Pattern-oriented Model for Exploring Systemic Toxicity and Biomaker Network Dynamics

Epidemiologic results have shown mixed and inconsistent results for human health effects, for instance in consideration of PFOA exposure. Some studies have found lipid metabolism and thyroid hormone effects; however, conclusive evidence has been elusive due to low environmental and occupational exposures to PFOA.

The time-dependent relationships between chemistries – including total cholesterol, low- and high-density lipoproteins, insulin, glucose, triiodothyronine, and thyroxine – were examined via information and network theoretic models. In order to validate data and gain insights into the interdependencies of biomarkers and PFOA, a pharmaco-kinetics/-dynamics (PKPD) model has been used. Results indicate that increasing PFOA, especially at higher levels, leads to a relatively small decrease in lipids and an increase in thyroid hormones. These results suggest that high levels of exposure have small effects on these aspects of metabolism, and thus shed some light into the potential systemic toxicity of PFOA in the environment from population exposures among the general population.

Beyond the PKPD model we perform a model-free information theoretic analyses that show: (i) the inference of functional network of biomarkers, or dynamical biomarker network (DBN) using a transfer entropy model; (ii) network and entropy based predictions of selected biomarker (cholesterol and T4); (iii) the identification of tipping point and of early warning variables; and, (iv) information-theoretic global sensitivity and uncertainty analyses (GSUA) for the identification of clusters of individuals whose biomarker importance is significantly different than the rest of the population considered.

Authors: 
Matteo Convertino, Yang Liu and Joe Servadio
Room: 
7
Date: 
Monday, September 24, 2018 - 18:45 to 19:00

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