GUIDE TO THE EXPOSOME
Metabolomics Drives Exposome Research for Precision Medicine
6.0 Introduction
In the previous chapter we saw how exposome research findings impacted the health of large populations. Here, we will explore the role of exposome research in clinical decision-making at the level of the individual. Precision medicine aims to deliver individualized prevention and treatment strategies. Although genomics has laid the foundation for tailored treatments and preventative strategies, it explains only a fraction of disease risk. The remaining influence comes from our environment including everything we eat, breathe, touch, and how our bodies respond to these inputs, breathe, touch, and how our bodies respond. Metabolomics is a high-resolution biochemical lens that serves as a standalone tool or a critical component of holistic exposome analysis, enabling real-time insight into environmental influences. This chapter outlines the role of mass spectrometry-based metabolomics in advancing exposome research, highlights findings from four recent exposome-focused webinars, and explores current challenges, emerging tools, and institutional strategies charting the path forward.
6.1 Introduction: Precision Medicine Requires Understanding the Exposome
The promise of precision medicine is to tailor health interventions to the unique biology of individuals. While human genomics has unlocked tremendous insight, it accounts for less than 20% of chronic disease risk1. The remaining risk is environmental and is notoriously dynamic, variable, and difficult to capture. These environmental influences, collectively referred to as the exposome, include everything from pollutants, diet, stress, behavior, as well as endogenous processes such as inflammation and metabolism. Metabolomics, particularly using high-resolution mass spectrometry, enables researchers to profile thousands of small molecules in human biospecimens. These metabolites reflect both endogenous processes and exogenous exposures, offering a comprehensive snapshot of physiological state. Moreover, unlike genomics and self-reporting, which have been heavily relied upon up to this point and are largely static and biased, metabolomics is dynamic and responsive to change and is not susceptible to lapses in patient reporting, making it uniquely suited to exposome research and precision health2. In the next section we will discuss cases that demonstrate the suitability and value of metabolomics to exposome-focused precision health studies.6.2 Leading Research
6.2.1 Case Study 1: Dr. Jessica Lasky-Su- Metabolomics at the center of diet, therapeutic response and aging
Traditional methods of assessing environmental exposures, such as questionnaires or single-timepoint measurements, often fall short in capturing the biological impact of lifestyle and environmental factors. Dr. Lasky-Su’s team addressed this gap using untargeted metabolomics in large cohort studies. To quantify the cumulative biological burden of environmental exposures, her group developed OMICAge, a metabolomics-based aging clock that offers a dynamic and responsive measure of biological aging, outperforming static models such as epigenetic clocks (Figure 6.1)3. Building on this framework, Lasky-Su’s team analyzed metabolomic profiles in over 10,000 participants, demonstrating that metabolic signatures more accurately map well-defined diets (e.g., Mediterranean and DASH diets) than self- reported food frequency questionnaires (not published). This analysis revealed that healthy diet-associated metabolomic profiles corresponded with reduced biological age, while unhealthy patterns accelerated aging.
6.2.2 Case Study 2: Dr. Fredrik Bäckhed – Microbiome-Metabolome Dynamics
Dr. Bäckhed’s research underscores the microbiome as a central component of the exposome, functioning both as a sensor and mediator of environmental exposures. The microbiota is essential for normal host physiology, transforming external inputs, such as diet and pollutants—into bioactive metabolites that modulate host biology. This process of sensing, transforming, and translating exposures into health or disease outcomes explains why individuals with similar environmental exposures often exhibit divergent biological responses, which is driven by the interplay between host genetics and microbiome composition.
His and other groups have shown that microbiota-driven differences in the metabolome can predispose individuals to obesity and type 2 diabetes in both humans and gnotobiotic mice9–11. A key example is imidazole propionate, a metabolite generated by microbial conversion of dietary histidine, which directly impairs insulin signaling through activation of the p38γ–p62–mTORC1 pathway12. This mechanistic link between microbial metabolism and host insulin resistance has been demonstrated in both humans and gnotobiotic mouse models13. In large European cohorts (SCAPIS and EPIC-Norfolk) untargeted metabolomics profiling of over 500 plasma metabolites revealed that nearly one-third of metabolites associated with impaired glucose control were microbiome-derived13. Notably, individuals with elevated imidazole propionate exhibited greater susceptibility to insulin resistance and obesity, reinforcing the microbiome’s role in metabolic dysfunction.
Dr. Bäckhed’s team further demonstrated that while personalized interventions— such as diet and exercise—can shift these microbiome-metabolite signatures, the effectiveness of such interventions varies based on an individual’s baseline microbiota composition. Importantly, metabolomics outperformed metagenomics in predicting type 2 diabetes outcomes, achieving an area under the curve (AUC) of 0.89 versus 0.69, even when reduced to a minimal 32-metabolite signature (Figure 6.2)13.
These findings showcase the unique power of metabolomics to reveal actionable host-microbe interactions, further emphasizing the exposome’s critical role in advancing precision medicine.
6.2.3 Case Study 3: Dr. Russell Bowler – Accelerated Metabolic Aging in Chronic Obstructive Pulmonary Disease
Not all smokers develop emphysema or chronic obstructive pulmonary disease (COPD), a long-standing clinical gap in understanding individual susceptibility to environmental exposures. The biological mechanisms that predispose some individuals to severe lung disease, while others remain resilient despite similar exposures are not well defined. In these studies, plasma metabolomics identified early signatures of pathophysiological change—including disruptions in oxidative stress, lipid metabolism, and inflammation—that often preceded the onset of overt disease (Figure 6.3)14. Notably, these molecular signatures recapitulate pathways characteristic of biological aging, suggesting that susceptible individuals experience premature or accelerated aging at the molecular level. Furthermore, distinct metabolomic clustering patterns emerged that stratified patients based on clinical characteristics15.
These findings underscore the power of metabolomics to uncover subclinical biological responses to environmental exposures, enabling earlier detection and more precise risk stratification in COPD. Importantly, the identified signatures highlight several modifiable factors influencing COPD susceptibility and progression, including smoking cessation, oxidative stress mitigation, lipid metabolism regulation, inflammation control, and reduction of co-exposures such as air pollution, which present actionable targets for early intervention and personalized disease management.
6.2.4 Case Study 4: Drs. John Chambers & Dorrian Low – Dietary Intake in Multi-Ethnic Asian Population
East Asian populations are disproportionately affected by type 2 diabetes (T2D) and metabolic syndrome, often developing insulin resistance and cardiometabolic complications at lower body mass indices compared to Western populations16 . This disparity highlights a critical gap in understanding how environmental exposures, particularly diet, interact with internal biology to drive disease risk in high- vulnerability groups.
To address this challenge, Drs. John Chambers and Dorrain Yanwen Low applied untargeted plasma metabolomics in the HELIOS and SG100K cohorts, quantifying 1,055 metabolites to develop objective biochemical signatures of habitual diet17. Using machine-learning feature selection of 3 to 39 metabolites per panel, they were able to accurately predict intake of region-specific foods and beverages such as coffee, tea, fish, and rice (Figure 6.4).
These composite metabolite scores outperformed single biomarker and questionnaire-based methods in both accuracy and longitudinal reproducibility over ~322 days. Importantly, these metabolite-derived dietary signatures demonstrated stronger associations with key cardiometabolic outcomes, including insulin resistance (HOMA-IR), BMI, fat mass index, hypertension, and carotid intima-media thickness, compared to self-reported dietary data. Notable metabolites within these panels included lipid-like compounds, amino acid derivatives, and xenobiotic markers linked to region-specific foods. For example, betainised compounds (e.g., homostachydrine and tryptophan betaine) were associated with roti and idli, 4-hydroxychlorothalonil (fungicide residue marker) related to legumes and vegetables, and ergothioneine linked to mushroom intake.
This study exemplifies how metabolomics-based dietary exposome profiling can bridge the gap between environmental behaviors and internal metabolic risk, offering scalable tools for precision nutrition and public health strategies.
6.3 Methodological Considerations: Top- Down, Bottom-Up, and Integrated Models
Studying the exposome and its effects is inherently challenging, as exposures are chemically diverse, heterogeneous in origin, and can be intermittent or continuous. Addressing this complexity requires thoughtful study design and a multifaceted research approach. A well-rounded exposome investigation often begins with a carefully designed human cohort study aimed at discovery. Top-down approaches, such as wearable sensors, air quality monitors, and geospatial models, are used to capture external exposures in real time. Simultaneously, bottom-up strategies, particularly untargeted metabolomics, enable researchers to measure internal chemical signatures that reflect both exogenous exposures and endogenous biological responses, without prior assumptions.
These studies depend on large, diverse cohorts with rich environmental, clinical, and behavioral data, coupled with standardized biospecimen collection. Longitudinal sampling adds critical temporal resolution, allowing differentiation between acute and chronic exposures, as well as adaptive versus pathological responses. When paired with clinical outcomes, metabolomics can generate hypotheses about how exposures influence disease trajectories in real-world populations.
To test these hypotheses, in vitro systems and animal models provide controlled environments where exposures can be replicated, doses precisely manipulated, and biological pathways isolated to confirm causal mechanisms. These experimental systems offer a level of control rarely possible in human studies. When findings from these models align with metabolomic signatures observed in humans, the evidence strengthens for a true biological pathway linking exposure to disease. This convergence, or meet-in-the-middle design (see Chapter 4), offers a structured approach to bridge observational associations with mechanistic insight18. By identifying and validating exposure-related metabolic intermediates, researchers clarify the biological relevance of specific exposures and uncover actionable biomarkers that can inform early intervention and precision prevention strategies.
6.4 Summary: Exposure to Actionable Insight
Significant progress has been made in integrating established technologies and analytical frameworks to characterize the exposome in biologically interpretable and actionable ways. Among these, metabolomics provides a distinct advantage: it enables simultaneous detection of both exogenous exposures and the endogenous metabolic responses they trigger (capturing external influences and internal physiological adaptations). This ability to capture dynamic, real-time physiological changes positions metabolomics and, by extension, exposomics as a cornerstone of precision medicine. Of all omics technologies, metabolomics offers the most immediate and integrative insight into phenotype, reflecting the combined effects of genetics, environment, and lifestyle.
When embedded within well-designed studies, inclusive and representative cohorts, and supported by integrative multi-omics analysis, exposomics can uncover previously unrecognized disease risk determinants, refine early intervention strategies, and enhance individualized care. As institutional investments grow and interdisciplinary collaborations expand the field is poised for broader impact. Moving forward, the priority is not merely to generate more data, but to advance integration, accessibility, and application of exposomic insights into real-world public health and clinical challenges. Advances in systems biology and integrative analytics are enabling a unified exposome framework, linking external measurements with internal biological signatures to reveal complex exposure-disease relationships.
References
1. Rappaport, S. M. Genetic Factors Are Not the Major Causes of Chronic Diseases. PloS One 11, 0154387 (2016).
2. Suhre, K. et al. Matching Drug Metabolites from Non-Targeted Metabolomics to Self-Reported Medication in the Qatar Biobank Study. Metabolites 12, 249 (2022).
3. Chen, Q. et al. OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records. (2023) doi:10.1101/2023.10.16.562114.
4. Horner, D. et al. A western dietary pattern during pregnancy is associated with neurodevelopmental disorders in childhood and adolescence. Nat. Metab. 7, 586–601 (2025).
5. Mendez, K. M. et al. Exploring the Varied Clinical Presentation of Pediatric Asthma through the Metabolome. Am. J. Respir. Crit. Care Med. (2025) doi:10.1164/rccm.202407-1382OC.
6. Tran, D. T. et al. Plasma pharmacometabolomics of inhaled corticosteroid-related adrenal suppression in asthma. J. Allergy Clin. Immunol. 155, 1857–1865 (2025).
7. Contreras, N. et al. Multiomic Integration Analysis for Monitoring Severe Asthma Treated With Mepolizumab or Omalizumab. Allergy 80, 1899–1911 (2025).
8. Nopsopon, T. et al. Untargeted metabolomic analysis reveals different metabolites associated with response to mepolizumab and omalizumab in asthma. ERJ Open Res. 10, (2024).
9. Bäckhed, F., Manchester, J. K., Semenkovich, C. F. & Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl. Acad. Sci. U. S. A. 104, 979–984 (2007).
10. Meijnikman, A. S., Gerdes, V. E., Nieuwdorp, M. & Herrema, H. Evaluating Causality of Gut Microbiota in Obesity and Diabetes in Humans. Endocr. Rev. 39, 133–153 (2018).
11. Turnbaugh, P. J., Bäckhed, F., Fulton, L. & Gordon, J. I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008).
12. Koh, A. et al. Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1. Cell 175, 947-961.e17 (2018).
13. Wu, H. et al. Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat. Med. 31, 2222–2231 (2025).
14. Gillenwater, L. A. et al. Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules. Netw. Syst. Med. 3, 159–181 (2020).
15. Gillenwater, L. A. et al. Multi-omics subtyping pipeline for chronic obstructive pulmonary disease. PloS One 16, 0255337 (2021).
16. Chan, J. C. N. et al. Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA 301, 2129–2140 (2009).
17. Low, D. Y. et al. Metabolic variation reflects dietary intake in a multi-ethnic Asian population. (2023) doi:10.1101/2023.12.04.23299350.
18. Chadeau-Hyam, M. et al. Meeting-in-the-middle using metabolic profiling - a strategy for the identification of intermediate biomarkers in cohort studies. Biomark. Biochem. Indic. Expo. Response Susceptibility Chem. 16, 83–88 (2011).
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