title: Quantification of lipoprotein subclasses by proton nuclear magnetic resonance-based partial least-squares regression models.
abstract: BACKGROUND
  Cardiovascular disease risk can be estimated in part on the basis of the plasma lipoprotein profile. Analysis of lipoprotein subclasses improves the risk evaluation, but the traditional methods are very time-consuming. Novel, rapid, and productive methods are therefore needed.
  
  
  METHODS
  We obtained plasma samples from 103 fasting people and determined the plasma lipoprotein subclass profiles by an established ultracentrifugation-based method. Proton nuclear magnetic resonance (NMR) spectra were obtained from replicate samples on a 600 MHz NMR spectrometer. From the ultracentrifugation-based reference data and the NMR spectra, we developed partial least-squares (PLS) regression models to predict cholesterol and triglyceride (TG) concentrations in plasma as well as in VLDL, intermediate-density lipoprotein (IDL), LDL, 3 LDL fractions, HDL, and 3 HDL subclasses.
  
  
  RESULTS
  The correlation coefficients (r) between the plasma TG and cholesterol concentrations measured by the 2 methods were 0.98 and 0.91, respectively. For LDL- and HDL-cholesterol concentrations, r = 0.90 and 0.94, respectively. For cholesterol concentrations in the LDL-1, LDL-2, and LDL-3 fractions, r = 0.74, 0.78, and 0.69, respectively, and for HDL subclasses HDL(2b), HDL(2a), and HDL(3), cholesterol concentrations were predicted with r = 0.92, 0.94, and 0.75, respectively. TG concentrations in VLDL, IDL, LDL, and HDL were predicted with correlations of 0.98, 0.85, 0.77, and 0.74, respectively. The cholesterol and TG concentrations in the main lipoprotein fractions and in LDL fractions and HDL subclasses predicted by the PLS models were 94%-100% of the concentrations obtained by ultracentrifugation.
  
  
  CONCLUSIONS
  NMR-based PLS regression models are appropriate for use in research in which analyses of the plasma lipoprotein profile, including LDL and HDL subclasses, are required in large numbers of samples.
authors.affiliation: Institute of Human Nutrition, Department of Food Science, Quality and Technology, The Royal Veterinary and Agricultural University, Frederiksberg, Denmark. mpe@kvl.de
authors.forename: Martin
authors.initials: M
authors.lastname: Petersen
authors.forename: Marianne
authors.initials: M
authors.lastname: Dyrby
authors.forename: Søren
authors.initials: S
authors.lastname: Toubro
authors.forename: Søren Balling
authors.initials: SB
authors.lastname: Engelsen
authors.forename: Lars
authors.initials: L
authors.lastname: Nørgaard
authors.forename: Henrik Toft
authors.initials: HT
authors.lastname: Pedersen
authors.forename: Jørn
authors.initials: J
authors.lastname: Dyerberg
mesh_terms: D000328:Adult; D000368:Aged; D002784:Cholesterol / Q000097:blood; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008074:Lipoproteins / Q000097:blood / Q000145:classification*; D008075:Lipoproteins, HDL / Q000097:blood; D008077:Lipoproteins, LDL / Q000097:blood; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D008875:Middle Aged; D014280:Triglycerides / Q000097:blood; D014461:Ultracentrifugation
chemical_list: D008074:Lipoproteins; D008075:Lipoproteins, HDL; D008077:Lipoproteins, LDL; D014280:Triglycerides; D002784:Cholesterol