Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures

Abstract

Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures. KEYWORDSBayesian inference, chemical mixtures, child health, longitudinal data, machine learning, neurodevelopment 4680

Associations of a Metal Mixture Measured in Multiple Biomarkers with IQ: Evidence from Italian Adolescents Living near Ferroalloy Industry

“…However, the epidemiologic literature on interactions between Cu and either Mn or Pb is sparse. One recent epidemiologic study of children in Mexico City used BKMR and reported a similar interaction between Cu and Pb and neurobehavior, where higher concentrations of Pb reduced the beneficial effect of Cu on infant neurodevelopment ( Liu et al. 2018 ).…”

“…However, the epidemiologic literature on interactions between Cu and either Mn or Pb is sparse. One recent epidemiologic study of children in Mexico City used BKMR and reported a similar interaction between Cu and Pb and neurobehavior, where higher concentrations of Pb reduced the beneficial effect of Cu on infant neurodevelopment (Liu et al 2018). Despite several differences in study design, including exposure timing (prenatal versus adolescent), age at outcome assessment (6-24 months versus adolescence), and sources of contaminants (unknown versus ferroalloy industry), this finding of a Cu-Pb interaction using BKMR is consistent with ours.…”

“…Yet other metals in the ferroalloy mixture, such as Pb and Cu, are also known to be neurotoxic (Bulcke et al 2017;Caito and Aschner 2017). Furthermore, prior studies have shown interactions between some of these metals on neurodevelopment (Claus Henn et al 2012;Gorell et al 1997;Lin et al 2013;Liu et al 2018;Menezes-Filho et al 2018). Despite the presence of multiple metals in ferroalloy emissions that may have additive or synergistic associations with neurobehavioral outcomes, few prior studies have considered the health effects of exposures to multiple metals (Lucchini et al 2012b;Menezes-Filho et al 2018).…”

Two-step approach for assessing the health effects of environmental chemical mixtures: application to simulated datasets and real data from the Navajo Birth Cohort Study

“…time series or longitudinal data). The BKMR approach [50] can handle repeated measures, and the more recently developed Bayesian varying coefficient kernel machine regression (BVCKMR) is designed to estimate the mixture effects on outcome trajectories [51]. These two approaches characterize the exposure-response surface and provide visualizations of the interaction effects between mixture components for longitudinal data.…”

An Important Need to Monitor from an Early Age the Neurotoxins in the Blood or by an Equivalent Biomarker

“…Beyond that, statistical programs would be invoked to assess the overall risk from such a mixture of levels. Such developments are currently being addressed by research examining multiple neurotoxin effects and also mathematical frameworks that can handle such complex mixtures [120,121].…”