Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS) †
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1,2,*, 3,4,5, 6,7,8
, 9, 10, 10, 2,11, 12, 13, 14, 15, 16, 17, 13, 18, A. Heather Eliassen 19,20, 21, 22, 23, 24,25, 26,27, 28,29, 30,31 and 19add
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, 9, 10, 10, 2,11, 12, 13, 14, 15, 16, 17, 13, 18, A. Heather Eliassen 19,20, 21, 22, 23, 24,25, 26,27, 28,29, 30,31 and 19
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1
Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT 84112, USA
2
Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
3
Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
4
Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
5
Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
6
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA
7
The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH 43210, USA
8
Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH 43210, USA
9
Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
10
Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA 02115, USA
11
Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
12
Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands
13
Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
14
Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
15
Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
16
College of Medicine, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
17
Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
18
COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 1165 Copenhagen, Denmark
19
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
20
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
21
Institute of Cancer Research, Department of Medicine, Medical University of Vienna, 1090 Vienna, Austria
22
Section of Nutrition and Metabolism, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
23
Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan
24
MRC Epidemiology Unit, Public Health, University of Cambridge, Cambridge CB2 1 TN, UK
25
The Francis Crick Institute, London NW1 1ST, UK
26
Turku Centre for Biotechnology, University of Turku, 20500 Turku, Finland
27
School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
28
Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
29
Life course epidemiology of adiposity and diabetes (LEAD) Center, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
30
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
31
Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
*
Author to whom correspondence should be addressed.
†
Disclaimer: Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.
Metabolites 2019, 9(7), 145; https://doi.org/10.3390/metabo9070145
Received: 10 June 2019
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Revised: 28 June 2019
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Accepted: 4 July 2019
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Published: 17 July 2019
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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.
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Keywords:
metabolomics; epidemiology; statistical analysis; reporting; analytical methods; data analysis; pre-processing
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MDPI and ACS Style
Playdon, M.C.; Joshi, A.D.; Tabung, F.K.; Cheng, S.; Henglin, M.; Kim, A.; Lin, T.; van Roekel, E.H.; Huang, J.; Krumsiek, J.; Wang, Y.; Mathé, E.; Temprosa, M.; Moore, S.; Chawes, B.; Eliassen, A.H.; Gsur, A.; Gunter, M.J.; Harada, S.; Langenberg, C.; Oresic, M.; Perng, W.; Seow, W.J.; Zeleznik, O.A. Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS). Metabolites 2019, 9, 145. https://doi.org/10.3390/metabo9070145
AMA Style
Playdon MC, Joshi AD, Tabung FK, Cheng S, Henglin M, Kim A, Lin T, van Roekel EH, Huang J, Krumsiek J, Wang Y, Mathé E, Temprosa M, Moore S, Chawes B, Eliassen AH, Gsur A, Gunter MJ, Harada S, Langenberg C, Oresic M, Perng W, Seow WJ, Zeleznik OA. Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS). Metabolites. 2019; 9(7):145. https://doi.org/10.3390/metabo9070145
Chicago/Turabian StylePlaydon, Mary C., 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. H. Eliassen, Andrea Gsur, Marc J. Gunter, Sei Harada, Claudia Langenberg, Matej Oresic, Wei Perng, Wei J. Seow, and Oana A. Zeleznik. 2019. "Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)" Metabolites 9, no. 7: 145. https://doi.org/10.3390/metabo9070145
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