Beyond genomics: understanding exposotypes through metabolomics

Abstract

BackgroundOver the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.Main textMetabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.ConclusionsComparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

Metabolic Signatures of the Exposome—Quantifying the Impact of Exposure to Environmental Chemicals on Human Health

“…It should be noted, however, that there is a risk for obtaining false positive exposome-health associations due to the complex correlation structure of the exposome. It is very challenging (1) to efficiently untangle the exposures that causally impact specific health outcomes from spuriously associated exposures and (2) to identify synergistic effects between exposures. Therefore, it is crucial to give a careful consideration to both in the selection of appropriate statistical methods as well as to the interpretation of the results [35].…”

Metabolomics: Basic Principles and Strategies

“…Clinical biomarkers and different metabotypes of disease severity correlated to exposures [72], and biological outcomes [73] have been studied and identified through metabolomics profiling, MWAS, and metabolomics fingerprinting and footprinting techniques in individuals and populations which will enable precision medicine and public healthcare [74][75][76][77][78][79]. Studies moving from genome-wide association studies (GWAS) to metabolome-wide association studies (MWAS) were first described in 2008 as "environmental and genomic influences to investigate the connections between phenotype variation and disease risk factors" [78][79][80]. Rattray and colleagues suggested exposotypes on single individual phenotypes and populations, in epidemiologic research, and disease risk using metabolome-wide association studies and impacts on precision medicine [79].…”

Lipidomic-Based Advances in Diagnosis and Modulation of Immune Response to Cancer

“…Following the discovery of the structure of DNA in 1953 [ 1 ], increasingly efficient technologies for the study of the whole genome (genomics) have enabled assessments of genome-based pathologies in large population cohorts [ 2 ]. However, since a broad number of factors, including environment, diet or lifestyle, are important in the etiology of diverse diseases such as cancer, a high-dimensional biological approach appears to be required [ 3 ]. A multi-omics/systems-level approach, which encompasses the combined analysis of data from genomics, RNA transcription (transcriptomics), proteins/peptides (proteomics) and metabolites (metabolomics), enables one to overlay gene information onto a complementary understanding of accrued molecular mechanisms [ 4 ].…”