Racial differences in heart failure with preserved ejection fraction in the ambulatory heart failure population

Racial differences in heart failure with preserved ejection fraction (HFpEF) have rarely been studied in an ambulatory, financially "equal access" cohort, although the majority of such patients are treated as outpatients. ^ Retrospective data was collected from 2,526 patients (2,240 Whites, 286 African American) with HFpEF treated at 153 VA clinics, as part of the VA External Peer Review Program (EPRP) between October 2000 and September 2002. Kaplan Meier curves (stratified by race) were created for time to first heart failure (HF) hospitalization, all cause hospitalization and death and Cox proportional multivariate regression models were constructed to evaluate the effect of race on these outcomes. ^ African American patients were younger (67.7 ± 11.3 vs. 71.2 ± 9.8 years; p < 0.001), had lower prevalence of atrial fibrillation (24.5 % vs. 37%; p <0.001), chronic obstructive pulmonary disease (23.4 % vs. 36.9%, p <0.001), but had higher blood pressure (systolic blood pressure > 120 mm Hg 77.6% vs. 67.8%; p < 0.01), glomerular filtration rate (67.9 ± 31.0 vs. 61.6 ± 22.6 mL/min/1.73 m2; p < 0.001), anemia (56.6% vs. 41.7%; p <0.001) as compared to whites. African Americans were found to have higher risk adjusted rate of HF hospitalization (HR 1.52, 95% CI 1.1 - 2.11; p = 0.01), with no difference in risk-adjusted all cause hospitalization (p = 0.80) and death (p= 0.21). ^ In a financially "equal access" setting of the VA, among ambulatory patients with HFpEF, African Americans have similar rates of mortality and all cause hospitalization but have an increased risk of HF hospitalizations compared to whites.^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

The role of serotonin in depression and clotting in the coronary artery disease population

Background: The mechanisms underlying the relationship between depression and acute coronary syndrome (ACS) remain unclear. Platelet serotonin has been associated with both depression and coronary artery disease in stable outpatients. Understanding the association between depression and platelet serotonin, during ACS, may explain some of the acute cardiovascular events seen in some individuals with depression. ^ Objectives: This study was designed to evaluate whether levels of platelet serotonin, during ACS, differ between individuals who screen positive for depression and individuals who screen negative for depression and to determine if a dose-response relationship exists between depressive symptoms and platelet serotonin levels. ^ Methods: In this cross-sectional study, data was collected on 51 patients hospitalized for ACS. Multiple linear regression models were used to determine if a relationship exists between depression and platelet serotonin levels. ^ Results: Of the 51 ACS patients, 24 screened positive for depression and 27 screened negative for depression. Platelet serotonin levels were not significantly different between the depressed group (942.10 ± 461.3) and the non-depressed group (1192.41 ± 764.3) (p= .293 and β= -4.093) and a dose-response relationship between depressive symptoms and platelet serotonin levels was not found (p= .250 and β= -.254). ^ Discussion: In this study, a relationship between depression and platelet serotonin levels was not found. Future research should focus on gaining a better understanding of the variables that may influence platelet serotonin levels in the ACS population. ^

Fibulin-2 stabilizes tumor extracellular matrix and drives malignant progression of lung adenocarcinoma

The ECM of epithelial carcinomas undergoes structural remodeling during periods of uncontrolled growth, creating regional heterogeneity and torsional stress. How tumors maintain ECM integrity in the face of dynamic biophysical forces is still largely unclear. This study addresses these deficiencies using mouse models of human lung adenocarcinoma. Spontaneous lung tumors were marked by disorganized basement membranes, dense collagen networks, and increased tissue stiffness. Metastasis-prone lung adenocarcinoma cells secreted fibulin-2 (Fbln2), a matrix glycoprotein involved in ECM supra-molecular assembly. Fibulin-2 depletion in tumor cells decreased the intra-tumoral abundance of matrix metalloproteinases and reduced collagen cross-linking and tumor compressive properties resulting in inhibited tumor growth and metastasis. Fbln2 deposition within intra-tumoral fibrotic bands was a predictor of poor clinical outcome in patients. Collectively, these findings support a feed-forward model in which tumor cells secrete matrix-stabilizing factors required for the assembly of ECM that preferentially favors malignant progression. To our knowledge, this is the first evidence that tumor cells directly regulate the integrity of their surrounding matrix through the secretion of matrix-stabilizing factors such as fibulin-2. These findings open a new avenue of research into matrix assembly molecules as potential therapeutic targets in cancer patients.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^

Predicting smokeless tobacco cessation in a blue -collar population

The use of smokeless tobacco products is undergoing an alarming resurgence in the United States. Several national surveys have reported a higher prevalence of use among those employed in blue-collar occupations. National objectives now target this group for health promotion programs which reduce the health risks associated with tobacco use.^ Drawn from a larger data set measuring health behaviors, this cross-sectional study tested the applicability of two related theories, the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), to smokeless tobacco (SLT) cessation in a blue-collar population of gas pipeline workers. In order to understand the determinants of SLT cessation, measures were obtained of demographic and normative characteristics of the population and specific constructs. Attitude toward the act of quitting (AACT) and subjective norm (SN) are constructs common to both models, perceived behavioral control (PBC) is unique to the TPB, and the number of past quit attempts is not contained in either model. In addition, a self-reported measure was taken of SLT use at two-month follow-up.^ The study population was comprised of all male SLT users who were field employees in a large gas pipeline company with gas compressor stations extending from Texas to the Canadian border. At baseline, 199 employees responded to the SLT portion of the survey, 118 completed some portion of the two-month follow-up, and 101 could be matched across time.^ As hypothesized, significant correlations were found between constructs antecedent to AACT and SN, although crossover effects occurred. Significant differences were found between SLT cessation intenders and non-intenders with regard to their personal and normative beliefs about quitting as well as their outcome expectancies and motivation to comply with others' beliefs. These differences occurred in the expected direction, with the mean intender score consistently higher than that of the non-intender.^ Contrary to hypothesis, AACT predicted intention to quit but SN did not. However, confirmatory of the TPB, PBC, operationalized as self-efficacy, independently contributed to the prediction of intention. Statistically significant relationships were not found between intention, perceived behavioral control, their interactive effects, and use behavior at two-month follow-up. The introduction of number of quit attempts into the logistic regression model resulted in insignificant findings for independent and interactive effects.^ The findings from this study are discussed in relation to their implications for program development and practice, especially within the worksite. In order to confirm and extend the findings of this investigation, recommendations for future research are also discussed. ^