Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)
Mary C. Playdon,
Amit D. Joshi,
Fred K. Tabung,
Susan Cheng,
Mir Henglin,
Andy Kim,
Tengda Lin,
Eline H. van Roekel,
Jiaqi Huang,
Jan Krumsiek,
Ying Wang,
Ewy Mathé,
Marinella Temprosa,
Steven Moore,
Bo Chawes,
A. Heather Eliassen,
Andrea Gsur,
Marc J. Gunter,
Sei Harada,
Claudia Langenberg,
Matej Oresic,
Wei Perng,
Wei Jie Seow,
Oana A. Zeleznik
Affiliations
Mary C. Playdon
Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT 84112, USA
Amit D. Joshi
Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
Fred K. Tabung
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA
Susan Cheng
Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
Mir Henglin
Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA 02115, USA
Andy Kim
Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA 02115, USA
Tengda Lin
Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
Eline H. van Roekel
Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands
Jiaqi Huang
Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
Jan Krumsiek
Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
Ying Wang
Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
Ewy Mathé
College of Medicine, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
Marinella Temprosa
Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
Steven Moore
Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
Bo Chawes
COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 1165 Copenhagen, Denmark
A. Heather Eliassen
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
Andrea Gsur
Institute of Cancer Research, Department of Medicine, Medical University of Vienna, 1090 Vienna, Austria
Marc J. Gunter
Section of Nutrition and Metabolism, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
Sei Harada
Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan
Claudia Langenberg
MRC Epidemiology Unit, Public Health, University of Cambridge, Cambridge CB2 1 TN, UK
Matej Oresic
Turku Centre for Biotechnology, University of Turku, 20500 Turku, Finland
Wei Perng
Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
Wei Jie Seow
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
Oana A. Zeleznik
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.