Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles

Marko Sysi-Aho (Corresponding Author), Aki Vehtari, Vidya Velagapudi, Jukka Westerbacka, Laxman Yetukuri, Robert Bergholm, Marja-Riitta Taskinen, Hannele Yki-Järvinen, Matej Oresic

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

Abstract

Motivation: 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.
Original languageEnglish
Pages (from-to)i519-i528
JournalBioinformatics
Volume23
Issue number13
DOIs
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

Fingerprint

Lipoproteins
Lipids
Regression
Serum
Chemical analysis
Lipid Metabolism
Bioinformatics
Computational Biology
Regression Model
Dynamic Modeling
Bayesian Model
High-dimensional Data
Fractionation
Profile
Technology

Keywords

  • serum lipids
  • lipid metabolism
  • lipidomics
  • lipids
  • lipoproteins
  • bayesian

Cite this

Sysi-Aho, M., Vehtari, A., Velagapudi, V., Westerbacka, J., Yetukuri, L., Bergholm, R., ... Oresic, M. (2007). Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. Bioinformatics, 23(13), i519-i528. https://doi.org/10.1093/bioinformatics/btm181
Sysi-Aho, Marko ; Vehtari, Aki ; Velagapudi, Vidya ; Westerbacka, Jukka ; Yetukuri, Laxman ; Bergholm, Robert ; Taskinen, Marja-Riitta ; Yki-Järvinen, Hannele ; Oresic, Matej. / Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. In: Bioinformatics. 2007 ; Vol. 23, No. 13. pp. i519-i528.
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Sysi-Aho, M, Vehtari, A, Velagapudi, V, Westerbacka, J, Yetukuri, L, Bergholm, R, Taskinen, M-R, Yki-Järvinen, H & Oresic, M 2007, 'Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles', Bioinformatics, vol. 23, no. 13, pp. i519-i528. https://doi.org/10.1093/bioinformatics/btm181

Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. / Sysi-Aho, Marko (Corresponding Author); Vehtari, Aki; Velagapudi, Vidya; Westerbacka, Jukka; Yetukuri, Laxman; Bergholm, Robert; Taskinen, Marja-Riitta; Yki-Järvinen, Hannele; Oresic, Matej.

In: Bioinformatics, Vol. 23, No. 13, 2007, p. i519-i528.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles

AU - Sysi-Aho, Marko

AU - Vehtari, Aki

AU - Velagapudi, Vidya

AU - Westerbacka, Jukka

AU - Yetukuri, Laxman

AU - Bergholm, Robert

AU - Taskinen, Marja-Riitta

AU - Yki-Järvinen, Hannele

AU - Oresic, Matej

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N2 - Motivation: 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.

AB - Motivation: 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.

KW - serum lipids

KW - lipid metabolism

KW - lipidomics

KW - lipids

KW - lipoproteins

KW - bayesian

U2 - 10.1093/bioinformatics/btm181

DO - 10.1093/bioinformatics/btm181

M3 - Article

VL - 23

SP - i519-i528

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 13

ER -

Sysi-Aho M, Vehtari A, Velagapudi V, Westerbacka J, Yetukuri L, Bergholm R et al. Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. Bioinformatics. 2007;23(13):i519-i528. https://doi.org/10.1093/bioinformatics/btm181