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Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles

Al-Majdoub, Mahmoud LU ; Herzog, Katharina LU ; Daka, Bledar; Magnusson, Martin LU ; Råstam, Lennart LU ; Lindblad, Ulf LU and Spégel, Peter LU (2018) In Metabolites 8(4).
Abstract

The plasma metabolome is associated with multiple phenotypes and diseases. However, a systematic study investigating clinical determinants that control the metabolome has not yet been conducted. In the present study, therefore, we aimed to identify the major determinants of the plasma metabolite profile. We used ultra-high performance liquid chromatography (UHPLC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) to determine 106 metabolites in plasma samples from 2503 subjects in a cross-sectional study. We investigated the correlation structure of the metabolite profiles and generated uncorrelated metabolite factors using principal component analysis (PCA) and varimax rotation. Finally, we investigated associations... (More)

The plasma metabolome is associated with multiple phenotypes and diseases. However, a systematic study investigating clinical determinants that control the metabolome has not yet been conducted. In the present study, therefore, we aimed to identify the major determinants of the plasma metabolite profile. We used ultra-high performance liquid chromatography (UHPLC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) to determine 106 metabolites in plasma samples from 2503 subjects in a cross-sectional study. We investigated the correlation structure of the metabolite profiles and generated uncorrelated metabolite factors using principal component analysis (PCA) and varimax rotation. Finally, we investigated associations between these factors and 34 clinical covariates. Our results suggest that liver function, followed by kidney function and insulin resistance show the strongest associations with the plasma metabolite profile. The association of specific phenotypes with several components may suggest multiple independent metabolic mechanisms, which is further supported by the composition of the associated factors.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Metabolites
volume
8
issue
4
external identifiers
  • scopus:85057222069
ISSN
2218-1989
DOI
10.3390/metabo8040078
language
English
LU publication?
yes
id
5a36a9e1-50b2-48e1-8e32-aa0491a5a06b
date added to LUP
2018-11-18 10:15:02
date last changed
2019-02-18 03:00:02
@article{5a36a9e1-50b2-48e1-8e32-aa0491a5a06b,
  abstract     = {<p>The plasma metabolome is associated with multiple phenotypes and diseases. However, a systematic study investigating clinical determinants that control the metabolome has not yet been conducted. In the present study, therefore, we aimed to identify the major determinants of the plasma metabolite profile. We used ultra-high performance liquid chromatography (UHPLC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) to determine 106 metabolites in plasma samples from 2503 subjects in a cross-sectional study. We investigated the correlation structure of the metabolite profiles and generated uncorrelated metabolite factors using principal component analysis (PCA) and varimax rotation. Finally, we investigated associations between these factors and 34 clinical covariates. Our results suggest that liver function, followed by kidney function and insulin resistance show the strongest associations with the plasma metabolite profile. The association of specific phenotypes with several components may suggest multiple independent metabolic mechanisms, which is further supported by the composition of the associated factors.</p>},
  articleno    = {78},
  author       = {Al-Majdoub, Mahmoud and Herzog, Katharina and Daka, Bledar and Magnusson, Martin and Råstam, Lennart and Lindblad, Ulf and Spégel, Peter},
  issn         = {2218-1989},
  language     = {eng},
  month        = {11},
  number       = {4},
  series       = {Metabolites},
  title        = {Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles},
  url          = {http://dx.doi.org/10.3390/metabo8040078},
  volume       = {8},
  year         = {2018},
}