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🔍 Exploring Pathways of Environmental Influence with Linda Lernel 🌿🧠

A deeper understanding of the pathways through which an environmental mixture operates is crucial to designing effective interventions. Linda Lernel and her team present an innovative methodology for estimating the natural direct and indirect effects, as well as the controlled direct effects of an exposure to a complex mixture on an outcome through a mediating variable.

We implemented Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) co-exposures on the mediator, (2) co-exposures and mediator on the outcome, and (3) selected covariates in the mediator and/or outcome. Using posterior predictive distributions of the mediator and outcome, we simulated counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects.

Our simulation study demonstrates that when exposure-mediator and exposure-mediator-outcome relationships are complex, BKMR Causal Mediation Analysis outperforms current mediation methods. We applied our methodology to quantify the contribution of birth length as a mediator between in utero co-exposure to arsenic, manganese and lead, and children’s neurodevelopmental scores, in a prospective birth cohort in Bangladesh.

Among younger children, we found a negative (adverse) association between metal mixing and neurodevelopment. We also found evidence that length at birth mediates the effect of mixed metal exposure on neurodevelopment for younger children. If birth length were set at its 75th percentile, the detrimental effect of metal mixing on neurodevelopment is attenuated, suggesting that nutritional interventions to increase fetal growth, and thus birth length, could block potentially the detrimental effect of metal mixing on neurodevelopment. 🌐🔬 #ScientificResearch #EnvironmentalMediation #ChildDevelopment

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