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

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Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles. / Al-Majdoub, Mahmoud; Herzog, Katharina; Daka, Bledar; Magnusson, Martin; Råstam, Lennart; Lindblad, Ulf; Spégel, Peter.

In: Metabolites, Vol. 8, No. 4, 78, 15.11.2018.

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TY - JOUR

T1 - Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles

AU - Al-Majdoub, Mahmoud

AU - Herzog, Katharina

AU - Daka, Bledar

AU - Magnusson, Martin

AU - Råstam, Lennart

AU - Lindblad, Ulf

AU - Spégel, Peter

PY - 2018/11/15

Y1 - 2018/11/15

N2 - 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.

AB - 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.

U2 - 10.3390/metabo8040078

DO - 10.3390/metabo8040078

M3 - Article

VL - 8

JO - Metabolites

JF - Metabolites

SN - 2218-1989

IS - 4

M1 - 78

ER -