An epidemiological introduction to human metabolomic investigations

Amit D. Joshi, Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
Ali Rahnavard, Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
Priyadarshini Kachroo, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kevin M. Mendez, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Wayne Lawrence, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Sachelly Julián-Serrano, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA.
Xinwei Hua, Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China.
Harriett Fuller, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Nasa Sinnott-Armstrong, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Fred K. Tabung, The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA.
Katherine H. Shutta, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Laura M. Raffield, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Burcu F. Darst, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. Electronic address: bdarst@fredhutch.org.

Document Type

Journal Article

Publication Date

7-17-2023

Journal

Trends in endocrinology and metabolism: TEM

DOI

10.1016/j.tem.2023.06.006

Keywords

epidemiology; high-dimensional statistical methods; integrative omics; metabolites; metabolomic epidemiology; quality control

Abstract

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Department

Biostatistics and Bioinformatics

Link to Full Text

Share

COinS