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      Clustering by Plasma Lipoprotein Profile Reveals Two Distinct Subgroups with Positive Lipid Response to Fenofibrate Therapy

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          Abstract

          Fibrates lower triglycerides and raise HDL cholesterol in dyslipidemic patients, but show heterogeneous treatment response. We used k-means clustering to identify three representative NMR lipoprotein profiles for 775 subjects from the GOLDN population, and study the response to fenofibrate in corresponding subgroups. The subjects in each subgroup showed differences in conventional lipid characteristics and in presence/absence of cardiovascular risk factors at baseline; there were subgroups with a low, medium and high degree of dyslipidemia. Modeling analysis suggests that the difference between the subgroups with low and medium dyslipidemia is influenced mainly by hepatic uptake dysfunction, while the difference between subgroups with medium and high dyslipidemia is influenced mainly by extrahepatic lipolysis disfunction. The medium and high dyslipidemia subgroups showed a positive, yet distinct lipid response to fenofibrate treatment. When comparing our subgroups to known subgrouping methods, we identified an additional 33% of the population with favorable lipid response to fenofibrate compared to a standard baseline triglyceride cutoff method. Compared to a standard HDL cholesterol cutoff method, the addition was 18%. In conclusion, by using constructing subgroups based on representative lipoprotein profiles, we have identified two subgroups of subjects with positive lipid response to fenofibrate therapy and with different underlying disturbances in lipoprotein metabolism. The total subgroup with positive lipid response to fenofibrate is larger than subgroups identified with baseline triglyceride and HDL cholesterol cutoffs.

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          Most cited references31

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          Mechanism of action of fibrates on lipid and lipoprotein metabolism.

          Treatment with fibrates, a widely used class of lipid-modifying agents, results in a substantial decrease in plasma triglycerides and is usually associated with a moderate decrease in LDL cholesterol and an increase in HDL cholesterol concentrations. Recent investigations indicate that the effects of fibrates are mediated, at least in part, through alterations in transcription of genes encoding for proteins that control lipoprotein metabolism. Fibrates activate specific transcription factors belonging to the nuclear hormone receptor superfamily, termed peroxisome proliferator-activated receptors (PPARs). The PPAR-alpha form mediates fibrate action on HDL cholesterol levels via transcriptional induction of synthesis of the major HDL apolipoproteins, apoA-I and apoA-II. Fibrates lower hepatic apoC-III production and increase lipoprotein lipase--mediated lipolysis via PPAR. Fibrates stimulate cellular fatty acid uptake, conversion to acyl-CoA derivatives, and catabolism by the beta-oxidation pathways, which, combined with a reduction in fatty acid and triglyceride synthesis, results in a decrease in VLDL production. In summary, both enhanced catabolism of triglyceride-rich particles and reduced secretion of VLDL underlie the hypotriglyceridemic effect of fibrates, whereas their effect on HDL metabolism is associated with changes in HDL apolipoprotein expression.
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            Lipoprotein particle analysis by nuclear magnetic resonance spectroscopy.

            Laboratory measurements of plasma lipids (principally cholesterol and triglycerides) and lipoprotein lipids (principally low-density lipoprotein [LDL] and low-density lipoprotein [HDL] cholesterol) are the cornerstone of the clinical assessment and management of atherosclerotic cardiovascular disease (CVD) risk. LDL particles, and to a lesser extent very-low-density lipoprotein [VLDL] particles, cause atherosclerosis, whereas HDL particles prevent or reverse this process through reverse cholesterol transport. The overall risk for CVD depends on the balance between the "bad" LDL (and VLDL) and "good" HDL particles. Direct assessment of lipoprotein particle numbers us now possible through nuclear magnetic resonance spectroscopic analysis.
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              Atherogenic lipoprotein phenotype. A proposed genetic marker for coronary heart disease risk.

              In a community-based study of 301 subjects from 61 nuclear families, two distinct phenotypes (denoted A and B) were identified by nondenaturing gradient gel electrophoretic analysis of low density lipoprotein (LDL) subclasses. Phenotype A was characterized by predominance of large, buoyant LDL particles, and phenotype B consisted of a major peak of small, dense LDL particles. Previous analysis of the family data by complex segregation analysis demonstrated that these phenotypes appear to be inherited as a single-gene trait. In the present study, the phenotypes were found to be closely associated with variations in plasma levels of other lipid, lipoprotein, and apolipoprotein measurements. Specifically, phenotype B was associated with increases in plasma levels of triglyceride and apolipoprotein B, with mass of very low and intermediate density lipoproteins, and with decreases in high density lipoprotein (HDL) cholesterol, HDL2 mass, and plasma levels of apolipoprotein A-I. Thus, the proposed genetic locus responsible for LDL subclass phenotypes also results in an atherogenic lipoprotein phenotype.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                12 June 2012
                : 7
                : 6
                : e38072
                Affiliations
                [1 ]Department of Microbiology and Systems Biology, TNO, Zeist and Leiden, The Netherlands
                [2 ]Analytical Sciences Division, The Leiden Amsterdam Centre for Drug Research, Leiden, The Netherlands
                [3 ]The Netherlands Bioinformatics Centre (NBIC), Nijmegen, The Netherlands
                [4 ]The Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, United States of America
                [5 ]Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
                Governmental Technical Research Centre of Finland, Finland
                Author notes

                Conceived and designed the experiments: DKA JMO BvO. Performed the experiments: DKA. Analyzed the data: KvB DBvS LDP CQL AAG. Contributed reagents/materials/analysis tools: KvB DBvS LDP BvO. Wrote the paper: KvB DBvS. Manuscript editing: KvB DBvS LDP CQL JMO AAG BvO DKA.

                Article
                PONE-D-11-03178
                10.1371/journal.pone.0038072
                3373573
                22719863
                a59f8440-2557-4ee7-8331-d497af954dbf
                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                History
                : 14 February 2011
                : 1 May 2012
                Page count
                Pages: 11
                Categories
                Research Article
                Biology
                Computational Biology
                Biochemical Simulations
                Systems Biology
                Theoretical Biology
                Mathematics
                Applied Mathematics
                Algorithms
                Statistics
                Biostatistics
                Medicine
                Cardiovascular
                Diagnostic Medicine
                Clinical Laboratory Sciences
                Drugs and Devices
                Drug Information
                Metabolic Disorders

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