Metabolomics in epidemiologic research: challenges and opportunities for early-career epidemiologists
The application of metabolomics to epidemiologic studies is increasing.
Aim of Review
Here, we describe the challenges and opportunities facing early-career epidemiologists aiming to apply metabolomics to their research.
Key Scientific Concepts of Review
Many challenges inherent to metabolomics may provide early-career epidemiologists with the opportunity to play a pivotal role in answering critical methodological questions and moving the field forward. Although generating large-scale high-quality metabolomics data can be challenging, data can be accessed through public databases, collaboration with senior researchers or participation within interest groups. Such efforts may also assist with obtaining funding, provide knowledge on training resources, and help early-career epidemiologists to publish in the field of metabolomics.
KeywordsMetabolomics Epidemiology Early-career scientists Challenges Opportunities
All authors wrote the manuscript. All authors read and approved the manuscript.
E.H. van Roekel was financially supported by Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant programme (Grant No. 2016/1620) and the GROW School for Oncology and Developmental Biology. E. Loftfield was supported by the Intramural Research Program of the National Institutes of Health, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services. R.S. Kelly was supported by a Discovery Award from The US Department of Defense (Grant No. W81XWH-17-1-0533), and a grant from the US NIH (Grant No. 1R01HL123915-01). O.A. Zeleznik was supported by grants from the NIH (Grant Nos. CA087969, CA050385). K.A. Zanetti was supported by the Extramural Research Program of the National Institutes of Health, Division of Cancer Control and Populations Sciences, National Cancer Institute, Department of Health and Human Services.
Compliance with ethical standards
Conflict of interest
All authors declare that they do not have conflict of interest.
Research involving human and/or animal participants
This article does not contain any studies with human and/or animal participants performed by any of the authors.
- Alkawaa, F. M., et al. (2017). Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. Journal of Proteome Research, 17(1), 337–347.Google Scholar
- Kirwan, J. A., et al. (2018). Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for “precision Medicine and pharmacometabolomics task group"-the metabolomics society initiative). Clinical Chemistry, 64(8), 1158–1182.CrossRefGoogle Scholar
- Moore, S. C., et al. (2018). A metabolomics analysis of body mass index and postmenopausal breast cancer risk. Journal of National Cancer Institute, 110(6), 588–597.Google Scholar