GUIDE TO THE EXPOSOME
Challenges Associated with Studying the Exposome
3.0 Introduction
Exposomics, the study of the exposome using metabolomics and other omics technologies, is an emerging field of research that has the potential to provide a better understanding of the complex relationship between external and internal environmental exposures and health outcomes. However, generating and interpreting exposome data is not without challenges. For one, the exposome is large, complex, and unpredictable in nature. Secondly, it is difficult to accurately measure exposures across a lifetime, and internal factors, such as genetics, influence the outcome of these exposures, adding further complexity to data interpretation. Thirdly, current models designed to infer causal relationships between exposures and outcomes have limited ability to evaluate combinatorial exposures over time and thus necessitate integrative computational models to disentangle these cause-and-effect relationships. Here, we discuss these limitations and their current impact on exposome research. In the next chapter, we provide point-by-point solutions to these challenges, many of which are currently offered by Metabolon.
3.1 Inconsistency of Exposure
Exposures vary with a person’s age, gender, race, ethnicity, and socioeconomic status and are highly variable throughout a person’s lifetime, making it difficult to parse out the contributions of each exposure and the cumulative effects of exposure mixtures, on a person’s health (Figure 3.1). This challenge is confounded by the transient nature of many exposures, including dietary metabolites, airborne pollutants, and stress hormones, among others, which can fluctuate by the hour, day, or season. Even though all exposures influence health in some way, their effects may not manifest until years later. Furthermore, the exposure metabolites themselves may be rapidly metabolized, meaning that single-timepoint samples may not capture relevant exposures. One study demonstrated this point particularly well by showing that single-timepoint estimates of mercury concentrations in the blood of a fish-eating population did not accurately reflect the temporal variability of methylmercury and compared to 3 sample averages, did not reliably indicate longer-term exposure1. This concept was also demonstrated by a similar study, which showed that single-point measurements of per- and polyfluoroalkyl substances (PFAS) were relatively poor estimates of longer-term exposure2.
Figure 3.1: Challenges in Exposome Research
3.2 Coverage of the Exposome
As the study of small molecules, metabolomics is essential to exposome studies, but there are technical limitations related to exposome coverage that hamper the generation of high-quality data. For instance, the databases used to analyze high-resolution mass spectrometry data often contain relatively few metabolites related to environmental chemicals, which makes it difficult to annotate these species. Additionally, untargeted metabolomic methods, which are widely used in exposome research, tend to be less sensitive than targeted protocols, making these approaches susceptible to missing exposures to nano- or sub-nanomolar levels of environmental metabolites.
Differences in biochemical profiles across biological sample types and the structural diversity of exposome metabolites are other challenges that should be considered. Not all chemicals are detectable in all matrices, and metabolites encompass a wide range of structures and ion features. Furthermore, many metabolites exist as isomers, which have identical masses but different structures, making metabolite annotation and subsequent study reproducibility particularly challenging (see Chapter 5 for further discussion on metabolite annotation).
In practice, a single MS1 peak can match multiple compounds in public databases because many metabolites share the same accurate mass, and fragments can overlap between structurally related molecules. Unless biochemical standards are available to identify metabolites confidently, exposome features or metabolites identified in a study may be misclassified. Thus, accurate identification to the highest possible confidence is imperative for meaningful interpretation of metabolomics data. Beyond the hurdles to correctly identifying metabolites, the lack of transparent and standardized annotation processes makes it difficult to reproduce data from different platforms.
Metabolon’s analytical tools and data analysis software offer viable solutions to most of these limitations, which are discussed in detail in Chapter 4.
3.3 Understanding the biological Response to Exposure
As mentioned above and in other chapters of this book, the vast and dynamic nature of the exposome along with individually based variables that influence the impact of exposure on outcome, are two of the biggest hurdles to generating high-quality data in exposome studies and in turn, drawing meaningful conclusions from those findings. Although it is impossible to account for every single variable in exposome studies, many variables can be recognized and their outcomes identified if: 1) the study population is large, 2) the study is conducted over a reasonably long period of time, and 3) relevant clinical data are collected alongside metabolomics data. Studies aimed at understanding mixed environmental exposures in various populations demonstrate this point nicely10,11. The challenges we then face are analyzing the plethora of samples that result from large population-based studies and interpreting those findings efficiently and accurately. Biochemical pathway analysis is highly complex, and it becomes more complex as more variables are considered. Addressing these challenges requires sophisticated analytical tools and support for large-scale sample analysis.3.4 Sample Collection: Timing and Matrix
Accurately interpreting exposome data is also complicated by the sheer number and varying magnitudes of potential exposures a person may encounter throughout their lifetime. Some exposures, such as air and water pollution, are notoriously difficult to measure because, although usually chronic in nature, they tend to be present at very low levels. On the other hand, some exposures, such as pesticides, may be limited to acute exposures and/or metabolized rapidly and therefore not readily detected in single-timepoint samples.
Limitations inherent to sample matrices themselves also deserve consideration. Blood and urine samples are excellent reflections of systemic exposures; however, chemicals that are sequestered, metabolized or act within tissues may be missed. A comprehensive exposome assessment often requires a multi-matrix approach. Lipophilic compounds are especially susceptible to uptake by tissues due to their hydrophobic nature. Additionally, as the site of detoxification, the liver also serves as a potential reservoir of exposome metabolites. Understanding the biological context of the sample matrix is imperative to accurate interpretation of the data. With the appropriate metabolomic study design, the time-varying exposures, biological response dynamics, and disease progression effects can be assessed in exposome studies.
3.5 Limited Tools
Many of the approaches used to study the exposome, especially -omics techniques, yield large and complex datasets that require sophisticated analytical techniques that leverage a variety of computational tools. It is also challenging to interpret genomic, transcriptomic, proteomic, and metabolomics datasets, particularly in the context of one another, because the relationship between the data and biological function is not always clear. Newer technologies such as omics, sensors, and geographic or spatial information are facilitating a better understanding of the exposome, yet challenges analyzing and interpreting large swaths of data remain. In the next chapter, we discuss Metabolon’s solutions to common limitations of today’s metabolomics tools.
3.6 Exposome- Specific Equity Gaps
One limitation of exposome research that is often overlooked is the equity gap in study populations. Minorities and high-risk populations have historically been, and unfortunately still are, underrepresented in scientific and clinical research12,13. Sadly, exposome studies are no exception14. In some cases, populations at an economic disadvantage are most impacted by specific environmental exposures, including toxic compounds and pollutants, yet these populations are often least represented. Biases in environmental epidemiology prevent the full understanding of how various exposures and exposure mixtures influence health outcomes. For this reason, ensuring that study designs include populations that are most relevant to the exposure being studied, regardless of other factors, is highly important for the future of exposome research. Along these lines, the underrepresentation of certain populations has also influenced reference levels for biomonitoring initiatives. Several studies have shown that baseline metabolite levels differ between ethnic groups15-19. This information is pertinent to accurately interpreting the effects of exposures on health, which further demonstrates the importance of improving minority representation in future exposome studies. Aside from the need for better representation, there are challenges associated with addressing this problem that should be considered. Establishing large- scale cohorts for population-based studies is time-consuming and expensive. Furthermore, ensuring adequate participation and retention among underrepresented populations has proven especially difficult owing to language barriers20,21, cultural barriers22,23, cultural stigma and misconceptions24,25, socioeconomic and logistical barriers26, and a lack of trust in researchers27,28. Addressing this complex challenge will require careful strategies consistently implemented over time, and efforts are currently underway by many scientists and industry stakeholders, including Metabolon, through our support of inclusive study designs and community-based research initiatives.References
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2. Bartell SM, Purdue MP, Rhee J, Nost TH, Rusiecki J, Steenland K. How well does a single blood sample represent long-term exposure for epidemiological studies of PFOA among men in the general population? Environ Int. Oct 2024;192:109056. doi:10.1016/j.envint.2024.109056
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