Thesis (M.Sc.)--Memorial University of Newfoundland, Faculty of Medicine, 2000. / Typescript. Bibliography: leaves 144-154.

Introduction Tobacco addiction, obesity, hypertension and physical inactivity are common risk factors of non-communicable diseases. Information on the prevalence of risk factors is needed for inter alia planning of services. Sample A community-based sample of 99 subjects of both genders, aged 20 to 59 years, was randomly selected. Method Smoking status and physical activity levels were determined using a questionnaire. Hypertension (systolic blood pressure >160mmHg / diastolic blood pressure >90mmHg) and obesity (body mass index (8MI) >30kg/m2, waist-hip ratio (WHR) >1(males), >0.84 (females» were measured. Results Of the sample, 25% smoked, 6% were hypertensive, 19%(8MI) and 12%(WHR) were obese, 23% inactive at work and 25% inactive during leisure time. Seventy-eight percent did not participate in sport. Each subject had at least one risk factor. Conclusion Socio-economical, behavioural, psychological and cultural factors appear to influence the prevalence rates. Despite relatively low prevalence rates, high-¬risk groups were identified such as male smokers and obese females. Promoting physical activity by physiotherapists as part of comprehensive intervention programmes appears especially appropriate due to its inter-relationship with other risk factors. Prevention and treatment of risk factors should be a health priority. / Dissertation (MSc (Physiotherapy Management))--University of Pretoria, 2005. / Physiotherapy / unrestricted

Cardiovascular disease (CVD) risk behaviors, namely tobacco smoking, hazardous alcohol use, poor diet and sedentary behavior, are more prevalent among people living with HIV (PLWH) than the general population. Qualitative evidence shows that PLWH report adopting unhealthy behaviors as a means of coping with the stress of living with HIV, including the adverse physiological symptoms of HIV infection, the psychological stress of being aware of one’s HIV status, and the physiological and psychological impacts of being on HIV treatment. These observations suggest that being HIV-positive may have a causal influence on CVD risk behaviors and that these causal effects likely differ across stages of the HIV continuum. To date, few quantitative studies have been conducted to examine these causal relationships. The goal of this dissertation was to explore the effects of HIV continuum stage on CVD risk behaviors and assess several plausible stress-coping mechanisms, as motivated by established stress-coping theory. This dissertation consisted of three studies. First, a systematic review was conducted to examine the existing quantitative evidence for the causal effects of HIV continuum stage on CVD risk behaviors. Findings from this review revealed that being HIV-positive is associated with excess smoking and drinking, and that while receipt of a positive HIV diagnosis is associated with short-term improvements in some CVD risk behaviors, these improvements are unlikely to be maintained long-term. Overall, however, the existing studies suffer important methodological limitations, notably inadequate characterization of HIV continuum stage. The second study was an empirical analysis of patterns of self-reported CVD risk behaviors across the HIV continuum among a population-based sample of 4,061 adults aged 40 years and over living in rural Agincourt district in South Africa. Results showed no consistent evidence of an association between HIV continuum stage and hazardous alcohol use or sedentary behavior. However, higher prevalence of smoking was observed specifically among males who were HIV-positive and aware of their status but not on treatment, compared to those who were HIV-negative. There was no evidence of mediation by various measures of physiological and/or psychological stress. The third study was an analysis of whether perceived life expectancy (PLE) modifies the effects of HIV continuum stage on CVD risk behaviors. Observed associations were most prominent among individuals with low PLE and null among those with high PLE. Overall, this dissertation contributed to greater understanding of the relationship between CVD risk behaviors among HIV-infected persons across the HIV continuum. Findings did not support a stress-coping hypothesis; however, PLE was found to be a potentially useful indicator of individuals who are most likely to smoke in the presence of HIV. This dissertation also fills evidence gaps among older adults in sub-Saharan Africa, an under-studied population with high and increasing burdens of both HIV and CVD. As HIV-positive population survive longer on antiretroviral therapy and the prevention of age-related conditions becomes increasingly important, these findings may help inform future research and the development of CVD prevention interventions.

Positive psychology has led a movement that concentrates on positive characteristics. The current study examined the relationship between positive emotions, negative emotions, flourishing, languishing, and cardiovascular functioning. The study uses guided imagery to help participants recall a negative emotional event and positive emotional event in a counterbalanced order. The reverse order allowed us to examine the differential contributions of stress buffering versus facilitated recovery effects to higher levels of heart rate variability (HRV). The study also examined the relationship between mental health categories and known cardiovascular disease risk. Univariate analysis of variance revealed that positive emotions can serve as a stress buffer and dampen cardiovascular responses to a negative event. Also, analysis revealed a trend for the prediction that positive emotions can facilitate cardiovascular recovery following a negative event. Exploratory analysis did not reveal differences between a facilitated recovery group and a buffering group for cardiovascular measures. Future studies should include tighter control to help compare the differential influences of stress facilitation and stress buffering on cardiovascular functioning. The results from the study indicate that it is still too early to tell whether mental health buffers those individuals from developing CVD, and to answer whether languishing increases the risk of CVD. Longitudinal studies of young individuals without a prior history of any risk of CVD and who are flourishing or languishing might help provide answers to these questions.

Cardiovascular risk factors have expanded to include personality and other psychological characteristics. Negative affect (NA) has a longstanding history in cardiovascular health, but the path by which NA leads to cardiovascular disease (CVD) is yet to be defined. The following study examined the relationship of high NA and low extroversion (EX) with physiological cardiovascular markers in a sample of non-medical, professional adults. Our results indicated that individuals high in NA and low in EX displayed a significantly lower platelet count and a significantly higher mean platelet volume. Individuals high in NA displayed a significantly lower cholesterol risk ratio, while individuals high in EX displayed significantly higher platelet counts. Personality was not significantly related to blood pressure, high or low density lipoproteins. Understanding the relationships among psychological variables and physiological markers will help clinical researchers design interventions that reduce the likelihood of CVD.

An accumulating evidence-base indicates that internalizing mental health disorders in adulthood are causally associated with cardiovascular diseases (CVD) and type-2 diabetes (T2DM). It is plausible, however, that the relationship between mental and cardiometabolic ill-health becomes established long before adulthood, and that externalizing problems (the other central domain of common psychopathology) are also involved. These questions, as well as questions on the mechanisms that underlie the relationships, have been insufficiently investigated. The overarching goal of this dissertation was to expand current knowledge on how common mental health problems increase cardiometabolic risk over the life course. First, the prospective association between childhood internalizing (emotional problems) and externalizing problems (hyperactivity and conduct problems) with CVD and T2DM risk in adolescence was assessed in data from the Avon Longitudinal Study of Parents and Children (ALSPAC, N=7,730). Results showed that hyperactivity problems were associated with insulin resistance (high HOMA-IR); that hyperactivity and conduct problems were each associated with high triglyceride levels; and that emotional problems were inversely associated with high triglyceride levels. These results suggest that childhood externalizing problems are an early life risk factor for CVD and T2DM and that childhood internalizing problems are not a risk factor or, that risk in these children does not become apparent until after adolescence. Second, the mechanisms underlying the prospective association of childhood hyperactivity and conduct problems with high levels of triglycerides in adolescence were investigated using causal mediation methods. Results showed that despite being associated with hyperactivity and with conduct problems, body mass index and lifestyle health behaviors including sleep, diet, physical activity, alcohol, and smoking, together these variables, as measured, mediated only 19.6 % and 19.3% of the associations of hyperactivity and conduct problems with triglycerides, respectively. These results would suggest that mechanisms other than body adiposity and unhealthy behaviors are also involved and that those mechanisms have a larger role in mediating these relationships. Alternatively, It is possible that the observed small role of health behaviors is due to error in measurement and therefore improving measurements for health behaviors should be a central focus of future work. Third and last, a systematic review of the literature on the relationship between childhood externalizing problems with CVD and T2DM risk was conducted. Studies were graded for propensity to bias. Evidence was summarized and assessed for consistency. Results strongly supported positive associations of externalizing problems with insulin resistance, T2DM, and with increased blood lipids among children and adolescents. Evidence suggested that associations are at least partly independent of body adiposity. Evidence provided mix support for the associations with T2DM and blood lipids in adults and with other outcomes in children or adults. Studies in children tended to be cross-sectional and to use valid and reliable assessment methods, whereas studies of adults tended to be prospective and to rely on less-valid, less reliable assessment methods. These results warrant more research, specifically prospective studies that track children into young adulthood, that employ well-validated measures of externalizing behaviors, that rely on repeated assessments of T2DM and CVD risk throughout follow-up, and that investigate mechanisms other than body adiposity and health behaviors. Overall, this dissertation has found that childhood externalizing problems are prospectively associated with elevated CVD and T2DM risk, specifically with elevated risk of increased levels of blood lipids and insulin resistance. Unlike studies in adults, this dissertation does not support a role of internalizing problems as risk factors. Among children with externalizing problems, risk becomes evident before adolescence and appears to be largely driven by pathways independent of unhealthy behaviors and body adiposity. Implications of this research’s findings for health practice were proposed. This dissertation identified several gaps and methodological shortcoming in the extant literature. Recommendations were made for future research, including fundamental next questions to investigate, and study designs and methodologies that are best suited to tackle those questions.

No description available.

Assessing the functional impact of genetic variants across the human genome is essential for understanding the molecular mechanisms underlying complex traits and disease risk. Genetic variation that causes changes in gene expression can be analyzed through parallel genotyping and functional genomics assays across sets of individuals. In particular, regulatory variants may impact transcription factor regulation. In this thesis, to map variants that impact the expression of many genes simultaneously through a shared transcription factor (TF), we use an approach in which the protein-level regulatory activity of the TF is inferred from genome-wide expression data and then genetically mapped as a quantitative trait. In Chapter 2, we developed a generalized linear model (GLM) to estimate TF activity levels in an individual-specific manner, and used it to analyze RNA-seq profiles from the Genotype-Tissue Expression (GTEx) project. A key feature is that we fit a beta-binomial GLM at the level of pairs of neighboring genes in order to control for variation in local chromatin structure along the genome and other confounding effects. As a predictor in our model, we use differential gene expression signatures from TF perturbation experiments. After estimating genotype-specific activities for 55 TFs across 49 tissues, in Chapter 3, we performed genome-wide association analysis on the virtual TF activity trait. This revealed hundreds of TF activity quantitative trait loci, or aQTLs, highlighting the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omic approach. Lastly, in Chapter 4, we studied the direct impact of genetic variants on TF binding by predicting genetic effects on TF binding affinity. Specifically, we predicted binding affinity on allele-specific binding data using TF binding models derived by the ProBound recently developed by our laboratory, and constructed a likelihood model to assess the performances across different binding models. This indicates that ProBound provides a promising tool for the prediction of genetic effects on in vivo TF binding.

Cardiovascular disease (CVD) affects millions of people and is a leading cause of death worldwide. CVD consists of a broad set of conditions including structural heart disease, coronary artery disease and stroke. Risk for each of these conditions accumulates over long periods of time depending on several risk factors. In order to reduce morbidity and mortality due to CVD, preventative treatments administered prior to first CVD event are critical. According to clinical guidelines, such treatments should be guided by an individual’s total risk within a window of time. A related objective is secondary prevention, or early detection, wherein the aim is to identify and mitigate the impact of a disease that has already taken effect. With the widespread adoption of electronic health records (EHRs), there is tremendous opportunity to build better methods for risk assessment and early detection. However, existing methods which use EHRs are limited in several ways: (1) they do not leverage the full longitudinal history of patients, (2) they use a limited feature set or specific data modalities, and (3) they are rarely validated in broader populations and across different institutions. In this dissertation, I address each of these limitations. In Aim 1, I explore the challenge of handling longitudinal, irregularly sampled clinical data, proposing discriminative and generative approaches to model this data. In Aim 2, I develop a multimodal approach for the early detection of structural heart disease. Finally, in Aim 3, I study how different feature inclusion choices affect the transportability of deep risk assessment models of coronary artery disease across institutions. Collectively, this dissertation contributes important insights towards building better approaches for risk assessment and early detection of CVD using EHR data and systematically assessing their transportability across institutions and populations.

Many areas of clinical informatics research rely on accurate and complete race and ethnicity (RE) patient information, such as estimating disease risk, assessing quality and performance metrics, and identifying health disparities. Structured data in the electronic health record (EHR) is an easily accessible source for patient-level information, however RE information is often missing or inaccurate in structured EHR data. Furthermore, current federal standards on RE categories have been acknowledged as inadequate, and in need of more granular realizations. While more difficult to extract data from, clinical notes provide a rich, nuanced and subjective source of information that can be leveraged to increase granularity and/or recover RE information missing in structured data. State-of-the-art clinical natural language processing (NLP) approaches can enable researchers to extract RE information from clinical notes, however, NLP has also been shown to inherit, exacerbate, and create new biased and harmful associations, especially in modern deep learning approaches. This thesis explores the relationships between direct and indirect explicit mentions of RE and RE inferences in clinical text annotated by humans, and leverages an approach to audit deep NLP models for their learned associations. We develop gold-standard annotations for information related to RE (RE indicators) and RE labels. We leverage four RE indicators: country of origin, spoken language, direct race, and direct ethnicity mention. We find high agreement between annotators for RE label assignments, and that sentences assigned RE categories have drastically different distributions of RE indicators. Furthermore, we find high agreement between structured and unstructured sources of RE information, and that unstructured data can be used to recover missing RE information in structured data. Leveraging the gold-standard RE annotations, we train a model to label sentences with RE information and audit the model to examine the alignment between salient features and RE indicators. While our models attain good classification performance, this does not translate into high overlap with RE indicators. We find evidence for learned associations that are benign mistakes, helpful but not strictly correct, and potentially harmful mistakes if not addressed by future work.