Gieger Christian, Geistlinger Ludwig, Altmaier Elisabeth, Hrabé de Angelis Martin, Kronenberg Florian, Meitinger Thomas, Mewes Hans-Werner, Wichmann H-Erich, Weinberger Klaus M, Adamski Jerzy, Illig Thomas, Suhre Karsten
Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
PLoS Genet. 2008 Nov;4(11):e1000282. doi: 10.1371/journal.pgen.1000282. Epub 2008 Nov 28.
The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.
代谢组学这一快速发展的领域旨在全面测量细胞或体液中理想状态下的所有内源性代谢物。由此,它提供了人体生理状态的功能性读数。与关键脂质、碳水化合物或氨基酸稳态变化相关的基因变异,不仅因其直接参与代谢物转化修饰而预期会表现出大得多的效应大小,而且还应能揭示此类变异的生化背景,尤其是在涉及酶编码基因时。为了验证这一假设,据我们所知,我们基于对KORA研究中284名男性参与者血清中363种代谢物的定量测量,开展了首次代谢组学全基因组关联研究(GWA研究)。我们发现常见单核苷酸多态性(SNP)与人体代谢稳态的显著差异存在关联,可解释高达12%的观察到的变异。使用某些代谢物浓度比值作为酶活性的替代指标,高达28%的变异可得到解释(P值为10^(-16)至10^(-21))。我们在编码酶的基因(FADS1、LIPC、SCAD、MCAD)中鉴定出四个基因变异,其相应的代谢表型(代谢型)与这些酶所活跃的生化途径明显匹配。我们的结果表明,常见的基因多态性会导致人群代谢组成的重大差异。这可能会带来一种基于基因分型和代谢特征相结合的个性化医疗保健新方法。这些由基因决定的代谢型可能预示着某种医学表型的风险、对特定药物治疗的反应,或对营养干预或环境挑战的反应。