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

被引：391

作者：

Wurtz, Peter ^{[1
,2
,3
]}

Kangas, Antti J. ^{[1
,2
,3
]}

Soininen, Pasi ^{[1
,2
,3
,4
]}

Lawlor, Debbie A. ^{[5
,6
]}

Smith, George Davey ^{[5
,6
]}

Ala-Korpela, Mika ^{[1
,2
,4
,5
,6
]}

机构：

[1] Univ Oulu, Computat Med, Fac Med, Aapistie 5A,POB 5000, FI-900141 Oulu, Finland

[2] Univ Oulu, Bioctr Oulu, Oulu, Finland

[3] Brainshake Ltd, Helsinki, Finland

[4] Univ Eastern Finland, Sch Pharm, NMR Metabol Lab, Kuopio, Finland

[5] Univ Bristol, Sch Social & Community Med, Bristol, Avon, England

[6] Univ Bristol, Med Res Council, Integrat Epidemiol Unit, Bristol, Avon, England

来源：

AMERICAN JOURNAL OF EPIDEMIOLOGY | 2017年 / 186卷 / 09期

基金：

英国惠康基金; 英国医学研究理事会; 芬兰科学院;

关键词：

amino acids; biomarkers; drug development; fatty acids; Mendelian randomization; metabolomics; nuclear magnetic resonance; serum; GENOME-WIDE ASSOCIATION; MENDELIAN RANDOMIZATION; LIPOPROTEIN SUBCLASSES; H-1-NMR SPECTROSCOPY; CARDIOVASCULAR RISK; INSULIN-RESISTANCE; GENETIC INFLUENCES; DNA METHYLATION; AMINO-ACIDS; FATTY-ACIDS;

D O I：

10.1093/aje/kwx016

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

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.

引用

页码：1084 / 1096

页数：13

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