Design and Analysis of Metabolomics Studies in Epidemiological Research: A Primer on -Omic Technologies
- ↵*Correspondence to Dr. John P. A. Ioannidis, Stanford Prevention Research Center, Stanford University School of Medicine, 1265 Welch Rd, Medical School Office Building Room X306, Stanford, CA 94305 (e-mail: jioannid@stanford.edu).
- Abbreviations: MS, mass spectrometry; NMR, nuclear magnetic resonance; PLS, partial least squares regression.
- Received January 31, 2014.
- Accepted May 7, 2014.
Abstract
Metabolomics is the field of “-omics” research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field.
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- © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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