The exposome, initially conceptualized by Wild in 2005,¹ represents the totality of environmental exposures experienced by an individual across their lifespan. The exposome complements genomic information by comprehensively incorporating external exposures (eg, environmental pollutants, diet, and lifestyle factors) and internal biological responses (eg, metabolomic profiles and inflammatory markers).² Although much attention has focused on genetic determinants of disease, environmental factors contribute to a majority of risks, with an estimated 70–90% of chronic disease risks attributable to environmental exposures.³ This highlights the need to assess lifelong environmental exposures to elucidate the complex interplay between genetics and environment in disease etiology. To translate the conceptual framework of exposome into empirical evidence, large-scale population studies and methodological innovations have been instrumental. Early exposome studies relied on large-scale epidemiological data, such as the Human Early Life Exposome (HELIX) project,⁴ which has systematically assessed early life environmental exposures and notably identified adverse impacts of diverse early life environmental exposures, including air pollution, environmental chemicals, and dietary patterns, on children’s developmental health outcomes.⁵ Building on this foundation, recent European initiatives such as the EXPANSE project have leveraged large population cohorts and advanced exposome analytics to integrate multi-omics data and digital health records. By linking environmental, lifestyle, and biological datasets, these projects aim to uncover how complex exposure mixtures shape molecular pathways and contribute to chronic disease development.⁶ These integrative strategies have provided stronger support for causal inference and a deeper understanding of biological mechanisms underlying the adverse impact of environmental factors on disease etiology. For example, utilizing epigenomic data, one research found prenatal tobacco exposure was associated with accelerated biological aging in adulthood.⁷ As the field progresses, there is a growing need for refined analytical tools and computational strategies to manage the scale and complexity of exposomic data and to support its application in precision public health.

Exposomic data are comprehensive in their nature. While the exposome also encompasses physical, behavioral, and social factors, the present commentary focuses specifically on the environmental chemical exposome.

Despite significant methodological progress, exposome research still faces key challenges. One of the most important limitations is the inability of current methods to fully capture the inherent complexity and variability of environmental exposures. Firstly, individuals are exposed to thousands of environmental exposures that fluctuate over time, space, intensity, and biological context. Capturing this dynamic and multifactorial exposure landscape with sufficient temporal resolution remains difficult, especially when relying on cross-sectional data or single-time point measurements.¹⁰ Secondly, a large proportion of chemical signals detected in biospecimens remain structurally unidentified, primarily due to limitations in existing chemical libraries and reference standards. This is especially true for emerging pollutants and their transformation products, which complicates interpretation, reduces consistency across studies, and introduces uncertainty into exposure estimations.^32,33 Thirdly, mechanistic biomarkers of environmental exposures, such as inflammation, oxidative stress, and metabolic disruption markers, may be influenced by inherent biological heterogeneities within the population, which complicates the interpretation of causal relationships with external exposures.³⁰

In addition to measurement difficulties, the interdisciplinary nature of exposome research (environmental science, analytical chemistry, epidemiology, and systems biology) also faces fundamental critical barriers related to data integration, standardization, and interpretation of biological meanings. Unified protocols and compatible data structures are needed to synthesize and interpret such data. Inconsistencies in metadata annotation, quality control procedures, and reporting standards hinder reproducibility and limit the potential for cross-study comparisons. Mechanistic understanding remains limited, and very few studies incorporate longitudinal designs, experimental validation or weight-of-evidence approaches to support causal interpretation.^24,34 Furthermore, there is no widely accepted framework for interpreting complex biological responses to environmental mixtures, which restricts the ability to translate internal signals into meaningful health insights.³³ Tackling these issues needs better coordination of data systems, stronger long-term cohort studies, and analysis approaches that are closely linked to biological mechanisms.

Future research needs to adopt a more integrated and harmonized approach. A key priority is improving chemical identification. Many detected environmental signals still lack structural annotation. Efforts should focus on expanding spectral databases, including more transformation products, and aligning mass spectrometry protocols across different studies to improve consistency and coverage.³⁵ Another important step is addressing the lack of temporal and contextual resolution in exposure data. This can be achieved by designing longitudinal studies that extensively collect repeated biological samples over time, as some exposome cohorts are beginning to conduct. The use of wearable devices and location-tracking technologies can help capture more detailed information about personal exposures at different life stages.^10,36 Moreover, artificial intelligence (AI) and exposome intelligence (EI) tools offer a transformative opportunity for scaling exposome science. EI refers to the use of AI-driven analytical frameworks to systematically identify, characterize, and prioritize environmental exposures across complex datasets.³⁷ Methods such as machine learning, network analysis, and causal modeling can be used to identify patterns, explore possible mechanisms, and improve predictions of health outcomes.³⁸ For example, AI-assisted molecular network analysis has already shown promise in identifying unknown environmental compounds and helping prioritize those that may require further investigations.^39,40 Finally, it is critical to build stronger connections between different scientific fields. Bringing together expertise from environmental health, data science, biology, and public health will be essential for training researchers who can interpret exposome data and apply it to real-world health problems. As the field moves toward a large-scale Human Exposome Project, similar in scope to the Human Genome Project, international cooperation, open data sharing, and stable funding will be necessary to realize its full impact on public health and disease prevention.³³

The exposome offers a valuable and comprehensive framework for understanding how environmental factors influence human health (Figure 3). Although methods for exposure assessment and data analysis have improved, researchers still face important challenges, including the limited ability to identify unknown chemicals, the difficulty of capturing exposure information over long periods, and the complexity of combining diverse datasets to uncover causal relationships. Addressing these gaps will require closer collaboration across disciplines, improved standardization of data collection and analysis, and the thoughtful use the application of novel tools such as wearable sensors and data-driven approaches. With continued progress, exposome research can go beyond identifying descriptive associations to understand how the total environment affects human health, which is critical for developing more effective disease prevention and health promotion strategies.