![]() ![]() However, metabolites are known to be affected by preanalytical conditions such as biospecimen collection, processing, and storage conditions therefore, experimental processes should be consistently applied across all biospecimens ( 15). Biospecimens that are typically collected in epidemiologic studies are suitable for metabolomics analysis ( 10, 14). Studies that use prospective sampling allow the assessment of temporal relationships ( 13). ![]() Case–control studies also involve biospecimen collection at the time of diagnosis, allowing for stronger metabolite–disease associations ( 11). Case–control and cross-sectional studies allow researchers to glean potential metabolomic differences by comparing individuals by disease or exposure status ( 12). Metabolomic epidemiology, as defined by Lasky-Su and colleagues ( 10) is “the field of scientific enquiry involving the systematic use of epidemiological methods and principles to study population-based variation in the human metabolome as it associates with health-related outcomes or exposures.” Case–control, cross-sectional, prospective cohort, and nested study designs are common types of metabolomic epidemiology studies ( 10–12). In contrast, NMR-based platforms provide detailed structural information on fewer metabolites and are nondestructive and fully quantitative ( 5). MS-based platforms have the advantages of broader metabolite coverage and higher sensitivity compared with NMR, but they are destructive to the sample, and technical reproducibility is variable ( 5). To detect metabolites in a sample, commonly used metabolomic platforms include mass spectrometry (MS)–based and nuclear magnetic resonance (NMR)–based techniques. Additionally, there are targeted analyses, which quantify a smaller number of predefined metabolites that are related in function and class ( 4). Semi-targeted studies profile hundreds of metabolites whose identity is defined from a range of chemical classes and metabolic pathways before experimentation ( 4). ![]() Untargeted studies aim to detect as many metabolites as possible using a global approach, where there is no a priori metabolite information leading to data acquisition ( 4). Two main analytic approaches are used in these studies: untargeted and semi-targeted profiling. It can be applied to estimate disease risk, elucidate biological mechanisms, and identify biomarkers for disease diagnosis and prognosis. Metabolomics has been shown to be a powerful tool for studying human health and biology. The emergence of the field can be traced to 1998, when the term “metabolome” was first introduced by Oliver and colleagues ( 3). Metabolomics is an “omics” approach focused on the large-scale analysis of the metabolome, the set of metabolites within a biological system ( 1, 2). This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. Studies were geographically diverse, including countries in Asia, Europe, and North America 27.3% of studies reported on participant race, the majority reporting White participants. Most studies used a nested case–control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography–tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. ![]() This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. An increasing number of cancer epidemiology studies use metabolomics assays. ![]()
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