title: Gene-gene and gene - clinical factors interaction in acute myocardial infarction: a new detailed risk chart.
abstract: OBJECTIVE
  The complex pathogenesis of acute myocardial infarction (AMI) implicates phenotypic and genetic heterogeneity. In this pilot case-control study single nucleotide polymorphism (SNP) in several inflammatory genes, such as interleukin (IL)-1beta, IL-6, IL-10, alpha-1-antichymotrypsin (ACT), tumor necrosis factor alpha (TNF)-alpha and interferon gamma (IFN)-gamma genes along with SNPs of genes regulating vascular functions (vascular endothelial growth factor; VEGF) and cholesterol synthesis (hydroxy-methyl-glutaryl CoA reductase; HMGCR) were investigated.
  
  
  METHODS
  Patients were genotyped with RT-PCR technique and data were analyzed with a new mathematical algorithm named Auto Contractive Map.
  
  
  RESULTS
  The Auto Contractive Map (AutoCM), was applied in AMI patients with the aim to detect and evaluate the relationships among genetic factors, clinical variables and classical risk factors. Genes were selected because their strong regulatory effect on inflammation and SNP in these gene were located in the promoter region. In the connectivity map generated by AutoCM a group of variables was directly linked with the AMI status; these were: gender (male), early age at onset (50-65 years), HMGCR gene (CC wild type genotype), IL-1betaCT, IL-6 GG and VEGF CC genotypes. This direct link suggested a possible pathogenetic association with AMI. Other genetic, clinical and phenotypic variables were associated to the disease under a statistically defined hierarchy showed in the new connectivity map generated by AutoCM.
  
  
  CONCLUSIONS
  These analyses suggested that genotypes of few inflammatory genes, a SNP in HMGCR gene, middle age, gender, low HDL and diabetes were very informative variables to predict the risk of AMI.
authors.affiliation: Department of Experimental Pathology, School of Medicine, University of Bologna, Italy. federico.licastro@unibo.it
authors.forename: F
authors.initials: F
authors.lastname: Licastro
authors.forename: M
authors.initials: M
authors.lastname: Chiappelli
authors.forename: E
authors.initials: E
authors.lastname: Porcellini
authors.forename: G
authors.initials: G
authors.lastname: Campo
authors.forename: M
authors.initials: M
authors.lastname: Buscema
authors.forename: E
authors.initials: E
authors.lastname: Grossi
authors.forename: F
authors.initials: F
authors.lastname: Garoia
authors.forename: R
authors.initials: R
authors.lastname: Ferrari
mesh_terms: D000208:Acute Disease; D000367:Age Factors; D017668:Age of Onset; D000368:Aged; D016022:Case-Control Studies; D008076:Cholesterol, HDL / Q000097:blood; D003920:Diabetes Mellitus / Q000378:metabolism* / Q000473:pathology; D005260:Female; D056726:Genetic Association Studies; D005838:Genotype; D006801:Humans; D006903:Hydroxymethylglutaryl CoA Reductases / Q000235:genetics*; D053583:Interleukin-1beta / Q000235:genetics*; D015850:Interleukin-6 / Q000235:genetics*; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000235:genetics* / Q000378:metabolism; D010865:Pilot Projects; D016133:Polymerase Chain Reaction; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012307:Risk Factors; D042582:Vascular Endothelial Growth Factor C / Q000235:genetics*
chemical_list: D008076:Cholesterol, HDL; D053583:Interleukin-1beta; D015850:Interleukin-6; C467486:VEGFC protein, human; D042582:Vascular Endothelial Growth Factor C; D006903:Hydroxymethylglutaryl CoA Reductases