Browsing by Subject "Cardiovascular disease"

In this dissertation, I conduct three studies focusing on differences in social groups and their health outcomes or opinions related to medical practices. In Chapter 1, I outline each study and its findings.

Chapter 2 focuses on the Great Recession in the United States (2007–2009) and how this crisis is associated with health disparities. In this chapter, I use nine waves spanning 16 years (1998-2014) of the RAND version of the Health and Retirement Study (HRS) to examine disparities in cardiovascular disease, stroke, or related death (e.g., circulatory disease) among Americans ages 50 and older to determine whether these health disparities among different educational groups narrowed, widened, or remained constant during and after the Great Recession. In general, findings from discrete-time hazard analyses suggest that the disparities remained relatively constant with some (nonsignificant) evidence of some narrowing between the most educated and least educated groups.

Chapter 3 examines whether there is symmetry in the effects of certain health behaviors—smoking, drinking, weight management—on cardiovascular disease, stroke, or related death between socioeconomic groups. This chapter also uses nine waves (1998–2014) of the RAND HRS. Discrete-time hazard regression analyses indicate that while those with higher socioeconomic status maintain better health outcomes regardless of health behaviors, the effects of health behaviors are not equally distributed. Poor weight management and higher numbers of daily cigarettes are associated with a greater increased risk for those with more education than for those with less. The effects of heavy drinking, however, are less severe for the those with the most education compared to those with the least education.

Chapter 4 studies the role of social change by investigating trends in approval for euthanasia among cohorts in the United States and how those trends are influenced by cohort replacement and religious attitudes. This chapter uses 16 waves of the General Social Survey (1985–2014) and estimates differences in approval ratings between cohorts using logistic regression models. Results show that baby boomers are significantly more likely to approve of euthanasia than either their predecessors or successors, suggesting a cohort effect. Individuals belonging to more conservative religious groups and displaying higher levels of religiosity are less likely to approve. With the meaningful effect of cohorts on approval for euthanasia, findings suggest that as baby boomers age and as the population becomes less religious, approval for euthanasia might increase again.

In this manuscript I seek to demonstrate the importance of gene-environment interactions in cardiovascular disease. This manuscript contains five studies each of which contributes to our understanding of the joint impact of genetic variation and environmental exposures to cardiovascular disease: a candidate gene study for gene-smoking interactions associated with early-onset coronary artery disease, an epidemiology study of the association between traffic-related air pollution and cardiovascular disease, a Genome-Wide Interaction Study for gene-by-traffic related air pollution interactions associated with peripheral arterial disease, a Genome-Wide Interaction Study for gene-by-traffic related air pollution interactions on coronary atherosclerosis burden, and a method for analyzing associations between high-dimensional genomics datasets.

Smoking is a strong risk factors for coronary artery disease, and may play a causative role in the incidence of coronary artery disease. Smoking had been implicated as a reason for heterogeneity observed in associations between genetic variants on chromosome three and coronary artery disease. I used a family-based early-onset coronary artery disease cohort (GENECARD) to study gene-smoking interactions. I also used data from the three independent cohorts to perform a meta-analysis of gene-smoking interactions focusing on the KALRN gene and Rho-GTPase pathway. I found significant evidence for gene-smoking interactions associations involving variants in KALRN and other Rho-GTPase pathway genes on chromosome 3.

Though the estimated increase in incident cardiovascular disease or cardiovascular events due to air pollution exposure is modest at 3-5%, the ubiquitous nature of air pollution exposures means it has a substantial population-level impact on cardiovascular disease. Historically genome-wide interaction studies with air pollution have not yielded genome-wide significant interactions, however by implementing statistical tools novel to this field I have discovered significant interactions between genetic variants and traffic-related air pollution that are associated with cardiovascular diseases.

I studied interactions associated with peripheral arterial disease and the number of diseased coronary vessels (an indicator for coronary artery disease burden) using race-stratified cohort study designs. With peripheral arterial disease I observed that variants in both BMP8A and BMP2 showed evidence for interactions in both European-American and African-American cohorts. In BMP8A I uncovered the first genome-wide significant interaction with air pollution associated with cardiovascular disease. BMP2 gene expression is upregulated after exposure to black carbon, a major component of diesel exhaust, and coding variants within this gene showed evidence for interaction. With the number of diseased coronary vessels I observed that variants in PIGR showed significant evidence for involvement in gene-traffic related air pollution interactions. I observed that coding variation within PIGR was associated with coronary artery disease burden in a gene-by-traffic related air pollution interaction model. As PIGR is involved in the immune response it represents a strong candidate gene discovered via an unbiased genome-wide scan.

The use of high dimensional data to study chronic disease is becoming commonplace. In order to properly analyze high-dimensional data without suffering from high false-discovery rate penalties, the data is often summarized in a way that takes advantage of the correlation structure. Two common approaches for this are principal components analysis and canonical correlation analysis. However neither of these approaches are appropriate when one preferentially desires to preserve structure within the data. To address this shortcoming I developed constrained canonical correlation analysis (cCCA). With cCCA one can evaluate the correlation between two high dimensional datasets while preferentially preserving structure in one of the datasets. This has uses when studying multi-variate outcomes such as cardiovascular disease using multi-variate predictors such as air pollution. Additionally cCCA can be used to create endophenotype factors that specifically explain the variation within a high-dimensional set of predictors (such as gene expression or metabolomics data) with respect to potential endophenotypes for cardiovascular disease, such as cholesterol measures.

High-throughput molecular profiling is being increasingly applied to identify novel biomarkers and mechanisms of health and disease. One such application is the use of mass spectrometry-based metabolomic profiling in cardiovascular disease (CVD), whose underlying pathophysiology and risk prediction models are incompletely understood. Two general approaches have been taken in these applications: targeted and non-targeted profiling. The targeted approach identifies and quantifies select known or potential biomarkers in CVD, often via isotope-labeled internal standards. The non-targeted approach attempts to profile the full spectrum of the metabolome, with identification of metabolites aided by existing spectral libraries. In contrast to many successful applications of targeted metabolomics to CVD, early applications of non-targeted profiling have resulted in several pitfalls due to lack of rigor in study design, immature technologic platforms, and challenges in metabolite identification and quantification both at the experimental and computational level. These pitfalls highlight the importance of experimental design and method development in non-targeted metabolomic profiling. The overall goal of this dissertation is to improve methods in non-targeted metabolomic profiling both at the experimental and computational level, and apply these improved methods to CVD human studies. Specifically, this dissertation aims to: 1) identify and modify factors that could influence metabolite identification and quantification in GC-MS based non-targeted profiling at the experimental level; (2) apply emerging methods for metabolite identification at the computational level to generate hypotheses for unknowns; and (3) apply metabolomic profiling to studies in human cardiovascular disease, using the refined methods from the first two aims.

For the first aim, we sought to identify and modify factors in GC-MS-based metabolomic profiling of human plasma that could influence metabolite identification and quantification at the experimental level. Our experimental design included two studies: 1) the limiting-dilution study, which investigated the effects of laboratory preparation and analysis on analyte identification and quantification, and 2) the concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We tested and confirmed our hypothesis that contaminants, concentration, intra- and inter-experiment variability are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized into recommendations for experimental design of GC-MS-based profiling of human plasma.

For the second aim, we applied emerging methods for metabolite identification level to generate hypotheses for unknowns at the computational level. Specifically, we tested and confirmed our hypothesis that integrating genomic, transcriptomic, and metabolomic data could generate hypotheses for unknowns. Combining the strengths of multiple omics platforms and metabolomic databases, we were able to generate hypotheses for three unknown metabolites implicated in CVD at the computational level.

For the third aim, we applied metabolomic profiling to two studies of CVD and tested the hypothesis that application of the refined methods developed in the first two aims of this dissertation are useful in CVD biomarker and mechanism discovery. In one study, we used heart failure with preserved ejection fraction (HFpEF) as a model to demonstrate the power of targeted metabolomic profiling in testing existing hypotheses of CVD biomarkers and mechanisms. In a second study, we used incident CVD events as a model to 1) apply the refined methods from the first two aims of this dissertation, and 2) demonstrate the power of non-targeted metabolomic profiling in generating novel hypotheses of CVD biomarkers and mechanisms.

This dissertation contributes to research in metabolomics and CVD in several ways. The most significant contribution is the set of recommendations for experimental design in non-targeted metabolomics, which has been incorporated into the workflow of non-targeted profiling at the Duke Molecular Physiology Institute for future studies. Additional contributions include the following: hypotheses for three unknowns implicated in incident CVD events, and novel biomarkers and mechanisms implicated in HFpEF and CVD. Future directions from this dissertation include the following: 1) application of the same principles to method development and validation of metabolomic profiling using other analytical technologies; 2) experimental validation of the hypotheses for unknowns generated by this dissertation; and 3) functional validation of the biomarkers and mechanisms implicated in CVD at the experimental level.

Background: The burden of cardiovascular disease (CVD) is rising in sub-Saharan Africa (SSA). Preeclampsia, a type of hypertensive disorder of pregnancy, is a unique risk factor for CVD among women, yet little is known about the postpartum cardiovascular risk among women with preeclampsia in SSA. Objective: To determine the incidence of hypertension, a major risk factor for CVD, at 6-months postpartum among Kenyan women with preeclampsia. Methods: This prospective cohort study included all pregnant or recently postpartum women with preeclampsia who were admitted to a national, referral hospital in western Kenya from January 20, 2020-March 19, 2020, when the study was unexpectedly paused due to the COVID-19 pandemic. Using home blood pressure monitoring technique, we described the trajectory of blood pressure after delivery. Bivariate and multivariable regression analyses were performed to investigate for risk factors associated with hypertension at 6-month follow-up. Results: Eight-six women with preeclampsia were enrolled prior to March 19, 2020, when the study was unexpected paused due to the COVID-19 pandemic. Among the 50 women who completed follow up, 38% (n=19) had hypertension. Blood pressure normalized for all women by 6 weeks after delivery but rose again beyond 12 weeks among those with hypertension at follow up. Maternal age, parity and history of preeclampsia in prior pregnancy or a previous pregnancy complication (preterm delivery or stillbirth) were associated with hypertension at follow up. Overall, rates of routine, postpartum clinic follow up were low (64%) among women despite nearly all (97%) attending clinic for infant immunizations following delivery. Conclusion: There is a high incidence of hypertension at 6-months postpartum among Kenyan women with preeclampsia, though low rates of postpartum follow up care indicate a potential missed opportunity for early CVD identification and prevention among this high risk-population.

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Variations in gene expression are potential risk factors for atherosclerosis, which is one of the most common forms of cardiovascular disease. We performed a localized Pearson correlation test in 372 individuals from seven datasets relevant to cardiovascular disease studies. The genomes of samples were separated into 20Mb windows and correlation tests were performed locally in these windows. The localized Pearson correlation test found chr3:115Mb–135Mb was tightly connected by significantly high proportion of highly correlated pairs (P value = 0.0266 with Z-test). LSAMP, GATA2, MBD4, and other genes in the region were considered associated with cardiovascular disease because they were involved in highly correlated pairs. Furthermore, these genes were also associated with cardiovascular disease by having significantly high SNP odds ratios (P value < 0.1) between patients and controls in an independent Duke University Medical Center database. In addition, a permutation test was performed to demonstrate that chr3:115Mb–135Mb might underlie the regulation of cardiovascular disease. Finally, the localized Pearson correlation test also found some other regions that could be associated with cardiovascular disease.

Background: Faced with the surging trends of cardiovascular diseases (CVDs) and the limited numbers of health professionals in Nepal, more innovative measures should be explored to tackle the challenges of CVD prevention and control. This study explored the feasibility of shifting some CVD-related tasks to the community by engaging female community health volunteers (FCHVs) for CVD risk screening. Methods: This study was conducted in a rural and an urban study site in Lalitpur (Kathmandu Valley), Nepal. Mixed methods were employed in this study. Ten FCHVs were recruited and trained to use the Cardiovascular Risk Scoring Chart adapted from the World Health Organization’s Package for Essential Non-Communicable Diseases (PEN). After the training, FCHVs administered cardiovascular risk factor questionnaires and used the risk scoring chart to screen eligible community residents in their catchment area. Using the data collected by FCHVs, a medical doctor calculated the second risk score with the same risk scoring chart. A kappa concordance test was used to compare these two sets of risk screening results for agreement, and a sensitivity and specificity test was conducted to assess the reliability of the FCHVs’ CVD risk screening results. Two focus group discussions were administered to investigate the FCHVs’ training and fieldwork experiences during the study. Results: There were 491 community residents screened for cardiovascular risk at two study sites. The mean level of agreement between the two sets of risk screening results was 94.5% (Kappa = 0.77, P < 0.05). The sensitivity of the FCHV screening test was 90.3% (95% CI [0.801, 0.964]); and the specificity was 97% (95% CI [0.948, 0.984]). In the FGD, FCHVs expressed a strong enthusiasm and readiness for NCD related work. Besides, all FCHVs agreed that they could manage their current workload and were confident that they could perform more tasks for the prevention and control of NCDs with the proper training. More NCD-related programs and training are called for by FCHVs. Conclusions: It is feasible to train FCHVs to use the simple cardiovascular scoring chart to screen and identify community residents at high risk of developing CVDs. Although FCHVs expressed interests in taking on more responsibility regarding the prevention and control of NCDs, further studies are needed to assess the feasibility of engaging FCHVs within the existing healthcare system.

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