Pathologic background to Apo E- and TG-rich lipoproteins revealed by discrimination via linear regression
Journal Title: Journal of Analytical Bio-Science - Year 2011, Vol 34, Issue 2
Abstract
Aiming to characterise atherosclerotic risk in individuals with Apo E- and TG-rich lipoproteins, the authors analysed the combined clinical data for Apo E and TG using discrimination via linear regression. The result of discrimination via linear regression showed that individuals above the regression line (+) had significantly higher Apo E and TG levels than those beneath the regression line (-), forming an Apo E- and TG-rich group. In the above-the-line (+) group, Apo E significantly correlated with increases in insulin resistance (HOMA-R) and SAA and decreases in HDL-C and Adipo, suggesting that a higher level of Apo E might indicate a higher risk of atherosclerosis in Apo E- and TG-rich individuals. Next, the sub-groups defined by the cut-off values for Apo E and TG were analysed. Among the sub-groups in the above-the-line (+) region, group C (high Apo E, normal TG) had significantly higher BMI, systolic blood pressure, and diastolic blood pressure than group A1 (normal Apo E and TG), while group D (high Apo E and TG) had significantly higher HOMA-R, SAA, BMI, and diastolic blood pressure and significantly lower HDL-C and Adipo than group A1. Group A1 had the highest levels of Apo E and TG, followed by groups C and D in decreasing order, and group D showed a high frequency of subjects with IGT and MS. These results are considered to suggest that the risk of atherosclerosis increases with rises in Apo E and TG levels. They also suggest that an Apo E- and TG-rich status may constitute a risk of atherosclerosis in MS and IGT.
Authors and Affiliations
Naoko Ikoshi
Pathologic background to Apo E- and TG-rich lipoproteins revealed by discrimination via linear regression
Aiming to characterise atherosclerotic risk in individuals with Apo E- and TG-rich lipoproteins, the authors analysed the combined clinical data for Apo E and TG using discrimination via linear regression. The result of...
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