Skip Navigation
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Internet Explorer is no longer a supported browser.

This website may not display properly with Internet Explorer. For the best experience, please use a more recent browser such as the latest versions of Google Chrome, Microsoft Edge, and/or Mozilla Firefox. Thank you.

Your Environment. Your Health.

COMPLEX MIXTURES OF ENDOCRINE DISRUPTING CHEMICALS IN RELATION TO COGNITIVE DEVELOPMENT

Export to Word (http://www.niehs.nih.gov//portfolio/index.cfm/portfolio/grantdetail/grant_number/F31ES030263/format/word)
Principal Investigator: Gibson, Elizabeth Atkeson
Institute Receiving Award Columbia University Health Sciences
Location New York, NY
Grant Number F31ES030263
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 15 Mar 2019 to 14 Mar 2022
DESCRIPTION (provided by applicant): 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 combination with didactic training and individual cross-disciplinary mentoring, comprises a comprehensive research training plan that will serve as a platform from which to launch a career as an independent investigator in environmental epidemiology.
Science Code(s)/Area of Science(s) Primary: 16 - Mixtures
Secondary: 03 - Carcinogenesis/Cell Transformation
Publications See publications associated with this Grant.
Program Officer Bonnie Joubert
Back
to Top