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

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

Author information

1
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
2
Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington.
3
Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts.
4
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York.
5
Division for Research in Community Interventions, National Institute of Perinatology, Mexico, Mexico.
6
Center for Research in Nutrition and Health, National Institute of Public Health, Cuernavaca, Mexico.
7
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
8
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

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.

KEYWORDS:

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

PMID:
30277584
PMCID:
PMC6522130
[Available on 2019-12-30]
DOI:
10.1002/sim.7947

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