Advancing translational exposomics: bridging genome, exposome and personalized medicine

Abstract

Understanding the interplay between genetic predisposition and environmental and lifestyle exposures is essential for advancing precision medicine and public health. The exposome, defined as the sum of all environmental exposures an individual encounters throughout their lifetime, complements genomic data by elucidating how external and internal exposure factors influence health outcomes. This treatise highlights the emerging discipline of translational exposomics that integrates exposomics and genomics, offering a comprehensive approach to decipher the complex relationships between environmental and lifestyle exposures, genetic variability, and disease phenotypes. We highlight cutting-edge methodologies, including multi-omics technologies, exposome-wide association studies (EWAS), physiology-based biokinetic modeling, and advanced bioinformatics approaches. These tools enable precise characterization of both the external and the internal exposome, facilitating the identification of biomarkers, exposure-response relationships, and disease prediction and mechanisms. We also consider the importance of addressing socio-economic, demographic, and gender disparities in environmental health research. We emphasize how exposome data can contextualize genomic variation and enhance causal inference, especially in studies of vulnerable populations and complex diseases. By showcasing concrete examples and proposing integrative platforms for translational exposomics, this work underscores the critical need to bridge genomics and exposomics to enable precision prevention, risk stratification, and public health decision-making. This integrative approach offers a new paradigm for understanding health and disease beyond genetics alone.

Fig. 1

Integrating exposomics and genomics to inform treatment strategies. Environmental exposures (lifestyle, social, chemical, and ecological factors) and genetic factors lead to heterogeneity across populations. In the absence of individualized data, conventional treatment strategies may produce variable responses, with some individuals experiencing suboptimal outcomes. Utilizing exposomic and genomic data to guide treatment strategies can improve efficacy and promote optimal health outcomes by accounting for inter-individual differences

Fig. 2

Concept of an exposomics platform. The green arrow indicates that we can move from internal exposure data to external exposure data, the red arrow indicates the translation of aggregate and cumulative external exposure to internal exposure; PBBK modelling is the (bidirectional) link between external and internal exposure. Personal sensors contribute to exposure assessment, and they can provide information related to ancillary exposure data as well (i.e. heart rate, intensity of activity, location etc.), not only environmental exposure data. The upper line includes also monitoring and chemical analysis data, but all of them related to environment