High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Abstract

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science.

Keywords: exposome; metabolomics; nursing; preterm birth.

Figure 1.

Work flow of liquid chromatography coupled with mass spectrometry (LC-MS) metabolomics experiments. The biological question dictates what samples to use. Samples are extracted for small metabolite molecules, which undergo chromatographic separation before being injected into and analyzed in a mass spectrometer. The feature extraction step obtains quantitative information regarding metabolite features from the LC-MS data, where a single metabolite may correspond to multiple features (peaks). Statistical analysis and bioinformatics (metabolic pathways and networks, data integration) are performed to address the biological question.

Figure 2.

Example coverage of human metabolism by a high-resolution metabolomics experiment. From a set of human plasma samples, we generated the data set via a Thermo Fisher Q Exactive mass spectrometer, with reverse-phase C18 liquid chromatography and negative ionization. The experiment produced 35,708 metabolite features, which were then matched to the KEGG pathway database within 2 parts per million mass accuracy. Data from this experiment match to 879 metabolites in the KEGG human metabolic map. Each black dot represents a tentatively matched metabolite. Light gray dots are from other species and are not considered as part of human metabolism by KEGG. Complemented by other ionization methods and chromatographic tools, the practical coverage will be further increased.

Figure 3.

High-resolution metabolomics cover the chemical categories in the Human Metabolome Database (HMDB). The data set described in Figure 2 was matched to 26,104 HMDB entries, within 2 part per million mass accuracy. This figure demonstrates the potential of this platform to capture both endogenous metabolites and exposure chemicals in the same samples.