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

Abstract

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.

Keywords: Mendelian randomization; amino acids; biomarkers; drug development; fatty acids; metabolomics; nuclear magnetic resonance; serum.

Figure 1.

Comparison of lipoprotein lipid and glucose quantification in an epidemiologic setting, using nuclear magnetic resonance (NMR) (2013) and routine clinical chemistry assays (y-axis) (n = 2,749 from the Avon Longitudinal Study of Children and Parents (ALSPAC) Mothers Cohort) (103). The correlation coefficients are 0.95 (A), 0.94 (B), 0.93 (C), 0.91 (D), and 0.96 (E). The lower concentration of low-density lipoprotein (LDL) cholesterol quantified by NMR than by the Friedewald approximation stems from the latter also containing intermediate-density lipoprotein cholesterol (104). The NMR-based LDL cholesterol refers specifically to cholesterol in the LDL particles with the sizes as defined in Web Figure 1. The correspondence of these measures varies slightly from cohort to cohort, but the correspondence is generally excellent between the clinical chemistry and the NMR for these measures. It is important to note that the comparisons illustrated here do not show strict analytic comparisons with samples undergoing identical processing and storage time, but rather indicate analytic consistency demonstrated in epidemiologic settings. No quantitative assessment of analytic correspondences is therefore made here. When it comes to potential clinical applications of metabolic profiling, more analytic and clinical testing is required, particularly with those metabolic measures that are intended to be used as part of diagnostic protocols. It is also to be expected that official accreditations of analytic and laboratory procedures will be a prerequisite for widespread clinical applications. HDL, high-density lipoprotein.

Figure 2.

Comparison of circulating fatty-acid quantification in an epidemiologic setting, nuclear magnetic resonance (NMR) and gas chromatography (y-axis) (n = 2,193 from the Cardiovascular Risk in Young Finns Study) (7). The correlation coefficients are 0.92 (A), 0.94 (B), and 0.94 (C). See note on Figure 1 for the analytic correspondence. DHA, docosahexaenoic acid; MUFA, monounsaturated fatty acid.

Figure 3.

Comparison of circulating β-hydroxybutyrate quantification in an epidemiologic setting, using nuclear magnetic resonance (NMR) and an enzymatic method (y-axis) (n = 56) (105). The correlation coefficient is 0.98. See note on Figure 1 for the analytic correspondence.

Figure 4.

Biomarker associations with cardiovascular event risk for selected polar metabolites quantified by both nuclear magnetic resonance (NMR) and mass spectrometry (MS). Filled squares indicate hazard ratios for incident cardiovascular disease, adjusted for age and sex, for 13,441 individuals (1,741 events) profiled by NMR. Open squares show the same biomarker associations in the Framingham Offspring Study (2,289 individuals and 466 events) profiled by MS. Circles indicate the biomarker associations compared for the same subset of 679 individuals (305 events) profiled both by NMR (filled circles) and MS (open circles). The figure is adapted from Würtz et al. (7). CI, confidence interval; HR, hazard ratio.