Abstract
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.
Highlights
Recent advances in high-throughput methods to characterize the human metabolome present an unprecedented opportunity to strengthen epidemiological research and broaden its scope.Metabolomics is being utilized to shed light on disease etiology through objective biomarkers of exposures that are otherwise fraught with measurement error; to refine or complement our current methods of phenotypic assessment; to understand biological pathways linking exposures to health outcomes; identify early onset disease; and subtype diseases with heterogeneous etiologies [1].Metabolomics is the comprehensive characterization of small molecules present in biospecimens such as plasma, urine, and stool
Consortium of Metabolomics Studies (COMETS) cohort representatives to gain insights into the strategies and procedures used for analyzing metabolomics data, including data pre-processing, analysis, and reporting of results
We conducted a survey of 47 participating COMETS cohort representatives to gain insights into the strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide
Summary
Recent advances in high-throughput methods to characterize the human metabolome present an unprecedented opportunity to strengthen epidemiological research and broaden its scope.Metabolomics is being utilized to shed light on disease etiology through objective biomarkers of exposures that are otherwise fraught with measurement error; to refine or complement our current methods of phenotypic assessment; to understand biological pathways linking exposures to health outcomes; identify early onset disease; and subtype diseases with heterogeneous etiologies [1].Metabolomics is the comprehensive characterization of small molecules present in biospecimens such as plasma, urine, and stool. Metabolomics has shown success in screening newborns for inborn errors of metabolism, for identifying candidate biomarkers for early disease detection, for some diseases like diabetes and cancer [3,4], for understanding disease mechanisms [5], and has been used to develop better measures of disease risk factors like smoking, diet, and obesity [6,7,8] As this emerging technology is increasingly incorporated into disease research, including epidemiological studies, a bottleneck to advancing the field is the complexity and lack of standard protocols or best practices for analyzing metabolomics data [9]. The purpose of this study was to summarize current practices of investigators participating in the international
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