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)
Published
Abstract
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
REFERENCES 37
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
School | |
---|---|
Department | |
Degree | |
Submission | |
Language |
|
Research Field | |
Palavra-chave | |
Committee Chair / Thesis Advisor |
![Preview image embargoed](/assets/No_Preview-c99a369db0c8f31173bf3fdc06f581cabae85282e8a747dd66f99b731b54aec0.png)
Primary PDF
Thumbnail | Title | Date Uploaded | Actions |
---|---|---|---|
![]() |
File download under embargo until 21 May 2025 | 2021-05-04 12:11:39 -0400 | File download under embargo until 21 May 2025 |
Supplemental Files
Thumbnail | Title | Date Uploaded | Actions |
---|