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

Stat Med. 2018 Dec 30;37(30):4680-4694. doi: 10.1002/sim.7947. Epub 2018 Sep 12.


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.

Keywords: Bayesian inference; chemical mixtures; child health; longitudinal data; machine learning; neurodevelopment.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bayes Theorem*
  • Child, Preschool
  • Cognition / drug effects
  • Dose-Response Relationship, Drug
  • Environmental Exposure / adverse effects*
  • Environmental Exposure / analysis
  • Female
  • Heavy Metal Poisoning, Nervous System / epidemiology
  • Heavy Metal Poisoning, Nervous System / etiology
  • Humans
  • Infant
  • Infant, Newborn
  • Markov Chains
  • Mexico / epidemiology
  • Models, Statistical
  • Monte Carlo Method
  • Neurodevelopmental Disorders / chemically induced*
  • Pregnancy
  • Pregnancy Trimesters / drug effects
  • Prenatal Exposure Delayed Effects / chemically induced
  • Prospective Studies
  • Regression Analysis