Physical activity among breast and prostate cancer survivors: Barriers, facilitators and maintenance following the FRESH START trial

Research provides evidence of the positive health effects associated with regular physical activity participation in all populations. Activity may prove to be especially beneficial in those with chronic conditions such as cancer. However, the majority of cancer patients and survivors do not participate in the recommended amount of physical activity. The purpose of this dissertation was to identify factors associated with physical activity participation, describe how these factors change as result of a diet and exercise intervention, and to evaluate correlates of long term physical activity maintenance. ^ For this dissertation, I analyzed data from the FRESH START trial, a randomized, single-blind, phase II clinical trial focused on improving diet and physical activity among recently diagnosed breast and prostate cancer survivors. Analyses included both parametric and non-parametric statistical tests. Three separate studies were conducted, with sample sizes ranging from 400 to 486. ^ Common barriers to exercise, such as “no willpower,” “too busy,” and “I have pain,” were reported among breast and prostate cancer survivors; however, these barriers were not significantly associated with minutes of physical activity. Breast cancer survivors reported a greater number of total barriers to exercise as well as higher proportions reporting individual barriers, compared to prostate cancer survivors. Just less than half of participants reduced their total number of barriers to exercise from baseline to 1-year follow-up, and those who did reduce barriers reported greater increases in minutes of physical activity compared to those who reported no change in barriers to exercise. Participants in both the tailored and standardized intervention groups reported greater minutes of physical activity at 2-year follow-up compared to baseline. Overall, twelve percent of participants reached recommended levels of physical activity at both 1- and 2-year follow-up. Self-efficacy was positively associated with physical activity maintenance, and the number of total barriers to exercise was inversely associated with physical activity maintenance. ^ Results from this dissertation are novel and informative, and will help to guide future physical activity interventions among cancer survivors. Thoughtfully designed interventions may encourage greater participation in physical activity and ultimately improve overall quality of life in this population. ^

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^

Mutation and association analysis of the PVR and PVRL2 genes in patients with non-syndromic cleft lip and palate.

Orofacial clefts (OFC; MIM 119530) are among the most common major birth defects. Here, we carried out mutation screening of the PVR and PVRL2 genes, which are both located at an OFC linkage region at 19q13 (OFC3) and are closely related to PVRL1, which has been associated with both syndromic and non-syndromic cleft lip and palate (nsCLP). We screened a total of 73 nsCLP patients and 105 non-cleft controls from the USA for variants in PVR and PVRL2, including all exons and encompassing all isoforms. We identified four variants in PVR and five in PVRL2. One non-synonymous PVR variant, A67T, was more frequent among nsCLP patients than among normal controls, but this difference did not achieve statistical significance.

Prevalence and determinants of type 2 diabetes among Filipino Americans in Houston metropolitan statistical area, Texas

Diabetes in adults (type 2) has emerged as a world health problem. Prevalence and risk factors have been found to vary in different populations. The wide range of prevalence rates worldwide indicates the importance of genetic and environmental factors in the etiology of the disease. The few available studies suggest that Filipinos are among the higher-risk groups for developing diabetes. This cross-sectional study estimated the overall prevalence rate of type 2 diabetes among Filipino Americans, ages 20–74 years and residents of Houston Metropolitan Statistical Area, Texas, to be 16.1%. The observed high prevalence was associated with age, sex, family history of diabetes, obesity, region of birth; and, in women, gestational diabetes and income. The diabetic Filipino Americans had a higher proportion of parental history of diabetes, medical history of hypertension, and history of smoking; were physically less active, but generally non-obese, compared with the United States diabetic population. ^

The socioeconomic status of families of hyperglycemic versus non-hyperglycemic children: A descriptive analysis

The proportion of children and adolescents living with type 2 diabetes mellitus (DM) is rising at an alarming rate. Studies have shown that poor dietary choices and sedentary behaviors account for progression of some of the most prevalent diseases in America, including obesity, heart disease and diabetes. Other studies have shown that genetics plays a role in the diabetic determination of an individual, although not very common. What are some of the differentiating factors between elevated and non-elevated fasting capillary glucose (FCG) levels in children of similar ages, knowing they spend a majority of their lives at home or at school? Why are some children acquiring diabetes while others are not? This study utilized an IRB-approved Family Demographic Survey to determine gender, family income, parent education levels, sedentary practices, and household size. Only those families who gave consent to take part in the study received a questionnaire. The statistical results were used to test the hypothesis that children living with elevated FCG levels are more likely to descend from families with lower incomes, and lower levels of education.^ With regard to household income and FCG status of non-hyperglycemic and hyperglycemic children (Table 4b), there are 10.4% more hyperglycemic children in the lower income bracket than non-hyperglycemic children in the same income bracket.^ With regard to maternal education and FCG status (Table 5b), there are 7.0% more hyperglycemic children in the high school or less maternal educational attainment level than non-hyperglycemic children in the same maternal educational level. The Pearson correlation of maternal education and FCG status showed a negative correlation value of -.035 (Table 5d). The higher the occurrence of hyperglycemia in a child, the lower the maternal educational status is. Household size ranges and averages are nearly identical in families of both hyperglycemic and non-hyperglycemic children. ^

Assessment of US Air Force student pilots with intermittent monofixation syndrome on a non-stereoptic dependent flight maneuver in pilot training

The United States Air Force School of Aerospace Medicine (USAFSAM) and Aeromedical Consult Service (ACS) have developed waiver criteria for pilots with subtle substandard depth perception. This is to allow United States Air Force (USAF) pilots with mild depth perception deficiency to continue flying duties while limiting the risk to flight safety and ensuring the availability of costly human resources. From 1999 to 2005, 166 aviators were given waivers for intermittent monofixation syndrome (IMFS). Of these, 96 were student pilots who performed slightly worse at stereoptic dependent flight maneuvers than student pilots (8,907) with normal depth perception (Lowry, 2006).^ This study's purpose is to evaluate the performance of the extended-trail maneuver, a non-stereoptic dependent flying maneuver, as executed by a cohort of 12 United States Air Force student pilots with intermittent monofixation syndrome versus the cohort of 100 student pilots with normal depth perception. These subjects are extracted from the cohorts examined by Lowry (2006) and the null hypothesis predicts no statistical difference in the performance of the non-stereoptic dependant flight maneuver extended-trail between student pilots with intermittent monofixation syndrome and those without the condition. ^

Exposure to nonsteroidal anti-inflammatory drugs and weight loss and hospitalization in non-small cell lung cancer patients

Background. Cancer cachexia is a common syndrome complex in cancer, occurring in nearly 80% of patients with advanced cancer and responsible for at least 20% of all cancer deaths. Cachexia is due to increased resting energy expenditure, increased production of inflammatory mediators, and changes in lipid and protein metabolism. Non-steroidal anti-inflammatory drugs (NSAIDs), by virtue of their anti-inflammatory properties, are possibly protective against cancer-related cachexia. Since cachexia is also associated with increased hospitalizations, this outcome may also show improvement with NSAID exposure. ^ Design. In this retrospective study, computerized records from 700 non-small cell lung cancer patients (NSCLC) were reviewed, and 487 (69.57%) were included in the final analyses. Exclusion criteria were severe chronic obstructive pulmonary disease, significant peripheral edema, class III or IV congestive heart failure, liver failure, other reasons for weight loss, or use of research or anabolic medications. Information on medication history, body weight and hospitalizations was collected from one year pre-diagnosis until three years post-diagnosis. Exposure to NSAIDs was defined if a patient had a history of being treated with NSAIDs for at least 50% of any given year in the observation period. We used t-test and chi-square tests for statistical analyses. ^ Results. Neither the proportion of patients with cachexia (p=0.27) nor the number of hospitalizations (p=0.74) differed among those with a history of NSAID use (n=92) and those without (n=395). ^ Conclusions. In this study, NSAID exposure was not significantly associated with weight loss or hospital admissions in patients with NSCLC. Further studies may be needed to confirm these observations.^

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.