title: A clustering analysis of lipoprotein diameters in the metabolic syndrome.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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; P < .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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Epidemiology, University of Alabama at Birmingham, School of Public Health, USA. LekkiWood@Gmail.com
authors.forename: Alexis C
authors.initials: AC
authors.lastname: Frazier-Wood
authors.forename: Stephen
authors.initials: S
authors.lastname: Glasser
authors.forename: W Timothy
authors.initials: WT
authors.lastname: Garvey
authors.forename: Edmond K
authors.initials: EK
authors.lastname: Kabagambe
authors.forename: Ingrid B
authors.initials: IB
authors.lastname: Borecki
authors.forename: Hemant K
authors.initials: HK
authors.lastname: Tiwari
authors.forename: Michael Y
authors.initials: MY
authors.lastname: Tsai
authors.forename: Paul N
authors.initials: PN
authors.lastname: Hopkins
authors.forename: Jose M
authors.initials: JM
authors.lastname: Ordovas
authors.forename: Donna K
authors.initials: DK
authors.lastname: Arnett
mesh_terms: D000328:Adult; D000368:Aged; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008074:Lipoproteins / Q000097:blood* / Q000737:chemistry; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood*; D008875:Middle Aged; D010316:Particle Size; D010641:Phenotype
chemical_list: D008074:Lipoproteins