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Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.

In recent years, metabolomic analyses have been increasingly performed in studies with an epidemiological design. Since 2012, the National Cancer Institute has recognized the importance of supporting these efforts by strategically building resources and infrastructure to move this area forward. This review outlines those efforts, including building infrastructure, leveraging existing resources, establishing the COnsortium of METabolomics Studies (COMETS), and improving rigor and reproducibility.

Greater attention to social factors, such as race/ethnicity, socioeconomic position, and others, are needed across the cancer continuum, including breast cancer, given differences in tumor biology and genetic variants have not completely explained the persistent Black/White breast cancer mortality disparity. In this commentary, we use examples in breast cancer risk assessment and survivorship to demonstrate how the failure to appropriately incorporate social factors into the design, recruitment, and analysis of research studies has resulted in missed opportunities to reduce persistent cancer disparities. The conclusion offers recommendations for how to better document and use information on social factors in cancer research and care by (1) increasing education and awareness about the importance of inclusion of social factors in clinical research; (2) improving testing and documentation of social factors by incorporating them into journal guidelines and reporting stratified results; and (3) including social factors to refine extant tools that assess cancer risk and assign cancer care. Implementing the recommended changes would enable more effective design and implementation of interventions and work toward eliminating cancer disparities by accounting for the social and environmental contexts in which cancer patients live and are treated.

BackgroundQuality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities.Aim of reviewA present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry.Key scientific concepts of reviewCommunity engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a “living guidance” document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field.

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day ‘Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies’ held at the National Cancer Institute’s Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.

IntroductionThe Metabolomics Quality Assurance and Quality Control Consortium (mQACC) organized a workshop during the Metabolomics 2022 conference.ObjectivesThe goal of the workshop was to disseminate recent findings from mQACC community-engagement efforts and to solicit feedback about a living guidance document of QA/QC best practices for untargeted LC–MS metabolomics.MethodsFour QC-related topics were presented.ResultsDuring the discussion, participants expressed the need for detailed guidance on a broad range of QA/QC-related topics accompanied by use-cases.ConclusionsOngoing efforts will continue to identify, catalog, harmonize, and disseminate QA/QC best practices, including outreach activities, to establish and continually update QA/QC guidelines.

IntroductionDuring the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC–MS-based untargeted metabolomics.ObjectivesThe Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document.MethodsPresentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance.ResultsLive polling results and follow-up discussions offered a broad international perspective on QA/QC practices.ConclusionsCommunity input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers.

Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.

BackgroundThe application of metabolomics to epidemiologic studies is increasing.Aim of ReviewHere, we describe the challenges and opportunities facing early-career epidemiologists aiming to apply metabolomics to their research.Key Scientific Concepts of ReviewMany challenges inherent to metabolomics may provide early-career epidemiologists with the opportunity to play a pivotal role in answering critical methodological questions and moving the field forward. Although generating large-scale high-quality metabolomics data can be challenging, data can be accessed through public databases, collaboration with senior researchers or participation within interest groups. Such efforts may also assist with obtaining funding, provide knowledge on training resources, and help early-career epidemiologists to publish in the field of metabolomics.

BackgroundThrough the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.Aim of reviewTo understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.Key scientific concepts of reviewWe observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.