Disentangling the consequences of type 2 diabetes on targeted metabolite profiles using causal inference and interaction QTL analyses.

Autor: Bocher O; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany., Singh A; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; Munich School for Data Science (MUDS), Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; Technical University of Munich (TUM), TUM School of Medicine and Health, Graduate School of Experimental Medicine, Munich, Germany., Huang Y; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; Technical University of Munich (TUM), TUM School of Medicine and Health, Graduate School of Experimental Medicine, Munich, Germany., Võsa U; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia., Reimann E; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia., Arruda A; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; Munich School for Data Science (MUDS), Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; Technical University of Munich (TUM), TUM School of Medicine and Health, Graduate School of Experimental Medicine, Munich, Germany., Barysenska A; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany., Kolde A; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.; Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia., Rayner NW; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany., Esko T; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia., Mägi R; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia., Zeggini E; Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.; TUM school of medicine and health, Technical University Munich and Klinikum Rechts der Isar, Munich, Germany.
Jazyk: angličtina
Zdroj: PLoS genetics [PLoS Genet] 2024 Dec 03; Vol. 20 (12), pp. e1011346. Date of Electronic Publication: 2024 Dec 03.
DOI: 10.1371/journal.pgen.1011346
Abstrakt: Circulating metabolite levels have been associated with type 2 diabetes (T2D), but the extent to which T2D affects metabolite levels and their genetic regulation remains to be elucidated. In this study, we investigate the interplay between genetics, metabolomics, and T2D risk in the UK Biobank dataset using the Nightingale panel composed of 249 metabolites, 92% of which correspond to lipids (HDL, IDL, LDL, VLDL) and lipoproteins. By integrating these data with large-scale T2D GWAS from the DIAMANTE meta-analysis through Mendelian randomization analyses, we find 79 metabolites with a causal association to T2D, all spanning lipid-related classes except for Glucose and Tyrosine. Twice as many metabolites are causally affected by T2D liability, spanning almost all tested classes, including branched-chain amino acids. Secondly, using an interaction quantitative trait locus (QTL) analysis, we describe four metabolites consistently replicated in an independent dataset from the Estonian Biobank, for which genetic loci in two different genomic regions show attenuated regulation in T2D cases compared to controls. The significant variants from the interaction QTL analysis are significant QTLs for the corresponding metabolites in the general population but are not associated with T2D risk, pointing towards consequences of T2D on the genetic regulation of metabolite levels. Finally, through differential level analyses, we find 165 metabolites associated with microvascular, macrovascular, or both types of T2D complications, with only a few discriminating between complication classes. Of the 165 metabolites, 40 are not causally linked to T2D in either direction, suggesting biological mechanisms specific to the occurrence of complications. Overall, this work provides a map of the consequences of T2D on Nightingale targeted metabolite levels and on their genetic regulation, enabling a better understanding of the T2D trajectory leading to complications.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Bocher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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