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Metabolites 2018, 8(4), 78; https://doi.org/10.3390/metabo8040078

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

1
Unit of Molecular Metabolism, Department of Clinical Sciences, Skåne University Hospital, Lund University, SE-205 02 Malmö, Sweden
2
Centre for Analysis and Synthesis, Department of Chemistry, Lund University, 223 62 Lund, Sweden
3
Department of Public Health and Community Medicine/Primary Health Care, The Sahlgrenska Academy, University of Gothenburg, 405 30 Göteborg, Sweden
4
Department of Clinical Sciences, Lund University, 221 00 Malmö, Sweden
5
Department of Cardiology, Skåne University Hospital, Lund University, 205 02 Malmö, Sweden
6
Department of Clinical Sciences in Malmö, Family and Community Medicine, Skåne University Hospital, Lund University, 205 02 Malmö, Sweden
These authors contributed equally to the work.
*
Author to whom correspondence should be addressed.
Received: 19 October 2018 / Revised: 12 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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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 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. View Full-Text
Keywords: metabolomics; glomerular filtration rate; insulin resistance; acylcarnitines; branched-chain amino acids metabolomics; glomerular filtration rate; insulin resistance; acylcarnitines; branched-chain amino acids
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Al-Majdoub, M.; Herzog, K.; Daka, B.; Magnusson, M.; Råstam, L.; Lindblad, U.; Spégel, P. Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles. Metabolites 2018, 8, 78.

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