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Developing High Performance Lipoprotein Density Profiling for Use in Clinical Studies Relating to Cardiovascular Disease

[Image: see text] Early detection of the beginning stage of cardiovascular disease (CVD) is an approach to prevention because the process is reversible at this stage. Consequently, several methods for screening for CVD have been introduced in recent years incorporating different analytical methods f...

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Detalles Bibliográficos
Autores principales: Larner, Craig D., Henriquez, Ronald R., Johnson, Jeffrey D., Macfarlane, Ronald D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2011
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3220625/
https://www.ncbi.nlm.nih.gov/pubmed/21970640
http://dx.doi.org/10.1021/ac2018124
Descripción
Sumario:[Image: see text] Early detection of the beginning stage of cardiovascular disease (CVD) is an approach to prevention because the process is reversible at this stage. Consequently, several methods for screening for CVD have been introduced in recent years incorporating different analytical methods for characterizing the population of blood-borne lipoprotein subclasses. The gold standard method for lipoprotein subclassification is based on lipoprotein density measured by sedimentation equilibrium using the ultracentrifuge. However, this method has not been adopted for clinical studies because of difficulties in achieving the precision required for distinguishing individuals with and without CVD particularly when statistical classification methods are used. The objective of this study was to identify and improve the major factors that influence the precision of measurement of lipoprotein density profile by sedimentation equilibrium analysis and labeling with a fluorescent probe. The study has two phases, each contributing to precision. The first phase focuses on the ultracentrifugation-related variables, and the second phase addresses those factors involved in converting the fluorescent lipoprotein density profile to a digital format compatible with statistical analysis. The overall improvement in precision was on the order of a factor of 5, sufficient to be effectively applied to ongoing classification studies relating to CVD risk assessment.