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Evaluation of Gemfibrozil Therapy: Predictive Response from Lipoprotein Subfraction Analysis

Overview
Journal Am J Ther
Specialty Pharmacology
Date 1999 Jul 29
PMID 10423623
Citations 3
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Abstract

Subjects with high-density lipoprotein cholesterol (HDL-C) values of less than 47 mg/dL (mean 35.6 +/- 5.5 mg/dL) were selected for this study to examine relationships between plasma lipids, lipoprotein components, and the outcome of gemfibrozil therapy. Changes in plasma lipoprotein subfractions were determined to better understand the previously observed variability of the responses in both HDL-C and triglycerides to gemfibrozil. Based on the data collected, an attempt was made to identify pretreatment lipid parameters that may be predictive regarding the efficacy of gemfibrozil therapy. Serum samples were analyzed at the outset and after the conclusion of 12 weeks of gemfibrozil therapy. Because the HDL-C response to therapy was highly variable, the data from patients were separated into two groups, responders (>20% increase in HDL-C) and nonresponders (<20% increase in HDL-C). The lipid components of lipoprotein subfractions were evaluated using multiple regression analysis yielding predictive models that show the relationship between specific lipoprotein subfractions and the percentage change in HDL-C and posttreatment triglyceride levels. Group classification was then predicted with 78% accuracy using specific lipoprotein subfractions to estimate an individual's percentage change in HDL-C. The major difference between the responder and nonresponder groups was their respective correlations between triglyceride-lowering and changes in HDL-C. In the responder group, there was a significant correlation between the changes in HDL-C and the lowering of triglycerides (r = 0.61, p = 0.03), whereas the nonresponder group showed no such correlation (r = 0.17, p = 0.52). The predictive model also proved to be highly accurate in forecasting the effectiveness of the triglyceride-lowering action of gemfibrozil in this group of patients.

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