Time-varying associations between prenatal metal mixtures and rapid visual processing in children

Abstract

BackgroundHumans are exposed to mixtures of chemicals across their lifetimes, a concept sometimes called the “exposome.” Mixtures likely have temporal “critical windows” of susceptibility like single agents and measuring them repeatedly might help to define such windows. Common approaches to evaluate the effects of chemical mixtures have focused on their effects at a single time point. Our goal is to expand upon these previous techniques and examine the time-varying critical windows for metal mixtures on subsequent neurobehavior in children.MethodsWe propose two methods, joint weighted quantile sum regression (JWQS) and meta-weighted quantile sum regression (MWQS), to estimate the effects of chemical mixtures measured across multiple time points, while providing data on their critical windows of exposure. We compare the performance of both methods using simulations. We also applied both techniques to assess second and third trimester metal mixture effects in predicting performance in the Rapid Visual Processing (RVP) task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) assessed at 6–9 years in children who are part of the PROGRESS (Programming Research in Obesity, GRowth, Environment and Social Stressors) longitudinal cohort study. The metals, arsenic, cadmium (Cd), cesium, chromium, lead (Pb) and antimony (Sb) were selected based on their toxicological profile.ResultsIn simulations, JWQS and MWQS had over 80% accuracy in classifying exposures as either strongly or weakly contributing to an association. In real data, both JWQS and MWQS consistently found that Pb and Cd exposure jointly predicted longer latency in the RVP and that second trimester exposure better predicted the results than the third trimester. Additionally, both JWQS and MWQS highlighted the strong association Cd and Sb had with lower accuracy in the RVP and that third trimester exposure was a better predictor than second trimester exposure.ConclusionsOur results indicate that metal mixtures effects vary across time, have distinct critical windows and that both JWQS and MWQS can determine longitudinal mixture effects including the cumulative contribution of each exposure and critical windows of effect.

Periconceptional and prenatal exposure to metal mixtures in relation to behavioral development at 3 years of age

“… 17 , 18 Only recently, however, have epidemiologic investigations begun to consider neurodevelopmental effects of time-varying multimetal mixtures. 19 – 22 These investigations are supported by longitudinal data on multimetal exposures that occur during the prenatal period and recently developed statistical methods that extend time-fixed mixtures methods to accommodate longitudinal exposure data. These time-varying multimetal mixture investigations have similar objectives as their time-fixed counterparts, with the additional objective of estimating metal-outcome relations at multiple time points, enabling the identification of sensitive periods of exposure.…”

We can and we must do better to protect children from drinking water contaminants

“…To this end, recent innovations in EH research, such as the analysis of baby teeth to pinpoint the timing of exposures in-utero and during early childhood should be incorporated into drinking water studies ( 17 ). Likewise, statistical methods that assess the impact of chemical mixtures ( 18 ), and gene-environment interactions ( 19 ) are critical to inform health-protective policies.…”

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

“…However, the nonparametric exposure-response function may be difficult to interpret and its fitting often needs a large sample size [13,14]. In addition, WQS and BKMR have been generalized to study environmental mixtures with several types of outcomes, such as WQS for longitudinal outcomes [15] and BKMR for time-to-event outcomes [16]. However, a general modeling framework that can alleviate the above limitations in environmental health research is still desired [17].…”