A clustering analysis of lipoprotein diameters in the metabolic syndrome

<p>Abstract</p> <p>Background</p> <p>The presence of smaller low-density lipoproteins (LDL) has been associated with atherosclerosis risk, and the insulin resistance (IR) underlying the metabolic syndrome (MetS). In addition, some research has supported the association...

Full description

Bibliographic Details
Main Authors: Frazier-Wood Alexis C, Glasser Stephen, Garvey W Timothy, Kabagambe Edmond K, Borecki Ingrid B, Tiwari Hemant K, Tsai Michael Y, Hopkins Paul N, Ordovas Jose M, Arnett Donna K
Format: Article
Language:English
Published: BMC 2011-12-01
Series:Lipids in Health and Disease
Subjects:
Online Access:http://www.lipidworld.com/content/10/1/237
id doaj-c252bd62a94d4ddeb114687a755dc8a8
record_format Article
spelling doaj-c252bd62a94d4ddeb114687a755dc8a82020-11-24T22:02:58ZengBMCLipids in Health and Disease1476-511X2011-12-0110123710.1186/1476-511X-10-237A clustering analysis of lipoprotein diameters in the metabolic syndromeFrazier-Wood Alexis CGlasser StephenGarvey W TimothyKabagambe Edmond KBorecki Ingrid BTiwari Hemant KTsai Michael YHopkins Paul NOrdovas Jose MArnett Donna K<p>Abstract</p> <p>Background</p> <p>The presence of smaller low-density lipoproteins (LDL) has been associated with atherosclerosis risk, and the insulin resistance (IR) underlying the metabolic syndrome (MetS). In addition, some research has supported the association of very low-, low- and high-density lipoprotein (VLDL HDL) particle diameters with components of the metabolic syndrome (MetS), although this has been the focus of less research. We aimed to explore the relationship of VLDL, LDL and HDL diameters to MetS and its features, and by clustering individuals by their diameters of VLDL, LDL and HDL particles, to capture information across all three fractions of lipoprotein into a unified phenotype.</p> <p>Methods</p> <p>We used nuclear magnetic resonance spectroscopy measurements on fasting plasma samples from a general population sample of 1,036 adults (mean ± SD, 48.8 ± 16.2 y of age). Using latent class analysis, the sample was grouped by the diameter of their fasting lipoproteins, and mixed effects models tested whether the distribution of MetS components varied across the groups.</p> <p>Results</p> <p>Eight discrete groups were identified. Two groups (N = 251) were enriched with individuals meeting criteria for the MetS, and were characterized by the smallest LDL/HDL diameters. One of those two groups, one was additionally distinguished by large VLDL, and had significantly higher blood pressure, fasting glucose, triglycerides, and waist circumference (WC; <it>P </it>< .001). However, large VLDL, in the absence of small LDL and HDL particles, did not associate with MetS features. These associations held after additionally controlling for VLDL, LDL and HDL particle concentrations.</p> <p>Conclusions</p> <p>While small LDL diameters remain associated with IR and the MetS, the occurrence of these in conjunction with a shift to overall larger VLDL diameter may identify those with the highest fasting glucose, TG and WC within the MetS. If replicated, the association of this phenotype with more severe IR-features indicated that it may contribute to identifying of those most at risk for incident type II diabetes and cardiometabolic disease.</p> http://www.lipidworld.com/content/10/1/237lipoprotein particle diameterinsulin resistancenuclear resonance spectroscopyMetabolic Syndromelatent class analysisGOLDNwaist circumferencehypertensionhypertriglyceridemiafasting glucose
collection DOAJ
language English
format Article
sources DOAJ
author Frazier-Wood Alexis C
Glasser Stephen
Garvey W Timothy
Kabagambe Edmond K
Borecki Ingrid B
Tiwari Hemant K
Tsai Michael Y
Hopkins Paul N
Ordovas Jose M
Arnett Donna K
spellingShingle Frazier-Wood Alexis C
Glasser Stephen
Garvey W Timothy
Kabagambe Edmond K
Borecki Ingrid B
Tiwari Hemant K
Tsai Michael Y
Hopkins Paul N
Ordovas Jose M
Arnett Donna K
A clustering analysis of lipoprotein diameters in the metabolic syndrome
Lipids in Health and Disease
lipoprotein particle diameter
insulin resistance
nuclear resonance spectroscopy
Metabolic Syndrome
latent class analysis
GOLDN
waist circumference
hypertension
hypertriglyceridemia
fasting glucose
author_facet Frazier-Wood Alexis C
Glasser Stephen
Garvey W Timothy
Kabagambe Edmond K
Borecki Ingrid B
Tiwari Hemant K
Tsai Michael Y
Hopkins Paul N
Ordovas Jose M
Arnett Donna K
author_sort Frazier-Wood Alexis C
title A clustering analysis of lipoprotein diameters in the metabolic syndrome
title_short A clustering analysis of lipoprotein diameters in the metabolic syndrome
title_full A clustering analysis of lipoprotein diameters in the metabolic syndrome
title_fullStr A clustering analysis of lipoprotein diameters in the metabolic syndrome
title_full_unstemmed A clustering analysis of lipoprotein diameters in the metabolic syndrome
title_sort clustering analysis of lipoprotein diameters in the metabolic syndrome
publisher BMC
series Lipids in Health and Disease
issn 1476-511X
publishDate 2011-12-01
description <p>Abstract</p> <p>Background</p> <p>The presence of smaller low-density lipoproteins (LDL) has been associated with atherosclerosis risk, and the insulin resistance (IR) underlying the metabolic syndrome (MetS). In addition, some research has supported the association of very low-, low- and high-density lipoprotein (VLDL HDL) particle diameters with components of the metabolic syndrome (MetS), although this has been the focus of less research. We aimed to explore the relationship of VLDL, LDL and HDL diameters to MetS and its features, and by clustering individuals by their diameters of VLDL, LDL and HDL particles, to capture information across all three fractions of lipoprotein into a unified phenotype.</p> <p>Methods</p> <p>We used nuclear magnetic resonance spectroscopy measurements on fasting plasma samples from a general population sample of 1,036 adults (mean ± SD, 48.8 ± 16.2 y of age). Using latent class analysis, the sample was grouped by the diameter of their fasting lipoproteins, and mixed effects models tested whether the distribution of MetS components varied across the groups.</p> <p>Results</p> <p>Eight discrete groups were identified. Two groups (N = 251) were enriched with individuals meeting criteria for the MetS, and were characterized by the smallest LDL/HDL diameters. One of those two groups, one was additionally distinguished by large VLDL, and had significantly higher blood pressure, fasting glucose, triglycerides, and waist circumference (WC; <it>P </it>< .001). However, large VLDL, in the absence of small LDL and HDL particles, did not associate with MetS features. These associations held after additionally controlling for VLDL, LDL and HDL particle concentrations.</p> <p>Conclusions</p> <p>While small LDL diameters remain associated with IR and the MetS, the occurrence of these in conjunction with a shift to overall larger VLDL diameter may identify those with the highest fasting glucose, TG and WC within the MetS. If replicated, the association of this phenotype with more severe IR-features indicated that it may contribute to identifying of those most at risk for incident type II diabetes and cardiometabolic disease.</p>
topic lipoprotein particle diameter
insulin resistance
nuclear resonance spectroscopy
Metabolic Syndrome
latent class analysis
GOLDN
waist circumference
hypertension
hypertriglyceridemia
fasting glucose
url http://www.lipidworld.com/content/10/1/237
work_keys_str_mv AT frazierwoodalexisc aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT glasserstephen aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT garveywtimothy aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT kabagambeedmondk aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT boreckiingridb aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT tiwarihemantk aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT tsaimichaely aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT hopkinspauln aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT ordovasjosem aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT arnettdonnak aclusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT frazierwoodalexisc clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT glasserstephen clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT garveywtimothy clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT kabagambeedmondk clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT boreckiingridb clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT tiwarihemantk clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT tsaimichaely clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT hopkinspauln clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT ordovasjosem clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
AT arnettdonnak clusteringanalysisoflipoproteindiametersinthemetabolicsyndrome
_version_ 1725833689518047232