A meta‐analysis of studies on the association of the platelet PlA polymorphism of glycoprotein IIIa and risk of coronary heart disease

The Pl(A2) polymorphism of the glycoprotein IIIa subunit of the fibrinogen receptor (GPIIb-IIIa) has been reported by some studies to be associated with an increased risk of coronary thrombosis. Following the first paper on the subject in 1996, a large number of studies have investigated the relationship between this polymorphism and coronary thrombosis, either at the epidemiological or at the cellular and molecular levels. The cellular and molecular studies have shown in a consistent manner that this polymorphism increases platelet responsiveness. In contrast, epidemiological studies have generated inconsistent results regarding the clinical impact of Pl(A2). We consider 12 epidemiological studies that investigate the link between presence/absence of this polymorphism and presence/absence of coronary heart disease. Each is a case-control study that reports an odds ratio. The studies are not directly comparable because they differ greatly in their patient pools and also in the way the data are analysed. We present several meta-analyses of these 12 studies. The simplest one is based on a standard frequentist random effects model with a normal distribution for the study effects (the per-study population log-odds ratios). We also consider a Bayesian version of this model, with a diffuse prior for the mean and variance of the normal distribution of the study effects. The conclusions from both of these analyses is about the same, and is that there is evidence that the Pl(A2) polymorphism is associated with an increased risk of coronary heart disease. A look at the reported log-odds ratios across studies suggests that the study effects do not come from a symmetric distribution. For this reason, we also consider semi-parametric priors for the distribution of the study effects. These priors are specifically designed for this kind of meta-analysis, and are based on a certain class of mixtures of Dirichlet priors. They can be designed to concentrate most of their mass around the family of normal distributions, but still allow for any other distribution. The semi-parametric Bayesian model continues to give evidence of an association between the Pl(A2) polymorphism and the risk of coronary heart disease.

[1]  J. Sethuraman,et al.  Convergence of Dirichlet Measures and the Interpretation of Their Parameter. , 1981 .

[2]  Peter Müller,et al.  A Bayesian Population Model with Hierarchical Mixture Priors Applied to Blood Count Data , 1997 .

[3]  J. Stricker,et al.  Association of the Platelet Glycoprotein IIIa PIA1/A2 Gene Polymorphism to Coronary Artery Disease but not to Nonfatal Myocardial Infarction in Low Risk Patients , 1998, Thrombosis and Haemostasis.

[4]  N. Laird,et al.  Meta-analysis in clinical trials. , 1986, Controlled clinical trials.

[5]  L. Sharples.  Identification and accommodation of outliers in general hierarchical models , 1990 .

[6]  C. Mamotte,et al.  PIa1/a2 polymorphism of glycoprotein IIIa and risk of coronary artery disease and restenosis following coronary angioplasty. , 1998, The American journal of cardiology.

[7]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[8]  M. Delgado-Rodríguez,et al.  Polymorphism of the Platelet Glycoprotein IIIa Gene in Patients with Coronary Stenosis , 1998, Thrombosis and Haemostasis.

[9]  R E Weiss,et al.  On Bayesian calculations for mixture likelihoods and priors. , 1999, Statistics in medicine.

[10]  M. Odawara,et al.  Platelet glycoprotein IIIa PlA polymorphism and Japanese diabetic patients with coronary heart disease , 1996, The Lancet.

[11]  A. Carter,et al.  Platelet glycoprotein IIIa PlA polymorphism and myocardial infarction. , 1996, The New England journal of medicine.

[12]  Daniel T. Larose,et al.  Grouped random effects models for Bayesian meta-analysis. , 1997, Statistics in medicine.

[13]  J. Sethuraman.  A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .

[14]  J. Weiss,et al.  A polymorphism of a platelet glycoprotein receptor as an inherited risk factor for coronary thrombosis. , 1996, The New England journal of medicine.

[15]  Radford M. Neal.  Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .

[16]  J. Carlquist,et al.  Associations between a polymorphism in the gene encoding glycoprotein IIIa and myocardial infarction or coronary artery disease. , 1999, Journal of the American College of Cardiology.

[17]  N. Samani,et al.  Glycoprotein IIIa polymorphism and risk of myocardial infarction. , 1997, Cardiovascular research.

[18]  D. Hicks,et al.  Platelet glycoprotein IIIa Pl A polymorphism in young men with myocardial infarction , 1996 .

[19]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[20]  T. Ferguson.  Prior Distributions on Spaces of Probability Measures , 1974 .

[21]  K. Hampton,et al.  Platelet glycoprotein IIIa gene polymorphism and myocardial infarction , 1996, The Lancet.

[22]  Hani John Doss.  Bayesian nonparametric estimation of location , 1983 .

[23]  M. Escobar.  Estimating Normal Means with a Dirichlet Process Prior , 1994 .

[24]  James O. Berger,et al.  Statistical Decision Theory and Bayesian Analysis, Second Edition , 1985 .

[25]  D. Arveiler,et al.  The Leu33/Pro Polymorphism (PIA1/PIA2 ) of the Glycoprotein IIIa (GPIIIa) Receptor Is Not Related to Myocardial Infarction in the ECTIM Study , 1997, Thrombosis and Haemostasis.

[26]  Eric R. Ziegel,et al.  Practical Nonparametric and Semiparametric Bayesian Statistics , 1998, Technometrics.

[27]  S. Lohr.  Statistics (2nd Ed.) , 1994 .

[28]  S. Jainta,et al.  Sex determination in Ellobius lutescens: The story of an enigma , 1998, Cytogenetic and Genome Research.

[29]  C. Antoniak.  Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .

[30]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .

[31]  M. Escobar,et al.  Bayesian Density Estimation and Inference Using Mixtures , 1995 .

[32]  David R. Jones,et al.  Meta-analysis: weighing the evidence. , 1995, Statistics in medicine.

[33]  A. Gelfand,et al.  Bayesian Semiparametric Median Regression Modeling , 2001 .

[34]  A. Carter,et al.  Association of the Platelet PlA Polymorphism of Glycoprotein IIb/IIIa and the Fibrinogen Bβ 448 Polymorphism With Myocardial Infarction and Extent of Coronary Artery Disease , 1997 .

[35]  P. Ridker,et al.  PIA1/A2 polymorphism of platelet glycoprotein IIIa and risks of myocardial infarction, stroke, and venous thrombosis , 1997, The Lancet.

[36]  Hani Doss.  Bayesian Nonparametric Estimation of the Median; Part I: Computation of the Estimates , 1985 .

[37]  Donald A. Berry,et al.  Statistical Methodology in the Pharmaceutical Sciences , 1989 .

[38]  A. Carter,et al.  Platelet glycoprotein IIIa PIA polymorphism in young men with myocardial infarction , 1996, The Lancet.

[39]  D. Schaid.  Mathematical and Statistical Methods for Genetic Analysis , 1999 .