Integrating omics and artificial intelligence in pediatric environmental health: tools, challenges, and cohort-based...

Early childhood is a critical developmental period during which exposure to multiple environmental chemicals is common and increasingly recognized as an important determinant of long-term health. However, conventional risk-assessment methods are often poorly equipped to address the complexity, variability, and interactive effects of combined environmental exposures.

This mini-review examines how advances in omics technologies-particularly metabolomics, epigenomics, and transcriptomics-have enhanced our ability to characterize biological responses to complex environmental chemical mixtures. It also explores how artificial intelligence (AI) and machine learning (ML) tools can improve understanding of the health impacts of environmental mixtures in children.

This review summarizes findings from international and regional cohort studies, including those from Saudi Arabia, that link chemical mixtures to early molecular alterations in metabolic, epigenetic, and gene-expression pathways (e.g., DNA methylation changes). These omics-based signatures often emerge before clinical symptoms and have been associated with neurodevelopmental, endocrine, and cardiometabolic outcomes. The review also discusses key methodological challenges, including small sample sizes, variability in analytical platforms, and the limited availability of validated pediatric biomarkers, and highlights strategies for cross-cohort harmonization and multi-omics data integration.

Integrating omics technologies with AI provides a powerful framework for predictive and mechanistic research in pediatric environmental health. This convergence supports the identification of early biomarkers of susceptibility, the development of exposomic risk-prediction models, and the design of precision prevention strategies aimed at reducing early-life exposure to harmful environmental chemical mixtures.

Highlights how integrating omics technologies with artificial intelligence and machine learning (AI/ML) improves characterization of environmental exposures and their underlying biological mechanisms. Synthesizes evidence from international pediatric cohorts, including studies from underrepresented regions, offering both global and regional perspectives. Provides a translational framework linking molecular biomarkers, exposure science, and prevention strategies to inform policies protecting children from complex environmental exposures.