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Detalle de: Lipoprotein(a) levels in familial hypercholesterolemia: an important predictor of cardiovascular disease independent of the type of LDL receptor mutation.

Title Lipoprotein(a) levels in familial hypercholesterolemia: an important predictor of cardiovascular disease independent of the type of LDL receptor mutation.
Abstract

Objectives The aim of this study was to determine the relationship between lipoprotein(a) [Lp(a)] and cardiovascular disease (CVD) in a large cohort of patients with heterozygous familial hypercholesterolemia (FH). Background Lp(a) is considered a cardiovascular risk factor. Nevertheless, the role of Lp(a) as a predictor of CVD in patients with FH has been a controversial issue. Methods A cross-sectional analysis of 1,960 patients with FH and 957 non-FH relatives recruited for SAFEHEART (Spanish Familial Hypercholesterolemia Cohort Study), a long-term observational cohort study of a molecularly well-defined FH study group, was performed. Lp(a) concentrations were measured in plasma using an immunoturbidimetric method. Results Patients with FH, especially those with CVD, had higher Lp(a) plasma levels compared with their unaffected relatives (p 50 mg/dl showed the highest cardiovascular risk compared with patients carrying the same mutations and Lp(a) levels Conclusions Lp(a) is an independent predictor of CVD in men and women with FH. The risk of CVD is higher in those patients with an Lp(a) level >50 mg/dl and carrying a receptor-negative mutation in the LDLR gene compared with other less severe mutations.

Apa Reference Alonso, R., Andres, E., Mata, N., Fuentes-Jiménez, F., Badimón, L., López-Miranda, J., Padró, T., Muñiz, O., Díaz-Díaz, J. L., Mauri, M., Ordovás, J. M., & Mata, P. (2014). Lipoprotein(a) Levels in Familial Hypercholesterolemia. Journal of the American College of Cardiology, 63(19), 1982–1989. 10.1016/j.jacc.2014.01.063
Doi 10.1016/j.jacc.2014.01.063
Publication Date May 2014
Publication Type Article
Researcher Eduardo Andrés-León
Ontology
Research Groups Computational Biomedicine and Health Data Science
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