exposome
What is the exposome?
Who coined the term exposome and when?
How does the exposome relate to genetics and epigenetics?
What tools and methods are used to study the exposome?
exposome, the complete set of environmental exposures throughout a person’s life, as well as how these exposures contribute and relate to disease and overall health. Both the concept and the term exposome were coined in 2005 by epidemiologist Christopher Wild; it may be a portmanteau of expose and the Latin suffix -ome (meaning “entirety of”).
The study of the exposome, known as exposomics, forms a critical link between genetics and epigenetics. Genetics provides the blueprint of an individual’s DNA, while the exposome represents the factors that interact with that blueprint, influencing how genes are expressed and potentially contribute to disease. Epigenetics, the study of changes in gene expression that are due to chemical modifications to DNA, acts as a crucial bridge mediating the interaction between the exposome and genetic factors. Building deeper knowledge of these complex interactions can not only facilitate better understanding of the causes of disease but also aid in the development of targeted preventive treatments for disease, especially those that are noncommunicable, such as heart disease or cancer.
The study of the exposome is inherently interdisciplinary, integrating aspects of environmental science, epidemiology, computational science, artificial intelligence (AI), and molecular medicine. The interdisciplinary nature of the concept is its strength, allowing for a breadth of information to be collected about three different aspects of the exposome: internal factors, which are unique to the individual; specific external factors, which include lifestyle factors and occupational (work-related) exposures; and general external factors, which encompass social and population-level categories such as education level and economic status.
Because of the variety of elements that are considered when understanding the exposome, some researchers may use modifiers to denote a focus on specific subsections. Examples include the airborne exposome, when referring to atmospheric exposure profiles, or the microbial exposome, which refers to the microorganisms that live in humans and their effects on human health.
Tools to examine the exposome
A wide variety of tools and methods are used to study the exposome. For example, wearable technology, sensors, and mobile devices can be used to provide quick updates about physical activity and air conditions and can track location, allowing for geospatial monitoring and mapping of exposures.
Other methods for examining the exposome include exposome association studies, which are built upon geospatial data and questionnaires that provide qualitative data, including essential information about lifestyle, diet, medications, occupation, and residence. Longitudinal studies can improve the accuracy and thoroughness of this information, as they are designed to track individuals at different ages and assess how exposures across the lifespan influence health outcomes. These studies are especially useful for combining biological, environmental, and health data for multifactorial analysis.
To provide insight into biological responses to exposures, scientists use biological sampling and a variety of specific omics technologies. For one, metabolomics focuses on small molecules, such as those found in blood, saliva, urine, and tissues. To analyze these samples, high-resolution mass spectrometry serves as a powerful tool to detect and quantify thousands of environmental chemicals and metabolites, even at very low levels. In addition, epigenomics is used to better understand DNA methylation and histone alterations, while transcriptomics can be used to study changes in gene expression through analysis of RNA transcripts. Proteomics may be employed to analyze proteins that are formed from these transcripts.
Statistical methods for understanding the exposome include an extensive assortment of regression-based models, among them deletion-substitution-addition (DSA). Taken alongside computational models and bioinformatics, these methods are able to integrate and determine associations in large, elaborate datasets, such as the human exposome, which can have hundreds of thousands of data points.
In addition, some researchers think that machine learning (ML) and AI could aid analysis of interactions that encompass the exposome. One reason is that AI can quickly analyze what may otherwise appear to be disparate data. ML techniques, including network analysis, can clarify relationships between specific exposures and diseases.
- Related Topics:
- environmental health
Advancements in exposomics
Given the complexity of the exposome, there are many entry points for research. For example, researchers can sample the environment and then survey people living within that area to hypothesize an effect of certain exposures. This is referred to as the bottom-up approach. Alternatively, the top-down approach takes the reverse course, first examining a group with common exposures measured through biospecimen collection or surveying and then attempting to identify common biological responses. Researchers use a combination of these approaches and others to build a comprehensive understanding of the exposome that provides confidence in causality between certain exposures and their effects on human health.