Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

• Main Authors: 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
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Metabolites
• Subjects: metabolomics; epidemiology; statistical analysis; reporting; analytical methods; data analysis; pre-processing
• Online Access: https://www.mdpi.com/2218-1989/9/7/145

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.