The joint effect of prenatal exposure to metal mixtures on neurodevelopmental outcomes at 20-40 months of age: evidence from rural Bangladesh

Abstract

BACKGROUND: Exposure to chemical mixtures is recognized as the real-life scenario in all populations, needing new statistical methods that can assess their complex effects. OBJECTIVES: We aimed to assess the joint effect of in utero exposure to arsenic, manganese, and lead on children's neurodevelopment. METHODS: We employed a novel statistical approach, Bayesian kernel machine regression (BKMR), to study the joint effect of coexposure to arsenic, manganese, and lead on neurodevelopment using an adapted Bayley Scale of Infant and Toddler Development™. Third Edition, in 825 mother-child pairs recruited into a prospective birth cohort from two clinics in the Pabna and Sirajdikhan districts of Bangladesh. Metals were measured in cord blood using inductively coupled plasma-mass spectrometry. RESULTS: Analyses were stratified by clinic due to differences in exposure profiles. In the Pabna district, which displayed high manganese levels [interquartile range (IQR): 4:8, 18 lg=dl], we found a statistically significant negative effect of the mixture of arsenic, lead, and manganese on cognitive score when cord blood metals concentrations were all above the 60th percentile (As 0:7 lg=dl, Mn 6:6 lg=dl, Pb 4:2 lg=dl) compared to the median (As = 0:5 lg=dl, Mn = 5:8 lg=dl, Pb = 3:1 lg=dl). Evidence of a nonlinear effect of manganese was found. A change in log manganese from the 25th to the 75th percentile when arsenic and manganese were at the median was associated with a decrease in cognitive score of − 0:3 ( − 0:5, − 0:1) standard deviations. Our study suggests that arsenic might be a potentiator of manganese toxicity. CONCLUSIONS: Employing a novel statistical method for the study of the health effects of chemical mixtures, we found evidence of neurotoxicity of the mixture, as well as potential synergism between arsenic and manganese.

Evaluating the effects between metal mixtures and serum vaccine antibody concentrations in children: a prospective birth cohort study

“…Single measurements of B-Pb were collected at birth in cord blood, and fingerstick lead tests were conducted when children were age 20-40 months (B-Pb pregnancy and B-Pb toddlerhood ), and between ages 4-5 years (B-Pb childhood ). Sample collection and laboratory procedures have been previously described in detail [2]. Briefly, umbilical cord venous blood was collected in trace element-free tubes at the time of delivery.…”

“…Globally, arsenic, manganese, and lead are commonly encountered metals that can negatively impact public health. While many studies have focused on examining the reproductive and neurotoxic effects of these metal exposures on children's health [1][2][3][4], few have examined their impact on immune functioning. Yet there is experimental and epidemiologic evidence that arsenic, manganese, and lead can affect the immune system.…”

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

“…Recent progress in understanding how chemical mixtures affect health can be largely attributed to collaboration between scientific fields, innovation of statistical methods, and prioritization from funding agencies (Carlin et al 2013;Taylor et al 2016). Metals are an important class of chemicals in which to conduct mixtures research given that they are ubiquitous, commonly co-occur in the environment, and have interactive potential (Claus Henn et al 2014;Valeri et al 2017;Wright et al 2006). Communities are exposed to mixtures of metals that may be both naturally occurring and from anthropogenic sources.…”

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

“…Specifically, in studying the joint effects of environmental exposures, weighted quantile sum regression (WQS) (9,10) and Bayesian kernel machine regression (BKMR) (11,12) are two popular modeling approaches. In each run of analysis, the WQS method assumes that all exposures are associated with the outcome in one direction, and then derives a one-dimensional weighted sum score of the exposures under the assumed direction for the estimation of overall effect.…”