AN ASSESSMENT OF AFLATOXIN B1, AND OTHER SELECTED VARIABLES, IN A RURAL ENVIRONMENT AND ASSOCIATED CANCER RISK (TEXAS)

A study to assess possible exposure to carcinogenic metabolites (aflatoxins) from a mold Aspergillus flavus has been made in a rice producing area of Brazoria County, Texas. One hundred samples of unmilled rice were analyzed by thin-layer chromatography (TLC) for the amount of aflatoxin produced by the mold during rice growth and storage. Two well water samples and two rice elevator dust samples were also checked for possible aflatoxin content. The mortality rates from gastrointestinal and urinary tract cancers in the rice-growing part of the county were compared with mortality rates in the nonrice-producing areas of the same county.^ This study was an outgrowth of an earlier investigation by Cech and co-workers in Brazoria County which focused on environmental differences, specifically on the quality of drinking water in the former residences of decedents from primary liver cancer. It also compared subjects who died from other causes. The author of this dissertation participated in this phase of the overall investigation by performing some of the chemical analyses and by preparing synographic maps of water quality, and thus, part of those results from the early phase is also included in this manuscript.^ No aflatoxin was detected by TLC methods. However, when extracts of rice dust were checked for mutagenesis by the Ames Salmonella-microsome assay as a supplement to the TLC analysis, the result suggested that these dusts might have contained mutagenic material. The age-adjusted mortality rates in the rice-growing area were higher than those in the comparison area for both male and female gastrointestinal tract cancer and for male urinary tract cancer, but the differences were not statistically significant. ^

RUNOFF FROM COAL PILES: EFFECTS ON THE ENVIRONMENT AND THE PUBLIC'S HEALTH

The use of coal for fuel in place of oil and natural gas has been increasing in the United States. Typically, users store their reserves of coal outdoors in large piles and rainfall on the coal creates runoffs which may contain materials hazardous to the environment and the public's health. To study this hazard, rainfall on model coal piles was simulated, using deionized water and four coals of varying sulfur content. The simulated surface runoffs were collected during 9 rainfall simulations spaced 15 days apart. The runoffs were analyzed for 13 standard water quality parameters, extracted with organic solvents and then analyzed with capillary column GC/MS, and the extracts were tested for mutagenicity with the Ames Salmonella microsomal assay and for clastogenicity with Chinese hamster ovary cells.^ The runoffs from the high-sulfur coals and the lignite exhibited extremes of pH (acidity), specific conductance, chemical oxygen demand, and total suspended solids; the low-sulfur coal runoffs did not exhibit these extremes. Without treatment, effluents from these high-sulfur coals and lignite would not comply with federal water quality guidelines.^ Most extracts of the simulated surface runoffs contained at least 10 organic compounds including polycyclic aromatic hydrocarbons, their methyl and ethyl homologs, olefins, paraffins, and some terpenes. The concentrations of these compounds were generally less than 50 (mu)g/l in most extracts.^ Some of the extracts were weakly mutagenic and affected both a DNA-repair proficient and deficient Salmonella strain. The addition of S9 decreased the effect significantly. Extracts of runoffs from the low-sulfur coal were not mutagenic.^ All extracts were clastogenic. Extracts of runoffs from the high-sulfur coals were both clastogenic and cytotoxic; those from the low-sulfur coal and the lignite were less clastogenic and not cytotoxic. Clastogenicity occurred with and without S9 activation. Chromosomal lesions included gaps, breaks and exchanges. These data suggest a relationship between the sulfur content of a coal, its mutagenicity and also its clastogenicity.^ The runoffs from actual coal piles should be investigated for possible genotoxic effects in view of the data presented in this study.^

The effects of ethnicity and ethnic composition of schools on physical activity and nutrition behaviors of 8th and 11th graders in Texas

With rates of obesity and overweight continuing to increase in the US, the attention of public health researchers has focused on nutrition and physical activity behaviors. However, attempts to explain the disparate rates of obesity and overweight between whites and Hispanics have often proven inadequate. Indeed, the nebulous term ‘ethnicity’ provides little important detail in addressing potential biological, behavioral, and environmental factors that may affect rates of obesity and overweight. In response to this, the present research seeks to test the explanatory powers of ethnicity by situating the nutrition and physical activity behaviors of whites and Hispanic into their broader social contexts. It is hypothesized that a student's gender and grade level, as well as the socioeconomic status and ethnic composition of their school, will have more predictive power for these behaviors than will self-reported ethnicity. ^ Analyses revealed that while ethnicity did not seem to impact nutrition behaviors among the wealthier schools and those with fewer Hispanics, ethnicity was relevant in explaining these behaviors in the poorest tertile of schools and those with the highest number of Hispanics. With respect to physical activity behaviors, the results were mixed. The variables representing regular physical activity, participation in extracurricular physical activities, and performance of strengthening and toning exercises were more likely to be determined by SES and ethnic composition than ethnicity, especially among 8th grade males. However, school sports team and physical education participation continued to vary by ethnicity, even after controlling for SES and ethnic composition of schools. In conclusion then, it is important to understand the intersecting demographic and social variables that define and surround the individual in order to understand nutrition and physical activity behaviors and thus overweight and obesity.^

Longitudinal and logistic analysis of endocrine hormone profiles, physical maturation, and candidate genes related to pubertal events in children

Children who experience early pubertal development have an increased risk of developing cancer (breast, ovarian, and testicular), osteoporosis, insulin resistance, and obesity as adults. Early pubertal development has been associated with depression, aggressiveness, and increased sexual prowess. Possible explanations for the decline in age of pubertal onset include genetics, exposure to environmental toxins, better nutrition, and a reduction in childhood infections. In this study we (1) evaluated the association between 415 single nucleotide polymorphisms (SNPs) from hormonal pathways and early puberty, defined as menarche prior to age 12 in females and Tanner Stage 2 development prior to age 11 in males, and (2) measured endocrine hormone trajectories (estradiol, testosterone, and DHEAS) in relation to age, race, and Tanner Stage in a cohort of children from Project HeartBeat! At the end of the 4-year study, 193 females had onset of menarche and 121 males had pubertal staging at age 11. African American females had a younger mean age at menarche than Non-Hispanic White females. African American females and males had a lower mean age at each pubertal stage (1-5) than Non-Hispanic White females and males. African American females had higher mean BMI measures at each pubertal stage than Non-Hispanic White females. Of the 415 SNPs evaluated in females, 22 SNPs were associated with early menarche, when adjusted for race ( p<0.05), but none remained significant after adjusting for multiple testing by False Discovery Rate (p<0.00017). In males, 17 SNPs were associated with early pubertal development when adjusted for race (p<0.05), but none remained significant when adjusted for multiple testing (p<0.00017). ^ There were 4955 hormone measurements taken during the 4-year study period from 632 African American and Non-Hispanic White males and females. On average, African American females started and ended the pubertal process at a younger age than Non-Hispanic White females. The mean age of Tanner Stage 2 breast development in African American and Non-Hispanic White females was 9.7 (S.D.=0.8) and 10.2 (S.D.=1.1) years, respectively. There was a significant difference by race in mean age for each pubertal stage, except Tanner Stage 1 for pubic hair development. Both Estradiol and DHEAS levels in females varied significantly with age, but not by race. Estradiol and DHEAS levels increased from Tanner Stage 1 to Tanner Stage 5.^ African American males had a lower mean age at each Tanner Stage of development than Non-Hispanic White males. The mean age of Tanner Stage 2 genital development in African American and Non-Hispanic White males was 10.5 (S.D.=1.1) and 10.8 (S.D.=1.1) years, respectively, but this difference was not significant (p=0.11). Testosterone levels varied significantly with age and race. Non-Hispanic White males had higher levels of testosterone than African American males from Tanner Stage 1-4. Testosterone levels increased for both races from Tanner Stage 1 to Tanner Stage 5. Testosterone levels had the steepest increase from ages 11-15 for both races. DHEAS levels in males varied significantly with age, but not by race. DHEAS levels had the steepest increase from ages 14-17. ^ In conclusion, African American males and females experience pubertal onset at a younger age than Non-Hispanic White males and females, but in this study, we could not find a specific gene that explained the observed variation in age of pubertal onset. Future studies with larger study populations may provide a better understanding of the contribution of genes in early pubertal onset.^

Effects of a health advocacy program to promote physical activity and nutrition behaviors among adolescents: An evaluation of the Young Leaders for Healthy Change program

Background. This study was designed to evaluate the effects of the Young Leaders for Healthy Change program, an internet-delivered program in the school setting that emphasized health advocacy skills-development, on nutrition and physical activity behaviors among older adolescents (13–18 years). The program consisted of online curricular modules, training modules, social media, peer and parental support, and a community service project. Module content was developed based on Social Cognitive Theory and known determinants of behavior for older adolescents. ^ Methods. Of the 283 students who participated in the fall 2011 YL program, 38 students participated in at least ten of the 12 weeks and were eligible for this study. This study used a single group-only pretest/posttest evaluation design. Participants were 68% female, 58% white/Caucasian, 74% 10th or 11th graders, and 89% mostly A and/or B students. The primary behavioral outcomes for this analysis were participation in 60-minutes of physical activity per day, 20-minutes of vigorous- or moderate- intensity physical activity (MVPA) participation per day, television and computer time, fruit and vegetable (FV) intake, sugar-sweetened beverage intake, and consumption of breakfast, home-cooked meals, and fast food. Other outcomes included knowledge, beliefs, and attitudes related to healthy eating, physical activity, and advocacy skills. ^ Findings. Among the 38 participants, no significant changes in any variables were observed. However, among those who did not previously meet behavioral goals there was an 89% increase in students who participated in more than 20 minutes of MVPA per day and a 58% increase in students who ate home-cooked meals 5–7 days per week. The majority of participants met program goals related to knowledge, beliefs, and attitudes prior to the start of the program. Participants reported either maintaining or improving to the goal at posttest for all items except FV intake knowledge, taste and affordability of healthy foods, interest in teaching others about being healthy, and ease of finding ways to advocate in the community. ^ Conclusions. The results of this evaluation indicated that promoting healthy behaviors requires different strategies than maintaining healthy behaviors among high school students. In the school setting, programs need to target the promotion and maintenance of health behaviors to engage all students who participate in the program as part of a class or club activity. Tailoring the program using screening and modifying strategies to meet the needs of all students may increase the potential reach of the program. The Transtheoretical Model may provide information on how to develop a tailored program. Additional research on how to utilize the constructs of TTM effectively among high school students needs to be conducted. Further evaluation studies should employ a more expansive evaluation to assess the long-term effectiveness of health advocacy programming.^

Role of obesity, weight gain, physical activity, and polymorphisms in the mTOR pathway and the risk of renal cell carcinoma

The interplay between obesity, physical activity, weight gain and genetic variants in mTOR pathway have not been studied in renal cell carcinoma (RCC). We examined the associations between obesity, weight gain, physical activity and RCC risk. We also analyzed whether genetic variants in the mTOR pathway could modify the association. Incident renal cell carcinoma cases and healthy controls were recruited from the University of Texas MD Anderson Cancer Center in Houston, Texas. Cases and controls were frequency-matched by age (±5 years), ethnicity, sex, and county of residence. Epidemiologic data were collected via in-person interview. A total of 577 cases and 593 healthy controls (all white) were included. One hundred ninety-two (192) SNPs from 22 genes were available and their genotyping data were extracted from previous genome-wide association studies. Logistic regression and regression spline were performed to obtain odds ratios. Obesity at age 20, 40, and 3 years prior to diagnosis/recruitment, and moderate and large weight gain from age 20 to 40 were each significantly associated with increased RCC risk. Low physical activity was associated with a 4.08-fold (95% CI: 2.92-5.70) increased risk. Five single nucleotide polymorphisms (SNPs) were significantly associated with RCC risk and their cumulative effect increased the risk by up to 72% (95% CI: 1.20-2.46). Strata specific effects for weight change and genotyping cumulative groups were observed. However, no interaction was suggested by our study. In conclusion, energy balance related risk factors and genetic variants in the mTOR pathway may jointly influence susceptibility to RCC. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.