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Paradoxically, the vast majority of research models intended to understand the relationship between exogenous exposures and lung disease are reduced to a single inhalant. This is understandable given the practical challenges of investigation, but is problematic in terms of translation to the real-world human condition. Furthermore, use of data from such models can lead to under-estimation of effect, which may adversely impact regulatory imperatives to protect public health based on the most robust information. Efforts to incrementally introduce layers of complexity to observational and experimental systems have revealed pathophysiology previously 'hidden' within simplified models. Capturing the effects of co-exposure to traffic-related air pollution and allergens is a paradigmatic example, and has demonstrated the influence of co-exposures across a plethora of clinical and sub-clinical endpoints within the respiratory tract; from DNA methylation in the epithelium, to inflammatory mediators and allergen-specific antibodies in the airway, to airflow limitation and symptoms, the addition of a common second exposure induces profound changes. Additionally, genetic variation alters the product of these relationships significantly, and capturing multi-dimensional interactions may reveal susceptible populations who are particularly impacted by these exposures and may merit focused measures for protection. Collectively, better modeling, and ultimately deeper knowledge, of these complex relationships has important implications for personalized health and prevention, development and refinement of pharmacologics, and public health responses to climate change and the staggering burden of pollution-driven disease worldwide.
PMID: 29909283 [PubMed - as supplied by publisher]