Bocher Ozvan, Singh Archit, Huang Yue, Võsa Urmo, Reimann Ene, Arruda Ana, Barysenska Andrei, Kolde Anastassia, Rayner Nigel W, Esko Tõnu, Mägi Reedik, Zeggini Eleftheria
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.
PLoS Genet. 2024 Dec 3;20(12):e1011346. doi: 10.1371/journal.pgen.1011346. eCollection 2024 Dec.
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.
循环代谢物水平与2型糖尿病（T2D）有关，但T2D对代谢物水平及其遗传调控的影响程度仍有待阐明。在本研究中，我们使用由249种代谢物组成的夜莺面板，在英国生物银行数据集中研究遗传学、代谢组学和T2D风险之间的相互作用，其中92%的代谢物对应于脂质（高密度脂蛋白、中间密度脂蛋白、低密度脂蛋白、极低密度脂蛋白）和脂蛋白。通过孟德尔随机化分析将这些数据与来自DIAMANTE荟萃分析的大规模T2D全基因组关联研究（GWAS）相结合，我们发现79种代谢物与T2D存在因果关联，除葡萄糖和酪氨酸外，所有这些代谢物都属于脂质相关类别。受T2D易感性因果影响的代谢物数量是前者的两倍，几乎涵盖所有测试类别，包括支链氨基酸。其次，使用相互作用定量性状基因座（QTL）分析，我们描述了在爱沙尼亚生物银行的独立数据集中一致重复的四种代谢物，与对照组相比，T2D病例中两个不同基因组区域的基因座对其调控减弱。相互作用QTL分析中的显著变异在普通人群中是相应代谢物的显著QTL，但与T2D风险无关，这表明T2D对代谢物水平的遗传调控有影响。最后，通过差异水平分析，我们发现165种代谢物与微血管、大血管或两种类型的T2D并发症相关，只有少数能区分并发症类别。在这165种代谢物中，40种在两个方向上都与T2D没有因果联系，这表明并发症发生存在特定的生物学机制。总体而言，这项工作提供了T2D对夜莺靶向代谢物水平及其遗传调控影响的图谱，有助于更好地理解导致并发症的T2D发展轨迹。
