Data in support of a central role of plasminogen activator inhibitor-2 polymorphism in recurrent cardiovascular disease risk in the setting of high HDL cholesterol and C-reactive protein using Bayesian network modeling

James P Corsetti; Peter Salzman; Dan Ryan; Arthur J Moss; Wojciech Zareba; Charles E Sparks

doi:10.1016/j.dib.2016.05.026

Data in support of a central role of plasminogen activator inhibitor-2 polymorphism in recurrent cardiovascular disease risk in the setting of high HDL cholesterol and C-reactive protein using Bayesian network modeling

Data Brief. 2016 May 21:8:98-104. doi: 10.1016/j.dib.2016.05.026. eCollection 2016 Sep.

Authors

James P Corsetti¹, Peter Salzman², Dan Ryan¹, Arthur J Moss³, Wojciech Zareba³, Charles E Sparks¹

Affiliations

¹ Department of Pathology and Laboratory Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
² Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
³ Department of Medicine - Cardiology Unit, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.

Abstract

Data is presented that was utilized as the basis for Bayesian network modeling of influence pathways focusing on the central role of a polymorphism of plasminogen activator inhibitor-2 (PAI-2) on recurrent cardiovascular disease risk in patients with high levels of HDL cholesterol and C-reactive protein (CRP) as a marker of inflammation, "Influences on Plasminogen Activator Inhibitor-2 Polymorphism-Associated Recurrent Cardiovascular Disease Risk in Patients with High HDL Cholesterol and Inflammation" (Corsetti et al., 2016; [1]). The data consist of occurrence of recurrent coronary events in 166 post myocardial infarction patients along with 1. clinical data on gender, race, age, and body mass index; 2. blood level data on 17 biomarkers; and 3. genotype data on 53 presumptive CVD-related single nucleotide polymorphisms. Additionally, a flow diagram of the Bayesian modeling procedure is presented along with Bayesian network subgraphs (root nodes to outcome events) utilized as the data from which PAI-2 associated influence pathways were derived (Corsetti et al., 2016; [1]).

Keywords: Bayesian network; Pathophysiology; Plasminogen activator inhibitor-2; Recurrent cardiovascular disease risk.