Toward a more holistic approach to the study of exposures and child outcomes
Creators: name: Lester, Barry M
name: Camerota, Marie
name: Everson, Todd M
name: Shuster, Coral L
name: Marsit, Carmen J
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Space Science
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Science Policy

description: <b>Aim:</b> The current work was designed to demonstrate the application of the exposome framework in examining associations between exposures and children's long-term neurodevelopmental and behavioral outcomes. <b>Methods:</b> Longitudinal data were collected from birth through age 6 from 402 preterm infants. Three statistical methods were utilized to demonstrate the exposome framework: exposome-wide association study, cumulative exposure and machine learning models, with and without epigenetic data. <b>Results:</b> Each statistical approach answered a distinct research question regarding the impact of exposures on longitudinal child outcomes. Findings highlight associations between exposures, epigenetics and executive function. <b>Conclusion:</b> Findings demonstrate how an exposome-based approach can be utilized to understand relationships between internal (e.g., DNA methylation) and external (e.g., prenatal risk) exposures and long-term developmental outcomes in preterm children. Environmental and psychosocial exposures have been recognized as important predictors of chronic disease risk and neurodevelopmental outcomes. The exposome is an established conceptual model that systematically examines the combined effects of biological, psychological, environmental and social exposures on health, well-being and development. This work was designed to apply the exposome framework to children's behavioral and neurodevelopmental outcomes and provide a roadmap for statistical approaches to assessing exposure-outcome relationships. Data was collected from a cohort of very preterm infants and included pre- and peri-natal exposure variables and behavioral and neurodevelopmental outcomes. Genomic DNA was collected from neonates for genome-wide DNA methylation analysis. Application of the exposome framework was demonstrated via three statistical approaches: exposome-wide association study, cumulative exposure models and random forest machine learning models. These techniques were then expanded to consider the joint role of exposures and epigenetics as predictors of child neurobehavioral outcomes. The results demonstrated the ability of an exposome-wide association study model to identify a cluster of exposure variables associated with child executive function. These findings were corroborated by findings from the cumulative exposure and machine learning models, suggesting that perinatal exposures predicted child executive function. Joint models showed independent and, potentially, interactive effects of exposures and epigenetic patterns on outcomes. These findings demonstrate the utility of the exposome framework for more holistically studying associations between exposures and child health and neurobehavioral outcomes.