Role of anxiety on vascular dysfunction

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations Spring 2016 Role of anxiety on vascular dysfunction Tiwaloluwa Adedamola Ajibewa University of Iowa Copyright 2016 Tiwaloluwa Ajibewa This thesis is available at Iowa Research Online: Recommended Citation Ajibewa, Tiwaloluwa Adedamola. "Role of anxiety on vascular dysfunction." MS (Master of Science) thesis, University of Iowa, Follow this and additional works at: Part of the Systems and Integrative Physiology Commons

2 ROLE OF ANXIETY ON VASCULAR DYSFUNCTION by Tiwaloluwa Adedamola Ajibewa A thesis submitted in partial fulfillment of the requirements for the Master of Science degree in Health and Human Physiology in the Graduate College of The University of Iowa May 2016 Thesis Supervisor: Assistant Professor Gary L. Pierce

3 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL MASTER S THESIS This is to certify that the Master s thesis of Tiwaloluwa Adedamola Ajibewa has been approved by the Examining Committee for the thesis requirement for the Master of Science degree in Health and Human Physiology at the May 2016 graduation. Thesis Committee: Gary L. Pierce, Thesis Supervisor Jess G. Fiedorowicz Amy L. Sindler

4 ACKNOWLEDGMENTS To my parents, John and Stella for their constant encouragement and love. I would also like to acknowledge Dr. Gary L. Pierce, for his expertise and guidance in writing my thesis, as well as for granting me the opportunity to be part of his amazing lab. Additionally, I would like to thank Dr. Jess Fiedorowicz and Jane Persons for their invaluable contributions to this manuscript. Lastly, I must acknowledge my committee, Dr. Pierce, Dr. Fiedorowicz, and Dr. Amy Sindler, for their time and expertise in the completion of this thesis. ii

5 ABSTRACT High anxiety is associated with an increased risk of developing cardiovascular disease (CVD), in particular, atherosclerotic coronary artery disease. However, the mechanisms by which anxiety contributes to the development of CVD are unclear. Unlike other common psychiatric disorders such as depression, anxiety and its effects on CVD risk has not been studied extensively. Moreover, whether elevated anxiety is associated with arterial stiffness and vascular endothelial dysfunction,biomarkers of CVD risk, in healthy adults and whether a psychological intervention designed to lower anxiety levels in healthy adults with moderate to high baseline anxiety levels ameliorates vascular dysfunction remains unclear. The purpose of this study was twofold; first to determine the extent to which moderate to high anxiety levels are associated with vascular dysfunction including aortic stiffness as measured by carotid-femoral pulse wave velocity (cf- PWV), carotid artery stiffness via ultrasound-based β-stiffness index, and forearm resistance artery function measured as peak forearm blood flow using venous occlusion plethysmograph (VOP). Secondly, to determine whether the empirically validated Acceptance and Commitment Training (ACT) anxiety intervention improved vascular function after 12 weeks and if this was associated with reductions in anxiety in adults with moderate to high baseline anxiety levels. Our results indicated that there was no association between increased anxiety levels and any of the three vascular outcomes of interest. Conversely, there was an association between the ACT intervention participation and improvement in forearm resistance artery function independent of age, sex, education, race/ethnicity, BMI and STAI Trait anxiety. Taken together, these data suggest that although higher State and Trait anxiety was not associated with aortic stiffness, carotid stiffness or forearm resistance artery function, and the ACT intervention was associated with improved peripheral resistance artery function. Additional studies are needed to determine whether this effect occurs earlier than 12 weeks and sustained longer that 12 weeks, and whether it occurs in adults with CVD risk factors (i.e. atherosclerosis), non-white racial/ethnic backgrounds and in resistance vessel function in response to intra-arterial vasoactive agonists such as acetylcholine. iii

6 PUBLIC ABSTRACT Anxiety disorders are one of the most common mental disorders nationally, costing greater than 42 billion dollars a year to the U.S. health care system. Anxiety has been shown to be strongly and independently associated with chronic medical conditions, such as hypertension and cardiovascular disease (CVD). However our understanding of the relation between CVD risk and anxiety is understudied and not well understood. The purpose of this project is to explore the relation between increased anxiety levels and decreased function of the large (aorta and carotid) and small arteries associated with CVD risk in individuals with moderate to high anxiety. Additionally, this study aimed to examine the role of a relatively novel and experimentally validated anxiety intervention program in improving artery function after 12 weeks. In 43 participants (age 35.8 ± 9.4 years) with moderate to high anxiety and 12 participants (age 47.9 ± 13.9 years) with low anxiety, increased anxiety was not associated with aortic or carotid artery stiffness, or small artery function in healthy adults. However, the anxiety intervention was associated with improvements in small artery function. Taken together, these data suggests that the anxiety intervention results in improved small artery function in healthy adults with moderate to high levels of anxiety. More work is needed in order to explore whether this effect occurs in adults with CVD risk factors (i.e. atherosclerosis), in non-white racial and ethnic groups and in other tests of small artery function. iv

7 TABLE OF CONTENTS LIST OF TABLES... vii LIST OF FIGURES... ix CHAPTER I INTRODUCTION... 1 Epidemiology of Anxiety and Cardiovascular Disease... 2 Anxiety and Vascular Dysfunction... 3 Anxiety and Arterial Stiffness... 5 Potential Mechanisms That Link Anxiety with Vascular Dysfunction... 6 Anxiety and Hypertension... 6 Obesity and Anxiety... 6 Anxiety and Systemic Inflammation... 7 Acceptance and Commitment Therapy... 8 ACT Effectiveness Hypothesis and Aims CHAPTER II TESTING AND METHODS Anxiety Classification Subject Recruitment ACT study Subject Recruitment TIVA study Study Visits Informed Consent-Visit Baseline Measurements-Visit Acceptance and Commitment Training Intervention-Visit Six Week Follow up Post Intervention-Visit Statistical Analysis CHAPTER III RESULTS Aim 1 Results Baseline Subject Characteristics Aim 1 results Aim 2 Results Baseline Subject Characteristics Aim 2 results CHAPTER IV DISCUSSION v

8 Aims and vascular outcomes Large elastic stiffness and anxiety Carotid β-stiffness Forearm Blood Flow Limitations/ Recommendations APPENDIX A: TABLES APPENDIX B: FIGURES REFERENCES vi

9 LIST OF TABLES Table A-1: Baseline subject demographic characteristics for ACT and TIVA study groups Table A-2: Baseline subject anxiety, inflammation and vascular outcomes for ACT and TIVA groups Table A-3: Baseline subject demographic characteristics of the intervention, non-intervention, and combined groups Table A-4: Baseline subject anxiety, inflammation and vascular outcomes of the intervention, non-intervention and combined groups Table A-5: Linear regression model State anxiety-aim 1 (unadjusted) Table A-6: Linear regression model Trait anxiety-aim 1 (unadjusted) Table A-7: Linear regression model State anxiety-aim 1 adjusted for sex, age, education, study membership, and race/ethnicity Table A-8: Linear regression model Trait anxiety-aim 1 adjusted for sex, age, education, study membership, and race/ethnicity Table A-9: Linear regression model State anxiety-aim 1 adjusted for Model 1+ lncrp, BMI and MAP Table A-10: Linear regression model Trait anxiety-aim 1 adjusted for Model 1+ lncrp, BMI and MAP Table A-11: Linear mixed model analysis: Effects of ACT on vascular outcomes (unadjusted) Table A-12: Linear mixed model analysis: Effects of ACT on vascular outcomes adjusted for age, sex, education, and race/ethnicity Table A-13: Linear mixed model analysis: Effects of ACT on vascular outcomes adjusted for BMI and STAI Trait anxiety Table A-14: Carotid-femoral PWV over time Table A-15: Carotid-femoral PWV over time Table A-16: Carotid-femoral PWV over time Table A-17: Carotid β-stiffness index over time Table A-18: Carotid β-stiffness index over time Table A-19: Carotid β-stiffness index over time Table A-20: Peak forearm blood flow over time Table A-21: Peak forearm blood flow over time vii

10 Table A-22: Peak forearm blood flow over time Table A-23: Linear mixed model analysis: Effects of anxiety on vascular outcomes (unadjusted) Table A-24: Linear mixed model analysis: Effects of anxiety on vascular outcomes (unadjusted) Table A-25: Linear mixed model analysis: Effects of anxiety on vascular outcomes, adjusted for age, sex, education, and race/ethnicity Table A-26: Linear mixed model analysis: Effects of anxiety on vascular outcomes, adjusted for age, sex, education, and race/ethnicity Table A-27: Linear mixed model analysis: Effects of anxiety on vascular outcomes, adjusted for Model 1+lnCRP, BMI, and MAP Table A-28: Linear mixed model analysis: Effects of anxiety on vascular outcomes, adjusted for Model 1+lnCRP, BMI, and MAP viii

11 LIST OF FIGURES Figure B-1: Linear regression dot-plot models for cf-pwv (unadjusted, model 1 and model 2) Figure B-2: Linear regression dot-plot models for carotid β-stiffness (unadjusted, model 1 and model 2) Figure B-3: Linear regression dot-plot models for Peak forearm blood flow (unadjusted, model 1 and model 2) Figure B-4: Scatterplots for vascular outcomes and State anxiety (unadjusted linear regression models) Figure B-5: Scatterplots for vascular outcomes and Trait anxiety (unadjusted linear regression models) Figure B-6: Linear mixed model analyses dot-plot models for effects of ACT on vascular outcomes Figure B-7: Changes in vascular outcomes from baseline to 12-weeks in control and intervention groups ix

12 1 CHAPTER I INTRODUCTION Major events in our own individual past, families, or community, influence us physically, emotionally, mentally, and in every facet of life. The stress of such events can lead to chronic psychological stress in the form of a mental illness, which can in turn cause physical impairments such as shortness of breath and nausea. The National Institute of Mental Health defines mental illness as a psychological, behavioral, or emotional disorder (excluding developmental and substance use disorders) of sufficient duration that meets the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders. Anxiety and depressive disorders are among the most common mental health disorders in the world, with anxiety disorders being the most common psychiatric disorder in the developed world (Gariepy et al., 2010). According to the National Institute of Mental Health, in 2012, there were approximately 43.7 million U.S. adults (18 and older) with one or more mental illness, or about 18.6 percent of the total U.S. population. Importantly, Kessler et al. (2005) estimated that 18.1 percent of U.S. adults have at least a 12 month long occurrence of anxiety-related symptoms and a lifetime prevalence of approximately 28%, although many of the cases are mild and not severe. Anxiety is common both domestically and internationally (Demyttenaere et al., 2004), and because of this prevalence, treating anxiety is a major challenge. In the United States alone, the cost of treating anxiety-related illnesses and symptoms was estimated to be greater than 42 billion dollars a year in 1999 (Kessler & Greenberg 2002), comparable to the cost of treating depression. Anxiety is ever-present and is experienced by everyone at some point. Anxiety is defined by a future-oriented mood state associated with preparation for possible harm that involves a priming of simultaneous excitatory and inhibitory input to the fight-flight-or-freeze mechanism when danger is perceived to be possible at a later point in time (Zinbarg, 1998 as cited by Blaney, Krueger, and Millon, 2014). According to the Diagnostic and Statistical Manual of Mental Disorders edition 5 (DSM-5), there

13 2 are several types of anxiety disorders, which include: Generalized Anxiety Disorder (GAD), panic disorder (PD), social anxiety disorder (SAD), and specific phobias (Blaney, Krueger, and Millon, 2014). Anxiety is a normal and common feeling that may at times be functionally adaptive in different situations to help motivate an individual to take appropriate vigilance or to change (Thurston et al., 2013). However, propensity for anxiety is also a trait that leads to a chronic state of anxiousness, that when experienced within improper situations or at high frequencies and intensities, lead to maladaptive function, and a classification as a disorder (Thurston et al., 2013). Anxiety is known to be strongly and independently associated with chronic medical conditions (such as hypertension), physical disability, and decreased quality of life (Roy-Byrne et al., 2008). Furthermore, individuals with anxiety symptoms often suffer from other psychiatric disorders such as depression. As a result of such coexisting psychiatric disorders (anxiety and comorbid depression), further exacerbation of anxiety symptoms are often observed. Epidemiology of Anxiety and Cardiovascular Disease Anxiety is associated with an increased risk for the development of cardiovascular diseases (CVD-in particular atherosclerotic coronary artery disease). However, the mechanisms by which anxiety contributes to an increased CVD risk remains unclear. Additionally, unlike many other common psychiatric disorders such as depression, anxiety and its effect on CVD risk has not been studied as extensively. Roest and colleagues (2010) reviewed the association between anxiety and risk of coronary heart disease (CHD), specifically examining the impact of anxiety on the development of CHD in individuals who were previously in good health. Roest and colleagues, systemically reviewed published articles between 1980 to May 2009, that included end points of cardiac death, myocardial infarction (MI), and cardiac events with that of anxiety diagnosis. They carefully chose 21 prospective studies that had subjects that were initially healthy at baseline who also had anxiety assessments. Overall, their results showed that there is an association between anxiety and incident CHD with a 26% increased risk of having CHD with the onset of anxiety. Additionally, anxiety was also associated with cardiac death.

14 3 Individuals who developed anxiety had a 48% increased risk of cardiac mortality. In regards to myocardial infarction (MI), Roest and colleagues determined that there was a general pattern between MIs and anxiety, but that there weren t enough studies present to have enough power in their statistical calculations to determine conclusively. Taken together, these findings are additional evidence to lend credibility to the association between anxiety and CVD. Although the mediators linking anxiety to CVD risk remains unclear, one possible mechanism could be from increase or worsening of CVD risk factors such as blood pressure, lipids, glucose, obesity, and smoking (Stillman et al., 2013). Another possible mechanism of anxiety and increased CVD could be found via increased sympathetic nerve activity that could mediate CVD via different pathological pathways such as arrhythmias, increased blood pressure, cardiac dysfunction, systemic inflammation, vascular dysfunction, and or decreased heart rate variability (Stillman et al., 2013). As a result of all these possible links, it is important to be able to properly establish the pathway(s) by which anxiety causes increased CVD risk, because CVD remains the leading cause of death and disability in Western societies. Although much has been learned in the prevention and treatment of CVD, anxiety s influence on CVD risk may be underestimated. Given that anxiety is a potentially modifiable CVD risk factor, it is an attractive target for CVD risk reduction strategies in order to save lives, decrease the financial burden on the healthcare system, and to better treat patients suffering from anxiety symptoms and improve their quality of life. Anxiety and Vascular Dysfunction Although the mechanism that connects anxiety to increased CVD risk is unknown, one possible mechanism to consider is endothelial dysfunction, a precursor to the development of atherosclerosis. Atherosclerosis and the accumulation of fatty streaks is typically the beginning of indications of vascular dysfunction in human beings, which then leads to a vicious cycle that further worsens vascular function. This results in decreased endothelial-derived NO production and bioavailability in the arterial wall, allowing for greater production of reactive oxygen species, leading to increased vascular oxidative

15 4 stress. As such, with pathopsychological anxiety, there is an increase in sympathetic activity that can then lead to systemic inflammation, which can then cause vascular damage to the endothelium. Consistent with this, Stillman et al., (2013) conducted the first study of its kind to examine anxiety and its association/influence on forearm resistance vessel function in response to intra-brachial artery infusion of endothelial-dependent and independent pharmacological agonists. Forearm resistance vessel endothelial function measures forearm blood flow in small resistance vessels via venous occlusion plethysmography (VOP). As such, Stillman and colleagues found a significant independent association between anxiety and decreased forearm resistance vessel function [(before and after controlling for risk factors i.e. body mass index (BMI)] within their atherosclerosis group but not within their healthy cohort. Furthermore, there was no significant association between peripheral conduit artery endothelial function (as assessed by brachial artery flow-mediated dilation, FMD) and anxiety, suggesting that vascular endothelial dysfunction in individuals with anxiety is selective to the resistance vessels. In contrast to Stillman and colleagues, Narita et al., (2006), found a significant interaction for high anxiety, assessed by the State-Trait Anxiety Inventory (STAI) survey, and decreased endothelial function as measured by brachial artery FMD. Narita and colleagues studied 54 elderly subjects without any history of CVD or major atherosclerotic risk factors. Specifically, after adjusting for confounding factors, they found that the STAI-trait score but not the STAI-state was negatively correlated with %FMD. Additionally, the group of individuals with the higher STAI-trait scores were found to have a lower %FMD, as compared to the lower STAI-trait group. The divergent results may be explained by different populations sampled by these studies, in that the cohort in Stillman et al. were older and had existing coronary artery atherosclerosis, as compared to the disease free cohort in the study done by Narita and colleagues. Taken together, the existing evidence suggests that high chronic anxiety levels are associated with vascular dysfunction of resistance arteries of older adults with atherosclerosis and conduit arteries of healthy older adults, but more studies are needed in order to understand the mechanism by which anxiety causes vascular dysfunction.

16 5 Anxiety and Arterial Stiffness Increased stiffness of the large central elastic arteries (aorta and carotids) are associated with higher CVD risk (Mitchell et al., 2010). As such, another possible factor that could lead to CVD in individuals with high anxiety is increased arterial stiffness, a biological process that is observed even in the absence of CVD or CVD risk factors such aging, hypertension, hyperglycemia, and obesity. Yeragani et al., (2006) explored the association between anxiety and arterial stiffness in a cohort of 48 aged matched individuals (23 normal controls, and 25 anxiety patients). Using the State Anxiety Inventory (SAI), along with two measures of arterial stiffness, brachial-ankle pulse wave velocity (ba- PWV) and arterial stiffness index, they found that the patients with high anxiety had significantly higher ba-pwv and arterial stiffness index% as compared to the normal controls. Furthermore, Seldenrijk et al., (2011) in their investigation of depression, anxiety and arterial stiffness, observed higher central augmentation index with individuals with depression and anxiety. Seldenrijk and colleagues examined 618 subjects to investigate whether individuals with diagnosed depression or anxiety disorders show increased vascular stiffness and explored the possible associations with psychiatric characteristics. The psychiatric characteristics were measured via the 30-item Inventory of Depressive Symptomatology (IDS) for depression, and the 21-item Beck Anxiety Inventory for anxiety. Central stiffness was measured by radial pulse wave velocity calculated by the augmentation index (the ratio of late systolic pressure augmentation and pulse pressure x 100 in percentage), and local stiffness was assessed by carotid artery ultrasonography calculated via carotid artery distensibility coefficient. They found that subjects with current depression or anxiety had a higher AIx75 compared with control subjects (16.0% vs. 13.0%). When examining specific psychiatric characteristics and arterial stiffness they found that subjects with lifetime comorbidity of anxiety and depression showed a higher AIx75 (15.3%) as compared to control subjects (13.3%). In short, these studies and others, provide evidence that arterial stiffness is higher with anxiety suggesting a possible link between anxiety and CVD risk.

17 6 Potential Mechanisms That Link Anxiety with Vascular Dysfunction Anxiety and Hypertension Hypertension may be another potential mechanism that links the increased risk of CVD with anxiety. Hypertension is highly associated with CVD events such as stroke, myocardial infarction, cardiovascular death, as seen in Franklin et al., (2001) and Domanski et al., (2002), but whether anxiety leads to the onset of hypertension or even worsens hypertension, needs further investigation. In this regard, Jonas et al., (1997) investigated whether or not symptoms of anxiety and depression are risk factors for developing hypertension. Using a sub-sample of the National Health and Nutrition Examination (NHANES) I data collected between the years of called the NHANES Epidemiological Follow-up study, they studied men and women between the ages of years old who had previously had normotensive blood pressure at baseline (when the NHANES I data was collected) and followed up with them for 7-16 years after they had completed the NHANES I. In their follow up visit, they took three blood pressure measurements, as well as asking the individuals whether or not a physician had prescribed hypertensive medications to them during the follow-up period. They discovered that the incident, and needed treatment for hypertension was higher with individuals who had been classified as having high or intermediate anxiety symptoms scores, compared to those who had been classified as having low anxiety scores. Additionally, when they looked at the depression outcomes, they found that incidence rates of hypertension along with treatment for hypertension was highest among individuals with high and intermediate depression scores. Taken together these data support the idea that moderate or high levels of anxiety and depression are possible predictive factors for future development of hypertension. Obesity and Anxiety When considering the role of anxiety within the realm of CVD, it is important to also examine anxiety and its interaction with obesity due to the large population of obese individuals in the world, and the impact obesity has on CVD. Obesity is a disorder that is related to an increased risk of metabolic

18 7 diseases, CVD and physical disabilities, (Capuron et al., 2011). Obesity is associated with low grade chronic inflammation, and has been associated with higher incidences of neuropsychiatric symptoms that include but are not limited to emotional distress (Dixon et al., 2003; Evans et al., 2005; Herva et al., 2006; Bean et al., 2008). Given the relation between obesity and inflammation, it is hypothesized that obesity leads to increased inflammation that results in increased anxiety or possibly that both inflammation and anxiety develop independent of each other in obese adults. Indeed, Capuron et al. (2011) in their study examining adults with morbid obesity awaiting bariatric surgery, found that trait anxiety was related to circulating CRP after adjusting for BMI, suggesting that anxiety was associated with inflammation in obese adults but the relation was not completely explained by higher adiposity. Strine et al., (2008), studied the extent to which anxiety and depression were associated with obesity among US adults using data collected from the 2006 Behavioral Risk Factor Surveillance System (BRFSS-a large population based US survey), that looked at the prevalence of major health and safety related behaviors and characteristics of US adults. Specifically, they studied the associations between anxiety and depression with obesity using the Patient Health Questionnaire-8 (PHQ-8) from the depression and anxiety modules within the 2006 BRFSS. They found that those with a lifetime diagnosis of anxiety were 30% more likely to be obese than those who haven t had an anxiety diagnosis, even after adjusting for sociodemographic characteristics. Taken together, these data are consistent with the idea that obesity and inflammation are important factors to consider and investigate in linking anxiety to CVD. Anxiety and Systemic Inflammation Another possible mechanism by which anxiety potentially increases the risk of developing CVD is via increased low grade systemic inflammation because of the strong link between CVD risk and inflammation (Ridker 2007). Pitsavos et al., (2006) reported a strong relation between common inflammatory markers and anxiety. Pitsavos and colleagues found that C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), homocysteine, and fibrinogen levels were significantly and independently associated with anxiety status in men, while women in their study cohort

19 8 had significant associations between anxiety, CRP, IL-6, homocysteine, fibrinogen, and white blood cell count after adjusting for confounding variables (i.e. BMI, total cholesterol, and social economic status measures such as annual income). In another study, Vogelzangs and colleagues (2013) examined the influence of anxiety in causing low grade chronic inflammation. They specifically examined four types of anxiety disorders which were Generalized Anxiety Disorder (GAD), social phobia (which is now considered SAD), Panic Disorder (PD), agoraphobia and their association with increased inflammatory markers (CRP, IL-6 and TNF-α) in 2288 individuals (ages 18-65) who had any of the four anxiety disorders as compared to healthy controls. The presence of anxiety disorders within the sample population was determined via the Composite Interview Diagnostic Instrument (CIDI) administered by a trained researcher. They found that men with current anxiety had higher CRP (1.13 mg/l) compared to men without anxiety (0.97 mg/l), although the significant associations were lost after adjusting for antidepressant use (p= 0.08), a marginally significant association remained. Furthermore, another study by Capuron et al. (2013) in adults with obesity, found higher inflammation (CRP and IL-6) even after adjusting for body mass index was associated with trait anxiety and depression as assessed by the revised Neuroticism-Extraversion-Openness personality inventory. These studies support the idea that inflammation may be a key mechanistic link contributing to higher CVD risk in adults with anxiety. Acceptance and Commitment Therapy Given the strong link between anxiety and CVD, is it possible that an intervention that reduces anxiety can lead to decreased CVD risk? The Acceptance and Commitment Therapy (ACT) is a relatively novel type of cognitive behavioral therapy intervention that is often used in clinical psychology that targets all forms of anxiety. ACT is defined as a psychological intervention based on modern behavioral psychology, [ ], that applies mindfulness and acceptance processes, and commitment and behavior change processes, to the creation of psychological flexibility ( Hayes et al., 2006). ACT is an empirically validated treatment by the American Psychological Association that was created and developed by Dr.

20 9 Steven C. Hayes. ACT looks at how an individual can change behavior by presently accepting their current state of anxiety, rather than going out of their way to change it. ACT does this by addressing the patient s goals and values, using them as a means of helping the patient change present and future behavioral patterns. ACT has six main tenets, or core treatment policies: 1) Acceptance, 2) Defusion, 3) Contact with the present moment, 4) Self as context, 5) Values, 6) Committed action (Powers, 2009). ACT targets each of these core processes with the goal of cultivating psychological flexibility (adjusting to changing and challenging circumstances that we all face). The first tenet of Acceptance, involves an active awareness of one s anxiety without attempts to change the onset or frequency of the feeling, but rather to fully embrace the anxiety without defense (Hayes et al., 2006). The second tenet is cognitive defusion, which involves changing the way an individual interacts with and relates to their thoughts, by creating a mental context wherein the unfavorable functions of the thoughts are diminished. Thirdly ACT strives to promote being present and keeping in contact with the present moment. Being present has the goal of having individuals experience the world more directly, leading to greater flexibility in behavior and actions. Seeing worry and anxiety as naturally occurring in everyone, but having the mindset of not allowing it to control your behavior. This is done by using language more as a means of noting and describing feelings/thoughts/events, and not simply to predict and judge the feelings/thoughts/events one may be experiencing (Hayes et al., 2006). The fourth tenet that ACT targets is using self as a context. The self as a context framework is based on an individual being aware of one s own experiences or thoughts, without being attached to them. The fifth tenet that ACT aims to address are values. We all have values that shape who we are, via our experiences and backgrounds. ACT, through a multitude of exercises helps individuals choose life directions in various domains (e.g. family, career, relationships, spirituality), allowing people to have a clearer path to a more vibrant and dynamic life. Lastly, ACT aims to spur individuals into developing larger patterns of effective action via concrete short, medium, and long term behavioral change goals that are consistent with their values (Hayes et al., 2006).

21 10 ACT Effectiveness Currently, there have been a couple studies that have looked at the efficacy of ACT in improving psychiatric symptoms in clinical populations. These studies have examined the effectiveness of ACT as compared to control conditions and treatments such as cognitive behavioral treatments. Powers et al., (2009) conducted a meta-analysis of 18 randomized-controlled trials of 917 subjects that completed ACT and determined that ACT was much more effective than controlled conditions, placebos or subjects assigned to waitlist but not significantly more effective than current methods such as cognitive behavioral therapy. Recently, A-Tjak and colleagues (2015) conducted a comprehensive meta-analysis of 39 randomized controlled trials of ACT found from PsycINFO, MEDLINE, and the Cochrane Central Register of Controlled Trials. All of the studies included in their meta-analysis met all of the following inclusion criteria: random assignment with at least one ACT based treatment, either an active or inactive control group, diagnosis of clinically relevant disorder such as anxiety, and at least 10 participants in the active condition(s) at posttreatment assessment. A-Tjak and colleagues found that ACT intervention did better than controlled conditions on both primary and secondary outcome measurements (life satisfaction/quality) at posttreatment as well as at follow up assessments. Consistent with Powers et al. (2009), they also concluded that the ACT treatment had no significant differences between wellestablished psychological treatments such as cognitive behavioral therapy, suggesting that ACT is just as effective as the best treatments currently available for anxiety disorders and depression. Given the link between anxiety levels and increased CVD risk, the goal of our study was twofold; first to determine the extent to which anxiety is related to vascular dysfunction independent of demographic and CVD risk factors, and secondly to determine whether the ACT intervention is effective on improving vascular function in individuals with baseline moderate to high anxiety levels independent of the aforementioned demographic and CVD risk factors. We hypothesized that individuals who report moderate to high levels of anxiety will demonstrate reduced vascular function independent of demographic and CVD risk factors. We further hypothesized that the ACT intervention would lead to

22 11 improvements in vascular function and would be associated with reductions in anxiety levels after 12 weeks as reported by Dindo et al., Hypothesis and Aims The study has the following hypothesis and aims: Hypothesis 1: We hypothesize that higher anxiety levels will be associated with reduced arterial function after primary adjustment for age, sex, race and socioeconomic factors (education level) and further adjustments for CVD risk factors (BMI, blood pressure, CRP). Aim 1a (primary): To determine the extent to which higher state and trait anxiety is independently associated with aortic stiffness among healthy adults varying in anxiety levels. Aim 1b (secondary): To determine the extent to which state and trait anxiety is independently associated with carotid artery stiffness and resistance artery endothelial function among healthy adults varying in anxiety levels. Hypothesis 2: We hypothesize that ACT intervention will result in improvements in arterial function in healthy adults with baseline moderate to high anxiety levels. Aim 2a (primary): To determine the extent to which ACT improves aortic stiffness after 12 weeks among healthy adults with baseline moderate-high anxiety levels. Aim 2b (secondary): To determine the extent to which ACT improves carotid artery stiffness and resistance artery endothelial function after 12 weeks among healthy adults with baseline moderate-high anxiety levels.

23 12 CHAPTER II TESTING AND METHODS Anxiety Classification The ACT (Acceptance and Commitment Training) on anxiety study used several different surveys in order to classify generalized anxiety from acute anxiety, as well as to distinguish it from depression and other mental health disorders. Out of theses series of surveys, the following surveys were used for anxiety and depression: the Anxiety Inventory (AI), Beck Depression Inventory II (BDI-II), the State Trait Anxiety Inventory (STAI), and the Short Form 36 Health Survey (SF-36). These surveys are common surveys used in clinical studies dealing with anxiety symptoms and depression. Symptoms of anxiety were assessed via the AI, a relatively short 21 item self-report survey on a four point scale (0-3), that looks at the anxiety symptoms that individuals have been experiencing during the past month and how much they have been bothered by each of the symptoms. Examples of symptoms include numbness or tingling, dizziness or lightheadedness, being scared and indigestion. Total scores for the AI range from 0 to 63, with scores between 0-9 being considered minimal anxiety. Scores that were between are marked as mild anxiety, while scores between are considered moderate anxiety symptoms, and lastly scores between are considered severe anxiety symptoms. Symptoms of depression were evaluated using the BDI-II, a self-reported 21 item survey on a four point scale (1-4), examining the mood of individuals within the past week. Total scores for the BDI-II range from 0-63, with higher scores suggesting greater severity of depression symptoms. Scores within the range of 0-13 being considered minimal symptoms, scores in the range of being mild, being considered moderate depressive symptoms, and scores above 29 being considered severe symptoms of depression (Faulconbridge et al., 2013). All of these items are in line with the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM IV). The STAI (form Y-consist of Y-1 and Y-2) is a 40 item self-reported survey on a four point scale (1-4), with questions based on the feelings the individual has been experiencing, such as I feel upset, I feel satisfied, etc. High levels of anxiety are marked by a score of 4. The STAI assesses two types of anxiety, the first of which is state anxiety (which has a 20 item questionnaire), and

24 13 the latter is trait anxiety (which has a separate 20 item self-reported questionnaire). State anxiety is anxiety that is onset as a result of an event that may be traumatizing, leading to an acute bout of anxiety, but essentially only affects how the person feels right at a specific moment in time. Trait anxiety, on the other hand, is anxiety that comes on as a result of chronic, longer term anxiety, and how that person generally feels. Response options for the first 20 questions of the questionnaire are as follows: an answer of 1 means that the individual is not at all in possession of the feeling indicated, an answer of 2 means the individual somewhat has the feelings indicated, an answer of 3 means that the person moderately so has the feelings stated, and an answer of 4 means that the individual very much so has the indicated feeling of the statement. Answers for the remaining questions (21-40) are as follows: an answer of 1 means that the individual almost never has the feeling indicated, an answer of 2 means the person sometimes has the feeling stated, an answer of 3 means that the person often has the feelings stated, and an answer of 4 means that the person almost always has the feelings indicated by the statement. Total scores for the STAI range from 20-80, and are obtained by adding up the values from each response. Specific cutoffs for the STAI surveys aren t available however, the higher an individual scores on the STAI, the higher their anxiety levels. The SF-36 survey, is a multi-purpose health survey that looks at the functional health and well-being of individuals. It is composed of a total of 36 questions, and is a generic measurement self-reported survey that looks at general health, limitations of physical activity, physical health and emotional health problems, as well as an individual s social activity profile. Subject Recruitment ACT study All procedures and informed consent were approved by the University of Iowa Institutional Review Board (IRB, # ). Subjects were recruited via mass to the University of Iowa and flyers to individuals who worked or lived within the surrounding areas of the University of Iowa hospital and clinics and Iowa City. Subjects that were recruited to participate in the ACT study were individuals who believe that they have high feelings of anxiety. s were sent out by the Translational Vascular Physiology lab at the University of Iowa, and contained a link to the GAD-7 anxiety inventory for

25 14 individuals to fill out. If a person scored high enough on the anxiety inventory, which was measured as a GAD-7 score of 10 or greater, then they were contacted by phone to go through a phone screen (which exclude subjects who have a history of cardiovascular disease, history of atherosclerotic risk factors, neurological disorders, and or individuals who were currently seeking treatment for anxiety or depression). If the person then passes the phone screen, they were then contacted in order to set days and times when they could come into the lab to undergo testing and to fill out the informed consent forms. Subject Recruitment TIVA study All procedures and informed consent were approved by the University of Iowa IRB (IRB ID # ). Healthy older men and women ages years of age (older group) along with healthy young men and women ages (younger group) were enrolled in the study to establish age-associated differences in large elastic artery stiffness and other related vascular outcomes. All subjects had no history of cardiovascular, metabolic or pulmonary diseases, and were non-smokers. Middle aged/older women recruited for the study were postmenopausal and were not on hormone replacement therapy. Young women were tested during the early follicular phase of their menstrual cycle (within 8 days of onset of menses) to control for differences in circulating estradiol concentrations. Subjects on anti-depressant or anti-anxiety medications were included only after they had been stable for 6 months and didn t change dosage or type of antidepressant or anti-anxiety medication during the study. Study Visits Informed Consent-Visit 1 The first visit of the ACT study was the informed consent visit. For this visit, subjects came in, and one of the research assistants went through the informed consent form with them, educating and informing them about their rights as a research subject, some of the risks involved in participating in the study, as well as describing the different tests and measurements that will be taken in each visit, giving them as much insight as possible into what to expect throughout the study duration. Next the subject read and completed a demographics and health history survey, along with a modified activity questionnaire

26 15 which looked into their physical activity at work and during their leisure time. Following those two surveys, the subject then completed a series of various health surveys (eight in total) which were: State- Trait Anxiety Inventory, Beck Anxiety and Depression Inventories. Next, anthropometric measurements were taken, specifically the subjects height and weight, hip and waist measurement (in order to determine hip and waist ratio-a marker of increased cardiovascular risk), as well as a resting electrocardiogram (ECG) to see if there were any cardiac abnormalities and or arrhythmias. Finally, prior to the subject leaving, a blood sample was taken by one of the research nurses. Baseline Measurements-Visit 2 The second visit of the ACT study, was the baseline visit. Subjects would come to Translational Vascular Physiology lab in the University of Iowa hospital and Clinics early in the morning after fasting for at least 8 hours overnight. In addition to fasting, subjects had to have been off of any vitamins, antioxidants or any substances that can cause changes in vasculature, approximately two weeks prior to their baseline visit. Additionally, subjects had to have refrained from drinking alcohol and any caffeinated products as well as major physical exercise within 8 hours of testing. A pregnancy test was first taken if applicable, and then seven electrodes were placed on the subject in order to monitor their heart rate and rhythm throughout the duration of the visit. Blood samples were collected. Following the collection of blood samples, two brachial blood pressure measurements were obtained and pulse wave velocity measurements were taken using applanation tonometry from NIHem immediately after the blood pressure measurements. Collection of Circulating factors (lipids, glucose, insulin, Norepinephrine, CRP) Blood circulating factors were collected by a trained research staff or nurse. Upon insertion of a small venous catheter into the median cubital vein of the subject by a research staff nurse, blood circulating factors including lipids, glucose, insulin and CRP were collected and taken to and analyzed by the University of Iowa diagnostic lab (University of Iowa Hospitals and Clinics) by standard methods.

27 16 Carotid-Femoral Pulse Wave Velocity (cf-pwv) With hypertension and normal aging, central elastic arteries become stiffer, diastolic pressure decreases, and central systolic and pulse pressures are augmented due to increased carotid-femoral pulse wave velocity (cf-pwv) and early return of reflected waves to the heart from the periphery (Nichols, 2005). Over the past several years, cf-pwv has become the gold standard of measuring arterial stiffness that occurs with ageing as well as with atherosclerosis and the dysfunction of blood vessels (Mitchell, 2009). Cf-PWV is a non-invasive technique where two pressure or flow waveforms are measured with the tonometer probe at a known distance apart and the distance between measurement sites is divided by the propagation time delay (Mitchell, 2009) in order to calculate the rate of which blood flows through the blood vessel giving us insight into the compliance of the vessel of interest. The stiffer the arterial wall, the higher the pulse wave velocity, while the more compliant the vessel, the lower the velocity of the waves traveling in the lumen, similar to water flowing through a metal pipe as compared to water flowing through a more compliant rubber garden hose. Measurements taken at the carotid and femoral arteries give cf-pwv, which is a surrogate of aortic PWV (Mitchell, 2009). For our study we measured pulse wave velocity at four different sites (radial, brachial, carotid, and femoral arteries), with each location s distance being measured using a tape measure. Calculations of pulse waves were done using the NIHem in order to see whether or not there is an improvement to large artery stiffness (aortic stiffness specifically) from baseline to post measurements, as a result of the ACT intervention. Common Carotid Artery Compliance Carotid artery compliance and Beta-stiffness index were determined noninvasively by highresolution ultrasonography (Logiq 7, GE Healthcare) of the right common carotid artery and contralateral assessment of carotid artery blood pressure via non-invasive carotid artery applanation tonometry respectively. Carotid artery diameters are measured approximately 2 cm proximal to the carotid bulb with the transducer placed at a 90 angle to the vessel by off-line analysis of DICOM images with image analysis software (Medical Imaging Applications, LLC). Maximal diameters and minimal diameters were measured in sync with carotid artery blood pressure waveforms. Carotid blood pressure waveforms were

28 17 calibrated using diastolic and mean brachial artery blood pressure obtained from standard brachial artery cuff blood pressure. The principle investigator, or an experienced lab member placed the transducer on the carotid artery of the subject and a visual recording of the subject s carotid blood flow was taken for 30 seconds. Non Invasive Blood Pressure Monitoring Non-invasive beat by beat blood pressure monitoring of all subjects was done throughout the duration of vascular measurements via the Nexfin machine. A small blood pressure cuff was placed around the middle finger of the subject, which then recorded their blood pressure at several time points in order to monitor any sort of changes that were occurring while the subjects were undergoing vascular measurements. Forearm Blood Flow Using Venous Occlusion Plethysmography Forearm blood flow was obtained using a technique known as venous occlusion plethysmography (VOP). VOP is a non-invasive technique that differs from brachial flow mediated dilation in the aspect that it measures small artery blood flow within the forearm vasculature and microvasculature. The VOP process includes the occlusion of the wrist and upper arm using two rapid cuff inflator units (E20/AG101, Hokanson, Inc.) as well as the plethysmograph machine (EC-6, Hokanson, Inc). For the ACT study, the right arm was the sole arm used throughout the baseline and post intervention visits. VOP follows a relatively simple methodology wherein the plethysmograph was calibrated with the appropriate size strain gauge to the widest area of the subject s forearm. Following the calibration of the plethysmograph, baseline forearm blood flow measurements were taken; first by inflating the wrist cuff to 250mmHg for 1 minute, and then at the one minute mark, inflating the upper arm cuff at 50mmHg on a 15 second cycling basis for 2 additional minutes. The reactive hyperemia portion of the test is done a couple minutes after the baseline measurement. It is conducted by first inflating the upper arm cuff to 250mmHg for 4 minutes, and at the four minute mark inflating the wrist cuff to 250mmHg. At the 5 minute mark, the upper arm cuff is deflated to 50mmHg and set to cycle at 15 sec. intervals for an additional 3 minutes, which mark the end of the test.

29 18 Blood Samples An intravenous catheter was placed by a nurse, and blood samples totaling approximately two and a half tablespoons were collected by one of the clinical research unit nurses, and was then analyzed and stored. Acceptance and Commitment Training Intervention-Visit 3 Visit 3 was the ACT intervention, which took place in and around the Iowa Hospitals and Clinics in Iowa City, IA. At the beginning of the intervention workshop, subjects filled out the STAI and BAI surveys. For the intervention, participants participated in the ACT workshop, and were introduced into different ways by which they can deal with their feelings of anxiety by developing behaviors that cultivate psychological flexibility. Participants are equipped with techniques of controlling their thoughts and feelings of anxiety, with the understanding that anxiety and worrying are natural processes, which do not have to control their lives and their ability to live a life of productivity. The intervention was led by two licensed practitioners with a sound knowledge of the ACT training. For those who were not randomized into the intervention group, they received an with the same surveys that were filled out by those in the one day ACT workshop. Six Week Follow up Six weeks following the ACT intervention, subjects in both the control and intervention groups were sent an with the series of eight health surveys which they completed during visit one that they then completed again. Post Intervention-Visit 4 Visit 4 is the post intervention visit that occurred approximately six weeks following the six weeks follow up (twelve weeks after the intervention). Subjects came in for a second experimental visit, where they were tested using the same vascular measurements used in visit two. Prior to the commencement of the vascular measurements, subjects completed all of the various health surveys for a

30 19 third time, and had the same vascular measurements completed in the same manner as seen in visit two. Once again, they were provided with an ambulatory blood pressure monitor, which they took home to be worn for a total duration of 24 hours. Statistical Analysis Aim 1: For descriptive analysis, a Wilcoxon Rank Sum (i.e., Mann-Whitney U-test) was performed for continuous variables and chi-square for categorical variables to determine group differences between ACT and TIVA cohorts. Then, a multivariate linear regression analysis was used to determine baseline associations between vascular outcomes and anxiety, using State and Trait anxiety scores as the predictor variable and vascular outcomes as the response variable with cf-pwv as the primary outcome of interest and carotid β-stiffness and forearm blood flow as the secondary outcomes. Model 1 was the unadjusted model, and then to determine whether demographic and socioeconomic factors influence the relations, a 2 nd model (Model 2) was adjusted for age, sex, race, education and study assignment, followed by a fully adjusted model (Model 3) for the second model+ BMI, mean blood pressure and CRP. Aim 2: A linear mixed models regression analysis was performed in SAS 9.3 using the PROC MIXED function to determine whether ACT intervention compared with control resulted in reductions in the primary outcome, aortic stiffness, as well as the secondary outcomes, carotid β-stiffness and peak forearm blood flow. Linear mixed models were used because of their ability to account for within-subject correlations between repeated measurements. These models specified an autoregressive variancecovariance matrix (AR-1), under the assumption that the repeated measures are more strongly correlated the closer they are in time, and less correlated the further apart they are in time. The primary and secondary outcomes are modeled as continuous and measured at two time points: baseline (Visit #2: preintervention) and at week 12 (visit #5: post-intervention). The reported β values in the primary analysis represents the units of difference in the intervention group compared to the control group for a given random effect. A secondary analysis was done using a general linear mixed models regression analysis for

31 20 change in State or Trait anxiety with change in vascular outcomes. The reported β values in the secondary analysis represent units of change in State or Trait anxiety on change in vascular outcomes over the 12 weeks. Note that STAI surveys for State and Trait anxiety were captured at all five visits, and that the ACT intervention occurred in between Visit #3 and Visit #4. Statistical significance was defined as alpha <0.05 for all models.

32 21 CHAPTER III RESULTS Aim 1 Results Baseline Subject Characteristics Forty-three adults (age 35.8 ± 9.4 years-(s.d.)) completed the ACT study, while 12 adults (age 47.9 ± 13.9 years) were included from the TIVA study, for a combined total of 55 individuals (33 females and 22 male participants). Subject characteristics are presented in Table A-1. Subjects within the two groups did not differ in body mass index (p=0.38), race/ethnicity (p=0.43) or education (p=0.43). However, subjects within the two groups showed a significant difference in age, with the ACT group averaging a little over 12 years older as compared to the TIVA group (35.8 ± 9.4 years vs ± 13.9 years, p=0.01). Additionally, there was a statistically significant difference in proportion of males and females between groups (p=0.03). There were 1.5 times as many females as compared to males within the ACT cohort, while the TIVA cohort, although a much smaller sample, had twice as many males as compared to females. Subject characteristics in relation to vascular outcomes and anxiety based on study affiliation is shown in table A-2. As expected, individuals in the ACT group exhibited higher State anxiety and Trait anxiety as compared to the TIVA cohort (p<0.01), as the ACT participants were recruited based on feelings of high anxiety, while the TIVA group was recruited based on their age (individuals ages 50-79). Furthermore, the two groups did not differ by CRP (p=0.45), systolic blood pressure (p=0.66), diastolic blood pressure (p=0.39), mean arterial pressure (p=0.41), carotid β-stiffness (p=0.12) or cf-pwv (p=0.17). Forearm blood flow was not measured in the TIVA group, therefore no comparison could be made for forearm blood flow within the two groups. Aim 1 results Table A-5 through A-10 along with Figures B-1 through B-5 display the associations between the anxiety measures and the three vascular outcomes for aim 1 of the study. In the unadjusted linear

33 22 regression model, we found that there was a borderline association between trait anxiety and carotid β- stiffness (p=0.05), but no significant interaction between carotid β-stiffness and State anxiety. Additionally, there were no other significant relations between trait and state anxiety with cf-pwv, carotid β-stiffness or peak forearm blood flow in model 1 which was adjusted for sex, age, study membership, and race/ethnicity, and model 2 which was adjusted for model 1+BMI, MAP and lncrp. Aim 2 Results Baseline Subject Characteristics Subject characteristics for aim 2 are presented in Table A-3. Aim 2 of the study was to determine the effect of the ACT intervention on the three vascular outcomes, cf-pwv, carotid β-stiffness and peak forearm blood flow. There were 24 individuals (15 females and 9 males, ages 34.8 ± 9.1 years) that were randomized into the intervention group, and 18 individuals (14 females, and 4 males, ages 37.7 ± 9.9 years) who were randomized into the control group, for a grand total 42 individuals. There was one less person in the ACT group for aim 2 as compared to aim 1 (42 as compared to 43) because one participant dropped out of the study following baseline measurements, but prior to randomization. At baseline, subjects within the intervention and control group did not differ significantly in age (34.8 ± 9.1 years vs ± 9.9 years, p=0.21), sex (p=0.29) or race/ethnicity (p=0.55). However, there was a near significant difference between baseline BMI of the two groups (p=0.05), with individuals in the control group having a higher BMI as compared to the intervention group (30.0 ± 5.2 kg/m 2 vs ± 4.6 kg/m 2 respectively). Individuals within the control group displayed significantly higher baseline Trait anxiety compared to the intervention group (57.0 ± 9.3 vs ± 7.6, p=0.01), as shown in Table A-4. CRP (p=0.13), systolic blood pressure (p=0.59), diastolic blood pressure (p=0.78), mean arterial pressure (p=0.73), carotid β- stiffness (p=0.32) and cf-pwv (p=0.85) all were not different between the two groups. Aim 2 results Aim 2, focused on the effects of the ACT intervention on vascular outcomes with cf-pwv as the primary outcome, and carotid β-stiffness along with peak forearm blood flow as the secondary outcome.

34 23 We found that there was a significant interaction between intervention participation and peak forearm blood flow in all three of our statistical models. In all models, intervention participation was associated with an increase in peak forearm blood flow (p=0.031, p=0.049, p=0.040, respectively) as compared to the control group, shown in Tables A-11 through A-13, and Figure B-6. The second model was adjusted for age, sex, race, and education while the third model was adjusted for Model 2 + BMI and STAI Trait anxiety because these two variables had failed randomization. However, there were no differences between the control and intervention groups in any of the models for cf-pwv (p=0.23, p=0.28, and p=0.27 in the unadjusted model and adjusted models 1 and 2 respectively) or carotid β-stiffness (p=0.32, p=0.28, and p=0.29 in the unadjusted and adjusted models 1 and 2 respectively). Mean values of vascular outcomes at baseline and at 12 weeks are shown in Table A-14 through A-22, and overall change from baseline to 12-weeks in both control and intervention groups with respect to the three vascular outcomes are shown in Figure B-7. With regards to the effects of anxiety on vascular outcomes, we found no association with changes in anxiety levels from baseline to 12-weeks (with both State and Trait anxiety), and its effects on the three vascular outcomes in all three of our linear mixed models, as shown in Tables A-23 through A- 28.

35 24 CHAPTER IV DISCUSSION The primary focus of this thesis project was to determine the extent to which higher anxiety states (State and Trait anxiety) are independently associated with vascular dysfunction as assessed by aortic stiffness among healthy adults with varying levels of anxiety. Additionally, we wanted to evaluate the extent to which higher anxiety states are associated with carotid artery stiffness and resistance artery endothelial function within the same sample of healthy adults. The secondary focus of this thesis project was to determine the extent to which the ACT anxiety intervention would improve aortic stiffness, carotid β-stiffness and peak forearm blood flow after 12 weeks in healthy adults with moderate-high baseline anxiety levels. Contrary to our primary hypothesis, we found that increased anxiety states were not associated with large elastic artery stiffness, carotid β-stiffness, or peak forearm blood flow in healthy adults, with the exception of Trait anxiety being marginally associated with carotid β-stiffness in an unadjusted regression model. For our secondary aims, randomization to the ACT anxiety intervention was associated with improvements in peak forearm blood flow in all three of our linear mixed models, but not with cf- PWV or carotid β-stiffness. Therefore, these data suggest that although higher State and Trait anxiety was not associated with aortic stiffness, carotid stiffness or forearm resistance artery endothelial function, the ACT intervention is associated with improved peripheral resistance artery function. In addition, ACT is associated with reduced State and Trait anxiety levels in healthy adults who have baseline moderate to high anxiety. Aims and vascular outcomes Large elastic stiffness and anxiety For the first part of aim 1, we wanted to determine the extent to which higher anxiety is independently associated with increased large elastic stiffness. Unlike, Yeragani et al. (2006), who found that the patients with high anxiety had significantly higher ba-pwv and arterial stiffness index% as

36 25 compared to the normal controls, we found no association between cf-pwv and increased anxiety, both with Trait and State anxiety measures. This may be a result of the manner by which we measured PWV, using cf-pwv rather than ba-pwv. Cf-PWV is an indicator of aortic stiffness and is the speed that the pulse travels from the heart to the carotid and femoral artery, while ba-pwv is the speed that the pulse travels from the heart to the brachial artery and ankle. This difference is significant because of the types of arteries that are being used for the measurement. The carotid is a large elastic artery, while the brachial artery is a less elastic artery. Thus, this difference in PWV measurements could be another reason for the differing results between our study and that of Yeragani et al., (2006). Further, cf-pwv is considered the gold standard measure of aortic stiffness (Seldenrijk et al., 2011) and is strongly associated with CVD risk (Mitchell et al., 2010) whereas the clinical relevance of ba-pwv is unknown. Therefore, interventions that target cf-pwv likely will have the greatest impact on reducing CVD risk. Carotid β-stiffness For the secondary analysis of our first aim, we determined whether or not increased anxiety was independently associated with higher carotid β-stiffness. We found a borderline association between Trait anxiety and carotid β-stiffness (p=0.05) in our unadjusted linear regression model, but no association between carotid β-stiffness and State anxiety. Furthermore, there were no associations between carotid β- stiffness and either of the anxiety measures in the remainder of our models. Similar to our study, Seldenrijk and colleagues (2011) investigated whether individuals with a diagnosis of depression or anxiety disorders demonstrated increased vascular stiffness using carotid artery ultrasonography. In line with our study, they found no significant associations between psychiatric characteristics (measured by BAI and the Inventory of Depressive Symptomatology) and carotid artery distensibility coefficient (the change in cross-sectional area per unit of pressure). Forearm Blood Flow The third vascular outcome that we investigated for the secondary analysis of our first aim was peak forearm blood flow during reactive hyperemia, a measure of forearm resistance artery endothelial

37 26 function. Cross-sectionally, we found that there was no association between higher anxiety and peak forearm blood flow in all three of our linear regression models. Similar to our study, Stillman et al., (2013) examined anxiety and its association with forearm resistance vessel endothelial function in older adults with atherosclerosis. They found an inverse association between anxiety symptoms and forearm resistance vessel dilation in response to intra-brachial artery infusions of acetylcholine (β= -.302, p=0.004) before and after controlling for risk factors such as BMI. However, it is important to note that Stillman and colleagues studied two distinct groups of older adults, one with and without atherosclerosis. In agreement with our study, they did not find an association between anxiety and forearm resistance vessel dilation in the older healthy adults without atherosclerosis. In contrast, they did find a significant correlation between forearm resistance vessel dilation and anxiety scores among the atherosclerotic group. As such, it could be that the differences within the sample that Stillman and colleagues studied, older adults with atherosclerosis, compared to our younger, disease free sample could explain divergent results with our study. Additionally, the method by which they stimulated increases in forearm blood flow differed from our study. Stillman et al., measured forearm blood flow in response to the administration of vasoactive agents (Nitroprusside, acetylcholine, and verapamil) at low, medium, and high doses using VOP, whereas our study measured forearm resistance vessel function in response to reactive hyperemia, a non-invasive measure of the peak vasodilatory capacity of resistance arteries, which could have possibly led to the differences with our findings. Importantly, we did find an association between increased peak forearm blood flow in those persons randomized to the ACT intervention participation compared with control in all three of our linear mixed models, in aim 2. These data suggest for the first time that the ACT intervention results in improved forearm resistance artery endothelial function in healthy adults without CVD with baseline moderate/high anxiety. However, more studies are needed to determine whether this effect occurs in adults with atherosclerosis and in forearm resistance vessel function in response to intra-arterial vasoactive agonists.

38 27 Limitations/ Recommendations As with any scientific study, there are some limitations within our proposed study. One possible limitation of the study is the fact that those randomized into the control group were not blinded. This could introduce an unconscious bias which could then influence the results that we see with the control group. Secondly, another possible limitation to the study is the adherence to the training/treatment from the one day ACT group intervention. During the intervention, subjects were given different techniques and methods that they could use for living with their anxiety symptoms. However, if the post intervention vascular measurements were taken 12 weeks after the ACT intervention, it is possible that adherence to the methods learned in ACT could have been forgotten, and as a result, increased anxiety levels could reappear thus undoing whatever benefits the ACT intervention could have conferred. Therefore, this could have contributed to the observed little or no changes in vascular outcomes. In addition it is important to note that we did not measure physical activity or dietary records, so it is possible that individuals in the treatment group increased their physical activity or improved their diet, which could have also influenced our results. Additionally, the short duration of the one day ACT group intervention, lasting six hours, may not necessarily be long enough to aid subjects in truly understanding how to effectively deal with their anxiety. The Acceptance and Commitment therapy (which is the longer term therapy that the ACT intervention is based on), would typically be held for multiple sessions over a longer period of time, granting subjects a greater amount of time to truly grasp methods of combating and dealing with anxiety. Nevertheless, studies have repeatedly validated this one day ACT approach. Another important factor that must be considered when evaluating this study is the comorbidity of anxiety and depression. Indeed, Pitsavos et al., (2006), indicated that there is an association between anxiety and depression and often times both psychiatric disorders occur together making it difficult to tease out which one is influencing the primary outcomes of interest. For our study, we screened for depression using the depression inventory, however because anxiety and depression disorders are

39 28 comorbid disorders, one depression inventory may not be enough to properly screen out individuals with greater yet less apparent depression. Additionally, there have been relatively few studies that have examined racial and ethnic components of anxiety in non-white groups, and as such, it is an important aspect that needs to be further considered in our study. The population of Iowa City and Johnson County is majority white, which is reflected in the makeup of our study population. In this regard, our results should be interpreted with caution when extrapolating the data to non-white racial and ethnic groups. This is important because in some cultures such as Korean culture, Logan et al., (2012), suggested that individuals are often even more reluctant than the general public to share their personal information including self-reported anxiety levels, as a result of their social and cultural upbringing. Thus, there may be bias in self-report of anxiety levels in different racial and ethnic groups. Furthermore, given that some racial and ethnic groups, such as African-American and Hispanic-Americans, demonstrate higher rates of hypertension and CVD additional studies are needed to determine the possible impact of anxiety on CVD and the potential effectiveness of ACT in these groups of individuals with moderate to high anxiety. Our study found that there was a significant difference in proportion of males and females between the ACT and TIVA groups within our first aim, because TIVA participants were recruited based on age whereas ACT participants were recruited based on moderate to high anxiety levels. As such, the ACT cohort had a higher proportion of females compared with the TIVA cohort. Pitsavos et al., (2006), found that women had significantly higher STAI scores as compared to men within their cohort (p=0.04) and Simonds and Whiffen (2003) reported that women are more likely to develop and be diagnosed with depression and anxiety as compared to men. This data from the literature supports our finding that women are more likely to suffer from internalizing and mood disorders such as anxiety and or depression as compared to men, who are more likely to suffer from externalizing disorders such as alcoholism or drug addiction (Seedat et al., 2009). In conclusion, through our investigation of high anxiety and vascular dysfunction, we found that increased anxiety states were not associated with large elastic artery stiffness, carotid β-stiffness, or peak

40 29 forearm blood flow. However, we found that individuals randomized into the ACT intervention showed improvements in peak forearm blood flow in all three of our linear mixed models, suggesting that the ACT intervention could possibly have a selective effect on resistance arteries rather than the large elastic arteries. Furthermore, the ACT intervention could be causing improvements in peak forearm blood flow by altering outcomes that we did not examine. Possible candidate outcomes include but are not limited to changes in sympathetic nerve activity, hypothalamic-pituitary axis hormones such as the stress hormone cortisol, or even possibly altering other psychological measures such as depression. As such, the future direction of the study will involve increased recruitment of highly anxious individuals (in order to increase the power of our study, as well as to have a greater number of males), and the incorporation of other outcome measures such as sympathetic nerve activity as measured by microneurography, and stress hormones such as cortisol, behavioral outcomes such as physical activity levels and dietary records, and other psychological measures such as depression and quality of life surveys.

41 30 APPENDIX A: TABLES Table A-1: Baseline subject demographic characteristics for ACT and TIVA study groups (Aim 1) ACT (n=43) TIVA (n=12) Combined total (n=55) p-value ACT vs. TIVA Mean (SD) Age (years) 35.8 (9.4) 47.9 (12.9) 38.5 (11.3) 0.01 Body mass index (kg/m 2 ) 28.0 (5.0) 26.3 (3.6) 27.6 (4.8) 0.38 N (%) Sex Male 14 (32.6%) 8 (66.7%) 22 (40%) Female 29 (67.4%) 4 (33.3%) 33 (60%) 0.03 Race/Ethnicity American Indian 0 (0%) 0 (0%) 0 (0%) Asian 3 (7.1%) 0 (0%) 3 (5.6%) Black 3 (7.1%) 0 (0%) 3 (5.6%) Hispanic 2 (4.8%) 2 (16.7%) 4 (7.4%) 0.43 Pacific Islander 1 (2.4%) 0 (0%) 1 (1.8%) White 33 (78.6%) 10 (83.3%) 43 (79.6%) Education 0.46 Less than High School High School Diploma Some College College Degree Graduate/Professional Degree Data are mean and standard deviation. 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (9.3%) 0 (0%) 4 (7.3%) 19 (44.2%) 7 (58.3%) 26 (47.3%) 20 (46.5%) 5 (41.7%) 25 (45.4%)

42 31 Table A-2: Baseline subject anxiety, inflammation and vascular outcomes for ACT and TIVA groups (Aim 1) ACT (n=43) TIVA (n=12) Combined total (n=55) p-value ACT vs TIVA Mean (SD) State Anxiety 42.7 (8.0) 26.6 (8.5) 39.2 (10.5) < Trait Anxiety 53.4 (8.9) 26.4 (5.2) 47.5 (13.9) < Systolic Blood Pressure (9.6) (8.6) (9.4) 0.66 (mmhg) Diastolic Blood Pressure 73.9 (6.9) 71.0 (8.2) 73.6 (7.0) 0.39 (mmhg) Mean Arterial Pressure 89.6 (7.1) 86.5 (7.7) 89.3 (7.2) 0.41 (mmhg) C-reactive Protein (mg/l) 2.2 (2.8) 1.2 (0.9) 2.0 (2.6) 0.45 Carotid β-stiffness index (U) 7.1 (2.3) 8.7 (3.3) 7.4 (2.6) 0.12 Peak Forearm Blood Flow (ml/100 ml FAV/min) 21.6 (6.5) N/A 21.6 (6.5) N/A Carotid-Femoral PWV (cm/sec) (249.2) (187.6) (236.3) 0.17 Data are mean and standard deviation. PWV, pulse wave velocity. FAV, forearm volume. MAP calculated using 2/3 of the mean of 24-hr diastolic blood pressure and 1/3 of the mean of 24-hr systolic blood pressure.

43 32 Table A-3: Baseline subject demographic characteristics of the intervention, non-intervention, and combined groups (Aim 2) ACT intervention (n=24) No Intervention (n=18) Combined total (n=42) p-value ACT vs. No Intervention Mean (SD) Age (years) 34.8 (9.1) (9.85) (9.41) 0.21 Body mass index (kg/m 2 ) 26.5 (4.6) (5.23) (5.14) 0.05 N (%) Sex Male 9 (37.5%) 4 (22.2%) 13 (30.9%) Female 15 (62.5%) 14 (77.8%) 29 (69.1%) 0.29 Race/Ethnicity American Indian Asian Black Hispanic Pacific Islander White Education Less than High School High School Diploma Some College College Degree Graduate/Professional Degree Data are mean and standard deviation. 0 (0.0%) 0 (0.0%) 0 (0.0%) 2 (8.3%) 1 (5.9%) 3 (7.3%) 2 (8.3%) 1 (5.9%) 3 (7.3%) 2 (8.3%) 0 (0.0%) 2 (4.9%) 0 (0.0%) 1 (5.9%) 1 (2.4%) 18 (75.0%) 14 (82.4%) 32 (78.1%) (0%) 0 (0.0%) 0 (0.0%) 0 (0%) 0 (0.0%) 0 (0.0%) 1 (4.2%) 3 (16.7%) 4 (9.5%) (50.0%) 6 (33.3%) 18 (42.9%) 11 (45.8%) 9 (50.0%) 20 (47.6%)

44 33 Table A-4: Baseline subject anxiety, inflammation and vascular outcomes of the intervention, nonintervention and combined groups (Aim 2) ACT Intervention (n=24) No Intervention (n=18) Combined total (n=42) p-value ACT vs. No Intervention Mean (SD) State Anxiety 41.3 (8.4) 45.0 (7.1) 42.9 (8.0) 0.12 Trait Anxiety 50.4 (7.6) 57.0 (9.3) 53.2 (8.9) 0.01 Systolic Blood Pressure (11.1) (7.3) (9.7) 0.59 (mmhg) Diastolic Blood Pressure 74.1 (6.9) 73.8 (7.3) 74.0 (7.0) 0.78 (mmhg) C-reactive Protein (mg/l) 2.0 (3.4) 2.5 (2.2) 2.2 (2.9) 0.13 Mean Arterial Pressure 90.2 (7.6) 88.9 (6.8) 89.6 (7.2) 0.73 (mmhg) Carotid β-stiffness Index 6.8 (2.2) 7.5 (2.4) 7.1 (2.3) 0.32 (U) Peak Forearm Blood Flow 22.4 (7.9) 20.5 (4.7) 21.4 (6.5) 0.50 (ml/100 ml FAV/min) Carotid-Femoral PWV (cm/sec) (310.3) (151.3) (252.1) 0.85 Data are mean and standard deviation. PWV, pulse wave velocity. FAV, forearm volume. MAP calculated using 2/3 of the mean of 24-hr diastolic blood pressure and 1/3 of the mean of 24-hr systolic blood pressure.

45 34 Table A-5: Linear regression model State anxiety-aim 1 (unadjusted) State Anxiety Regression coefficient Unadjusted SE p-value Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow PWV, pulse wave velocity. Table A-6: Linear regression model Trait anxiety-aim 1 (unadjusted) Trait Anxiety Regression coefficient Unadjusted SE p-value Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow PWV, pulse wave velocity.

46 35 Table A-7: Linear regression model State anxiety-aim 1- adjusted for sex, age, education, study membership, and race/ethnicity State Anxiety Regression coefficient Model 1 1 SE p-value Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Model 1 adjusted for sex, age, education, study membership, and race/ethnicity. PWV, pulse wave velocity. Table A-8: Linear regression model Trait anxiety-aim 1 adjusted for sex, age, education, study membership, and race/ethnicity Trait Anxiety Regression coefficient Model 1 1 SE p-value Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Model 1 adjusted for sex, age, education, study membership, and race/ethnicity. PWV, pulse wave velocity.

47 36 Table A-9: Linear regression model State anxiety-aim 1 adjusted for Model 1+ lncrp, BMI and MAP State Anxiety Regression coefficient Model 2 2 SE p-value Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Model 2 adjusted for Model 1+lnCRP, BMI and MAP. PWV, pulse wave velocity. Table A-10: Linear regression model Trait anxiety-aim 1 adjusted for Model 1+ lncrp, BMI and MAP Trait Anxiety Regression coefficient Model 2 2 SE p-value Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Model 2 adjusted for Model 1+lnCRP, BMI and MAP. PWV, pulse wave velocity.

48 37 Table A-11: Linear mixed model analysis: Effects of ACT on vascular outcomes (unadjusted)- Aim 2 Regression coefficient SE p-value Unadjusted Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow PWV, pulse wave velocity. Table A-12: Linear mixed model analysis: Effects of ACT on vascular outcomes adjusted for age, sex, education, and race/ethnicity- Aim 2 Regression coefficient SE p-value Adjusted 1 Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Adjusted for age, sex, education, and race/ethnicity. PWV, pulse wave velocity. Table A-13: Linear mixed model analysis: Effects of ACT on vascular outcomes adjusted for age, sex, education, and race/ethnicity + BMI and STAI Trait anxiety- Aim 2 Regression coefficient SE p-value Adjusted 2 Carotid-Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Adjusted for age, sex, education, and race/ethnicity + BMI and STAI Trait Anxiety. PWV, pulse wave velocity.

49 38 Table A-14: Carotid-femoral PWV over time- Aim 2 Combined Groups Mean SE 95% CI Baseline , weeks , Data are mean and standard deviation. PWV, pulse wave velocity, with values in cm/sec. Table A-15: Carotid-femoral PWV over time- Aim 2 Intervention Group Mean SE 95% CI Baseline cm/sec , Weeks cm/sec , Data are mean and standard deviation. PWV, pulse wave velocity, with values in cm/sec. Table A-16: Carotid-femoral PWV over time- Aim 2 Control Group Mean SE 95% CI Baseline , Weeks , Data are mean and standard deviation. PWV, pulse wave velocity, with values in cm/sec.

50 39 Table A-17: Carotid β-stiffness index over time- Aim 2 Combined Groups Mean SE 95% CI Baseline , Weeks , 7.81 Data are mean and standard deviation. Table A-18: Carotid β-stiffness index over time- Aim 2 Intervention Group Mean SE 95% CI Baseline , Weeks , 8.66 Data are mean and standard deviation. Table A-19: Carotid β-stiffness index over time- Aim 2 Control Group Mean SE 95% CI Baseline , Weeks , 7.58 Data are mean and standard deviation.

51 40 Table A-20: Peak forearm blood flow over time- Aim 2 Combined Groups Mean SE 95% CI Baseline , Weeks , Data are mean and standard deviation. Values are in ml/100 ml FAV/min. FAV, forearm volume. Table A-21: Peak forearm blood flow over time- Aim 2 Intervention Group Mean SE 95% CI Baseline , Weeks , Data are mean and standard deviation. Values are in ml/100 ml FAV/min. FAV, forearm volume. Table A-22: Peak forearm blood flow over time- Aim 2 Control Group Mean SE 95% CI Baseline , Weeks , Data are mean and standard deviation. Values are in ml/100 ml FAV/min. FAV, forearm volume.

52 41 Table A-23: Linear mixed model analysis: Effects of State anxiety on vascular outcomes (unadjusted)- Aim 2 State Regression coefficient SE p-value Unadjusted Carotid Femoral PWV Carotid -stiffness Peak Forearm Blood Flow PWV, pulse wave velocity. Table A-24: Linear mixed model analysis: Effects of Trait anxiety on vascular outcomes (unadjusted)- Aim 2 Trait Regression coefficient SE p-value Unadjusted Carotid Femoral PWV Carotid -stiffness Peak Forearm Blood Flow PWV, pulse wave velocity.

53 42 Table A-25: Linear mixed model analysis: Effects of State anxiety on vascular outcomes, adjusted for age, sex, education, and race/ethnicity (Model 1)- Aim 2 State Anxiety Regression coefficient SE p-value Adjusted 1 Carotid Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Adjusted for age, sex, education, and race/ethnicity (Model 1). PWV, pulse wave velocity. Table A-26: Linear mixed model analysis: Effects of Trait anxiety on vascular outcomes, adjusted for age, sex, education, and race/ethnicity (Model 1)- Aim 2 Trait Anxiety Regression coefficient SE p-value Adjusted 1 Carotid Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Adjusted for age, sex, education, and race/ethnicity (Model 1). PWV, pulse wave velocity.

54 43 Table A-27: Linear mixed model analysis: Effects of State anxiety on vascular outcomes, adjusted for Model 1+lnCRP, BMI, and MAP- Aim 2 State Anxiety Regression coefficient SE p-value Adjusted 2 Carotid Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Adjusted for Model 1+lnCRP, BMI, and MAP. PWV, pulse wave velocity. Table A-28: Linear mixed model analysis: Effects of Trait anxiety on vascular outcomes, adjusted for Model 1+lnCRP, BMI, and MAP- Aim 2 Trait Anxiety Regression coefficient SE p-value Adjusted 2 Carotid Femoral PWV Carotid -stiffness Peak Forearm Blood Flow Adjusted for Model 1+lnCRP, BMI, and MAP. PWV, pulse wave velocity.

55 44 APPENDIX B: FIGURES Figure B-1: Linear regression dot-plot models for cf-pwv (unadjusted, model 1 and model 2) Model 1 adjusted for age, sex, education, study membership, ethnicity. Model 1 adjusted for age, sex, education, study membership, ethnicity. Model 2 adjusted for model 1+lnCRP, BMI and MAP. Figure B-2: Linear regression dot-plot models for carotid β-stiffness (unadjusted, model 1 and model 2) Model 1 adjusted for age, sex, education, study membership, ethnicity. Model 2 adjusted for model 1+lnCRP, BMI and MAP.

56 45 Figure B-3: Linear regression dot-plot models for Peak forearm blood flow (unadjusted, model 1 and model 2) Model 1 adjusted for age, sex, education, study membership, ethnicity. Model 2 adjusted for model 1+lnCRP, BMI and MAP. Forearm blood flow data from the ACT cohort only.

57 46 Figure B-4: Scatterplots for vascular outcomes and State anxiety (unadjusted linear regression models) Peak forearm blood flow data from the ACT cohort only. Figure B-5: Scatterplots for vascular outcomes and Trait anxiety (unadjusted linear regression models) Peak forearm blood flow data from the ACT cohort only.

58 47 Figure B-6: Linear mixed model analyses dot-plot models for effects of ACT on vascular outcomes Model 1 adjusted for sex, age, race, and education. Model 2 adjusted for BMI and STAI trait anxiety. Figure B-7: Changes in vascular outcomes from baseline to 12-weeks in control and intervention groups