Association between Cystatin C and MRI Measures of Left Ventricular Structure and Function: Multi-Ethnic Study of Atherosclerosis

Introduction. Reduced kidney function, approximated by elevated cystatin C, is associated with diastolic dysfunction, heart failure, and cardiovascular mortality; however, the precise mechanism(s) that account for these relationships remains unclear. Understanding the relationship between cystatin C and subclinical left ventricular (LV) remodeling, across ethnically diverse populations, may help explain the mechanisms underlying the association of kidney dysfunction with heart failure and cardiovascular mortality. Methods. Measures of cystatin C and LV parameters were obtained from the multi-ethnic study of atherosclerosis (MESA) cohort at baseline (N = 4, 970 with complete data on cystatin C and LV parameters). LV parameters; LV end-diastolic (LVEDV) and end-systolic volumes (LVESV), LV mass (LVM), concentricity (LV mass/LV end-diastolic volume), and LV ejection fraction (LVEF) were measured using magnetic resonance imaging. Nested linear models were used to examine the relationship between higher quartiles of cystatin C and LV parameters, with and without adjustment for demographics, height, and weight, and traditional cardiovascular risk factors. Similar analyses were performed stratified by ethnicity and gender. Results. A fully adjusted model demonstrated a linear relationship between higher quartiles of cystatin C and lower LVEDV, (Mean ± SE, 128 ± 0.7, 128 ± 0.7, 126 ± 0.7, 124 ± 0.8 mL; P = 0.0001). Associations were also observed between higher quartiles of cystatin C and lower LVESV (P = 0.04) and concentricity (P = 0.0001). In contrast, no association was detected between cystatin C and LVM or LVEF. In analyses stratified by race and gender, the patterns of association between cystatin C quartiles and LV parameters were qualitatively similar to the overall association. Conclusion. Cystatin C levels were inversely associated with LVEDV and LVESV with a disproportionate decrease in LVEDV compared to LVM in a multi-ethnic population. This morphometric pattern of concentric left ventricular remodeling, may in part explain the process by which kidney dysfunction leads to diastolic dysfunction, heart failure and cardiovascular mortality

Importance of the lipid-related pathways in the association between statins, mortality and cardiovascular disease risk : the multi-ethnic study of atherosclerosis

PURPOSE: Estimating how much of the impact of statins on coronary heart diseases (CHD), cardiovascular disease (CVD), and mortality risk is attributable to their effect on low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglycerides. METHODS: A semi-parametric g-formula estimator together with data from the Multi-Ethnic Study of Atherosclerosis (a prospective multi-center cohort study) was utilized to perform a mediation analysis. A total of 5280 participants, men and women of various race/ethnicities from multiple sites across the United States, were considered in the current study. RESULTS: The adherence adjusted total relative risk reduction (RRR) estimate (95% confidence interval) of statins on CHD was 14% (-16%, 37%), and the indirect component through LDL was 23% (-4%, 58%). For CVD, the total RRR was 23% (2%, 40%), and the indirect component through LDL was 5% (-13%, 25%). The total RRR of mortality was 18% (-1%, 35%), and the indirect component through LDL was -4% (-17%, 12%). The estimated indirect components through HDL and triglycerides were close to zero with narrow confidence intervals for all 3 outcomes. CONCLUSIONS: The estimated effect of statins on mortality, CVD, and CHD appeared to be independent of their estimated effect on HDL and triglycerides. Our study provides evidence that the preventive effect of statins on CHD could be attributed in large part to their effect on LDL. Our g-formula estimator is a promising approach to elucidate pathways, even if it is hard to make firm conclusions for the LDL pathway on mortality and CV

Metabolic Syndrome Derived from Principal Component Analysis and Incident Cardiovascular Events: The Multi Ethnic Study of Atherosclerosis (MESA) and Health, Aging, and Body Composition (Health ABC).

Background. The NCEP metabolic syndrome (MetS) is a combination of dichotomized interrelated risk factors from predominantly Caucasian populations. We propose a continuous MetS score based on principal component analysis (PCA) of the same risk factors in a multiethnic cohort and compare prediction of incident CVD events with NCEP MetS definition. Additionally, we replicated these analyses in the Health, Aging, and Body composition (Health ABC) study cohort. Methods and Results. We performed PCA of the MetS elements (waist circumference, HDL, TG, fasting blood glucose, SBP, and DBP) in 2610 Caucasian Americans, 801 Chinese Americans, 1875 African Americans, and 1494 Hispanic Americans in the multiethnic study of atherosclerosis (MESA) cohort. We selected the first principal component as a continuous MetS score (MetS-PC). Cox proportional hazards models were used to examine the association between MetS-PC and 5.5 years of CVD events (n = 377) adjusting for age, gender, race, smoking and LDL-C, overall and by ethnicity. To facilitate comparison of MetS-PC with the binary NCEP definition, a MetS-PC cut point was chosen to yield the same 37% prevalence of MetS as the NCEP definition (37%) in the MESA cohort. Hazard ratio (HR) for CVD events were estimated using the NCEP and Mets-PC-derived binary definitions. In Cox proportional models, the HR (95% CI) for CVD events for 1-SD (standard deviation) of MetS-PC was 1.71 (1.54-1.90) (P < 0.0001) overall after adjusting for potential confounders, and for each ethnicity, HRs were: Caucasian, 1.64 (1.39-1.94), Chinese, 1.39 (1.06-1.83), African, 1.67 (1.37-2.02), and Hispanic, 2.10 (1.66-2.65). Finally, when binary definitions were compared, HR for CVD events was 2.34 (1.91-2.87) for MetS-PC versus 1.79 (1.46-2.20) for NCEP MetS. In the Health ABC cohort, in a fully adjusted model, MetS-PC per 1-SD (Health ABC) remained associated with CVD events (HR = 1.21, 95%CI 1.12-1.32) overall, and for each ethnicity, Caucasian (HR = 1.24, 95%CI 1.12-1.39) and African Americans (HR = 1.16, 95%CI 1.01-1.32). Finally, when using a binary definition of MetS-PC (cut point 0.505) designed to match the NCEP definition in terms of prevalence in the Health ABC cohort (35%), the fully adjusted HR for CVD events was 1.39, 95%CI 1.17-1.64 compared with 1.46, 95%CI 1.23-1.72 using the NCEP definition. Conclusion. MetS-PC is a continuous measure of metabolic syndrome and was a better predictor of CVD events overall and in individual ethnicities. Additionally, a binary MetS-PC definition was better than the NCEP MetS definition in predicting incident CVD events in the MESA cohort, but this superiority was not evident in the Health ABC cohort

Mature students using mobile devices in life and learning

The paper reports on research concerned with learners’ uses of mobile technologies based on an international survey that targeted students registered in selected master’s and doctoral programmes in Australia, Hong Kong, Portugal, Sweden, and the United Kingdom. The survey findings were enriched by local knowledge, as the authors administered questionnaires in their own countries. The research gives an account of uses of handheld devices by students from departments of education, educational technology, engineering, and information technology in the domains of learning, work, social interaction and entertainment. The paper illuminates learners’ choices in the midst of evolving social practices, and challenges the common preconception that mobile devices are not suitable for academic study. In today’s global education marketplace, educators must know the technology habits and expectations of their students, including those from other countries. Knowing about students’ previous practices and the techno-cultural setting they come from can help institutions determine what mobile applications are most appropriate to support learning

Comparative analysis of methods for detecting interacting loci

<p>Abstract</p> <p>Background</p> <p>Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.</p> <p>Results</p> <p>We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding <it>multiple </it>sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.</p> <p>Conclusion</p> <p>This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: <url>http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list</url>.</p

Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.Comment: 7 pages, 7 figure

Associations of Plasma Phospholipid Omega-6 and Omega-3 Polyunsaturated Fatty Acid Levels and MRI Measures of Cardiovascular Structure and Function: The Multiethnic Study of Atherosclerosis

Background. The association between plasma omega-6 fatty acids and cardiovascular disease (CVD) is unclear, and discrepancy remains concerning the cardiovascular benefit of the omega-3 fatty acid alpha-linolenic acid. Methods. Associations of plasma phospholipid fatty acid levels (arachidonic acid, linoleic acid, eicosapentaenoic acid, docosahexaenoic acid (DHA), and alpha-linolenic acid) with cardiac magnetic resonance imaging measures of left ventricular (LV) mass, LV volume, ejection fraction, stroke volume, and aortic distensibility were investigated in 1,274 adults. Results. Results of multivariate analysis showed no statistically significant associations of plasma omega-6 or omega-3 levels with cardiac magnetic resonance imaging measures. Stratification by gender revealed a positive association between DHA and LV mass in women (β = 1.89, P = 0.02; P interaction = 0.003) and a trend for a positive association between DHA and ejection fraction in men (β = 0.009, P = 0.05; P interaction = 0.03). Conclusion. Additional research is warranted to clarify the effects of plasma DHA on cardiac structure and function in women versus men