# Complex Mixtures of Endocrine Disrupting Chemicals in Relation to Cognitive Development

> **NIH NIH F31** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $32,095

## Abstract

Project Summary/Abstract
Endocrine disrupting chemicals (EDCs) include multiple classes of chemicals that have been used extensively
in consumer products. Mounting evidence from toxicological and epidemiological studies suggest EDCs are
developmental neurotoxicants, and EDC exposure during the critical in utero period is associated with adverse
child cognitive development. Unfortunately, current research focuses on individual EDCs and largely ignores
joint and interactive effects of EDCs and the overall effect of the EDC mixture. To assess exposure to multiple
EDCs simultaneously, one must consider the high dimensionality of the exposure matrix and the complex
correlation structures across chemicals in statistical analyses. To address limitations of existing methods, we
propose to adapt a robust technique that is well-established for pattern recognition and dimensionality
reduction in machine learning. We specifically aim to use Latent Dirichlet Allocation (LDA), a type of robust
Bayesian non-negative matrix factorization, to determine the patterns of exposure to four ubiquitous classes of
EDCs known to cross the placenta—polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls
(PCBs), phenols (e.g., bisphenol A), and phthalates—and to characterize the relationship between these
exposure patterns and cognitive development. LDA is empirically-driven so that the researcher does not need
to specify a priori the number of patterns, and the non-negativity constraint enhances the interpretability of the
patterns identified. For our health model, we will use a supervised approach that allows child cognitive scores
to inform the LDA solution, thereby enabling identification of patterns most relevant to the outcome. We will
conduct this work using the existing infrastructure of the Columbia Center for Children’s Environmental Health
Mothers and Newborns Study, a longitudinal birth cohort of mother-child dyads. We will also establish
reproducibility of the method by creating a user-friendly R package so that other researchers can easily apply
LDA in environmental epidemiology, and we will verify transferability and functionality of the method on a
separate cohort. This will be the first study to assess the interacting and overall effects of multiple EDCs on
child cognitive development, introducing LDA as a straight-forward tool for the analysis of complex mixtures in
epidemiology. If successful, this method has broader implications for environmental epidemiology, as it can
easily be applied to other environmental mixtures of interest. The activities encompassed by this proposal
(study design, data management, advanced statistics, machine learning, data science, and presentation of
findings) cover the set of fundamental research skills required by a scientist entering the interdisciplinary field
of environmental epidemiology in the era of Big Data and Precision Public Health. The applied experience
gained from carrying out this research, in combina...

## Key facts

- **NIH application ID:** 9893708
- **Project number:** 5F31ES030263-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Elizabeth Atkeson Gibson
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $32,095
- **Award type:** 5
- **Project period:** 2019-03-15 → 2022-03-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9893708

## Citation

> US National Institutes of Health, RePORTER application 9893708, Complex Mixtures of Endocrine Disrupting Chemicals in Relation to Cognitive Development (5F31ES030263-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9893708. Licensed CC0.

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