Metabolomic epidemiology offers insights into disease aetiology

Harriett Fuller, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Yiwen Zhu, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Jayna Nicholas, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Haley A. Chatelaine, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA.
Emily M. Drzymalla, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Afrand K. Sarvestani, Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
Sachelly Julián-Serrano, Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA.
Usman A. Tahir, Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Nasa Sinnott-Armstrong, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Laura M. Raffield, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Ali Rahnavard, Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
Xinwei Hua, Department of Cardiology, Peking University Third Hospital, Beijing, China.
Katherine H. Shutta, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Burcu F. Darst, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. bdarst@fredhutch.org.

Document Type

Journal Article

Publication Date

10-1-2023

Journal

Nature metabolism

Volume

5

Issue

10

DOI

10.1038/s42255-023-00903-x

Abstract

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Department

Biostatistics and Bioinformatics

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