Baroni_2003_BMC.Med.Genet_4_8

BACKGROUND: Current evidence demonstrates that positive family history and several alterations in lipid metabolism are all important risk factors for coronary artery disease (CAD). All lipid abnormalities themselves have genetic determinants. Thus, objective of this study was to determine whether 6 genetic variants potentially related to altered lipid metabolism were associated with CAD and with lipid abnormalities in an Italian population. These genetic variables were: apolipoprotein E (Apo E), Apo AI, Apo CIII, Apo B, lipoprotein lipase (LPL) and the hepatic lipase (LIPC) genes. Furthermore, an 8 years prospective analysis of clinical cardiovascular events was related to the various genetic markers.
METHODS: 102 subjects with established coronary artery disease and 104 unrelated normal subjects were studied. CAD Patients were followed up for 8 years, and clinical CAD outcomes (a second coronary angioplasty (PTCA), myocardial infarction, coronary artery by-pass graft (CABG), cardiovascular deaths), available from 60 subjects, were related to the genetic variants by multiple regression analysis. Results. Of the six lipid loci studied (for a total of 11 polymorphisms) only the apolipoprotein E, Apo B and LIPC polymorphisms distinguished between case and controls. However, multivariate analysis accounting for clinical and metabolic predictors of CAD showed that only the ApoB Xba1 and ApoE4 polymorphism associated with CAD in this Italian population. When lipid parameters were related to genotypes, the ApoE, ApoB, and LIPC gene polymorphisms were associated to various markers of dyslipidaemia in the CAD patients, confirming previous reports. When the occurrence of a second cardiovascular event was related to genotypes, an independent role was observed for the LIPC gene T202T variant.
CONCLUSIONS: variation in LIPC (hepatic lipase) gene associates with clinical outcomes in Italian patients with established CAD. Further studies on the LIPC gene in CAD patients are warranted, in particular looking at the possible influences on clinical outcomes.