title: Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles.
abstract: BACKGROUND
  Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.
  
  
  RESULTS
  We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi
authors.forename: Marko
authors.initials: M
authors.lastname: Sysi-Aho
authors.forename: Aki
authors.initials: A
authors.lastname: Vehtari
authors.forename: Vidya R
authors.initials: VR
authors.lastname: Velagapudi
authors.forename: Jukka
authors.initials: J
authors.lastname: Westerbacka
authors.forename: Laxman
authors.initials: L
authors.lastname: Yetukuri
authors.forename: Robert
authors.initials: R
authors.lastname: Bergholm
authors.forename: Marja-Riitta
authors.initials: MR
authors.lastname: Taskinen
authors.forename: Hannele
authors.initials: H
authors.lastname: Yki-Järvinen
authors.forename: Matej
authors.initials: M
authors.lastname: Oresic
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008055:Lipids / Q000097:blood*; D008074:Lipoproteins / Q000097:blood*; D024821:Metabolic Syndrome / Q000097:blood*; D046228:Microarray Analysis / Q000379:methods*
chemical_list: D008055:Lipids; D008074:Lipoproteins