Quantitative serum NMR metabolomics in large-scale epidemiology a primer on -omic technology

Peter Wurtz, Antti J Kangas, Pasi Soininen, Debbie Lawlor, George Davey Smith, Mika Ala-Korpela

Research output: Contribution to journalArticle (Academic Journal)peer-review

114 Citations (Scopus)
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Detailed metabolic profiling in large-scale epidemiological 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 and has already been employed to profile 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 nuclear 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 etiological understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
Original languageEnglish
Article numberkwx016
Pages (from-to)1084-1096
Number of pages13
JournalAmerican Journal of Epidemiology
Issue number9
Early online date10 May 2017
Publication statusPublished - Nov 2017


  • metabolic profiling
  • biomarkers
  • metabolomics
  • serum
  • Mendelian randomization
  • drug development
  • amino acids
  • fatty acids
  • NMR

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