Background and aims: Evidence continues to accumulate that athero-protective effects of high-density lipoprotein (HDL) depend to some degree on effective HDL functionality and that such functionality can become degraded in the setting of chronic inflammation. To investigate this issue, we have studied a group of post-myocardial infarction patients with high levels of C-reactive protein as an indicator of chronic inflammation and with concurrently high levels of HDL cholesterol. For these patients we have demonstrated high-risk for recurrent cardiac events as well as a strong association of risk with a polymorphism of the gene (SERPINB2) for plasminogen activator inhibitor-2 (PAI-2) presumptively reflective of an important role for fibrinolysis in risk. However, additional processes might be involved. The current work sought to characterize processes underlying how PAI-2 might be involved in the generation of risk.
Methods: Multivariate population data were leveraged using Bayesian network modeling, a graphical probabilistic approach for knowledge discovery, to generate networks reflective of influences on PAI-2 polymorphism-associated risk.
Results: Modeling results revealed three individual networks centering on the PAI-2 polymorphism with specific features providing information relating to how the polymorphism might associate with risk. These included racial dependency, platelet clot initiation and propagation, oxidative stress, inflammation effects on HDL metabolism and coagulation, and induction and termination of fibrinolysis.
Conclusions: Beyond direct association of a PAI-2 polymorphism with recurrent risk in post-myocardial infarction patients, results suggest that PAI-2 likely plays a key role leading to risk through multiple pathophysiologic processes. Such knowledge could potentially be valuable with individualization of patient care.
Keywords: Atherosclerosis; Bayesian network; C-reactive protein; Fibrinolysis; High-density lipoprotein; Plasminogen activator inhibitor-2.
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