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Air pollution-induced systemic inflammation and oxidative stress are among potential underlying mechanisms that mediate the associations between air pollution and metabolic risk factors such as obesity and insulin resistance. Furthermore, both obesity and insulin resistance are two important risk factors for non-alcoholic liver disease, one of the most common liver diseases in the United States. Although in controlled animal studies, exposure to elevated fine particulate matter (PM2.5) has been associated with increased abdominal adiposity and liver fat accumulation, few epidemiologic studies examined these associations among adults. In this work, we first examined the associations of short-term exposure to air pollution, measured at the central and local air pollution monitors, with biomarkers of oxidative stress, including myeloperoxidase and 8-epi-prostaglandin F2α (8-epi-PGF2α) among participants from the community-based Framingham Heart Study Offspring cohort. We used linear regression models and linear mixed-effects models with random intercepts, and adjusted for demographic variables, individual- and area-level measures of socioeconomic position, clinical and lifestyle factors, weather, and temporal trend. We found positive associations of black carbon, a correlate of local traffic pollution, with myeloperoxidase, and of PM2.5 and sulfate with 8-epi-PGF2α across multiple moving averages. Participants with diabetes appeared to be more susceptible. In the next project, we examined the associations of residential proximity to the nearest major roadway and annual average PM2.5 with body mass index (BMI) and abdominal adiposity among participants from the multidetector computed tomography (MDCT) study, a substudy that recruited participants from the Framingham Offspring and Third Generation cohorts. We estimated residential-based annual average PM2.5 concentrations using a spatial-temporal model, and estimated subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) volumes by the MDCT scan. In this study, we found that living closer to a major roadway was associated with higher overall and abdominal adiposity. Finally, we examined the associations of residential proximity to the nearest major roadway and annual average PM2.5 with liver fat attenuation among participants from the MDCT study. In this study, liver CT attenuation in Hounsfield units was measured by abdominal MDCT scan and we defined hepatic steatosis as having a liver-to-phantom ratio ≤ 0.33. We found more liver fat (lower liver-to-phantom ratio) and higher odds of hepatic steatosis among participants who lived closer to a major roadway than those who lived further away. However, residential-based PM2.5 estimations were not associated with liver fat. Overall, we showed positive associations between short-term exposure to ambient air pollution and biomarkers of oxidative stress. We also showed that participants who lived closer to a major roadway had higher BMI, higher abdominal adiposity, and more liver fat than those who lived further away. Our observations were consistent with previous findings in animal studies and we extended these associations to adults from large community-based cohorts. Future studies are necessary to identify traffic-related factors that are associated with adipose tissue deposition other than particulate matter and to extend these findings by examining changes in abdominal adiposity or liver fat accumulation in relation to traffic-related components.

This dissertation assesses the associations of residential proximity to a major roadway and residential ambient fine particulate matter exposure with artery calcification, a correlate of atherosclerosis, among Framingham Heart Study participants. It also explores using quantile regression to model outcomes with distributions similar to that of artery calcification, which is zero-inflated and right-skewed, an excess of zeros followed by a long tail to the right. Chapter I examines associations of residential distance to a major roadway and residential fine particulate matter (PM2.5) exposure with coronary artery calcium Agatston score (CAC) among Framingham Heart Study participants living in the Northeastern U.S. CAC is a quantitative estimate of total coronary atheroma and a strong risk factor for coronary heart disease. We observed no associations of these exposures with the presence, extent or progression of CAC. Chapter II examines associations of residential distance to a major roadway and residential PM2.5 exposure with thoracic aortic calcium Agatston score (TAC) and abdominal aortic calcium Agatston score (AAC), correlates of systemic atherosclerosis. There were no associations of these exposures with the presence or extent of TAC or AAC or with AAC progression. The direction of some estimates ran counter to expectation. Overall, these exposures were not strongly related to artery calcification among participants living in a region with relatively low levels of and little variation in PM2.5. Chapter III assesses quantile regression performance when modeling outcomes with zero-inflated or right-skewed distributions, such as CAC. Quantile regression has the potential to identify associations of exposures with shifts in parts of the distribution of an outcome that linear regression may miss. When analyzing data in Chapters I and II, we found quantile regression performed poorly when modeling CAC. In an effort to better understand this performance, we examined quantile regression for simulated outcomes that had varying amounts of zero-inflation and skew. Quantile regression performed poorly for some scenarios in which there was zero-inflation, suggesting traditional multivariable quantile regression may not work well for zero-inflated outcomes. When using quantile regression in practice, users should check model performance by assessing shapes of covariate-outcome relationships and looking for unreasonable model predictions.