title: Ion mobility analysis of lipoprotein subfractions identifies three independent axes of cardiovascular risk.
abstract: OBJECTIVE
  Whereas epidemiological studies show that levels of low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) predict incident cardiovascular disease (CVD), there is limited evidence relating lipoprotein subfractions and composite measures of subfractions to risk for CVD in prospective cohort studies.
  
  
  RESULTS
  We tested whether combinations of lipoprotein subfractions independently predict CVD in a prospective cohort of 4594 initially healthy men and women (the Malmö Diet and Cancer Study, mean follow-up 12.2 years, 377 incident cardiovascular events). Plasma lipoproteins and lipoprotein subfractions were measured at baseline with a novel high-resolution ion mobility technique. Principal component analysis (PCA) of subfraction concentrations identified 3 major independent (ie, zero correlation) components of CVD risk, one representing LDL-associated risk, a second representing HDL-associated protection, and the third representing a pattern of decreased large HDL, increased small/medium LDL, and increased triglycerides. The last corresponds to the previously described "atherogenic lipoprotein phenotype." Several genes that may underlie this phenotype-CETP, LIPC, GALNT2, MLXIPL, APOA1/A5, LPL-are suggested by SNPs associated with the combination of small/medium LDL and large HDL.
  
  
  CONCLUSIONS
  PCA on lipoprotein subfractions yielded three independent components of CVD risk. Genetic analyses suggest these components represent independent mechanistic pathways for development of CVD.
authors.affiliation: Center for Human Genetic Research, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA.
authors.forename: Kiran
authors.initials: K
authors.lastname: Musunuru
authors.forename: Marju
authors.initials: M
authors.lastname: Orho-Melander
authors.forename: Michael P
authors.initials: MP
authors.lastname: Caulfield
authors.forename: Shuguang
authors.initials: S
authors.lastname: Li
authors.forename: Wael A
authors.initials: WA
authors.lastname: Salameh
authors.forename: Richard E
authors.initials: RE
authors.lastname: Reitz
authors.forename: Göran
authors.initials: G
authors.lastname: Berglund
authors.forename: Bo
authors.initials: B
authors.lastname: Hedblad
authors.forename: Gunnar
authors.initials: G
authors.lastname: Engström
authors.forename: Paul T
authors.initials: PT
authors.lastname: Williams
authors.forename: Sekar
authors.initials: S
authors.lastname: Kathiresan
authors.forename: Olle
authors.initials: O
authors.lastname: Melander
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Krauss
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood; D002318:Cardiovascular Diseases / Q000097:blood*; D008076:Cholesterol, HDL / Q000097:blood*; D008078:Cholesterol, LDL / Q000097:blood*; D002851:Chromatography, High Pressure Liquid; D002852:Chromatography, Ion Exchange; D015331:Cohort Studies; D005260:Female; D006801:Humans; D017136:Ion Transport; D008079:Lipoproteins, VLDL / Q000097:blood*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011336:Probability; D011379:Prognosis; D011446:Prospective Studies; D018570:Risk Assessment
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D008079:Lipoproteins, VLDL