Per- and Polyfluoroalkyl Substances and Cortisol Biomarkers in Wild Bottlenose Dolphins (Tursiops truncatus) from Charleston, South Carolina and Indian River Lagoon, Florida Restricted; Files Only

Bennett, Baylin (Spring 2021)

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Th goal of this thesis is to identify associations between the presence of blood per- and polyfluoroalkyl substances (PFAS) levels and blood cortisol levels in wild bottlenose dolphins from off the coast of Charleston, South Carolina and Indian River Lagoon, Florida. Wild bottlenose dolphin PFAS and cortisol levels were previously obtained. Associations were assessed using linear regression, Tobit regression, and parametric quantile regression controlling for bottlenose dolphin age and sex and year and location at time of sample collection. Further, results were stratified by bottlenose dolphin sex and age. Free cortisol showed statistically significant negative associations with PFOS, PFOA, and PFHxS and positive associations with PFTriA. Bound cortisol showed statistically significant positive associations with PFOS, PFDA, PFDoDA, PFDS, PFTA, PFTriA, and PFUA. Total cortisol showed negative associations with PFOA and PFHxS and positive associations with PFDA, PFDoDA, PFTA, and PFTriA. Using Tobit regression to account for detection limits and parametric quantile regression as a biomarker tool are both novel approaches to assessing the presence of associations between wild bottlenose dolphin blood PFAS and blood cortisol. Taken together, the results indicate a relationship between PFAS levels and cortisol levels. Blood cortisol is a biomarker for stress.  Stress has been proposed as playing a potential role in consequent autoimmune disease. Given that the wild bottlenose dolphins off the coast of Charleston, South Carolina share a crucial dietary fish source with the Gullah/Geechee population, who have a profound disparity in lupus prevalence, further research should be conducted to define the role PFAS through dietary consumption might have in developing autoimmune diseases.

Table of Contents

Introduction 8

Materials and Methods 9

Sample Collection 9

Table 1 9

Statistical Analyses 9

Linear Regression 10

Tobit Regression 10

Parametric Quantile

Regression 11

Results 12

Linear Regression 12

Figure 1 21

Tobit Regression 22

Figure 2 26

Parametric Quantile Regression 27

Figure 3 34

Discussion 35

Limitations 35


Appendix 39

Linear Regression 39

Supplemental Figure 1 39

Supplemental Figure 2 40

Supplemental Figure 3 41

Supplemental Figure 4 42

Supplemental Figure 5 43

Tobit Regression 44

Supplemental Figure 6 44

Supplemental Figure 7 45

Supplemental Figure 8 46

Supplemental Figure 9 47

Supplemental Figure 10 48

Parametric Quantile Regression 49

Supplemental Figure 11 49

Supplemental Figure 12 50

Supplemental Figure 13 51

Supplemental Figure 14 52

Supplemental Figure 15 53

About this Master's Thesis

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  • English
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