Cumulative risk curves of 1-year and overall mortality.

Abbreviations: NDCCB, non-dihydropyridine calcium channel blockers.</p

Baseline characteristics of patients with chronic obstructive pulmonary disease and acute myocardial infarction.

Baseline characteristics of patients with chronic obstructive pulmonary disease and acute myocardial infarction.</p

Cumulative risk curves of major adverse cardiac events (MACE) in 1 year.

Abbreviations: NDCCB, non-dihydropyridine calcium channel blockers.</p

Outcomes of patients with chronic obstructive pulmonary disease and acute myocardial infarction.

Outcomes of patients with chronic obstructive pulmonary disease and acute myocardial infarction.</p

In-hospital treatments and events in patients with chronic obstructive pulmonary disease and acute myocardial infarction.

In-hospital treatments and events in patients with chronic obstructive pulmonary disease and acute myocardial infarction.</p

β-blockers after acute myocardial infarction in patients with chronic obstructive pulmonary disease: A nationwide population-based observational study

BackgroundPatients with chronic obstructive pulmonary disease (COPD) less often receive β-blockers after acute myocardial infarction (AMI). This may influence their outcomes after AMI. This study evaluated the efficacy of β-blockers after AMI in patients with COPD, compared with non-dihydropyridine calcium channel blockers (NDCCBs) and absence of these two kinds of treatment.Methods and resultsWe conducted a nationwide population-based cohort study using data retrieved from Taiwan National Health Insurance Research Database. We collected 28,097 patients with COPD who were hospitalized for AMI between January 2004 and December 2013. After hospital discharge, 24,056 patients returned to outpatient clinics within 14 days (the exposure window). Those who received both β-blockers and NDCCBs (n = 302) were excluded, leaving 23,754 patients for analysis. Patients were classified into the β-blocker group (n = 10,638, 44.8%), the NDCCB group, (n = 1,747, 7.4%) and the control group (n = 11,369, 47.9%) based on their outpatient prescription within the exposure window. The β-blockers group of patients had lower overall mortality risks (adjusted hazard ratio [95% confidence interval]: 0.91 [0.83–0.99] versus the NDCCB group; 0.88 [0.84–0.93] versus the control group), but the risk of major adverse cardiac events within 1 year was not statistically different. β-blockers decreased risks of re-hospitalization for COPD and other respiratory diseases by 12–32%.ConclusionsThe use of β-blockers after AMI was associated with a reduced mortality risk in patients with COPD. β-blockers did not increase the risk of COPD exacerbations.</div

Patient inclusion and exclusion.

Abbreviations: AMI, acute myocardial infarction; COPD, chronic obstructive pulmonary disease; NDCCB, non-dihydropyridine calcium channel blockers.</p

Inference of Cross-Level Interaction between Genes and Contextual Factors in a Matched Case-Control Metabolic Syndrome Study: A Bayesian Approach

<div><p>Genes, environment, and the interaction between them are each known to play an important role in the risk for developing complex diseases such as metabolic syndrome. For environmental factors, most studies focused on the measurements observed at the individual level, and therefore can only consider the gene-environment interaction at the same individual scale. Indeed the group-level (called contextual) environmental variables, such as community factors and the degree of local area development, may modify the genetic effect as well. To examine such <i>cross-level interaction</i> between genes and contextual factors, a flexible statistical model quantifying the variability of the genetic effects across different categories of the contextual variable is in need. With a Bayesian generalized linear mixed-effects model with an unconditional likelihood, we investigate whether the individual genetic effect is modified by the group-level residential environment factor in a matched case-control metabolic syndrome study. Such cross-level interaction is evaluated by examining the heterogeneity in allelic effects under various contextual categories, based on posterior samples from Markov chain Monte Carlo methods. The Bayesian analysis indicates that the effect of rs1801282 on metabolic syndrome development is modified by the contextual environmental factor. That is, even among individuals with the same genetic component of <i>PPARG</i>_Pro12Ala, living in a residential area with low availability of exercise facilities may result in higher risk. The modification of the group-level environment factors on the individual genetic attributes can be essential, and this Bayesian model is able to provide a quantitative assessment for such cross-level interaction. The Bayesian inference based on the full likelihood is flexible with any phenotype, and easy to implement computationally. This model has a wide applicability and may help unravel the complexity in development of complex diseases.</p> </div

The variance parameters represent the variability among areas for each SNP.

<p>Numbers are posterior means and standard deviations of variance components under the Bayesian conditional logistic regression model.</p

The posterior distributions of for = 1, …, 5 SNP, respectively, under the Bayesian unconditional likelihood model.

<p>The posterior distributions of for  = 1, …, 5 SNP, respectively, under the Bayesian unconditional likelihood model.</p