Lipoprotein predictors of cardiovascular events in statin-treated patients with coronary heart disease. Insights from the Incremental Decrease in End-points through Aggressive Lipid-lowering Trial (IDEAL)
2008 (English)In: Annals of Medicine, ISSN 0785-3890, E-ISSN 1365-2060, Vol. 40, no 6, 456-464 p.Article in journal (Refereed) Published
Background. Few studies have looked into the ability of measurements of apolipoprotein B (apoB) and apolipoprotein A-1 (apoA-1) or apoB/apoA-1 to predict new coronary heart disease (CHD) events in patients with CHD on statin treatment. Aims. In the IDEAL trial, to compare lipoprotein components to predict CHD events and to what degree differences in those parameters could explain the observed outcome. Methods. We compared the ability of treatment with atorvastatin 80 mg/day to that of simvastatin 20-40 mg/day to prevent CHD events in patients with CHD and used Cox regression models to study the relationships between on-treatment levels of lipoprotein components to subsequent major coronary events (MCE). Findings. Variables related to low-density lipoprotein cholesterol (LDL-C) carried more predictive information than those related to high-density lipoprotein cholesterol (HDL-C), but LDL-C was less predictive than both non-HDL-C and apoB. The ratio of apoB to apoA-1 was most strongly related to MCE. However, for estimating differences in relative risk reduction between the treatment groups, apoB and non-HDL-C were the strongest predictors. Interpretation. The on-treatment level of apoB/apoA-1 was the strongest predictor of MCE in the pooled patient population, whereas apoB and non-HDL-C were best able to explain the difference in outcome between treatment groups. Measurements of apoB and apoA-1 should be more widely available for routine clinical assessments. © 2008 Informa UK Ltd. (Informa Healthcare, Taylor & Francis AS).
Place, publisher, year, edition, pages
2008. Vol. 40, no 6, 456-464 p.
Apolipoproteins, CHD, Lipoproteins, Prediction, Statin treatment
National CategoryNatural Sciences
IdentifiersURN: urn:nbn:se:liu:diva-50220DOI: 10.1080/07853890801964955OAI: oai:DiVA.org:liu-50220DiVA: diva2:271116