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Ambient Air Pollution and Preeclampsia: Looking Back and Moving Forward.

Preeclampsia is a common pregnancy complication and a top contributor to maternal and fetal morbidity and mortality, affecting 2% to 8% of all pregnancies globally. Ambient particulate matter air pollution has been recognized as an independent risk factor for cardiovascular morbidity and mortality in the general population, and recent meta-analyses suggested that ambient particulate matter and gaseous air pollution are associated with increased odds of preeclampsia. Although the biological mechanisms underlying the association between air pollution and preeclampsia are largely unknown, several potential pathways have been suggested, including inflammation, oxidative stress, endothelial dysfunction, and more recently DNA methylation (Figure). However, significant knowledge gaps exist in the current literature. First, although it is widely recognized that preeclampsia with different onset and severity has distinct pathogenic mechanisms as well as maternal and fetal complications, few studies have examined the association between air pollution and the onset and severity of preeclampsia. Second, limited research has been conducted to assess the impact of source-specific air pollutants or to identify critical windows of exposure for preeclampsia. Such information may enhance our understanding of the underlying biological mechanisms and help inform the design of more targeted preventive strategies. In this issue of Hypertension, Assibey-Mensah et al used electronic medical records with linked birth certificate data on 16 116 pregnant women from 2 hospitals in Monroe County, New York, during 2009 to 2013 to examine the association between wintertime fine particulate matter (PM 2.5 ) and preeclampsia. The authors determined earlyand late-onset preeclampsia using the date of clinical diagnosis or gestational age at time of diagnosis. The data were then linked with source-specific ambient PM 2.5 estimated by land-use regression models to generate women’s month-specific exposures. The authors found that exposure to PM 2.5 , wood smoke, and traffic pollution in early pregnancy during winter was associated with increased odds of early-onset preeclampsia. This study represents one of the first efforts to bridge several critical gaps by considering the onset of preeclampsia, assessing source-specific PM 2.5 , and identifying critical windows of exposure. It also shows several challenges in the field to move this line of research forward. First, when stratifying preeclampsia by its onset or severity, the low incidence rate in each category dramatically decreases statistical power, making it hard to be studied in traditional pregnancy and birth cohorts. Similar to Assibey-Mensah et al, many studies in the field utilized electronic medical records data and administrative data such as birth certificates. Although these realworld data are increasingly available, challenges remain in algorithmically determining and extracting phenotypes from these complex data sets with not only structured (eg, diagnosis codes) but also richer unstructured (eg, physician notes) information. Diagnosis codes-based case definitions are known to have varying misclassification errors across many disease areas, including hypertension. Leveraging the rich information other than just the diagnosis codes, especially the unstructured clinical notes, can drastically increase the accuracy of these computable phenotypes. More efforts on developing standardized, automated, and computerized phenotype algorithms are needed to minimize potential misclassifications. Second, although some modeled estimates of the 6 criteria air pollutants at high spatiotemporal resolutions are publicly available, limited resources exist for source-specific particulate matter air pollution, which usually relies on data collected from stationary monitors. Future efforts are needed to explore innovative methods such as integrations of multisource data (eg, stationary monitored data and remote sensing data) to assess source-specific particulate matter with large spatiotemporal coverages and high resolutions. Third, despite the existence of statistical methods to account for autocorrelated time-varying exposures such as distributed lag models and survival models with time-varying covariates and coefficients, they have not been widely used in studying ambient air pollution and preeclampsia. Future studies need to implement these methods to identify susceptible windows of exposure. In summary, the study by Assibey-Mensah et al is a critical first step in understanding how source-specific PM 2.5 may impact the onset of preeclampsia. Moving forward, several gaps need to be addressed. First, it would be critical to conduct mechanistic studies to examine the molecular mechanisms such as epigenetic factors in mediating the association between ambient air pollution and preeclampsia and to elucidate The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. From the Department of Epidemiology, College of Public Health and Health Professions and College of Medicine (H.H., J.Z.) and Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville (J.B.). Correspondence to Hui Hu, Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd, CTRB 4224, Gainesville, FL 32610. Email huihu@ufl.edu Ambient Air Pollution and Preeclampsia Looking Back and Moving Forward