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Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

机译:大规模流行病学中定量血清核磁共振代谢组: - M-底漆

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摘要

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.
机译:大规模流行病学研究中的详细代谢分析已经发现了用于心细截体疾病的新型生物标志物,并阐明了既定危险因素的分子协会。基于核磁共振光谱的定量代谢源性平台已发现广泛使用,已经探讨了超过400,000种血液样品。每种样本量化超过200种代谢措施;除了常规用于流行病学的许多生物标志物之外,该方法同时提供细粒脂蛋白亚类分析和定量循环脂肪酸,氨基酸,葡糖生成相关代谢物,以及来自多种代谢途径的许多其他分子。在这里,我们专注于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。我们突出了危险因素,孟德尔随机化的使用的分子特征,以及流行病学代谢分析的研究设计与分析的关键问题。我们还详细介绍了代谢分析数据与遗传学的集成如何增强药物开发。我们讨论为什么定量代谢分析在流行病学和生物库中普遍存在。虽然新陈代谢分析的大规模应用仍然是新颖的,但似乎综合生物标志物数据将有助于对各种疾病和能力预测疾病风险的能力,有可能转化为多种临床环境。

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