituents of PM2.5 in the Personalized Environment and Genes Study (PEGS) cohort consisting of 9414 subjects centered in NC, USA. We utilized land-use regression (LUR) predictions from the Center for Air, Climate, and Energy Solutions (CACES) and the Atmospheric Composition Analysis Group (ACAG). For increased spatial resolution, we included cumulative exposure to volatile organic compounds (VOC) based on the sum of exponentially decaying contributions from the EPA Toxic Release Inventory and the density of major roads within a 5km radius of a participant’s address. We will use logistic regression with quantile g-computation, adjusting for age, gender, income, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and mean air pollution mixtures from 2000-2016. RESULTS:The PEGS cohort reported a high prevalence of autoimmune diseases with 3177 (33.7%) reporting 1+ AI disease. In our specific outcomes of interest, 1173 (12.5%) reported psoriasis (398) and/or eczema (873). Preliminary results for PM2.5 composition show that the mixture components of sulfate, black carbon, and nitrate contribute to a positive overall conditional odds ratio for risk of AI skin disease. CONCLUSIONS:Using publicly available LUR air pollution data joined with the PEGS cohort, we elucidate the potential important components of particulate exposure on AI skin disease. KEYWORDS: mixtures, air pollution, autoimmune disease, LUR,