Phthalates, chemicals found in a variety of consumer goods and personal care products, may adversely affect fetal neurodevelopment. Women are exposed to a mixture of phthalates during pregnancy because of the common presence of these chemicals in consumer goods. The aim of this study is to investigate potential associations between phthalate exposure during the first trimester of gestation and Intelligence Quotient (IQ) scores of 3-year old children.

Abstract Weighted Quantile Sum Regression for Analyzing Correlated Predictors Acting Through a Mediation Pathway on a Biological Outcome By Bhanu M. Evani, Ph.D. A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2017. Major Director: Robert A. Perera, Asst. Professor, Department of Biostatistics This work examines mediated effects of a set of correlated predictors using the recently developed Weighted Quantile Sum (WQS) regression method. Traditionally, mediation analysis has been conducted using the multiple regression method, first proposed by Baron and Kenny (1986), which has since been advanced by several authors like MacKinnon (2008). Mediation analysis of a highly correlated predictor set is challenging due to the condition of multicollinearity. Weighted Quantile Sum (WQS) regression can be used as an alternative method to analyze the mediated effects, when predictor correlations are high. As part of the WQS method, a weighted quartile sum index (WQSindex) is computed to represent the predictor set as an entity. The predictor variables in classic mediation are then replaced with the WQSindex, allowing for the estimation of the total indirect effect between all the predictors and the outcome. Predictors having a high relative importance in their association with the outcome can be identified by examining the empirical weights for the individual predictors estimated by the WQS regression method. Other constrained optimization methods (e.g. LASSO) focus on reducing dimensionality of the correlated predictors to reduce multicollinearity. WQS regression in the context of mediation is studied using Monte Carlo simulation for mediation models with two and three correlated predictors. WQS regression’s performance is compared to the classic OLS multiple regression and the regularized LASSO regression methods. An application of these three methods to the National Health and Nutrition Examination Survey (NHANES) dataset examines the effect of serum concentrations of Polychlorinated Biphenyls (independent variables) on the liver enzyme, alanine aminotransferase ALT (outcome), with chromosomal telomere length as a potential mediator. Keywords: Multicollinearity, Weighted Quantile Sum Regression, Mediation Analysis

Drinking groundwater is the primary way humans accumulate arsenic. Chronic exposure to inorganic arsenic (iAs) (over decades) has been shown to be associated with multiple health effects at low levels (5-10 ppb) including: cancer, elevated blood pressure and cardiovascular disease, skin lesions, renal failure, and peripheral neuropathy. Using hypertension (or high blood pressure) as a surrogate marker for cardiovascular disease, we examined the effect of iAs alone and in a mixture with other metals using a cross-sectional study of adults in United States (National Health and Examination Survey, NHANES, 2005-2010) adjusting for covariates: urinary creatinine level (mg/dL), poverty index ratio (PIR, measure of socioeconomic status, 1 to 5), age, smoking (yes/no), alcohol usage, gender, non-Hispanic Black, and overweight (BMI>=25). A logistic regression model suggests that a one-unit increase in log of inorganic arsenic increases the odds of hypertension by a factor of 1.093 (95% Confidence Interval=0.935, 1.277) adjusted for these covariates , which indicates that there was not significant evidence to claim that inorganic arsenic is a risk factor for hypertension. Biomonitoring data provides evidence that humans are not only exposed to inorganic arsenic but also to mixtures of chemicals including inorganic arsenic, total mercury, cadmium, and lead. We tested for a mixture effect of these four environmental chemicals using weighted quantile sum (WQS) regression, which takes into account the correlation among the chemicals and with the outcome. For one-unit increase in the weighted sum, the adjusted odds of developing hypertension increases by a factor of 1.027 (95% CI=0.882,1.196), which is also not significant after taking into account the same covariates. The insignificant finding may be due to the low inorganic arsenic concentration (8-620 μg /L) in US drinking water, compared to those in countries like Bangladesh where the concentrations are much higher. Literature provides conflicting evidence of the association of inorganic arsenic and hypertension in low/moderate regions; future studies, especially a large cohort study, are needed to confirm if inorganic arsenic alone or with other metals is associated with hypertension in the United States.

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