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The Environmental Contribution to Health and Disease: Analytical Tools for Monitoring and Diagnosis

Jul 2026 · Environments · Vol 13, pp. 416 · 0 citations · 316 references

TL;DR

The proposed framework highlights the need for multidisciplinary collaboration and advanced computational tools to address the complexity of exposome research and may support early disease detection, personalized prevention strategies, and the development of evidence-based environmental, occupational, and public health policies aimed at reducing the burden of environment-related diseases.

Abstract

Environmental exposures throughout life play a major role in shaping human health and contribute to the onset and progression of numerous chronic diseases, including neurodegenerative disorders and cancer. These effects arise from complex interactions between environmental pollutants, diet, lifestyle, occupation, genetic background, and other biological and socioeconomic factors. Although recent advances in omics technologies have substantially improved our understanding of the molecular mechanisms underlying these interactions, most studies continue to adopt reductionist approaches that fail to capture the complexity of the environment–health–disease continuum. We propose a comprehensive conceptual framework based derived from the existing literature for investigating lifelong environmental exposures through a multilevel and multidimensional strategy. Four complementary levels are defined: (i) molecular alterations characterized using ionomics, metallomics, proteomics, metabolomics, transcriptomics, genomics, and integrated multi-omics; (ii) tissue and organ alterations assessed by imaging technologies and radiomics; (iii) population-level variability investigated through epidemiological approaches (epidemiomics); and (iv) the integration and interpretation of heterogeneous datasets using artificial intelligence. Representative applications of these methodologies are discussed to illustrate their potential for identifying biomarkers, elucidating disease mechanisms, and improving exposure assessment. Particular attention is given to the integration of molecular, imaging, and epidemiological data to establish more robust causal relationships between environmental exposures and health outcomes. The proposed framework highlights the need for multidisciplinary collaboration and advanced computational tools to address the complexity of exposome research. Ultimately, this integrative approach may support early disease detection, personalized prevention strategies, and the development of evidence-based environmental, occupational, and public health policies aimed at reducing the burden of environment-related diseases.

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