Association of the 894G>T polymorphism in the endothelial nitric oxide synthase gene with risk of acute myocardial infarction

Background: This study was designed to investigate the association of the 894G>T polymorphism in the eNOS gene with risk of acute myocardial infarction (AMI), extent of coronary artery disease (CAD) on coronary angiography, and in-hospital mortality after AMI. Methods: We studied 1602 consecutive patients who were enrolled in the GEMIG study. The control group was comprised by 727 individuals, who were randomly selected from the general adult population. Results: The prevalence of the Asp298 variant of eNOS was not found to be significantly and independently associated with risk of AMI (RR = 1.08, 95%CI = 0.77–1.51, P = 0.663), extent of CAD on angiography (OR = 1.18, 95%CI = 0.63–2.23, P = 0.605) and in-hospital mortality (RR = 1.08, 95%CI = 0.29–4.04, P = 0.908). Conclusion: In contrast to previous reports, homozygosity for the Asp298 variant of the 894G>T polymorphism in the eNOS gene was not found to be associated with risk of AMI, extent of CAD and in-hospital mortality after AM

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Impact of the metabolic syndrome and its components combinations on arterial stiffness in Type 2 diabetic men

Aim. Arterial stiffness (AS) is a risk marker of atherosclerosis and coronary artery disease, yet its association with metabolic syndrome (MS) in diabetic patients is not established. The aim of this study was to investigate possible association of MS or its components with AS in diabetic population and to identify the MS definition which better correlates with AS. Methods. Overall, 98 type-2 diabetic men, mean age 64 +/- 10 years, were classified into groups according to the presence of MS, using the National Cholesterol Educational Program-Adult Treatment Panel III (NCEP-ATPIII) and International Diabetes Federation (IDF) definition. AS was estimated using carotid-femoral pulse wave velocity (PWV). For between-group comparisons and correlations between MS and it's components with AS, t-test and Pearson's correlation coefficient were employed, respectively. For multivariable analysis a linear regression model was used. Results. PWV in those with (72.5%) and without NCEP-ATPIII MS was 13.4 +/- 2.9 vs 12 +/- 3.2 m/s (P=NS) and in those with (79.6%) and without IDF MS 13.6 +/- 2.8 vs 11 +/- 3.2 m/s (P=0.036). AS positively correlated with IDF MS (r=0.332, P=0.036), increased blood pressure (r=0.324, P=0.037), and the combination of increased waist circumference according to IDF with hypertension (r=0.380, P=0.013); no correlation with NCEP-ATPIII MS was detected. In multivariable analysis, age, hypertension, and IDF MS were independently associated with AS (beta=2.52, P=0.039). Conclusion. IDF MS is independently associated with increased AS in diabetic men. Additionally, abdominal obesity, hypertension and older age were likely to be associated with increased AS. PWV measurement may be indicated in such patients. [Int Angiol 2009;28:490-5