Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures

Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) Project

PURPOSE

The Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) Project is a mother-child pregnancy and birth cohort originally initiated in the mid-1990s to explore: (1) whether enhanced mobilisation of lead from maternal bone stores during pregnancy poses a risk to fetal and subsequent offspring neurodevelopment; and (2) whether maternal calcium supplementation during pregnancy and lactation can suppress bone lead mobilisation and mitigate the adverse effects of lead exposure on offspring health and development. Through utilisation of carefully archived biospecimens to measure other prenatal exposures, banking of DNA and rigorous measurement of a diverse array of outcomes, ELEMENT has since evolved into a major resource for research on early life exposures and developmental outcomes.

PARTICIPANTS

n=1643 mother-child pairs sequentially recruited (between 1994 and 2003) during pregnancy or at delivery from maternity hospitals in Mexico City, Mexico.

FINDINGS TO DATE

Maternal bone (eg, patella, tibia) is an endogenous source for fetal lead exposure due to mobilisation of stored lead into circulation during pregnancy and lactation, leading to increased risk of miscarriage, low birth weight and smaller head circumference, and transfer of lead into breastmilk. Daily supplementation with 1200 mg of elemental calcium during pregnancy and lactation reduces lead resorption from maternal bone and thereby, levels of circulating lead. Beyond perinatal outcomes, early life exposure to lead is associated with neurocognitive deficits, behavioural disorders, higher blood pressure and lower weight in offspring during childhood. Some of these relationships were modified by dietary factors; genetic polymorphisms specific for iron, folate and lipid metabolism; and timing of exposure. Research has also expanded to include findings published on other toxicants such as those associated with personal care products and plastics (eg, phthalates, bisphenol A), other metals (eg, mercury, manganese, cadmium), pesticides (organophosphates) and fluoride; other biomarkers (eg, toxicant levels in plasma, hair and teeth); other outcomes (eg, sexual maturation, metabolic syndrome, dental caries); and identification of novel mechanisms via epigenetic and metabolomics profiling.

FUTURE PLANS

As the ELEMENT mothers and children age, we plan to (1) continue studying the long-term consequences of toxicant exposure during the perinatal period on adolescent and young adult outcomes as well as outcomes related to the original ELEMENT mothers, such as their metabolic and bone health during perimenopause; and (2) follow the third generation of participants (children of the children) to study intergenerational effects of in utero exposures.

TRIAL REGISTRATION NUMBER

NCT00558623.

Estimators for longitudinal latent exposure models: examining measurement model assumptions

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.