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Plasma lipoproteins are complex particles of lipid and proteins primarily designated for extracellular transport of acyl glycerides and cholesterol. We conventionally measure lipid molecules included in these particles as empirical parameters for evaluation of lipoprotein concentration. However, such “concentration” of lipid represents neither the molecules of independent behaviour assumed for regular chemical reactions nor those of lipoprotein particles that in fact behave as independent “molecules” in solution. Early theoretical work by Shen and Kézdy proposed number of each component lipid molecule packed in a lipoprotein particle of various subclasses, based on the size of the particles, space occupying volume of each lipid molecule, and a simple structural model that the particles are spherical composed of surface monomolecular layer of phospholipid and unesterified cholesterol, and spherical core of triglyceride and esterified cholesterol . This model has become a prototype of various other models derived to interpret more complex experimental findings. Lipoprotein subclasses are analyzed traditionally by ultracentrifugation and electrophoresis. However, the former is time-consuming, uneconomical and requires a large quantity of sample while the latter is only semi-quantitative. Hara and Okazaki have developed techniques to analyze lipoprotein subclasses by using molecular sieve high performance liquid chromatography(GP-HPLC)for years, and this technique has become one of the standard methods to analyze plasma lipoproteins. In principle, the GP-HPLC analysis gives particle size information along with the amounts of their lipid contents. On the other hand, a more-recently developed NMR analysis technique provides particle size and number at the same time. Since lipoprotein particle size is associated with some pathological conditions and their risk predicting power,

Gel Permeation HPLC Analysis Validates a Classical Spherical Space-filling Model for Lipoprotein Structure Opening Future Potential of the Technology