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Distribution of apolipoprotein ai-containing lipoprotein subclasses in plasma of normolipidemic subjects.
Author(s) -
Ragheb F. Atmeh,
Amani Z Kasasbeh,
Mohammad R Abu Odeh
Publication year - 1970
Publication title -
acta biochimica polonica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.452
H-Index - 78
eISSN - 1734-154X
pISSN - 0001-527X
DOI - 10.18388/abp.2010_2407
Subject(s) - subclass , in vivo , lipoprotein , apolipoprotein b , lipoprotein(a) , plasma lipoprotein , distribution (mathematics) , chemistry , cholesterol , mathematics , biochemistry , biology , immunology , antibody , genetics , mathematical analysis
The distribution of apoA-I among apoA-I-containing lipoprotein (AI-Lp) subclasses in plasma was studied by immunoblotting utilizing agarose gel matrix incorporating anti-apoA-I as the transfer medium. Nine AI-Lp subclasses were detected in the plasma of normolipidemics, with relative molecular masses ranging from 70,000 to ≥ 354,000 and diameters from 7.12 to ≥ 11.6 nm. The mass distribution of AI-Lp subclasses was significantly different between males and females, and some subclasses increased gradually with age while others decreased. There was a significant strong positive correlation between subclass 1 (M(r) 70,000–75,000) and subclass 3 (M(r) 105,000–126,000) in all subjects and age groups. Analysis of similar AI-Lp or HDL subclasses reported in the literature showed variability in the sizes reported by various workers. This stresses the need for a unified classification of such subclasses, and this work contributes to this direction. The quantitative nature of the method used in this work compared with the semiquantitative approaches used earlier makes it a better method for the study of the quantitative changes of the subclasses in various physiological and pathological states. The method helps to generate ideas for in vitro and in vivo studies of apoA-I exchange among subclasses and in vivo kinetic studies. Conclusion. Plasma level of the AILp subclasses varied quantitatively with age and gender, and strong correlations were detected between some subclasses. This work contributes to a better classification of AI-Lp subclasses according to their size. Comparison of the method used here with the methods reported in the literature revealed its advantages.

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