Opening up the "Black Box": Metabolic phenotyping and metabolome-wide association studies in epidemiology

• Published in: Journal of clinical epidemiology Vol. 63; no. 9; pp. 970 - 979
• Main Authors: Bictash, Magda, Ebbels, Timothy M, Chan, Queenie, Loo, Ruey Leng, Yap, Ivan K.S, Brown, Ian J, de Iorio, Maria, Daviglus, Martha L, Holmes, Elaine, Stamler, Jeremiah, Nicholson, Jeremy K, Elliott, Paul
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.09.2010; Elsevier; Elsevier Limited
• Subjects: Automation; Biological and medical sciences; Biomarkers; Biomarkers - metabolism; Epidemiology; General aspects; Genetic diversity; Health risks; Humans; INTERMAP; Internal Medicine; INTERSALT; Medical sciences; Metabolic phenotyping; Metabolites; Metabolome - genetics; Metabolomics - methods; Metabolomics - standards; Metabonomics; Miscellaneous; Molecular Epidemiology - standards; NMR spectroscopy; Phenotype; Population levels; Public health. Hygiene; Public health. Hygiene-occupational medicine; Reproducibility of Results; Risk Assessment; Spectrum analysis; Statistical models; Studies; Biomarkers; INTERSALT; Metabolic phenotyping; NMR spectroscopy; INTERMAP; Metabonomics; Warning; Biological marker; Exploration; Epidemiology; Spectrometry; Association; Public health
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/j.jclinepi.2009.10.001

Abstract Background Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic, other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels. Objectives We describe here the main procedures in large-scale metabolic phenotyping and their application to metabolome-wide association (MWA) studies. Methods By use of high-throughput technologies and advanced spectroscopic methods, application of metabolic profiling to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification. Discussion Metabolic profiling at epidemiologic scale requires optimization of experimental protocol to maximize reproducibility, sensitivity, and quantitative reliability, and to reduce analytical drift. Customized multivariate statistical modeling approaches are needed for effective data visualization and biomarker discovery with control for false-positive associations since 100s or 1,000s of complex metabolic spectra are being processed. Conclusion Metabolic profiling is an exciting addition to the armamentarium of the epidemiologist for the discovery of new disease-risk biomarkers and diagnostics, and to provide novel insights into etiology, biological mechanisms, and pathways.