Würtz Peter, Kangas Antti J, Soininen Pasi, Lawlor Debbie A, Davey Smith George, Ala-Korpela Mika
Am J Epidemiol. 2017 Nov 1;186(9):1084-1096. doi: 10.1093/aje/kwx016.
Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
大规模流行病学研究中的详细代谢谱分析发现了心血管代谢疾病的新型生物标志物,并阐明了既定风险因素的分子关联。基于核磁共振波谱的定量代谢组学平台已得到广泛应用,已对超过40万份血样进行了分析。每个样本可定量200多种代谢指标;除了许多流行病学中常规使用的生物标志物外,该方法还能同时提供细粒度的脂蛋白亚类分析以及循环脂肪酸、氨基酸、糖异生相关代谢物和来自多种代谢途径的许多其他分子的定量分析。在此,我们重点关注磁共振代谢组学在大规模流行病学中定量循环生物标志物的应用。我们强调风险因素的分子特征、孟德尔随机化的应用以及流行病学代谢谱分析的研究设计和分析的关键问题。我们还详细介绍了代谢谱数据与遗传学的整合如何促进药物开发。我们讨论了为什么定量代谢谱分析在流行病学和生物样本库中越来越普遍。尽管代谢谱分析的大规模应用仍然新颖,但全面的生物标志物数据似乎有可能有助于对各种疾病的病因理解和预测疾病风险的能力,并有可能转化为多种临床应用。