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EFFECTS OF PSYCHOLOGICAL STRESS ON GLUCOCORTICOID SENSITIVITY
OF INFLAMMATORY RESPONSE TO INFLUENZA VACCINE CHALLENGE
IN HEALTHY MILITARY COLLEGE STUDENTS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Vorachai Sribanditmongkol
Graduate Program in Nursing
The Ohio State University
2013
Dissertation Committee:
Dr. Jeremy Neal, Advisor
Dr. Donna McCarthy Beckett
Dr. Thelma Patrick
Copyright by
Vorachai Sribanditmongkol
2013
ABSTRACT
Background: Influenza and other infectious diseases are critical barriers to the
health and readiness of military units worldwide with reported rates of annual influenza
infection as high as 45%. Vaccination to prevent infections stimulates a transient,
inflammatory response, counterbalanced by the anti-inflammatory effects of increased
cortisol secretion which enhances antibody production for seroprotection. Paradoxically,
chronically-stressed individuals have elevated cortisol levels, but have poorer antibody
response to vaccination. Evidence suggests that chronic stress impairs immune cell
glucocorticoid sensitivity (GCS), leading to excessive production of proinflammatory
cytokines. This pathway may contribute to impaired immune responses to vaccination
and increased risk of infectious illness in military personnel in high stress areas of
service.
Purpose: The study was conducted to determine if psychological stress
diminishes GCS and regulation of proinflammatory cytokine production in a population
of healthy military students and personnel. It is hypothesized that subjects with greater
psychological stress will have lower GCS in an ex vivo laboratory model of influenza
vaccine challenge.
Methods: A cross sectional design was used with a convenience sample of
healthy, military college students and personnel (n = 61). Subjects completed the
Perceived Stress Scale (PSS) and trait subscale portion of the State-Trait Anxiety
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Inventory (STAI-T) as measures of psychological stress and provided a blood sample.
Whole blood was incubated in the presence of influenza vaccine and dexamethasone to
evaluate cytokine production and GCS. Associations between psychological stress and
cytokine production were evaluated using correlation and linear regression.
Results: Pearson correlations, Analysis of Variance (ANOVA) with post-hoc
Dunnett's T3 procedure, and Multiple Regressions were utilized for statistical analyses.
PSS and vaccine-stimulated cytokine production were not significantly correlated. Oneway ANOVA and post-hoc Dunnett's T3 Test revealed significant differences in cytokine
concentrations in the 3 ex vivo conditions (i.e., Unstimualted, Vaccine-stimulated, and
DEX-inhibited) (p<0.001). Results of the Pearson correlations showed that PSS scores
were inversely related to GCS (p<0.05) for all 4 vaccine-stimulated cytokines. Finally,
multiple regression models controlling for age, gender, race, and student cumulative
grade point average (GPA) revealed a negative relationship between PSS and GCS of
vaccine-stimulated production of IL-1β (β = -0.420, t = -3.55, p<0.01), IL-6 (β = -0.296,
t = -2.36, p<0.05), and TNF-α (β = -0.259, t = -2.060, p<0.05), but not IFN-γ.
Conclusions: Findings from this study suggest a biologic pathway through which
perceived psychological stress might alter the inflammatory immune response to
influenza vaccination and expand understanding of how stress might impact immune
function in military populations.
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DEDICATION
This dissertation is dedicated to my amazing children, Kien and Kalee for giving
me their unconditional love, patience, and joy. Kids, I am truly blessed to be your dad,
and look forward to some much needed fun times with you both soon. I also dedicate this
work to my family and to the loving memory of my late mom, Linda, who modeled the
true essence of human compassion and steadfast dedication to family, friends, and
nursing. Mom, I cherish the memories of our long, meaningful talks and the sharing of
your wisdom through your creative analogies. Your drive for seeking out answers to
life's questions taught me so much in a global and personal sense. Thank you, mom for
inspiring me to join the Navy and see the world, become a nurse to help others in need,
and begin this intellectual journey. For my dad, Voravit ("Jim"), your life's journey from
Thailand in seek of the "American Dream" has always inspired me to strive to be the best
that I can be for you, myself and my family. To my siblings, Nai, Alee, and Thai, your
unyielding love and beliefs in my ability to attain my highest aspirations sustained my
momentum throughout this time as well. I hope to make you all proud.
And most importantly, to Angie, who is the "love" of my life. Your selfless
sacrifice, unwavering support, encouragement, and faithful love that you've provided me
and our family over the years overwhelms my heart with gratitude beyond measure and
expression. I am forever grateful to God for our lives together, this shared
accomplishment, and the continued journey that lies ahead!
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ACKNOWLEDGMENTS
It is with the highest admiration and most sincere gratitude that I acknowledge Dr.
Jeremy Neal as my advisor and mentor. His constant leadership, support, and relentless
encouragement during the course of my doctoral studies helped me to step outside of my
comfort-zone and grow in many ways that I had never imagined possible. His seemingly
endless energy and genuine passion for research is contagious, and has excited me as I
look forward to a promising future as Navy Nurse researcher and scientist. I also extend
my deepest appreciation to Dr. Donna McCarthy Beckett, and Dr. Thelma Patrick, who
both served as vital members of my dissertation committee and readily shared their
expertise, guidance, and encouragement. Specifically, I am grateful to Dr. McCarthy
Beckett for opening my eyes to laboratory-based nursing research; taking me under her
"wing" and making me feel comfortable within this exciting facet of nursing. Dr.
McCarthy Beckett, the generosity and compassion that you demonstrate to all nursing
students in this challenging program is beyond approach. Dr. Patrick, I am also inspired
by your commitment and genuine drive to "see" your students achieve success, and I
thank you so much for your enduring support of my doctoral education. All members of
my dissertation committee embodied the utmost professionalism and demonstrated
unconditional human kindness that I aspire to emulate throughout my career.
I also extend my sincere appreciation to Dr. Laura Szalacha and Dr. Christopher
Holloman for their statistical analysis expertise and guidance of my dissertation research.
Thanks to Dr. Runfeng Jing for welcoming me into the biomedical lab, always keeping
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me on my toes, and expeditiously running my biomarker assays. I especially want to
thank Dr. Priscilla Koeplin for providing me her time, critical expertise, and
encouragement during the last weeks leading to the completion of this dissertation.
Thank you to my "original" fellow doctoral student champion-cohort, Dr. Sharon Hill
Cheatham, Dr. Rika Tanda, and Will Matcham. Thank you all for being my friends and
support group throughout my doctoral studies. I wish you all success!
I extend my most sincere gratitude to the best, PhD-prepared, Navy Nurse Corps
Officers ("Angels") I know. Thank you, CAPT Jacqueline Rychnovsky, CAPT (Retired)
Angelica Almonte, CAPT (Retired) Elizabeth Barker, and CAPT (Select) Lisa Osborne
for the mentorship and friendship each of you has given me over the years. Also thanks to
all of the Army, Navy and Marine, and Air Force students who volunteered from the
Reserve Officer Training Corps Unit, and mostly for your future military service.
I would also like to acknowledge the U.S. Navy Nurse Corps' Duty Under
Instruction (DUINS) scholarship program, which provided me the opportunity, time, and
full-tuition support throughout my doctoral program. Special thanks to Sigma Theta Tau,
International Honor Society of Nursing (Epsilon Chapter) for their generous research
award. Lastly, additional funds for laboratory supplies were provided from Newton Fund
contributions through the College of Nursing at The Ohio State University.
*The views expressed in this work are those of the author and do not reflect the
official policy or position of the Department of the Navy, Department of Defense, or the
United States Government.*
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VITA
December 19, 1970 ........................................Born-Columbus, Ohio
June 1989 .......................................................Gahanna Lincoln High School
1989 to 1994 ..................................................Enlistment in U.S. Navy (Active Duty)
June 1998 .......................................................B.S. in Nursing, The Ohio State University
1998 to present ...............................................Commissioned as Naval Officer, Nurse
Corps
May 2005 .......................................................M.S. in Nursing, University of San Diego
September 2008 to present .............................College of Nursing, The Ohio State
University
FIELDS OF STUDY
Major Field: Nursing
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TABLE OF CONTENTS
Page
Abstract .......................................................................................................................... ii
Dedication ..................................................................................................................... iv
Acknowledgments .......................................................................................................... v
Vita ............................................................................................................................... vii
List of Tables................................................................................................................. xi
List of Figures .............................................................................................................. xii
List of Abbreviations................................................................................................... xiii
CHAPTER 1................................................................................................................... 1
INTRODUCTION .................................................................................................. 1
CHAPTER 2................................................................................................................. 13
REVIEW OF LITERATURE ............................................................................... 13
Overview of Stress in ROTC Students ................................................................. 13
Biological Overview of Stress Response ............................................................. 15
Normal Immune Response .................................................................................... 19
Immune Response to Vaccination ............................................................ 20
Inflammatory Response to Vaccination .................................................... 21
Cortisol Regulation of Inflammatory Cytokine Production...................... 25
Effect of Chronic Stress on Immune Function ..................................................... 26
Chronic Stress and Inflammation.............................................................. 27
Chronic Stress and Cortisol Regulation of Inflammatory Cytokines ...... 27
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Chronic Stress and Poor Antibody Production ......................................... 33
Paradox ................................................................................................................. 34
Immunosuppression versus Glucocorticoid Resistance ............................ 35
Immunosuppression ...................................................................... 35
Glucocorticoid Resistance ............................................................ 36
Restatement of Purpose of the Study .................................................................... 37
CHAPTER 3................................................................................................................. 39
METHODS AND PROCEDURES....................................................................... 39
Research Design.................................................................................................... 39
Sample and Setting ............................................................................................... 39
Measures ............................................................................................................... 40
Data Collection Procedure .................................................................................... 46
Protection of Human Subjects .............................................................................. 49
Data Analysis ........................................................................................................ 50
CHAPTER 4................................................................................................................. 54
RESULTS ............................................................................................................. 54
Descriptive Statistics............................................................................................. 54
Psychosocial Variable Data Results...................................................................... 58
Associations Among Demographics, Psychosocial Variables, and GCS ............. 58
Results for Research Aim 1 .................................................................................. 60
Result for Research Aim 2 .................................................................................... 61
Results for Research Aim 3 .................................................................................. 66
CHAPTER 5................................................................................................................. 70
DISCUSSION AND CONCLUSION................................................................... 70
Discussion ............................................................................................................. 71
Psychological Stress.................................................................................. 71
Psychological Stress and Vaccine-stimulated Cytokine Production ........ 71
DEX-induced Suppression of Vaccine-stimulated Cytokine Production . 72
Psychological Stress and GCS…………………………………..……….73
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Study Limitations .................................................................................................. 74
Implications for Future Research .......................................................................... 75
Conclusion ............................................................................................................ 77
BIBLIOGRAPHY ........................................................................................................ 79
APPENDIX A: Demographic Data Questionnaire Form............................................. 93
APPENDIX B: Perceived Stress Scale (PSS) ............................................................ 104
APPENDIX C: State-Trait Anxiety Inventory (STAI) .............................................. 106
APPENDIX D: Informed Consent Form ................................................................... 112
APPENDIX E: HIPAA Authorization to Participate in Research Form ................... 118
APPENDIX F: Human Subjects Approval to Conduct Research .............................. 122
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LIST OF TABLES
Table
Page
Table 1 Demographic Characteristics of Sample...............................................................55
Table 2 Educational Characteristics of Sample.................................................................56
Table 3 Military Service Characteristics of Sample..........................................................57
Table 4 Psychosocial Characteristics of Samples..............................................................58
Table 5 Correlations among Demographics and Psychosocial Characteristics.................59
Table 6 Correlations among Demographics and GCS of Cytokine Production................60
Table 7 Mean Proinflammatory Cytokine Production Values and Experimental
Conditions..............................................................................................................62
Table 8 Summary of ANOVA for Mean Differences in Cytokine Production among
3 Ex Vivo Conditions............................................................................................66
Table 9 Correlations among PSS and GCS of Proinflammatory Cytokines......................67
Table 10 Summary of Multiple Regressions of PSS and GCS..........................................69
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LIST OF FIGURES
Figure
Page
Figure 1 Cortisol and Regulation of Proinflammatory Cytokines.......................................5
Figure 2 Psychological Stress, Impaired GCS, and Proinflammatory Cytokines................7
Figure 3 Conceptual Model with Sequence of Specific Aims............................................9
Figure 4 Blood Collection, Cultures, and Assays..............................................................48
Figure 5 Differences in Cytokine Production among the 3 ex vivo Conditions................65
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ABBREVIATIONS
ACTH= adrenocorticotropic hormone
PG=prostaglandins
BMI=body mass index
PNI=psychoneuroimmunology
CRH= corticotropin-releasing hormone
PSS=perceived stress scale
DEX=dexamethasone
ROTC=Reserve Officer Training Corps
GC=glucocorticoid
STAI=state-trait anxiety inventory
GCS=glucocorticoid sensitivity
TNF-α=Tumor necrosis factor alpha
g=gram
Th1=T-helper 1 cell
HPA=hypothalamic-pituitary-adrenal axis
Th2=T helper 2 cell
IFN-γ=Interferon gamma
µL=microliter
IL-1β=Interleukin-1 beta
µM=micromole
IL-6=Interleukin-6
µM/L=micromoles/liter
L=liter
M=mole
mg=milligram
mL=milliliter
nM=nanomole
NROTC=Naval Reserve Officer Training Corps
OSU =The Ohio State University
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CHAPTER 1
INTRODUCTION
Studies of the effects of psychological stress on inflammatory and other immune
responses in active and reserve military personnel is sparse, and is nearly non-existent in
military Reserve Officer Training Corps (ROTC) college students. The latter population
will enter the ranks and face multiple significant stressors, including military combatsupport and humanitarian deployments, family separations and reintegration transitions
from the battlefield to the home-front (Hoge, Auchterlonie, & Milliken, 2006; Lapierre,
Schwegler, & LaBauve, 2007; T. Smith, Leardmann, C. Smith, Jacobson, & Ryan, 2007).
Studies of deployed military personnel and training recruits demonstrate that,
despite being a presumably fit and healthy population, military individuals are at
increased risk for influenza, posing a significant health risk to the readiness of military
units worldwide (Balicer, Huerta, Levy, Davidovitch, & Grotto, 2005; Earhart, et al.,
2001; Makras, Alexiou-Daniel, Antoniadis, & Hatzigeorgiou, 2001). Influenza outbreak
rates have been reported as high as 45% (Klontz, et al., 1998; Makras, et al., 2001),
surpassing reported influenza rates in U.S. civilian populations (Centers for Disease
Control and Prevention [CDC], 2013; Snyder, Mancuso, & Aldous, 2006).
Vaccines are available but are not always effective. A growing body of literature
suggests that cortisol and the inflammatory response are both critical to the immune
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response following vaccination. However, chronically-stressed individuals commonly
have a poorer antibody immune response and report more illness-like symptoms
following vaccination (Burns, Carroll, Ring, Harrison, & Drayson, 2002a; Burns,
Drayson, Ring, & Carroll, 2002b; S. Cohen, Miller, & Rabin, 2001; Glaser et al., 1992;
Glaser, Sheridan, Malarkey, MacCallum, & Kiecolt-Glaser, 2000; Kiecolt-Glaser, Glaser,
Gravenstein, Malarkey, & Sheridan, 1996; M. Morag, A. Morag, Reichenberg, Leier, &
Yirmiya, 1999). The subjects in these studies tended to be older adults experiencing
chronic stress as a primary caregiver and/or spouse of a family member with dementia or
cancer (Burns, et al., 2002a; Burns, et al., 2002b; S. Cohen, et al., 2001; Glaser, et al.,
1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag, et al., 1999). Little is
known about the effects of stress on the immune response to vaccination in military
populations. It is plausible that mild to moderate stressors in the daily life of young,
healthy military personnel exert similar effects on immune responses to vaccine
challenge. Military personnel could be at risk for stress-induced changes in inflammatory
cytokine production following vaccination, placing them at higher risk for infectious
disease.
Although stress experienced by a military student population differs from stress
experienced by active duty military members, there are parallels that may allow a
convenience sample in this study to serve as a proxy in determining the effects of
psychological stress on immune responses to vaccine challenge. While militaryassociated stressors cannot be completely eliminated, reducing the risk for infectious
illness in the military is a national research priority. Ex vivo vaccine challenge is a useful
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model for examining the effects of psychological stress on inflammatory responses to
immune triggers. The purpose of this study was to determine if a seasonal, inactivated,
trivalent influenza vaccine elicited a measurable inflammatory response ex vivo; and if
the glucocorticoid sensitivity (GCS) of cytokine production was related to psychological
stress.
Background
Overview of Immune Response
The immune response to vaccination is described in the context of the immune
response and in the combined regulation by the immune and neuroendocrine systems.
Immune Response to Vaccination
Within the immune system, the response to vaccination involves the coordination
of a wide variety of immune cells. The initial recognition of an antigen (i.e. influenza
vaccine) occurs through its presentation to cells by antigen presenting cells (APCs).
Once presented by the APCs, helper T cells recognize the antigen and process and
present it to the β lymphocytes (β cells) to stimulate β-cell proliferation and
differentiation into plasma cells, which produce antibodies or immunoglobulin (Phillips,
2012; Kindt, Osborne, Goldsby, & Kuby, 2006). Antibodies secreted by β cells include
immunoglobulin A (IgA), IgE, IgM, IgG, and IgD (Segerstrom & Miller, 2004).
Vaccination with an antigen not previously encountered induces a primary
antibody response, with the earliest antibody to be produced (IgM) peaking
approximately 5 days after vaccination. Interaction between activated T- and β-cells
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leads to the production of high affinity or very specific antibodies in bodily fluids such as
IgG (found primarily in the blood) and IgA (found mainly at mucosal surfaces).
Secondary antibody responses occur through repeated natural antigenic exposure or
following repeated vaccination against more common pathogens such as influenza.
Secondary antibody responses are usually more rapid and of greater magnitude as a result
of some of the activated T- and β-cells becoming long-lived immune memory cells
(Phillips, 2012).
Vaccines, such as influenza vaccine consist of inactivated or dead viruses and
induce a thymus-dependent antibody response that requires helper T cell involvement
(Phillips, 2012). Another type of vaccination elicits a thymus-independent response,
requires helper T cells in antigen recognition, and relies solely upon antigen recognition
through β-cells (e.g., meningococcal A or tetanus). These vaccine types do not elicit as
strong or maintained response as thymus-dependent vaccines (Phillips, 2012). A third
vaccine type uses a conjugate vaccine to enhance the response to thymus-independent
antigens by attaching a protein to the antigen, stimulating an immune response involving
helper T-cell recognition (e.g., haemophilus influenzae type B).
Immune and Neuroendocrine System Reponses to Vaccination
Besedovsky and Sorkin (1977) provided the first evidence from animal studies of
a bidirectional communication flow from the activated immune system to the
hypothalamus, suggesting that the brain is involved in the immune response through an
immune-neuroendocrine network. The neuroendocrine response to an antigen (in this
study using the influenza vaccine) is represented in Figure 1.
4
Vaccination stimulates a transient increase in proinflammatory cytokine
production which stimulates cortisol secretion through direct effects on the adrenal cortex
and by releasing corticotrophin-releasing hormone (CRH) from the hypothalamus and
adrenocorticotropic hormone (ACTH) from the anterior pituitary. Proinflammatory
cytokine production is then counter-regulated by anti-inflammatory effects of increased
cortisol release (Elenkov, et al., 2000a; Tsigos & Chrousos, 2002) and by production of
anti-inflammatory cytokines (Elenkov, et al., 2000b; Sapolsky, Romero, & Munck, 2000;
Vedhara, et al., 1999). This cross-regulation of neuroendocrine and inflammatory
responses is necessary for T-cell and β-cell proliferation, antibody synthesis, and
seroprotection (Burns, et al., 2002a; Burns, et al., 2002b; S. Cohen, et al., 2001; Elenkov,
et al., 2000a; Glaser, et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag,
et al., 1999; Sapolsky, et al., 2000; Tsigos & Chrousos, 2002).
Figure 1
Cortisol and Regulation of Proinflammatory Cytokines
Figure 1. Illustration of
(+): Stimulates/Produces
(-): Inhibits
HPA Axis activation and
cortisol regulation of
proinflammatory
cytokine production in
response to influenza
vaccine.
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Cortisol is a hormone that is activated in stressful situations, providing the
neuroendocrine link between stress and immunity. The field of science that developed
from this discovery is psychoneuroimmunology (PNI), the study of the relationships
among brain, behavior and immunity, reflecting the effects of psychological stress on
endocrine and immune function (Ader, 2000). PNI is an appropriate model to study
psychological stress and inflammation as risk predictors of increased susceptibility to
infection (Blalock, 2005; Blalock & Smith, 2007; Elenkov, et al., 2000b).
The immune response to influenza vaccine in the presence of psychological stress
is depicted in Figure 2. Psychological stress activates the HPA axis to elicit cortisol
secretion, which can have significant effects on regulation of inflammatory and immune
responses (see Figure 1). Acute stress is known to activate the HPA axis, resulting in
short bursts of increased cortisol secretion. Acute increases in cortisol secretion just prior
to vaccination have been found to enhance antibody production in healthy individuals
(Edwards, et al., 2006).
In contrast, chronic stress leads to prolonged activation of the HPA axis,
prolonged elevation of cortisol levels, suppressed immune responses (Agarwal &
Marshall, 2001; Marshall, et al., 1998; Plotnikoff, Faith, Murgo, & Good, 2007),
diminished viral infection clearance (Glaser & Kiecolt-Glaser, 2005), and poorer
antibody response to vaccination (Burns, et al., 2002a; Burns, et al., 2002b; S. Cohen, et
al., 2001; Glaser, et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag, et
al., 1999; Vedhara, et al., 1999).
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Figure 2
Psychological Stress, Impaired GCS, and Proinflammatory Cytokines
(+): Stimulates/Produces
(-): Inhibits
(X): Disrupts
Figure 2. Illustration of psychological stress and impaired GCS of proinflammatory
cytokine production in response to influenza vaccine.
Psychological stress exerts globally suppressive effects on immune responses
including reduced humoral immunity, reduced lymphocyte proliferation, and reduced
cytotoxic function of natural killer cells (M. Cohen, et al., 2002; Herbert & S. Cohen,
1993; Kiecolt-Glaser, Marucha, Malarkey, Mercado, & Glaser, 1995). However, in the
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context of sustained psychological stress, individuals exhibit decreased or blunted
cortisol responses to acute stress (Heim, Ehlert, & Hellhammer, 2000; Miller, Cohen, &
Ritchey, 2002), persistent elevations in serum levels of proinflammatory cytokines, and
poorer antibody response following vaccination (Burns, Carroll, et al., 2002; Burns,
Drayson, et al., 2002; Cohen, et al., 2001; Glaser, et al., 1992; Glaser, et al., 2000b;
Kiecolt-Glaser, et al., 1996a; Morag, et al., 1999). The traditional concept of stressinduced immunosuppression does not offer a clear explanation as to why and how
chronic stress affects the inflammatory response.
It has been proposed that chronic psychological stress can lead to glucocorticoid
resistance in which the immune system demonstrates a diminished or sensitivity to
glucocorticoids (GCS) that normally control excessive inflammation. Impaired GCS
leads to HPA axis dysregulation and a subsequent, feed-forward cycle of uncontrolled
proinflammatory cytokine responses to stress (Miller, et al., 2002). The basic principle
used to assess GCS ex vivo is to induce cytokine production (via a bacterial endotoxin or
a mitogen stimulant) and co-incubate cells with serial dilutions of DEX to determine the
inhibition of cytokine production. Studies using this approach have shown reduced GCS
of immune cells and subsequent higher production levels of proinflammatory cytokine
IL-6 and TNF-α in the presence of varying concentrations of DEX in chronically stressed
populations (DeRijik, et al., 1997; Miller, et al., 2002; Rohleder, J. Wolf, & O. Wolf,
2010; Wirtz, et al., 2003). In this study, GCS was measured by examining the inhibitory
effect of DEX on production of IL-6, IL-1β, IFN-γ, and TNF-α by influenza vaccinestimulated peripheral blood cells. A lower percent inhibition of vaccine-stimulated
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cytokine production indicates a lower GCS (or greater resistance) to anti-inflammatory
effects of DEX.
The effect of chronic stress on the immune response is the basis for the
hypothesized relationships in this study. Figure 3 presents the conceptual model for this
research. From left to right, the conceptual model depicts the hypothesized relationships
between increased psychological stress and impaired GCS in response to vaccine
stimulation as denoted in the 3 specific aims of the study (Figure 3).
Figure 3
Conceptual Model with Sequence of Specific Aims
Figure 3. Graphical representation of conceptual model with sequence of specific aims
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Purpose of the Study
The purpose of this study was to determine whether psychological stress
affects proinflammatory cytokine production by peripheral blood cells in response
to influenza vaccine challenge and dexamethasone, a synthetic form of cortisol.
The central hypothesis is that higher perceived stress and anxiety can downregulate GCS of immune cells and result in elevated proinflammatory cytokine
production in response to vaccine challenge.
Specific Aims
•
Determine if psychological stress affects proinflammatory cytokine production by
peripheral blood cells stimulated ex vivo by Influenza Vaccine
•
Determine if dexamethasone (DEX) suppresses vaccine-stimulated cytokine
production
•
Determine if psychological stress predicts GCS (percent inhibition of stimulated
cytokine production to DEX)
Hypotheses
•
Peripheral blood cells from subjects with higher psychological stress (based on PSS
& STAI-T scores) will have higher production of cytokines (↑IFN-γ, ↑IL-1β, ↑IL-6,
↑TNF-α) in response to stimulation by Influenza Vaccine
•
DEX will reduce vaccine-stimulated cytokine production of IFN-γ, IL-1β, IL-6, and
TNF-α
•
Psychological stress (PSS & STAI-T scores) will predict GCS (percent inhibition of
cytokine production to DEX)
10
Conceptual Framework
Psychoneuroimmunology (PNI)
PNI is the study of the relationships among brain, behavior and immunity,
reflecting the effects of psychological stress on endocrine and immune function (Ader,
2000). PNI has aided scientists in understanding how psychological, neuroendocrine and
immunologic events can affect health.
Psychological stress. Psychological stress is defined as an individual’s
experience of negative events or perceptions of distress, typically associated with the
inability to cope (Cohen, et al., 2001), and occurring when an individual perceives that
environmental demands strain or exceed coping and adapting capacity (S. Cohen,
Janicki-Deverts, & Miller, 2007). Psychological stress activates the HPA axis, leading to
increased secretion of glucocorticoids such as cortisol (Everly & Lating, 2002).
Glucocorticoid sensitivity (GCS). Glucocorticoids have a fundamental role in
regulating inflammatory and neuroendocrine responses to a pathogen exposure (Anisman
& Merali, 2003; Charmandari, Tsigos, & Chrousos, 2005; Kiecolt-Glaser, McGuire,
Robles, & Glaser, 2002; Petrovsky, 2001; Raison & Miller, 2003). GCS refers to the
sensitivity of immune cells to glucocorticoid hormones that normally terminate the
inflammatory response (Bailey, Engler, Hunzeker, & Sheridan, 2003; Marques,
Silverman, & Sternberg, 2009; Miller, et al., 2002; Stark et al., 2001). It has been
proposed that chronic psychological stress and prolonged activation of the HPA and
sympathetic-adrenal medullary (SAM) axes may result in loss of counter-regulatory
responses in stimulated lymphocytes (Miller, et al., 2002; Raison & Miller, 2003).
11
Influenza vaccine (ex vivo challenge). Inflammatory responses to influenza
vaccination are mild compared to responses to influenza infection. When influenza
vaccine is used to stimulate the immune response en vivo and ex vivo, researchers can
study production of cytokines that drive the inflammatory response (Tsai et al., 2005),
and study alterations in immunological responses to challenge under well-controlled
conditions (Glaser & Kiecolt-Glaser, 2005). Because influenza vaccination is considered
safe, it provides an excellent model for studying immune inflammatory responses to
antigen challenge in populations required to have an annual influenza vaccination (i.e.,
military personnel and college ROTC students).
Significance of this Study
This study is broadly focused on improving the health of military service
members. Infection-related illness and hospitalizations are significant barriers to health
and readiness of military units world-wide. Outbreaks of influenza among military
personnel, including military student trainees, exceed rates seen in civilian population
although the reasons for this remain unknown. Individuals in the military are under
considerable stress. Consequently, stress-induced effects on GCS may lead to
dysregulation of the inflammatory response to vaccination. This research will increase
understanding of the effects of psychological stress on the inflammatory response, and
may have implications regarding psychological stress effects on immune responses to
vaccination in military personnel, students, and other highly-stressed populations.
12
CHAPTER 2
REVIEW OF LITERATURE
Introduction
A growing body of literature suggests that chronic psychological stress impairs
the immune response to vaccination or viral challenge. It has been reported that military
individuals, despite being a presumably fit and healthy population, have higher rates of
infections such as influenza than do civilian populations, posing a significant health risk
to the readiness of military units worldwide (Balicer, et al., 2005; Klontz, et al., 1998;
Makras, et al., 2001; Snyder, et al., 2006). This chapter reviews the literature on
psychological stressors in military personnel and Reserve Officer Training Corps
(ROTC) cadets, the physiology of the stress response, normal responses to immune
stimuli, and the effects of chronic stress on the immune response to vaccine challenge.
This literature informed the design of this study, conducted to determine if psychological
stress affects influenza vaccine-induced production of pro-inflammatory cytokines in
whole blood of healthy ROTC college students.
Overview of Stress in ROTC students
Research on the psychological effects of stress on immune function in active and
training military personnel is sparse. These populations are either currently facing or will
encounter considerable stress due to long military combat-support and humanitarian
13
deployments, family separations, and reintegration transitions from the battlefield to the
home-front. Negative effects of stress on immune function could place these otherwise
healthy individuals at risk for infections. Although the stress experienced by military
students is different from active duty military members, there are some parallels that
permit the military student population to serve as a proxy in determining the effects of
psychological stress on immune function in military personnel.
ROTC is a military scholarship and leadership program designed to prepare
college students for military service as Commissioned Officers in the U.S. Armed Forces
upon graduation. ROTC college students have potentially stressful experiences similar to
those of civilian college students; however, superimposed are military training duties,
physical fitness standards, and high academic achievement performance demands
required for ROTC scholarship and entrance into active duty military service as a
Commissioned Officer.
Some students participating in ROTC are active duty members, National
Guardsmen/Reservists, and veterans who may have been previously exposed to the
stressors associated with military deployment. Many returning Iraqi and Afghan military
veterans have returned to college. Students who are new to college and who are also
actively serving as military members may experience culture shock when transitioning
from military to civilian institutions such as college. Zinger and Cohen (2010) reported
that student veterans describe post traumatic stress disorder (PTSD), depression, physical
injury, lack of structure in civilian life, and difficulties with personal relationships and
social functioning.
14
Cadets train together in their respective military service groups, but are evaluated
as individuals on their potential to be future military officers. The student is constantly in
direct competition with all members of his or her own military service peer group.
Cadets report academic and military performance concerns and workload as sources of
stress. These stressors are inversely associated with cadet well-being and performance
outcomes (Adler, McGurk, Stetz, & Bliese, 2003). Cadets have also reported anticipatory
stress, time management pressures, sleep deprivation, military leadership and physical
fitness performance evaluations, conflicts between teamwork and competitive grading,
and inexperience in the leadership role (Gold & Friedman, 2000). Although research
involving military students is sparse, perceived stress has been shown to have negative
effects on service academy cadets’ physical health and susceptibility to illness and
physical injury (Glaser, et al., 1999; Lee, Meehan, Robinson, Mabry, & Smith, 1992). A
growing body of literature suggests that psychological stress may play a role in the
pathogenesis of respiratory infections (Cohen, et al. 2012a), which could explain the
increased risk of respiratory infections such as influenza in military personnel (Balicer, et
al., 2005; Earhart, et al., 2001; Klontz, et al., 1998; Makras, et al., 2001; Snyder, et al.,
2006).
Biological Overview of Stress Response
Stress
Stress is often described as a response, a stimulus, or a bidirectional exchange or
transaction (Lyon, 2000). Lazarus and Folkman (1984) described stress as an internal
15
process and relationship between the person and the environment wherein an individual
appraises an event as a threat. The stress response is classically described as the body's
reaction to a perceived or real threat to well-being (Charmandari, et al., 2005; Chrousos,
2000). The stress response involves activation of the hypothalamic-pituitary-adrenal
(HPA) axis and/or the sympathetic nervous system (SNS) to aid an organism in
physiologically dealing with the threat and restoring well-being. Stress responses are the
physiological and psychological consequences of the appraisal of the event as a stressor.
A stressor may be any condition or external stimulus that poses a physical or
psychological challenge and elicits a stress response.
The two main types of stressors are categorized as psychological and biological.
Both stressor types can interact to magnify or alter the stress response. Psychological
stressors can be real or imagined environmental events that prime activation of the stress
response through cognitive appraisal mechanisms (Chrousos, 2009). Before an event can
elicit the stress response, an individual must perceive the event as stressful.
Psychological stress is defined as an individual’s experience of negative events or
perceptions of distress and negative affect typically associated with the inability to cope
with or respond adequately to a stressor (Cohen, et al., 2001; Cohen, et al., 2007).
Examples of probable psychological stressors for ROTC cadets are peer-to-peer
competitiveness for leadership roles and ROTC scholarship, or taking examinations.
Psychological stress can be classified as either acute (i.e., short term, generally lasting
days to weeks) or chronic (i.e., longer term, generally lasting weeks to months). Acute
stress is commonly due to the occurrence of a major life event or trauma; chronic stress is
16
most often the result of the accumulation of day-to-day stress such as caregiving or
poverty. Both acute and chronic stress can have long term consequences for the
individual (McEwen, 1998; Selye, 1985, 1998). Biological stressors can activate the
stress response without cognitive perception or appraisal of the stressor (Everling &
Lating, 2002). An example of a probable biological stressor for ROTC cadets is intensive
physical fitness training activities.
Stress Response
The mind and body react to stressors through activation of several complex
physiologic responses at the level of the central nervous system; these responses affect
behavior and all major organ systems (Charmandari, et al., 2005). The stress response is
tightly controlled and maintained by a variety of feedback loops (Gold & Chrousos,
2002).
During the perception of an event appraised to be potentially harmful, the cerebral
cortex and limbic systems activate the hypothalamus (Blalock, 2005; Tsigos & Chrousos,
2002) which controls the release of corticotrophin-releasing hormone (CRH) from
paraventricular nuclei (PVN) of the hypothalamus. In the acute stress response,
characterized by increased circulating cortisol levels, there is a surge of CRH secretion
which causes a surge in the secretion of adrenal corticotrophic hormone (ACTH) from
the anterior pituitary, resulting in increased secretion of glucocorticoids such as cortisol
from the adrenal cortex (Charmandari, et al., 2005). The central stress response also
activates neural pathways to increase release of androgens and mineralocorticoids from
17
the adrenal cortex, these also act on the HPA axis to potentiate its activity and increase
cortisol secretion (Charmandari, et al., 2005).
The elevated levels of cortisol terminate the stress response by acting on the HPA
axis through a negative feedback loop to reduce the release of CRH and ACTH, allowing
serum cortisol levels to return to basal levels (Burke, Davis, Otte, & Mohr, 2005;
Jacobson & Sapolsky, 1991; Sapolsky, et al., 2000). This negative feedback is mediated
through two types of intracellular receptors. The majority of these receptors, known as
mineralocorticoid receptors or type I receptors, are located within the hippocampus.
Type I receptors respond to low concentrations of cortisol in basal conditions and play a
role in the normal circadian activity of the HPA Axis. Type II glucocorticoid receptors
(GR) are located within the hippocampus, hypothalamus, and pituitary gland (Claes,
2004; Stark, et al., 2001) and respond to both basal and stress-induced elevations of
glucocorticoids. With chronic stress, basal levels of cortisol secretion do not follow the
normal circadian pattern of cortisol release over 24 hours, suggesting stress induced
dysregulation of the HPA axis. Further evidence of HPA axis dysregulation comes from
research using magnetic resonance imaging, finding that chronic exposure to elevated
glucocorticoids decreases hippocampal volume (McEwen, 1998). As the hippocampus is
a site of negative feedback for the HPA axis, the decrease in functional hippocampal
integrity may contribute to continued elevation of cortisol levels and dysregulation of the
HPA axis as seen in patients with post traumatic stress disorder (PTSD), Cushing's
Syndrome, and depression (Lee, Ogle, & Sapolsky, 2002). Studies using animal models
of chronic stress found that GR in areas of the prefrontal cortex, hippocampus, and
18
hypothalamus are differentially regulated under chronic stress conditions (Mizoguchi,
Ishige, Aburdad, & Tabira, 2003; Stark, et al., 2001). These findings suggest that the
hippocampus, as well as GR, may be integral in the dysregulation of the HPA axis with
chronic stress.
Normal Immune Response
Protection against stress, tissue injury, and invading pathogens are controlled
through innate (non-specific) and adaptive (specific) immune responses McCance,
Huether, Brashers, & Rote, 2009; Segerstrom & Miller, 2004). When exposed to
immune stimuli, the innate immune system is activated, and resident cells are actively
attracted and migrate to the site of injury, resulting in cellular phagocytosis and removal
of debris from dead pathogens. This activity signals the initiation of acute inflammation
in which macrophages and other cells are stimulated to produce pro-inflammatory
mediators such as cytokines.
During the typical innate inflammatory response, concurrent system activation of
the adaptive immune response occurs, and macrophages present antigens (foreign, nonself cells) for recognition, killing, and removal. The adaptive immune response requires
recognition of the foreign antigens as non-self to preserve and avoid attack of host (self)
cells which would cause autoimmune disease. Humoral and cell-mediated immune
responses both comprise the adaptive immune system (Kindt, et al., 2006). The humoral
immune branch, which involves antibody (immunoglobulin) production, is made of β
lymphocytes (β cells) that secrete antigen-specific antibodies that bind and neutralize
pathogens, protecting against bacterial, parasitic infections and disease (Kindt, et al.,
19
2006). Cell-mediated immunity consists of both T helper lymphocytes (CD4+) and
cytotoxic T lymphocytes (CTLs) (CD8+), which protect through killing intracellular and
virally infected cells. β cells mature in the bone marrow and T lymphocytes migrate and
mature in the thymus gland; both are necessary for humoral and cellular immune
responses (Kindt, et al., 2006). When activated by immune challenge, CD4+ T cells can
differentiate into either T helper-1 (Th1) or T helper-2 (Th2) cells. The Th1 immune
response is associated with a strong cell-mediated killing/CTL response, whereas a Th2
response is characterized by a humoral or antibody-mediated immune response (Esser, et
al., 2003). Collectively, all are crucial in the elimination of infections.
Immune Response to Vaccination
Influenza vaccine consists of inactivated or attenuated viruses, and induces a
thymus-dependent antibody response that requires helper T cell involvement (Phillips,
2012). Within the immune system, the response to vaccination involves the coordination
of a wide variety of immune cells. Initially antigen presenting cells (APCs), such as
dendritic cells and macrophages, present influenza antigen to the T helper cells for
antigen recognition. Once T helper cells have recognized the antigen, the stimulation and
differentiation of β cells into plasma cells occurs. Plasma cells then produce antibodies
or immunoglobulin such as IgA, IgE, IgM, IgG, and IgD (Phillips, 2012; Kindt, et al.,
2006; Segerstrom & Miller, 2004). Vaccination with an antigen not previously
encountered induces a primary antibody response, wherein the earliest antibody (IgM) is
produced with peaks occurring approximately 5 days after vaccination. Interaction
between activated T- and β-cells leads to the production of high affinity antibodies in
20
bodily fluids such as IgG (found mainly in the blood) and IgA (found mainly at mucosal
surfaces). Secondary antibody responses occur against more common pathogens, such as
influenza, when the immune system has been previously exposed to the antigen, either
through natural occurrence or by previous vaccination. Secondary antibody responses are
usually more rapid and of greater magnitude as some of the previously activated T and βcells become long-lived immune memory cells, which are readily available to respond to
subsequent immune antigenic challenges (Phillips, 2012).
Inflammatory Response to Vaccination
Another essential aspect of the immune response that occurs during vaccination is
an inflammatory response, providing immediate host defense and initiating adaptive
immune responses to pathogen exposure. Inflammation is a complex defense mechanism
involving interactions among multiple classes of mediators and cell types. Inflammation
is influenced by the production of cytokines, small polypeptides secreted by white blood
cells (WBCs) attracted to sites of injury or infection (Abbas, Lichtman, & Pillai, 2009;
Murphy, Travers, & Walport, 2007; Kumar, Abbas, Fausto, & Aster, 2009).
Inflammatory cytokines have a diverse range of biological effects that include directing
WBCs toward sites of injury or infection, stimulating the production of other mediators
involved in the inflammatory response, and enhancing the cytotoxic capacity of certain
WBC classes (Miller, et al., 2002).
Cytokines are classified as either proinflammatory or anti-inflammatory.
Proinflammatory cytokines such as IFN-γ, IL-1β, IL-6, and TNF-α are of special interest
in PNI research because they are the primary regulators of innate immune and
21
inflammatory responses (Maes, Christophe, Bosmans, Lin, & Neels, 2000; Maes, et al.,
1998a; Maes, et al., 1998b) and have been useful biomarkers of alterations in the immune
and stress response systems with chronic stress. The proinflammatory cytokines IL-1β,
IL-6, and TNF-α, are released in the early stages of an immune response from a variety of
cell types, including macrophages, vascular endothelial cells, fibroblasts, and neurons
(Li, et al., 2008; Sasaki, et al., 2002).
IFN-γ plays a critical role in both innate and adaptive immunity and has antiviral,
immunoregulatory and anti-tumor properties (Schroder, Hertzog, Ravasi, & Hume,
2004). IFN-γ is predominantly produced by natural killer T cells as part of the innate
immune response, followed by CD4+ Th1 lymphocytes and CD8+ cytotoxic T
lymphocytes (CTL) once antigen-specific immunity develops (Schoenborn, & Wilson,
2007; Schroder, et al., 2004). IFN-γ plays an important role in the differentiation of
antigen-inexperienced CD4+ cells (Th0 cells) into Th1 cells. Additionally, IFN-γ
suppresses Th2 cell differentiation and secretion of anti-inflammatory cytokines such as
IL-4 and IL-10 (Schroder, et al., 2004). Because excessive IFN-γ expression is
associated with a number of autoinflammatory and autoimmune diseases(Schroder, et al.,
2004), it is of particular interest to this study which examines the effects of perceived
psychological stress on inflammatory responses to ex vivo vaccine challenge.
IL-1β is another potent inflammatory cytokine, produced primarily by monocytes,
but also by activated macrophages, dendritic cells and certain epithelial cells in response
to infection and injury (Li, et al., 2008; Sasaki, et al., 2002). Increased levels of IL-1β
have been reported in patients with infections, acute inflammation, trauma (i.e., post-
22
surgical) and chronic inflammatory conditions such as atherosclerosis, ischemic disease,
and cancer. It is also increased in healthy subjects after strenuous exercise (Li, et al.,
2008; Shabakhti, et al., 2004).
IL-6 is one of the most studied cytokines in relation to psychological stress and
health outcomes (Kiecolt-Glaser, et al., 2003; Ridker, Hennekens, Buring, & Rifai, 2000;
Steptoe, Hamer, & Chida, 2007). Concomitantly produced with IL-1β and/or TNF-α,
increased levels of IL-6 are commonly seen in chronically stressed individuals. IL-6 is
produced by monocytes, macrophages, and non-lymphoid cells in response to stress,
tissue injury, or infection. In addition to involvement in the activation of plasma
lymphocytes and synthesis of β-cells, IL-6 uniquely exhibits two contrasting
inflammatory effects important in the transition from acute to chronic inflammation
(Gabay, 2006; Kaplinski, Marin, Montero-Julian, Mantovani, & Farnier, 2003). In
models of acute inflammation, IL-6 exhibits anti-inflammatory Th2 properties by
activating the HPA axis, resulting in cortisol release (Goebel, Mills, Irwin, & Ziegler,
2000) to resolve inflammation. In contrast, prolonged elevations of IL-6 have been
shown to favor a Th1 response and contribute to pathogenesis of rheumatoid arthritis and
colitis (Calcagni & Elenkov, 2006; Kaplinski, et al., 2006; Miller, et al., 2002).
Typically, the HPA axis prevents the excessive peripheral release of IL-6 through cortisol
effects following acute stress. However, resistance to the inhibitory effects of
glucocorticoids on proinflammation has been shown to result in higher systemic levels of
IL-6 in chronically stressed individuals when compared to healthy control subjects
(Miller, et al., 2002).
23
TNF-α, a proinflammatory cytokine, is produced mainly by activated
macrophages that plays a key role in the local inflammatory response and helps to control
infections. Increased release of TNF-α concomitantly with IL-1β and IL-6 secretion
occurs with sepsis and contributes to the pathobiology of septic shock (Goebel, et al.,
2000). Circulating levels of TNF-α are also increased in both young and older adults
under chronic stress (Wright, et al., 2004).
During an immune challenge such as vaccination, the immune system is activated
to produce inflammatory cytokines. Measuring the production of proinflammatory
cytokines, such as IFN-γ, IL-1β, IL-6, and TNF-α may be helpful in examining
inflammatory responses to vaccine challenge in stressed military students. Typically,
vaccination stimulates a transient increase in plasma levels of proinflammatory cytokines
as part of the innate and adaptive immune responses. This increase in inflammatory
cytokines has a profound effect on the HPA axis, inducing glucocorticoid release of
cortisol (Tsigos & Chrouos, 2002). IL-1β, IL-6, and TNF-α stimulate secretion of
cortisol through direct effects on the cells of the adrenal cortex and indirect effects on
CRH release from the hypothalamus and ACTH from the anterior pituitary (Petrovsky,
2001; Petrovsky & Harrison, 1997). Thus, the proinflammatory release of cytokines due
to vaccination is counterbalanced by the anti-inflammatory effects of increased cortisol
secretion (Elenkov, et al., 2000a; Tsigos & Chrousos, 2002) and reciprocal Th2 derived
anti-inflammatory cytokine activity (Elenkov, et al., 2000b; Sapolsky, et al., 2000;
Vedhara et al., 1999). The cross-regulation of neuroendocrine and inflammatory
responses is needed for T-cell and β-cell proliferation, antibody synthesis, and
24
seroprotection (Burns, et al., 2002a; Burns, et al., 2002b; Cohen, et al., 2001; Elenkov, et
al., 2000b; Glaser, et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag, et
al., 1999; Sapolsky, et al., 2000; Tsigos & Chrousos, 2002).
Cortisol Regulation of Inflammatory Cytokine Production
Cortisol, the end-product of the stress response and HPA axis activation, inhibits
nearly all aspects of the immune system, including the inflammatory response to immune
stimuli (Gold & Chrousos, 2002; Tsigos & Chrousos, 2002). Glucocorticoids influence
the balance between Th1derrived proinflammatory and Th2 anti-inflammatory cytokine
secretion (Elenkov, 2004). For example, proinflammatory cytokines such as IL-1β, TNFα, and IFN-γ are down regulated by cortisol (Elenkov, et al., 2000a; Hu, Li, Meng, &
Ivashkiv, 2003) in contrast, the production of anti-inflammatory cytokines such as IL-10
and IL-4 is enhanced by cortisol (Elenkov, & Chrousos, 1999, 2002; Schuld, et al., 2003).
The inhibitory role of glucocorticoids (e.g., cortisol) provides host protection
from the detrimental consequences of an overactive inflammatory response (Anisman &
Merali, 2003; Charmandari, et al., 2005; Kiecolt-Glaser, et al., 2002; Petrovsky, 2001).
Glucocorticoids have been used as anti-inflammatory treatment since the 1930s
(Chrousos, 2000). Glucocorticoid steroidal medications are generally thought to be
immunosuppressive (Chrousos, 2000); however, more recent evidence suggests the
interplay between the immune system, stress system, and inflammatory responses is more
complex than previously believed (Cohen, et al., 2012a; Miller, et al., 2002; Rohleder,
2012; Tsigos & Chrousos, 2002). Normally, cortisol decreases production of
proinflammatory cytokines, abating the inflammatory response. Glucocorticoid
25
responsiveness or sensitivity to the effects of cortisol has an important role in regulating
the cytokine balance and susceptibility to inflammatory, autoimmune and infectious
diseases (Cohen, et al., 2012a; Miller, et al., 2002; Rohleder, 2012; Tsigos & Chrousos,
2002).
During the acute or initial phase of inflammation with vaccine exposure, cellular
immune responses occur through Th1 lymphocyte secretion of proinflammatory
cytokines IFN-γ and TNF-α, which recruit macrophages, cytotoxic T lymphocytes, and
natural killer cells to kill and remove antigenic debris (Elenkov, 2004). Cortisol also
plays an important role in modulating inflammatory responses to immune stimuli through
its effects on acquired immunity, primarily through the stimulation of Th2 lymphocytes
(Chrousos, 2000). As a result of elevated cortisol and glucocorticoid stimulation of Th2
lymphocytes the secretion of anti-inflammatory cytokines IL-4 and IL-10 occurs to
suppress Th1 proinflammatory cytokines, and to affect humoral (antibody) immunity by
promoting differentiation of β lymphocytes into plasma cells to produce antibodies
(Elenkov, 2004). The shift from the initial cellular (Th1/proinflammatory) to humoral
(Th2/anti-inflammatory) immune responses indicates the suppressive effects of cortisol
on proinflammatory cytokine production (Calcagni & Elenkov, 2006; Elenkov, 2004;
Glaser, et al., 2001).
Effect of Chronic Stress on Immune Function
Psychological stress can affect immune function in response to vaccination or
viral challenge (Black, 2003). Chronic stress affects inflammation, cortisol regulation of
cytokine production levels and antibody production during vaccination.
26
Chronic Stress and Inflammation
Psychological stress is known to increase production of inflammatory cytokines
and cortisol in otherwise healthy individuals (Hamer & Steptoe, 2007). Both physical
and psychological stress provokes transient increases in cytokines; in particular IL-6
(DeRijik et al., 1997; Zhou, Kusnecov, Shurin, DePaoli, & Rabin, 1993). Kiecolt-Glaser
(2003) suggested that the increase in IL-6 may be due in part to stress behaviors such as
smoking, over eating, excessive alcohol use, insufficient sleep, or leading a sedentary
lifestyle without exercise that affect IL-6 production. Increased levels of IL-6 can persist
for as long as 3 years beyond the stress experience (Kiecolt-Glaser, et al., 2003).
Acute inflammation is a limited beneficial response, particularly during infectious
challenge. In contrast, chronic inflammation may result from persistent infection,
prolonged exposure to inflammatory stimuli or toxins, or presence of autoimmune
disease. Transition to chronic inflammation occurs when acute inflammatory responses
cannot be resolved. Chronic inflammation is prolonged and may last for weeks, months
or longer. Because chronic inflammation may not manifest the typical signs or symptoms
seen with acute inflammation, a person with chronic, systemic, low-grade inflammation
may appear asymptomatic and healthy, but could be at risk for infection and disease.
Chronic Stress and Cortisol Regulation of Inflammatory Cytokines
Cortisol, increased in response to elevated levels of cytokines, is also activated in
stressful situations, and is the neuroendocrine link between stress, inflammation, and
immunity. Psychological stress activates the HPA axis to elicit cortisol secretion, which
may have significant effects on regulation of inflammatory and immune responses.
27
Acute stress exposure activates the HPA axis, resulting in short bursts of increased
cortisol secretion. Acute increases in cortisol secretion just prior to vaccination enhance
antibody production in healthy individuals (Edwards et al., 2006). However, chronic
stress exposure leads to prolonged activation of the HPA axis and prolonged elevation of
cortisol levels and suppressed immune responses (Agarwal & Marshall, 2001; Marshall,
et al., 1998; Plotnikoff, Faith, Murgo, & Good, 2007), diminished viral infection
clearance (Glaser & Kiecolt-Glaser, 2005), and poorer antibody response to vaccination
(Burns, et al., 2002a; Burns, et al., 2002b; Cohen, et al., 2001; Glaser, et al., 1992;
Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag, et al., 1999; Vedhara, et al.,
1999).
The prolonged elevations of proinflammatory cytokines that result from chronic
stress induce a decrease in glucocorticoid receptors in the central nervous system, causing
hypothalamic CRH-secreting cells to become insensitive to increasing cortisol
concentrations and dampening normal negative feedback responses. Exposure to high
amounts of stress, coupled with an impaired response to glucocorticoids due to
diminished cortisol concentrations or impaired GR function, predisposes individuals to
upper respiratory infections (Cohen, et al., 2012a) as well as autoimmune and
inflammatory, glucocorticoid-resistant, inflammatory disease (Burnsides, et al., 2012;
Silverman & Sternberg, 2008). Chronic activation of the HPA axis with prolonged
cortisol elevations seen in individuals under chronic stress or GR hypersensitivity leads to
suppression of proinflammatory/Th1 immune responses and increases risk for infection
(Chrousos, 1995; Chrousos & Gold, 1992; Elenkov, Chrousos, et al., 2000b). These
28
types of conflicting findings are barriers to understanding how stress and inflammation
impact immune responses, factoring in infection and disease risk. Nonetheless, if
inflammation is not controlled, illness and disease can occur.
In earlier research, psychological stress was shown to exert globally suppressive
effects on immune responses including reduced humoral immunity, reduced lymphocyte
proliferation, and reduced cytotoxic function of natural killer cells. A sustained elevation
in cortisol caused by psychological stress favors a persistent bias toward a Th2 response,
which impairs innate Th1 proinflammatory responses to pathogens (Cohen, et al., 2002;
Herbert & Cohen, 1993; Kiecolt-Glaser, et al., 1995). However, in the context of
sustained or chronic psychological stress, individuals exhibited decreased or blunted
cortisol responses to acute stress (Heim, et al., 2000; Miller, Cohen, & Ritchey, 2002),
persistent elevations in serum levels of proinflammatory cytokines, and poorer antibody
response following vaccination (Burns, et al., 2002a; Burns, et al., 2002b; Cohen, et al.,
2001; Glaser, et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag, et al.,
1999). The traditional concept of stress-induced immunosuppression does not offer a
clear and definitive explanation for the immune effects of chronic stress. It has been
proposed that chronic psychological stress, resulting in the prolonged activation of the
HPA axis and elevation of serum cortisol levels, may also result in downregulation of the
expression and function of glucocorticoid receptors. This functional loss of
glucocorticoid sensitivity (GCS) or glucocorticoid resistance (GCR) leads to HPA axis
dysregulation and a subsequent, feed-forward cycle of uncontrolled proinflammatory
cytokine responses or biased Th1 responses (Miller, et al., 2002).
29
Chronic stress can lead to glucocorticoid resistance in which the immune system
demonstrates a diminished sensitivity to glucocorticoids that normally control excessive
inflammation. Impaired GCS or GCR can be inherited or acquired, and is a major
problem in the glucocorticoid steroid treatment of many inflammatory diseases including
asthma, ulcerative colitis, systemic lupus erythematosus, and rheumatoid arthritis, where
as many as 30% of patients may be glucocorticoid resistant (Burnsides, et al., 2012;
Hearing, Norman, Smyth, Foy, & Dayan, 1999). Additional support for significant GCR
comes from previous studies reporting that an estimated 30 % of the normal healthy
population is glucocorticoid nonresponsive or resistant, or has impaired GCS (Burnsides,
et al., 2012; Hearing, et al., 1999).
Evidence of the effects of chronic stress and impaired GCS of inflammation
comes from studies of animals as well as humans. Animal studies demonstrate the
possibility that chronic stress diminishes the immune system’s responsiveness or
sensitivity to GCs that normally controls excessive inflammation. Researchers, using
social disruption as a stress model, found that socially defeated mice had reduced GCS to
stressed-induced elevations of GCs (e.g., corticosterone) in lipopolysaccharide (LPS)
stimulated splenocytes, resulting in higher IL-6 production (Stark, et al., 2001).
Mirroring these findings, socially disrupted stress-induced defeated mice with decreased
GCS were found to have greater risk for increased inflammation and mortality from
experimental influenza infection and septic shock (Padgett, Marucha, & Sheridan, 1998;
Quan, et al., 2001). These findings warrant research in humans, particularly in military
personnel who often are experiencing, have encountered, or can expect to face stressors
30
of long family separations (i.e. social disruption, chronic stress) during deployments, and
challenging reintegration transitions from the battlefield to the home-front.
GCS is most often measured through ex vivo assays, where immune cells of
chronically stressed people (as compared to their respective control groups) are harvested
and incubated with a bacterial endotoxin (e.g., LPS, tetanus) or a mitogen (e.g.,
phytohemagglutinin [PHA]) in combination with various concentrations of synthetic GCs
such as dexamethasone (DEX), hydrocortisone, or prednisone to measure dose response.
Studies using this approach have shown reduced GCS of immune cells, and subsequent
higher production levels of proinflammatory cytokine IL-6 and TNF-α in the presence of
varying concentrations of DEX from samples of spousal caregivers of dementia patients
(Bauer, et al., 2000), parents of children undergoing treatment for cancer (Miller, et al.,
2002), and stress-related syndromes such as extreme exhaustion in industrial employees
(Wirtz, et al., 2003), chronic fatigue (ter Wolbeek, et al., 2008), and depression (Miller,
Pariante, & Pearce, 1999).
Miller and colleagues (2002) demonstrated that peripheral blood cells obtained
from 25 young adults (n =25) parenting a child undergoing medical treatment for cancer
produced higher levels of the proinflammatory cytokine IL-6 when exposed to
dexamethasone (a potent synthetic form of cortisol) compared with cells from 25 nonchronically stressed adult parents of healthy children without cancer Miller, et al., 2002).
Wirtz and colleagues (2003), found that blood monocytes from highly exhausted,
middle-age, healthy, male industrial workers had reduced responsiveness to DEX
inhibition of LPS-stimulated IL-6 production than did non-exhausted workers (p =0
31
.003). Although this study only focused on healthy male subjects, the authors suggested
that reduced GCS may lead to sustained cytokine production once monocytes have
encountered a biological stressor such as LPS. They also proposed that altered regulation
of proinflammatory cytokine production may serve as one possible pathway linking
exhaustion with increased risk of atherosclerosis in healthy and other at- risk populations.
Cohen and colleagues (2012a) proposed that chronic exposure to a major stressful
life event may result in glucocorticoid receptor resistance (i.e., GCR or decreased GCS),
and in turn, result in a failed down-regulation of the inflammatory response, and
subsequent increased signs and symptoms of upper respiratory infection to rhino virus
exposure. Subjects were assessed for chronic stress, exposed to the rhino virus, and
monitored in quarantine for 5 days for signs of respiratory infection and illness. GCR
was assessed prior to viral challenge using a standard ex vivo model, wherein leukocytes
were co-incubated with lipopolysaccharide (LPS) and DEX, and proinflammatory
cytokine production were measured in supernatants, obtained from nasal fluid samples.
Cohen and colleagues (2012) found that subjects who reported higher levels of stress also
had higher GCR (lower GCS) prior to viral exposure produced higher proinflammatory
cytokines when they were infected. Higher GCR (i.e., impaired GCS) predicted greater
production of proinflammatory cytokines, IL-6 and TNF-α, obtained from nasal tissue
fluids among infected and healthy subjects. According to the authors of this study, this
finding indicates that chronic stress may induce glucocorticoid resistance or lower GCS,
which in turn interferes with appropriate regulation and glucocorticoid control of
inflammation.
32
Chronic Stress and Poor Antibody Production
Psychological stress has been shown to enhance the risk for infectious disease,
prolong infection-related illnesses, and reduce antibody production (Konstantinos &
Sheridan, 2001; Burns, et al., 2002a; Burns, et al., 2002b; S. Cohen, et al., 2001; Glaser,
et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996; Morag, et al., 1999). In a
seminal study, Kiecolt-Glaser and colleagues (1996) found impaired antibody responses
to influenza vaccination in Alzheimer’s caregivers relative to matched controls; only 37%
of the highly stressed caregivers demonstrated an increase in antibody response at 1
month following vaccination (Kiecolt-Glaser, et al., 1996).
Other studies have found that men and women chronically stressed by caring for a
spouse with dementia had distinct deficits in both cellular and humoral immune responses
to the influenza vaccine compared with well-matched control non-caregiver subjects
(Kiecolt-Glaser, et al., 1996a; Vedhara, et al., 2003; Vedhara, et al., 1999). Although
conceptually distinct, stress and depression involve activation of the HPA axis and
increased plasma levels of proinflammatory cytokines (Anisman & Merali, 2003). In
much of the literature on psychosocial stress these two constructs are measured together.
Depression is associated with higher proinflammatory responses to antigen challenge
(Glaser, Robles, Sheridan, Malarkey, & Kiecolt-Glaser, 2003; Zhou, et al., 1993). Glaser
and colleagues (2003) found that individuals reporting more depressive symptoms
showed increases in IL-6 serum levels two weeks following influenza vaccination when
compared to those who reported less depressive symptoms.
33
The protection offered through antiviral vaccines is contingent upon the humoral
as well as the cell-mediated immune response and is reduced in chronically stressed
individuals (Deng, Jing, Campbell, & Gravenstein, 2004). Chronic psychological stress
impaired antibody responses in younger adults following influenza, rubella, or hepatitis B
vaccinations (Burns, Carroll, Drayson, Whitham, & Ring, 2003; Burns, et al., 2002b;
Glaser, et al., 1992; Glaser, et al., 2000; Miller, et al., 2004; Morag, et al., 1999). Stress
also reduces the antibody response to antibacterial vaccines (Glaser, et al., 2000; Burns,
et al., 2003; Burns, et al., 2002b). These studies show that individuals who reported
greater stress or anxiety had poorer, delayed, and shorter-lived immune responses to
recommended and seasonal vaccinations. Individuals with poorer responses to vaccines
have higher rates of clinical illness associated with respiratory viruses, herpes virus, and
Epstein-Barr virus (Cohen et al., 1998; S. Cohen, Tyrell, & Smith, 1991; Glaser, et al.,
1999; Plotkin, 2001).
Paradox
Despite world-wide use of the influenza vaccine, the effect of individual factors
on vaccination outcomes is complex and not completely understood. The literature does
not clearly explain why chronically stressed individuals who have elevated cortisol
exhibit persistent elevations of serum inflammatory cytokines such as IL-6, and poorer
antibody response following vaccination (Burns, et al., 2002a; Burns, et al., 2002b;
Cohen, et al., 2001; Glaser, et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996;
Morag, et al., 1999; Miller, et al., 2004). If stress downregulates immune responses
through increased secretion of cortisol, then proinflammatory cytokines, such as IL-6
34
should be suppressed as well. As vaccine stimulated proinflammatory cytokine
production initially increases and elicits cortisol secretion, a shift from Th1 to Th2
activity promotes β-lymphocyte proliferation, differentiation, and antibody production
(Burns, et al., 2002a; Burns, et al., 2002b; Cohen, et al., 2001; Elenkov, et al., 2000a;
Glaser, et al., 1992; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1996b; Morag, et al., 1999;
Sapolsky, et al., 2000; Tsigos & Chrousos, 2002). If this is the case, there should be a
decrease in the production of Th1 proinflammatory cytokines, which would permit an
increase in Th2 activity, and possibly greater antibody production in response to chronic
stress.
Much of the relevant literature regarding the relationships among psychological
stress, inflammation, and immune function are focused on the effects of glucocorticoids.
The two basic models that describe the effects of glucocorticoids in immune function
during stress are the immunosuppression and the glucocorticoid-resistance models.
Immunosuppression versus Glucocorticoid Resistance
Immunosuppression
The immunosuppression model's main tenet is that stress suppresses the immune
function in a way that results in greater vulnerability to infectious diseases (Miller, et al.,
2002):
Stress is assumed to downregulate immunity by (a) activating autonomic nervous
system fibers that descend from the brain to lymphoid organs, (b) triggering the
secretion of hormones and neuropeptides that bind to white blood cells and alter
35
their function, and (c) inducing immunomodulatory coping behaviors, such as
cigarette smoking and alcohol consumption (p. 531).
The immunosuppression model does not provide an explanation for how stress might
influence diseases and symptoms whose central feature is excessive inflammation. The
immunosuppression model focuses on the suppression of inflammatory cytokine
responses to immune stimuli. The paradox with this theory is that if stress downregulates
immunity through the triggering of the stress response with secretion of cortisol, then
proinflammatory cytokines should be suppressed as well. There should be a decrease in
the production of inflammatory mediators and possibly enhanced antibody production in
response to chronic stress. However, chronic stress is associated with elevated levels of
circulating biomarkers of inflammation and poorer antibody response to vaccination.
Glucocorticoid Resistance
In an attempt to explain this paradox, Miller and colleagues (2002) proposed the
glucocorticoid resistance model, which serves as the foundation of this study's
overarching hypothesis: chronic stress diminishes leukocyte responsiveness to the antiinflammatory effects of glucocorticoid hormones (i.e. cortisol). This model begins with
stress-induced activation of the HPA and sympathetic adrenal medullary (SAM) axes.
Chronic stress results in prolonged exposure to elevated levels of glucocorticoids, leading
to downregulated expression or altered function of glucocorticoid receptors. Miller also
found that downregulation of receptors impairs the cell’s ability to respond to the normal
36
anti-inflammatory actions of cortisol. This process can also increase risk of diseases
associated with inflammation by creating a persistent proinflammatory milieu.
For the purposes of this study, the term glucocorticoid sensitivity (GCS) will be
used in lieu of glucocorticoid resistance. The term sensitivity is a more appropriate
description in this study, which is designed to determine whether perceived psychological
stress affects virus-induced cytokine production by leukocytes of healthy military
students, and if dexamethasone (a synthetic form of cortisol) suppresses cytokine
production. For the purposes of this study, we have defined GCS as the percent reduction
in vaccine-induced cytokine production (Bailey, et al., 2003; S. Cohen, et al., 2012a;
Marques, et al., 2009; Miller, et al., 2002; Stark, et al., 2001).
Military individuals as well as veterans and military college students may be
under considerable stress. Consequently, stress may reduce GCS causing loss of normal
suppressive effects of cortisol on production of proinflammatory cytokines in response to
an immune stimulus such as attenuated influenza virus. This hypothesis has not
previously been examined among healthy military personnel or military college student
subjects, and may prove useful in explaining the high rates of influenza infection in
military personnel.
Purpose of this Study
Studies demonstrate that despite being a presumably fit and healthy population,
military training recruits, cadets, and deployed personnel have higher rates of upper
respiratory infections, such as influenza, than do civilian populations. This poses a
significant health risk to the readiness of military units world-wide.
37
Literature suggests that psychological stress may play a role in the pathogenesis
of respiratory infections (Cohen et al. 2012a), which could explain the increased risk of
respiratory infections such as influenza in military personnel. The inflammatory
response, in particular production of proinflammatory cytokines, plays a key role in the
antibody response to immune stimuli such as vaccination. Cortisol plays a key role in
abating the inflammatory response to promote antibody production. We hypothesized
that with chronic stress, cells lose their sensitivity to effects of glucocorticoids, resulting
in a protracted inflammatory response and reduced antibody production.
Research conducted on military students is sparse despite the fact that previous
studies have shown that perceived stress has negative effects on cadet physical health and
susceptibility to illness (Glaser, et al., 1999; Lee, et al., 1992). Thus, the current study
was designed to determine if perceived psychological stress affects influenza vaccineinduced production and regulation of pro-inflammatory cytokines in whole blood of
healthy ROTC college students.
38
CHAPTER 3
STUDY DESIGN AND METHODS
Chapter 3 presents the research design, the sample/setting and the measures and
instruments used in the study. Procedures for collecting and processing blood specimens
and biomarker assays are described. The protection of human subjects, data collection,
and a plan for data analysis are discussed.
Research Design
A cross-sectional descriptive design was used to accomplish the specific aims of
the study.
Sample and Setting
This was a study of full-time military college students regardless of gender. The
inclusion criteria included the following: able to read, write, and speak English; 18-39
years old; general good health with no pre-existing conditions (e.g., asthma, Crohn’s
disease, Cushing’s disease, cardiovascular disease, metabolic syndrome, Type II diabetes,
atherosclerosis, COPD, chronic pain, pregnancy/lactation); no recent acute illness or use
of antibiotic, steroidal or anti-inflammatory, or anti-depressant medications in the past 6
weeks (with the exception of oral contraceptives). The criteria were designed to
minimize the interference of factors known to affect the production of proinflammatory
39
cytokines or immune function. Subjects were recruited via flier advertisements and faceface contact with military college students in the Tri-Service military service Reserve
Officer Training Corps at a large Midwest public university.
Measures
Study variables included 1) psychological stress, 2) cytokine production, and 3)
glucocorticoid sensitivity (GCS). Demographic data were also collected about factors
that could affect study results.
Demographic Data:
Demographic Survey Form (Appendix A). This questionnaire was used to collect
pertinent demographic data, including: General Information (i.e., age, race, ethnicity,
gender and marital status); Military Information (i.e., rank/pay-grade/ROTC student rank,
years of military service, military service branch, active duty/reserve duty status,
commissioned/enlisted status, designator/ specialty, deployment history, date and length
of most recent deployment, and combat/combat support exposure); and Student
Information (i.e., cumulative GPA, student class rank, and scholarship status).
Psychological Stress:
Psychological stress is defined as an individual’s experience of negative events or
perceived distress and negative mood. In this study, psychological stress was measured
using the Perceived Stress Scale (PSS) and the trait subscale portion of the State-Trait
Anxiety Inventory (STAI-T).
40
Perceived Stress Scale (PSS). The PSS has been used in numerous populations
and has been widely translated into other languages (Cohen & Janicki-Deverts, 2012b;
Cohen, et al., 1983; Glaser, et al., 1999). It is a 10-item questionnaire used to elicit an
individual’s evaluation of stressful experiences in the past month (Appendix B). The
responses are rated on a Likert-scale from 0 to 4, or from “never” to “very often” (0 =
Never, 1 = Almost never, 2 = Sometimes, 3 = Fairly often, 4 = Very often). A score is
determined by reversing the scores on the 4 positive items, and then summing across all
10 items for a global score. Scores can range from 0 to 40, with higher scores indicating
greater stress. PSS scores in the range of 0-13 indicate low stress, those in the range of
14-26 indicate moderate stress, and those in the range of 27-40 indicate high stress
(Cohen & Janicki-Deverts, 2012b). The PSS was designed to be used in individuals with
at least a junior high school education and can be administered in 5 minutes or less. No
special training is required to administer or score the instrument and all instructions for
scoring were clearly described in the original publication.
Psychometric properties of the PSS are appropriate for this study. Reliability and
internal validity have been reported as high Cronbach's α coefficients (0.84 to 0.86) (S.
Cohen, Kamarck, & Mermelstein, 1983; Polit & Beck, 2008). It is a valid and reliable
instrument across diverse populations (Cohen, et al., 1983) and was previously used with
2 military academy student samples as well as other military populations (Glaser, et al.,
1999; Taylor, et al., 2008b).
State-Trait Anxiety Inventory (STAI-T). The trait portion subscale was used to
measure self-reported anxiety, as the second measure of psychological stress. The STAI-
41
T measures longstanding, enduring anxiety and includes a series of 20 items (Appendix
C). Trait anxiety represents a predisposition to react with anxiety in perceived stressful
situations: trait anxiety scores are higher in psychoneurotic, chronically stressed, and
depressed people (Spielberger, Gorsuch, & Lushene, 1983; Taylor, et al., 2008b). The
STAI-T targets how respondents “generally feel” using a 4-point Likert-type scale (e.g.,
“I am a steady person”, “I lack self-confidence”) with possible responses ranging from
“almost never” to “almost always.” Other examples of items are “I feel pleasant,” “I
worry too much about something that does not matter” and “I make decisions easily”
(Spielberger, et al., 1983). The STAI-T inventory is scored by reverse coding each
positive item and then summing across all items. Scores range from 20 to 80, with lower
scores indicating less trait anxiety and higher scores indicating higher trait anxiety.
Reliability and internal validity are acceptable and have been reported as 0.79 to
0.93 (Spielberger, et al., 1983; Taylor, et al., 2008b); stability has been established
through test-re-test correlation (Barnes, Harp, & Jung, 2002; Grös, Antony, Simms, &
McCabe, 2007). Raw scores obtained from STAI-T for this study were treated as
continuous variables for data analysis. A median split technique was used to identify
high and low anxiety groups, and to determine the strength of correlation with continuous
PSS scores.
Cytokine Production:
Production of IFN-γ, IL-1β, IL-6, and TNF-α in whole blood was measured by exvivo stimulation of whole blood with attenuated influenza virus. Ex vivo stimulation of
whole blood was used in this study because it is a useful tool in investigating cytokine
42
responses to a various stimuli, including bacterial endotoxin (i.e., LPS or PHA), antigens
(i.e., influenza, tetanus, or hepatitis vaccines), allergens, and antibiotics (Thurm &
Halsey, 2005). It is also useful in determining the effects that potential inhibitors (e.g.,
pharmacological agents such as corticosteroids or synthetic glucocorticoids such as DEX)
may have on inflammatory processes (Thurm & Halsey, 2005; Creed, et al., 2009; Cohen
& Janicki-Deverts, 2012a). Using ex vivo whole blood culture provides an approximation
of the state of circulating cells and their interactions occurring in vivo. Whole blood was
selected over isolated leukocytes for cytokine measures because whole blood assays are
believed to be better reflections of in vivo cytokine activity, since whole blood contains
physiological concentrations of factors that influence immune cell function.
Additionally, the use of ex vivo whole blood cultured conditions alleviates placing human
subjects at increased risk for illness or unnecessary increased stress-induced immune
changes that could confound outcome measures (Thurm & Halsey, 2005).
Whole blood was stimulated with Afluria trivalent inactivated influenza vaccine
that contained the following three strains for 2011-2012: A/California/7/09 (H1N1)-like
virus (pandemic (H1N1) 2009 influenza virus); A/Perth /16/2009 (H3N2)-like virus; and
B/Brisbane/60/2008-like virus (CSL Biotherapies, Parkville, Australia). Influenza
vaccine was used in this study as an immuno-stimulant to trigger cytokine production ex
vivo because it provides an ideal context to study the general model of stress leading to
disease via effects on the HPA axis and inflammatory regulation (Cohen & JanickiDeverts, 2012a). Influenza vaccine has been widely used in studies as a mild immune
trigger to examine individual differences in both in vivo and ex vivo inflammatory
43
responses in young and elderly adult populations (Posthouwer, Voorbij, Grobbee,
Numans, & van der Bom, 2004; Skowronski, et al., 2003; van der Beek, Visser, & de
Maat, 2002; Glaser, et al., 2003; Tsai, et al., 2005). The inflammatory responses induced
by influenza vaccine are substantially milder and have a more transient effect on cytokine
responses. Ex vivo influenza vaccine-stimulated inflammatory responses, as used in this
study, may more closely mimic immune responses that occur in vivo than using ex vivo
stimulation of cytokine production through endotoxin exposure with LPS. The ex vivo
use of LPS may not reflect immune responses in vivo, as LPS works through a different
immune pathway than influenza vaccine, which requires less incubation period and is
elicited through a much more powerful, stimulated secretion of cytokines from
monocytes (Thurm & Halsey, 2005). Thus, the use of ex vivo influenza vaccine as an
immuno-stimulant of cytokine production in this current study is a useful model for
examining individual differences in inflammatory responses to immune challenge.
Glucocorticoid Sensitivity (GCS)
GCS is defined as the capacity of immuno-stimulated leukocytes to respond to the
normal anti-inflammatory actions of glucocorticoids (Bamberger, Schulte, & Chrousos,
1996; DeRijik, et al., 1997; Miller, et al., 2002; Rohleder, et al., 2010; Wirtz, et al.,
2003). Assays to determine glucocorticoid sensitivity include the dexamethasone
suppression test (Chriguer, et al., 2005; Ebrecht et al., 2000; Syed, Redfern, & Weaver,
2010), inhibition of peripheral blood mononuclear cell (PBMC) proliferation (Chriguer,
et al., 2005), quantification of the number and affinity of glucocorticoid receptors (GRs)
in PBMCs (Chriguer, et al., 2005), and glucocorticoid suppression of either mitogen or
44
antigen-stimulated cytokine production by peripheral blood leukocytes (PMCs) (DeRijik,
et al., 1997; Ebrecht, et al., 2000; Miller, et al., 2002; Syed, et al., 2010; Wirtz, von
Känel, Rohleder, & Fischer, 2004; Wirtz, et al., 2003). For the purposes of this study,
glucocorticoid suppression of cytokine production by PBC in whole blood stimulated
with influenza vaccine was examined.
Dexamethasone (DEX) has been widely used as a glucocorticoid (GC) (DeRijik,
et al., 1997; Miller, et al., 2002; Rohleder, et al., 2010; Wirtz, et al., 2003) in studies
examining GCS (Wirtz, et al., 2003). The basic principle used to assess GCS is to induce
cytokine production and co-incubate cells with serial dilutions of DEX to determine the
inhibition of cytokine production. In this study, DEX phosphate (Sigma-Aldrich, St.
Louis, MO; Material Number D2915-100MG, Batch SLBB7572V) was used at a final
concentration of DEX 200 nM to approximate GC levels commonly found in vivo during
moderate stress (Agarwal & Marshall, 2001; Miller, et al., 2002).
GCS was measured by examining the inhibitory effect of DEX on production of
IFN-γ, IL-1β, IL-6, and TNF-α by influenza vaccine-stimulated peripheral blood cells.
The following formula was used to quantify percent inhibition of cytokine production:
% Inhibition = 1- stimulated cytokine level with DEX
X100
stimulated cytokine level w/out DEX
e.g.,
% Inhibition = 1- 20 pg/ml
X100 = 80% inhibition of cytokine production
100 pg/ml
A lower percent inhibition of cytokine production indicated lower GCS (greater
resistance) to anti-inflammatory effects of DEX.
45
Data Collection Procedure
Military students were approached face-to-face and study information was
presented via an IRB-approved oral script by the investigator. Flyers were posted on
university bulletin boards in the ROTC facility. In order to prevent undue influence
during the consent process, the investigator’s military rank was not included on
recruitment flyers, advertisements, or in any subsequent email/phone/face-to-face
communications with the military student participants. The investigator wore civilian
business attire during all interactions and explained his role as a doctoral student.
Prospective subjects were also informed that all data were confidential and would not be
divulged to military Command Staff. Subjects were made aware that participation was
voluntary and that they could withdraw from the study at any time without consequence.
All potential subjects were encouraged to ask questions and voice concerns regarding
participation. If the potential subject agreed to participate after the study was explained
and time was provided to ask questions, informed (Appendix D) and HIPAA (Appendix
E) consents were completed.
Following enrollment, subjects were seated in a private room in the ROTC Unit
building and completed the demographic data form, the 10-item PSS, and 20-item STAIT. Blood (20 ml) was drawn from each subject’s antecubital vein into heparin-coated
vacutainer tubes (Becton-Dickinson, Franklin Lakes, NJ; BD Model Number 367880)
using 21-gauge butterfly needles. All subjects required only one venipuncture. The
blood tubes from each individual were placed in an individually labeled, biologic
specimen bag on an ice-pack and transported from the ROTC unit to the laboratory by the
46
investigator. All specimens were processed within 2 hours of collection. To control for
diurnal variations of endogenous cortisol effects on inflammatory cytokine production
levels, all blood was drawn between 9:00 a.m. and 3:00 p.m. The total time commitment
averaged 20 minutes for each subject. Each subject received a $10 gift card as
compensation for their time.
Ex-vivo cytokine production
Three ml of whole blood was diluted 1:1 with complete 1640 RPMI medium
containing no fetal calf serum (Gibco by Life Technologies) and 1.8 ml of the diluted
blood was placed into each of 3 wells of a 6-well culture plate [Figure 4]. Well A was
diluted blood with 0.2 ml medium to capture spontaneous, cytokine production in nonstimulated cells, Well B was blood with 0.1 ml of the influenza vaccine and 0.1ml of
medium to capture vaccine-induced cytokine production, and Well C was blood and 0.1
ml of influenza vaccine and 0.1 ml of dexamethasone to determine glucocorticoid
sensitivity. The final volume in each well was 2.0 ml.
The samples were then incubated for 72 hours at 37°C with 5% CO2. At 72
hours, the culture fluid was aspirated from each well and centrifuged at 500g (1250 rpm)
for 10 minutes. The cell-free supernatant fluids were stored in 500 μL aliquots in a -80°C
freezer until batch-assays for cytokine levels were performed (De Groote, et al., 1992;
Maes, et al., 1998a).
47
Figure 4
Blood Collection, Cultures, and Assays
A
B
C
Figure 4. Illustration of Blood Culture Well Conditions (A) Spontaneous/non-stimulated,
B) Stimulated and C) Inhibited), Incubation Periods, and Processing Supernatants for
Cytokine Assays.
Measurement of Cytokine Production
The concentrations of IFN-γ, IL-1β, IL-6, and TNF-α in the culture fluid
supernatants were determined using quantitative multiplex array technology (Meso Scale
Discovery [MSD]). The MSD 4-Plex I Ultra-Sensitive Kits demonstrate good intra-assay
and inter-assay precision (MSD Catalog; Product Number K15009C-2). To control for
any variation between assay kits, samples were batched by subject for analysis and all
samples were assayed in duplicate. Intra-assay and inter-assay coefficients of variation
48
are < 5% and < 10%, respectively. Sensitivity of detection varied by cytokine: IFN-γ =
0.55 pg/ml; IL-1β = 2.4 pg/ml; IL-6 = 0.7 pg/ml; and TNF-α = 0.2 pg/ml. All samples
were run at the same time according to the manufacturer’s instructions.
Protection of Human Subjects
Approval
The study was reviewed and approved by the Institutional Review Board for
Biomedical Research at The Ohio State University (see Appendix E; Protocol #
2012H0079).
Potential Risks
The single-time venous blood draw may be slightly painful and cause bruising
and/or light-headedness. Rarely, syncope or infection at the site may result. To
minimize risk, only the investigator, who is skilled at phlebotomy, performed all blood
draws. If a subject became light-headed, the investigator would remain with the subject
until he/she was fully recovered. These risks were minor compared to the knowledge
expected to be gained from this study. There were no long-term consequences of subject
participation.
Potential Benefits
There were no direct benefits to individual participants.
49
Confidentiality
All subjects were assigned a 3 digit number and no personal, identifiable data
were collected in this study. The subject numbers were the only identification method
used to link blood specimen data to questionnaire data. For example, demographic form
(#001), PSS (#001), STAI-T (#001), and blood specimen tube (#001), followed similarly
by (#002) for the next subject. Consent forms were securely stored in a locked file
cabinet. Subject data were stored on a secure server only accessible to the research team
by using an encrypted password. These measures minimized risk for breach of subject
confidentiality.
Data Analysis
Sample Size Calculation
A sample size of 55 achieved 90% power at alpha of 0.05 to detect an R-Squared
of 0.15 (medium effect) attributable to 1 predictor in a multiple regression model,
adjusting for 3 control variables with an R-Squared of 0.10 (Cohen, 1998).
Data Analysis
Demographic information was described using frequencies and descriptive
statistics, and was explored to determine normality of the distribution.
Data were analyzed using the Statistical Package for Social Sciences (SPSS)
software (version 19.0) (IBM Cooperation, Armonk, NY). Cytokine data were reviewed
for outliers, missing data, and errors. Cytokine values that were extreme and biologically
impossible were considered erroneous and cleaned from the dataset. Only 14 cytokine
50
values (1.9% of the total measurements) from 6 different subjects were removed.
Untransformed concentrations of each cytokine are reported as mean and standard
deviation.
Statistical Analysis
Descriptive statistics (frequencies, percentages, means, and standard deviations)
were estimated for all variables. Following univariate analyses, bivariate data analyses
were conducted using Pearson correlation coefficients for relationships between
continuous variables, and one-way Analysis of Variance (ANOVA) testing with post-hoc
procedures to test for significance and differences between each previously described ex
vivo cytokine condition. Multiple regression models were fit to examine if PSS predicts
GCS (percent inhibition of influenza vaccine-stimulated cytokine production) of IFN-γ,
IL-1β, IL-6, and TNF-α, controlling for age, gender, race, and student cumulative grade
point average (GPA). Significance level was set a priori at 0.05.
Specific Aims, Hypotheses, and Statistical Analysis Plans
Aim 1. Determine if psychological stress affects proinflammatory cytokine production by
peripheral blood cells stimulated ex vivo by Influenza Vaccine.
Hypothesis1. Peripheral blood cells from subjects with higher psychological
stress (based on PSS & STAI-T scores) will secrete more IFN-γ, IL-1β, IL-6, or
TNF-α in response to stimulation by Influenza Vaccine.
51
Analysis Plan 1: Bivariate analysis for this aim was conducted through
estimating Pearson Correlation to assess the relationship among PSS and
stimulated cytokine production.
Aim 2. Determine if dexamethasone suppresses vaccine-stimulated production of IFN-γ,
IL-1β, IL-6, and TNF-α.
Hypothesis 2a. There will be significant differences in cytokine concentration in
each culture condition (spontaneous, vaccine-stimulated, DEX+vaccine).
Hypothesis 2b. DEX will significantly reduce vaccine-induced cytokine
production.
Analysis Plan 2: A one-way Analysis of Variance (ANOVA) with the
post-hoc procedure, Dunnett's T3 test, was used to test for significant
effects of DEX on vaccine-induced production of IFN-γ, IL-1β, IL-6, and
TNF-α.
Aim 3. Determine if psychological stress predicts GCS (percent inhibition of stimulated
cytokine production).
Hypothesis 3. Psychological stress (as measured by PSS scores) will be the
strongest predictor of glucocorticoid sensitivity (GCS)–DEX suppression of
vaccine-induced cytokine production of IFN-γ, IL-1β, IL-6, and TNF-α.
Analysis Plan 3: The relationship between Stress (PSS) and GCS of each
cytokine was estimated with Pearson correlations, followed by multiple
52
regression models controlling for age, gender, race, and student
cumulative GPA.
53
CHAPTER 4
RESULTS
Descriptive Statistics
The results of this study are organized into four sections. The first section
describes the characteristics of the sample. The second section presents the results of the
psychosocial measures. The third section describes data analysis of the demographics,
psychosocial variables, proinflammatory cytokine production concentrations, and
glucocorticoid sensitivity (GCS). The fourth section presents the results related to each
aim of the study.
The purpose of the study was to determine if higher psychological stress
diminished GCS and regulation of proinflammatory cytokine production in a population
of military students. It was hypothesized that subjects with greater psychological stress
would have lower GCS, producing higher proinflammation subsequent to an ex vivo
influenza vaccine challenge. Descriptive and inferential statistics were used to address
the study hypotheses. Data were analyzed using the Statistical Package for SPSS, version
19.0.
Demographic Characteristics
A convenience sample of 61 healthy male and female military (active duty,
reservist, and veteran) full-time college students with an average age of 22 years (SD =
54
4.1, ranging from 18 to 37 years) who met the inclusion criteria were enrolled over a
period of 3 months from July through September, 2012. The demographic data for the
sample were obtained via a self-report questionnaire form (Appendix A). Information
included age, race, gender, level of education, marital status, current military rank, and
previous military experience. Demographic characteristics of the sample are displayed in
Table 1. Educational and military service characteristics of the sample are presented in
Table 2 and 3, respectively.
Table 1
Demographic Characteristics of the Sample (n =61)
________________________________________________________________________
n
(%)
Gender
Male
46
(75)
Female
15
(25)
Race/Ethnicity
White
African American
Asian
Native American Indian
Unknown
Marital Status
Married
Resides with spouse
Does not geographically
reside with spouse
53
1
5
1
1
(86)
(2)
(8)
(2)
(2)
6
(10)
2
(3)
17
35
(28)
(57)
1
(2)
Single
Significant other involved
No significant other involved
Divorced
Children
Yes
3
(5)
No
58
(95)
________________________________________________________________________
55
Table 2
Educational Characteristics of the Sample (n =61)
________________________________________________________________________
n
(%)
Educational level
College graduates
11
(18)
Current Graduate Students
3
(5)
Current Undergraduate Students
47
(77)
Student Class Standing
Freshman
Sophomore
Junior
Senior
Graduate-level student
9
20
4
25
3
(15)
(33)
(7)
(41)
(5)
Student Cumulative Grade Point Average (GPA)
4.0 GPA
4
(6)
> 3.8 - < 4.0 GPA
9
(15)
3.5-3.79 GPA
16
(26)
3.0-3.49 GPA
20
(33)
> 2.0 - < 3.0 GPA
12
(20)
< 2.0 GPA
0
(0)
________________________________________________________________________
56
Table 3
Military Service Characteristics of the Sample (n =61)
________________________________________________________________________
n
(%)
Military Service Branch
Army
12
(20)
Navy and Marine Corps
46
(75)
Air Force
3
(5)
Military Duty Status
Active Duty
Active Reserves
Inactive Reserves
13
7
41
(21)
(12)
(67)
Years of Military Service
1-4 years
5-8 years
9-16 years
50
8
3
(82)
(13)
(5)
Deployment History
Yes
No
9
52
(14)
(85)
Number of Deployments
0 Deployment
1 Deployment
2-4 Deployments
> 5 Deployments
51
2
5
3
(84)
(3)
(8)
(5)
Deployment Type Experience (n = 15)
Combat
Combat- Support
Humanitarian
Other (i.e. submarine deterrent patrol)
Multiple Types
1
3
2
5
4
(7)
(20)
(13)
(33)
(27)
Military ROTC Scholarship Status
Full Scholarship Student
36
(59)
Partial Scholarship Student
2
(3)
Non-Scholarship Student
23
(38)____________________
Note. Under Military Service Branch, subject numbers and percentages for Marine Corps
and Navy are combined, as both are within the Department of the Navy. This allows the
overall total sample percentages to equal 100%.
57
Differences in demographic data across gender were examined with contingency
table analyses. There were no statistically significant differences for this sample among
gender, racial/ethnicity, marital status, education, military service branch, military status,
military physical fitness assessment results, military body composition assessment,
deployment history, type of deployment, length of deployment, or years of military
service, scholarship recipient category, and student class standing.
Psychosocial Variables Data Results
Forty-eight percent (n = 29) of the sample had experienced a "Major LifeStressor", 46% (n =28) denied any "Major Life-Stressor", and 6% (n = 4) were unsure of
any "Major Life-Stressor." Findings from the10-item Perceived Stress Scale (PSS) and
the 20-item trait subscale in the Spielberger State Trait Anxiety Inventory-Trait (STAI-T)
and are shown in Table 4.
Table 4
Psychosocial Characteristics of the Sample (n = 61)
________________________________________________________________________
M
(SD)
Score Range
________________________________________________________________________
PSS Score
12.82 (6.32)
(0-36)
STAI-T Score
37.11 (9.46)
(23-77)___
Associations among Demographics, Psychosocial Variables, and GCS
Demographics and Psychosocial Characteristics
Correlations between demographic variables and psychosocial variables are
presented in Table 5. PSS and STAI-T scores were positively correlated (r = 0.627, p
58
<0.01). Demographic variables were not correlated with psychological stress as
measured by the PSS and STAI-T.
Table 5
Correlations among Demographics and Psychosocial Characteristics (n =61)
________________________________________________________________________
Measure
GPA BMI
Years in Number
PSS
STAI-T
Military Deployments
________________________________________________________________________
Age
.037
.197
.783**
-.683**
.114
.184
GPA
------
BMI
Years in
Military
Number
Deployments
.091
.096
.009
-.152
-.067
------
.145
-.167
.092
-.109
.148
.179
-.115
-.164
------
-.753
------
PSS
*
p<0.05, **p<0.01
**
------
.627** _
Demographics and GCS of Cytokine Production
GPA was positively correlated with GCS for only TNF-α (r = 0.281, p = 0.028);
the higher the student GPA, the higher GCS (percent inhibition) of TNF-α production, as
illustrated in Table 6. GPA was the only demographic variable that was significantly
correlated with GCS of cytokine production.
59
Table 6
Correlations among Demographics and GCS of Cytokine Production (n =61)
________________________________________________________________________
Measure
GPA BMI Years in Number
GCS
GCS
GCS
GCS
Military Deployments (IFN-γ) (IL-1β) (IL-6) (TNF-α)
________________________________________________________________________
Age
.037 .197 .783**
-.683**
.176
-.089
.043
.096
GPA
.096
.009
.109
.220
.061
.281*
------ .145
-.167
-.171
-.178
-.238
-.225
.201
-.026
.103
.087
------ .091
BMI
Years in
Military
------
Number
Deployments
*
p<0.05, **p<0.01
-.753**
------
-.165
.044 -.126 .086
______________________________
Results for Research Aim 1
The first research aim was to determine if psychological stress affects
proinflammatory cytokine production by peripheral blood cells stimulated ex vivo by
influenza vaccine.
Psychological Stress and Stimulated Cytokine Production
Psychological and spontaneous, non-stimulated cytokine production was
examined. Spontaneous cytokine production obtained from non-stimulated (control)
cultures demonstrated that psychological stress, as measured by the PSS and STAI-T,
was not statistically significant in the relationship to production values of IFN-γ, IL-1β,
IL-6, and TNF-α (all ps>0.05).
Then the extent to which psychological stress influenced cytokine production as
stimulated ex vivo by influenza vaccine was examined. Bivariate analysis was conducted
by estimating Pearson correlation to assess the relationship among PSS and stimulated
60
cytokine production. Stimulated cytokine production, obtained from influenza stimulated
cultures, demonstrated that psychological stress as measured by the scores obtained on
PSS and STAI-T was not statistically significant in the relationship to stimulated cytokine
production of IFN-γ, IL-1β, IL-6, and TNF-α.
Results for Research Aim 2
The second research aim was to determine if dexamethasone (DEX) suppresses
vaccine-stimulated production of IFN-γ, IL-1β, IL-6, and TNF-α.
Differences in Proinflammatory Cytokine Production and ex vivo Conditions
To determine the suppressive effects of DEX on influenza vaccine-stimulated
cytokine production, mean cytokine production was compared and examined for
differences in 3 experimental groups. As shown in Table 7, the mean values of cytokine
production for IFN-γ, IL-1β, IL-6, and TNF-α were obtained from the following
conditions: (a) spontaneous production of cytokines in non-stimulated cells, (b) influenza
vaccine-induced cytokine production, and (c) DEX-suppression of vaccine-induced
cytokine production (as a measure of glucocorticoid sensitivity).
61
Table 7
Mean Proinflammatory Cytokine Production Values and Experimental Conditions
________________________________________________________________________
Cytokine
Experiment
n
M
(SD)
________________________________________________________________________
IFN-γ
a) Spontaneous
61
2.80
6.53
b) Stimulated
61
1262.23
1224.75
c) Inhibited
61
496.47
523.80
IL-1β
a) Spontaneous
b) Stimulated
c) Inhibited
59
61
61
9.75
11.64
2.53
31.14
14.66
3.16
IL-6
a) Spontaneous
b) Stimulated
c) Inhibited
55
60
60
34.46
323.99
102.34
98.81
312.68
149.63
TNF-α
a) Spontaneous
59
4.77
11.07
b) Stimulated
61
74.54
86.04
c) Inhibited
61
13.15
59.31 _
Note. Ex vivo conditions for Cytokine production (a, b, and c):
a) Spontaneous (unstimualted/control): culture medium only
b) Stimulated: culture medium with Influenza Vaccine 9 µg/100µl
c) Inhibited: culture medium with Influenza Vaccine 9 µg/100µl and DEX (200 nM)
All values are in picograms per milliliter (ρg/mL).
One-way analysis of variance (ANOVA) was used to test for differences in mean
cytokine production levels of IFN-γ, IL-1β, IL-6, and TNF-α among the 3 ex vivo
experimental conditions (Figure 5 and Table 8). Based on significant ANOVA findings,
a multiple comparison post-hoc procedure using the Dunnett's T3 method was conducted
to evaluate pair-wise contrasts.
IFN-γ Production among ex vivo Conditions
The mean cytokine production of IFN-γ was: for condition (a) (Spontaneous) 2.80
(SD = 6.53), for (b) (Stimulated) 1262.23 (SD = 1224.75), and for condition (c)
(Inhibited) 496.47 (SD = 532.80). A one-way ANOVA was performed to assess
62
differences in the 3 ex vivo conditions. The 3 conditions differed, F (df
= 1, 2)
= 40.93,
p<0.001. There was a moderate effect size with a partial eta, η = 0.313, and the observed
power was strong (1.00). The R2 of 0.313 indicates that the 3 ex vivo conditions account
for 31.3% of the variance in IFN-γ production. Based on the significant ANOVA for
differences in the 3 conditions, a multiple comparison post-hoc procedure was conducted.
The Dunnett's T3 method, appropriate in this situation due to the unequal variances in the
sample, was used for all pair-wise contrasts. All of the pairs of condition groups were
significantly different (p<0.001) as shown in Figure 5.
IL-1β Production among ex vivo Conditions
The mean cytokine production of IL-1β was: for condition (a) (Spontaneous)
9.75 (SD = 31.4), condition (b) (Stimulated) 11.64 (SD = 14.66), and condition (c)
(Inhibited) 2.53 (SD = 3.16). A one-way ANOVA was performed to assess for
differences in the 3 ex vivo conditions. The 3 conditions differed, F (df
= 1, 2)
= 3.596, p
=0.029. There was a small effect size (η = 0.039), and the observed power was moderate
(0.660). The R2 of 0.039 indicates that the 3 ex vivo conditions account for only about
3.9% of the variance in IL-1β production. Based on the significant ANOVA for
differences in the 3 conditions, a multiple comparison post-hoc procedure was conducted.
Condition Groups (b) Stimulated and (c) Inhibited were found to be significantly
different (p<0.001) as shown in Figure 5.
63
IL-6 Production among Ex vivo Conditions
The mean cytokine production of IL-6 was: for condition (a) (Spontaneous) 34.46
(SD = 98.81), condition (b) (Stimulated) 323.99 (SD = 312.68), and condition (c)
(Inhibited) 102.34 (SD = 149.63). A one-way ANOVA was performed to assess for
differences in the 3 ex vivo conditions. The 3 conditions differed, F (df
= 1, 2)
= 30.241,
p<.001. There was a moderate effect size (η = .260), and the observed power was strong
(1.00). The R2 of 0.260 indicates that the 3 ex vivo conditions account for only about
26.0% of the variance in IL-6 production. Based on the significant ANOVA for
differences in the 3 conditions, a multiple comparison post-hoc procedure was conducted.
All of the condition groups were found to be significantly different (p<.001) as illustrated
in Figure 5.
TNF-α Production among ex vivo Conditions
The mean cytokine production of TNF-α was: for condition (a) (Spontaneous)
4.77 (SD = 11.07), for condition (b) (Stimulated) 74.55 (SD = 86.04), and condition (c)
(Inhibited) 13.15 (SD = 10.29). A one-way ANOVA was performed to assess for
differences in the 3 ex vivo conditions. The 3 conditions differed, F (df
= 1, 2)
= 34.168,
p<.001. There was a moderate effect size (η = .277) and the observed power was strong
(1.00). The R2 of 0.277 indicates that the 3 ex vivo conditions account for only about
27.0% of the variance in TNF-α production. Based on the significant ANOVA for
differences in the 3 conditions, a multiple comparison post-hoc procedure was conducted.
All of the condition groups were found to be significantly different (p<.001) as displayed
in Figure 5.
64
Figure 5
Differences in Cytokine Production among the 3 ex vivo Conditions
IL-1β Production
IFN-γ Production
Condition:
Condition:
a) Spontaneous
a) Spontaneous
b) Stimulated
b) Stimulated
c) Inhibited
c) Inhibited
Note: Effect of
different ex vivo
conditions on the
65
release of IFN-γ,
TNF-α Production
IL-6 Production
IL-1β, IL-6, and
TNF-α. Results
Condition:
Condition:
are given as
a) Spontaneous
a) Spontaneous
means ± standard
b) Stimulated
b) Stimulated
error of the mean
c) Inhibited
c) Inhibited
65
(SEM).
Table 8
Summary of ANOVA for Mean Differences in Cytokine Production among the 3 ex vivo
Conditions (n =61)
________________________________________________________________________
Sum of
df
Mean
F
p
η
R2
Squares
Square
________________________________________________________________________
IFN-γ
Between groups
48439748.958
2
24219874.479 40.493** .000 .313 .313
Within groups
1.065E8
178
598117.861
Total
2.187E8
181
________________________________________________________________________
IL-1β
Between groups
2818.608
2
1409.304
3.596* .029
.039 .039
Within groups
69758.296
178
391.901
Total
84031.826
181
________________________________________________________________________
IL-6
Between groups 2678300.289
2
1339150.145 30.241** .000 .260 .260
Within groups
7616651.082
172
44282.855
Total
14608639.412
175
________________________________________________________________________
TNF-α
Between groups
175701.330
2
87850.665
34.168** .000 .277 .277
Within groups
457662.785
178
2571.139
Total
808565.369
181_______________________________________
*
p<0.05, **p<0.01
Results for Research Aim 3
The third research aim was to determine if psychological stress predicts GCS.
Psychological Stress and Immune Glucocorticoid Sensitivity (GCS)
The relationship between stress (PSS) and GCS of each cytokine was estimated
with Pearson correlations, followed by multiple regression models controlling for age,
gender, race, and student cumulative GPA.
66
Higher PSS scores were significantly correlated with lower GCS for IL-1β, IL-6,
and TNF-α (see Table 9). An inverse relationship existed between psychological stress
and GCS with reference to continuous raw score data obtained by the PSS and GCS as
measured by percent inhibition of IFN-γ, IL-1β, IL-6, and TNF-α. Although there is an
inverse relationship between PSS continuous raw scores and GCS of IFN-γ, this
relationship was not statistically significant; further analysis through multiple regressions
was not warranted.
Table 9
Correlations among PSS and GCS of Proinflammatory Cytokines (n =61)
________________________________________________________________________
Measure
PSS
GCS
GCS
GCS
GCS
IFN-γ
IL-1β
IL-6
TNF-α
________________________________________________________________________
PSS
----.198
-.420**
-.296*
-.259*______
*p<0.05, **p<0.01
Following Pearson correlation estimation of the relationship between stress (PSS)
and GCS of each cytokine, multiple regression models were conducted on PSS and GCS
of IL-1β, IL-6, and TNF-α, controlling for age, gender, race, and student cumulative
GPA.
PSS and GCS of IFN-γ Production
Based on the non-significant IFN-γ results in the preceding analysis, F (df = 1, 59)
= 2.42, p>0.125, further analysis was not conducted.
67
PSS and GCS of IL-1β Production
A multiple regression was conducted with the following predictor variables: PSS,
age, gender, race, and GPA, with GCS of IL-1β as the outcome variable. The model
produced an R2 of 0.176, which was statistically significant, [F (df
= 1,59)
= 12.637,
p<0.01]. PSS, age, gender, race, and GPA accounts for 17.6% of the variance in GCS of
IL-1β. There was a strong negative relationship between psychological stress (via PSS
raw score) and GCS (percent inhibition) of IL-1β production (β = -0.420, t = -3.55,
p<0.01) when controlling for age, gender, race, and GPA. The results of the regression
analysis are shown in Table 10.
PSS and GCS of IL-6 Production
A multiple regression was conducted with the following predictor variables: PSS,
age, gender, race, and GPA, with GCS of IL-6 as the outcome variable. The model
produced an R2 of 0.088. PSS, age, gender, race, and GPA can account for 8.80% of the
variance in GCS of IL-6. There was a strong negative relationship between
psychological stress (via PSS raw score) and GCS (percent inhibition) of IL-6 production
(β = -0.296, t = -2.36, p<0.05) when controlling for age, gender, race, and GPA. The
results of the regression analysis are shown in Table 10.
PSS and GCS of TNF-α Production
A multiple regression was conducted with the following predictor variables: PSS,
age, gender, race, and GPA, with GCS of TNF-α as the outcome variable. The model
produced an R2 of .067, which was statistically significant, [F (df
68
= 1,59)
= 4.246, p<.05].
PSS, age, gender, race, and GPA accounted for 6.7% of the variance in GCS of TNF-α.
There was a strong negative relationship between psychological stress (via PSS raw
score) and GCS of TNF-α production (β = -0.259, t = -2.060, p<0.05) when controlling
for age, gender, race, and GPA. The results of the regression analysis are shown in Table
10.
Table 10
Summary Multiple Regressions of PSS and GCS of IL-1β, IL-6, and TNF-α (n =61)
________________________________________________________________________
Variable
B
SE B
β
t
p
R2
________________________________________________________________________
GCS of IL-1β
-1.724
.485
-.420
-3.555**
.001
.176
GCS of IL-6
-1.332
.564
-.296
-2.360*
.022
.088
GCS of TNF-α
-.640
*p<0.05, **p<0.01
.310
-.259
-2.060*
.022
.067
69
CHAPTER 5
DISCUSSION AND CONCLUSION
This study was conducted to determine if psychological stress levels experienced
by military students might affect the inflammatory response to vaccine challenge,
explaining in part increased susceptibility to influenza infection in military personnel.
Using whole blood obtained from healthy ROTC college students, this study specifically
sought to ascertain: (a) whether self-reported psychological stress influenced cytokine
production stimulated ex vivo by influenza vaccine, (b) the suppressive effects of DEX on
influenza vaccine-stimulated cytokine production, and (c) if psychological stress predicts
GCS (percent inhibition of stimulated cytokine production). It was hypothesized that
subjects with greater psychological stress will have lower GCS in an ex vivo laboratory
model of influenza vaccine challenge.
Chapter 5 provides an explanation of results and integrates current study findings
with existing literature. The limitations of the study are discussed as are implications and
future directions for research.
70
Discussion
Psychological Stress
The average score on the 10-item Perceived Stress Scale (PSS) was 12.82, similar
to U.S. norms (Cohen, et al., 1983), but lower than the average score of 16.78 reported by
young adults 18 to 25 years of age (Cohen & Janicki-Deverts, 2012b). Only one subject
in the sample reported high stress. Because the PSS and STAI-T were highly correlated,
self-reported psychological stress is likely consistent with the way each subject would
rate their perceived stress on any given day, adding to the validity of the PSS used in this
study. PSS and STAI-T scores were correlated; the lower PSS scores may indicate the
sample in this study was not as stressed as other similarly aged adults in college or
military service, warranting future research focused on active duty military populations.
Psychological Stress and Vaccine-Stimulated Cytokine Production
It was hypothesized that peripheral blood cells from subjects with higher selfappraised evaluations of psychological stress would have higher production of cytokines
(IFN-γ, IL-1β, IL-6, and TNF-α) in response to stimulation by influenza vaccine. The
results demonstrate that military students with higher PSS scores had higher stimulated
production of IFN-γ, IL-1β, IL-6, and TNF-α in response to influenza vaccine challenge
ex vivo, but this relationship was not statistically significant as hypothesized. This may
be a result of sample size, the young age of the subjects, or the low scores on the PSS.
71
DEX-induced Suppression of Vaccine-stimulated Cytokine Production
It was hypothesized that DEX would significantly reduce vaccine-induced
cytokine production of IFN-γ, IL-1β, IL-6, and TNF-α. The mean cytokine production
levels under 3 culture conditions were compared: (a) Spontaneous [non-stimulated
control], (b) Stimulated [using influenza vaccine], and (c) Inhibited [using vaccine and
DEX], which would evidence the suppressive effects of DEX on vaccine-stimulated
production of IFN-γ, IL-1β, IL-6, and TNF-α. Results demonstrated that all condition
pairs for cytokine production of IFN-γ, IL-1β, IL-6, and TNF-α were statistically
significant in influenza vaccine-stimulated and DEX-Inhibited conditions, demonstrating
that DEX had significant effect in the suppression of vaccine-stimulated cytokine
production. This finding is consistent with previous studies reporting the effects of ex
vivo DEX-induced, glucocorticoid suppression of inflammatory cytokine production
(Miller, et al., 2002; Cohen et al., 2012a).
Most of the subjects were male, consistent with the general military population as
reported by the Defense Manpower Data Center (2009). Although gender did not have a
significant relationship with psychological stress (PSS scores), GCS, or production of
proinflammatory cytokines, male subjects exhibited higher cytokine concentration values
in all three ex vivo experimental culture conditions. This finding is supported by Wirtz
and colleagues (2004), who reported that IL-6 and TNF-α production were higher in
men, and glucocorticoid-induced inhibition of LPS-stimulated IL-6 and TNF-α cytokine
production, was less pronounced in men than in women.
72
Psychological Stress and GCS
It was hypothesized that psychological stress (as measured by PSS scores) would
be the strongest predictor of GCS, with the inhibitory effect of DEX on vaccinestimulated cytokine production of IFN-γ, IL-1β, IL-6, and TNF-α less in cells from
subjects with higher stress compared to cells from subjects with lower stress. The results
verified that a higher PSS score was negatively related to lower GCS for IL-1β, IL-6, and
TNF-α only. These data are consistent with previous research suggesting that chronic
stress may induce glucocorticoid resistance or lower GCS (Cohen, et al., 2012a). The
negative relationship between PSS and GCS of IFN-γ production was not significant.
Vaccine-stimulated production of IFN-γ was increased without significant suppression by
DEX, suggesting that IFN-γ production may be less sensitive to suppressive effects of
cortisol as well. The reason for this remains unclear, and requires further research to
better understand the effects of stress on antibody responses to influenza production and
resistance to influenza infection.
GPA was statistically and positively correlated with GCS for only TNF-α; the
higher the student GPA, the higher the percent inhibition of proinflammatory cytokine
production of TNF-α (higher GCS). This is consistent with prior research demonstrating
that examination stress suppresses immune responses with a downregulation of Th1
proinflammatory cytokines in favor of an increase in Th2 anti-inflammatory cytokines
(Kang & Fox, 2001).
An analysis was conducted to determine which variable best predicts GCS of
cytokine production. Age, gender, race, and student GPA were entered into four
73
regression models with PSS. After controlling for these variables, multiple linear
regression analysis revealed that psychological stress, as measured by the PSS score, was
the strongest predictor of GCS of stimulated cytokine production for IL-1β, IL-6, and
TNF-α, but not IFN-γ. These findings are consistent with findings that chronic stress can
diminish the immune system GCS in spousal caregivers of dementia patients (Bauer, et
al., 2000), parents of children undergoing treatment for cancer (Miller, et al., 2002), and
stress-related syndromes such as vital exhaustion (Wirtz, et al., 2003), chronic fatigue (ter
Wolbeek, et al., 2008), and depression (Miller, et al., 1999).
Study Limitations
This study had a number of limitations. The cross-sectional design prevents
making any causal inferences regarding the relationship between psychological stress and
overall glucocorticoid sensitivity of peripheral blood leukocytes stimulated with
influenza vaccine. The small sample size is a limitation to generalizability. The study
population tended to be homogenous and primarily male as a result of convenience
sampling of military students.
Although female reproductive hormones (i.e. progesterone and estrogen) and
contraceptive use influence on inflammatory cytokine activity were accounted for, the
sample size and gender distribution impaired the examination of gender effects on GCS
and inflammatory cytokine production. Because the sample was not racially diverse,
effects and differences in cytokine production levels based on race could not be
determined. Subsequent studies should use recruitment strategies aimed at enrolling
74
more women and more racially and ethnically diverse subjects, allowing generalization to
the diverse U.S. military population.
The psychological stress scores were not evenly distributed, and may have
affected overall results about the relationship between psychological stress and
spontaneous and stimulated cytokine production. An alternative for future studies would
be to use the PSS as a screening tool for inclusion, appropriately and equally matching
number of subjects into tertile stress groups (i.e., low, moderate, and high stress).
Implications for Future Research
Despite the overall sample’s lower than average self-reported psychological
stress, findings indicate that higher psychological stress resulted in a significant
correlation with lower glucocorticoid sensitivity to suppressive effects of DEX on
cytokine production in a healthy, young adult population. Application of this study’s
design and findings to future research has potential implications for military health and
readiness; this is particularly true since dysregulated glucocorticoid regulation of
inflammatory cytokine production and impaired immune responses, as measured in other
studies, has strong associations with adverse, relevant health outcomes in stressed
populations.
The process of antibody formation is complex and unfolds in stages over a period
of weeks to months in response to vaccination. Future research would benefit by using
repeated measures of stress and cytokines over time to better describe the timing, change,
and magnitude of cytokine production responses to vaccine challenge in relation to
psychological stress. Ex vivo vaccine challenge is a useful model for examining the
75
effects of psychological stress on inflammatory responses to immune triggers under wellcontrolled conditions (Glaser & Kiecolt-Glaser, 2005; Tsai et al., 2005). This study
demonstrated that a seasonal, inactivated, trivalent influenza vaccine elicits a measurable
and statistically significant inflammatory response ex vivo. Future studies with longer
monitoring periods, and repeated collection of neuroendocrine, inflammatory, and
immune measures such as cortisol, inflammatory cytokines, and influenza antibody titers
would be useful in determining the extent of stress effects on humoral response and longterm antibody protection.
Investigation of stress differences and effects in cytokine production levels across
race and ethnic populations should be another primary goal of future research. When
compared to Caucasian subjects, African-Americans tend to exhibit higher basal and
stimulated inflammatory markers (Kiecolt-Glaser et al., 2003), as decreased GCS to
DEX, and increased disease-related morbidity (Federico, et al., 2005). Reproducing this
study in a larger, more racially and/or ethnically diverse population would be a valuable
scientific contribution.
Overall findings from this study, coupled with previous research findings
(Hearing, et al., 1999; Miller, et al., 2002; Wirtz, et al., 2004; Wirtz, et al., 2003; Cohen,
et al., 2012a), support the central hypothesis that higher perceived stress could impair
GCS of cells capable of producing proinflammatory cytokines. Future research, using
similar measures and influenza antibody titer, could help determine if the extent of GCS
and cytokine production predicts a greater risk of decreased antibody production
following influenza vaccination in high-stress populations.
76
The design and findings of this study provide a meaningful method of framing
future studies about the effects of stress-reduction interventions, such as tai chi, in at-risk
or immune-suppressed populations (McCain, Gray, Walter, & Robins, 2005; McCain et
al., 2003; McCain & Smith, 1994). Findings can help nurse researchers and clinical
nurse practitioners conceptualize health promotion interventions and infectious disease
risk outcomes for immunizations in otherwise healthy military personnel.
Conclusion
Military personnel are often subjected to stressful situations and events, such as
active combat, deployment, reintegration, and physical performance standards. Despite
being a presumably fit and healthy population, military training recruits, cadets, and
deployed personnel have higher rates of upper respiratory infection-related illness and
hospitalizations than civilian populations. This remains as a significant barrier to health
and readiness of military units worldwide. Vaccines are readily available for many
diseases, but are not always effective. Research increasingly suggests that chronic stress
and inflammation are critical to immune response, health, and disease risk following
vaccination. Stress effects on neuroendocrine and immune responses to vaccination
remain complex, and the susceptibility for increased disease risk, for certain populations
is still unclear. This study provides a model for testing the assumption that chronic stress
impairs normal regulation of cytokine production in response to vaccination.
This study was the first to demonstrate a significant relationship between selfreported stress levels and GCS of proinflammatory cytokine production in response to ex
vivo exposure to influenza vaccine in a sample of young, healthy, military college
77
students reporting lower than average psychological stress. The results can serve as a
beginning point for a greater understanding of stress and immune responses. It can also
provide support for researchers and clinicians striving to improve military service
member health and readiness. The overall results highlight the importance of considering
that psychological stress might alter glucocorticoid sensitivity and regulation of
inflammatory responses to vaccination in healthy, young adults.
78
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APPENDIX A: DEMOGRAPHIC DATA QUESTIONNAIRE FORM
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APPENDIX B: PERCEIVED STRESS SCALE (PSS)
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APPENDIX C: STATE TRAIT ANXIETY INVENTORY (STAI)
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APPENDIX D: INFORMED CONSENT FORM
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APPENDIX E: HIPAA AUTHORIZATION TO PARTICPATE IN RESEARCH
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APPENDIX F: HUMAN SUBJECTS APPROVAL TO CONDUCT RESEARCH
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