Background: Thyroid hormones serve a host of functions including metabolism, growth, and development. Endocrine disrupting chemicals (EDCs) can cause perturbations to thyroid hormone homeostasis, leading to adverse health effects. Risk assessment of thyroid disruptors requires an approach mechanistically linking toxicological and epidemiological data across multiple scales. Dynamic models can serve the integrating role.
Objective: (1) Construct a dynamic model of the hypothalamus-pituitary-thyroid (HPT) axis, (2) use the model to establish a reference human thyroid population model, and (3) use the population model to predict thyroid-disrupting mechanisms of EDCs.
Methods: An ordinary differential equation (ODE)-based deterministic model of the HPT axis was constructed to capture the feedback regulation between T3, T4, and TSH, their synthesis, metabolism, and plasma buffering. The initial model representing an average euthyroid condition was then optimized by using the NHANES thyroid profile data to establish a reference thyroid population model. Pearson correlation and weighted multiple linear regression of the thyroid profile data and/or optimized model parameters to urinary EDCs including sodium iodide symporter (NIS) inhibitors, environmental phenols, and perfluorinated chemicals were then performed. Hierarchical clustering of EDCs based on thyroid hormone profile and/or optimized model parameters was performed.
Results: The deterministic model recapitulated the mean levels of free T3, free T4, TSH, total T3 and total T4 of a general human population. The model can simulate primary or secondary hyper- or hypothyroid conditions. Using the NHANES dataset, a virtual thyroid population was established with optimized parameter distributions. Correlation analysis (1) confirmed the thyroid-disrupting mechanisms of well-characterized EDCs such as perchlorate and (2) made predictions for novel thyroid-disrupting mechanisms of a number of chemicals such as thiocyanate. Multiple linear regression demonstrated the negative association of thyroid hormones with a number of EDCs however the associations with TSH varied, suggesting different thyroid-disrupting mechanisms. Hierarchical clustering demonstrated the usefulness of optimized model parameters as additional features to help refine chemical grouping.
Conclusions: A dynamic model of the HPT axis can be used to infer novel mechanistic information of thyroid EDCs and it can become an important tool in risk assessment of EDCs by incorporating future in vitro testing data.
Table of Contents
Table of Contents
Â§ Physiology of the Hypothalamus-Pituitary-Thyroid (HPT) Axis
Â§ Pathology of the HPT Axis and Prevalence of Thyroid Disease
Â§ Role of Endocrine Disrupting Chemicals (EDCs) in Thyroid Disease
o Environmental Phenols
o Nitrate, Perchlorate and Thiocyanate
Â§ Current Movement in Chemical Toxicity Testing
Â§ Role of Dynamic Modeling in Risk Assessment of EDCs
Â§ Objectives of the Thesis Study
Â§ Construction of the Computational Model of the Human HPT Axis
o Construction of the Deterministic Model
o Construction of the Population Model
Â§ Statistical Methods
o Study Design and Population
o Study Variables
o Statistical Analysis
Â§ Deterministic HPT Model
Â§ Population HPT Model
Â§ Statistical Summary of NHANES Data
Â§ Correlation Analysis
Â§ Hierarchical Clustering
LIMITATIONS AND FUTURE RESEARCH
About this Master's Thesis
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|File download under embargo until 12 June 2019||2018-08-28||File download under embargo until 12 June 2019|