A Bayesian Approach to Assess Prenatal Exposures to Metal Mixtures and Associations with Latent Patterns of Neurodevelopmental Trajectories

Abstract

Due to the rise of exposomic research, there is increasing interest in complex mixtures which reflect real life exposure scenarios. Currently, there is limited literature on how exposure to metal mixtures affect neurodevelopmental trajectories. Furthermore, the few existing studies typically do not account for heterogeneity of response to exposure among children, and instead assume that exposure to metal mixtures impacts each child’s health to the same degree. In reality, there may be latent subgroups of children with distinct neurodevelopment trajectories, and the impacts of exposure to metal mixtures may differ across these subgroups.We investigate these questions by developing a two-stage statistical model combining growth mixture modeling and Bayesian kernel-learning methods. This novel statistical approach first identifies latent subgroups with growth mixture modeling using time-varying neurodevelopment scores. In a second step, we use a new statistical model called Bayesian varying coefficient kernel machine regression (BVCKMR), to separately analyze each latent subgroup to determine how metal mixture exposures at a single exposure window affect neurodevelopmental trajectories, while adjusting for covariates. BVCKMR can handle dozens of metal mixture components, allowing us to visualize interactions between up to four metal mixture components, while existing studies typically only focus on pairwise interactions.We illustrate this approach by applying it to the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) prospective cohort study based in Mexico City. We will present the association between prenatal metal mixtures and child neurodevelopment, as assessed using the Bayley Scales of Infant and Toddler Development. We demonstrate that the relative importance and interaction patterns between metal mixture components differs by latent class, and show that latent classes may be differentially vulnerable to metal mixtures.