Daily Stressors and Inflammation Among Family Dementia Caregivers

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Jean-Philippe Gouin, M.A.

Graduate Program in Psychology

The Ohio State University

2011

Dissertation Committee:

Janice K. Kiecolt-Glaser, Advisor

Charles F. Emery

Michael W. Vasey

Jean-Philippe Gouin

2011

Abstract

Acute laboratory stressors elicit elevations in circulating inflammatory biomarkers. Chronic stressors, such as family dementia caregiving, promote a state of chronic low-grade inflammation. The recurrent daily stressors associated with chronic stress may lead to repeated and sustained activation the inflammatory system. The goals of the present study were to evaluate whether greater exposure and reactivity to daily stressors fueled increased inflammation among family dementia caregivers, compared to noncaregiving controls. This cross-sectional study included 78 family dementia caregivers and 105 noncaregiving controls. A semi-structured interview, the Daily

Inventory of Stressful Events, assessed the occurrence of daily stressors in the past 24 hours; self-report questionnaires evaluated mood, health, and health behaviors; a blood sample provided data on two inflammatory markers, C-reactive protein (CRP) and interleukin-6 (IL-6). Results showed that caregivers were more likely to experience multiple stressors in the past 24 hours than noncaregiving controls. The occurrence of multiple daily stressors was associated with greater CRP, and exposure to multiple daily stressors mediated the relationship between parental caregiving and increased CRP.

Statin use moderated the relationship between daily stressors and IL-6 production; daily stressors were related to IL-6, but only among participants not using statins. Furthermore, among participants not using statins, the chronic stress of caregiving amplified IL-6, but not CRP, responses to daily stressors. The associations between daily stressors and

ii inflammation remained significant even after adjusting for differences in health and health behaviors. These results indicate that the cumulative effect of daily stressors promotes sustained elevations in inflammation. Greater exposure to daily stressors among family dementia caregivers may promote the chronic low-grade inflammation and the enhanced health risk observed in this chronically-stressed population.

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To my family.

Acknowledgments

I would like to thank my advisor, Dr. Janice Kiecolt-Glaser, for her wonderful mentorship and for providing me with exciting research opportunities. I would also like to thank Dr. Charles Emery and Dr. Mike Vasey for their time and helpful feedback. A thank you also goes to Dr. Maurice Eastridge who served as the Graduate Faculty

Representative on my dissertation defense.

I would also like to thank my lab mate, Liisa Hantsoo, for her countless revisions of the manuscript and help throughout my graduate studies at OSU. A special thank you also goes to my friends Tammy Schuler, LaBarron Hill, Eleshia Morrison, Gizem Erdem,

Molly Martinez, and Kristy Hall. They were always there to incite me to work when it was time to work and to play when it was time to play. I would also like to thank Vance for bringing joy in everyday of my life. Finally, I also wish to thank my family for their constant support.

I would also like to thank the Fonds de la Recherche en Santé du Québec for its financial support throughout my doctoral studies at the Ohio State University and the

NIH for its funding of the parent study (Grant # AG025732).

Vita

October 21st, 1981…………………………………………….Born, Québec City, Canada

2004...... B.A. Psychology, Laval University, Canada

2006 ………………………...M.Ps. Clinical Psychology, University of Montréal, Canada

2009………………………………..M.A. Clinical Psychology, The Ohio State University

Publications 1. Fagundes, C.P., Murray, D., Hwang, B.S., Gouin, J.P., Thayer, J.F., Sollers, J.J., Shapiro, C., Malarkey, W., & Kiecolt-Glaser, J.K. Sympathetic and Parasympathetic Activity in Cancer-Related Fatigue: More Evidence for a Physiological Substrate in Cancer Survivors. (In Press). Psychoneuroendocrinology.

2. Gouin, JP. Chronic Stress and Immune Dysregulation. (In Press). American Journal of Lifestyle Medicine.

3. Kiecolt-Glaser, JK, Gouin, JP, Glaser, R, Malarkey, WB, Pang, N. (2011). Childhood Adversity Heightens the Impact of Later-Life Caregiving Stress on Telomere Length and Inflammation. Psychosomatic Medicine, 73(1), 16-22.

4. Gouin, J.P., & Kiecolt-Glaser, J.K. (2011). The Impact of Psychological Stress on Wound Healing: Methods and Mechanisms. Immunology and Allergy Clinics of North America,31(1), 81-93.

5. Gouin, J.P., Connors, J., Kiecolt-Glaser, J.K., Glaser, R. Malarkey, W.B., Atkinson, C., Beversdorf, D., & Quan, N. (2010). Altered Expression of Circadian Rhythm Genes Among Individuals With a History of Depression. Journal of Affective Disorders. 126 (1-2):161-166.

6. Gouin JP, Carter CS, Pournajafi-Nazarloo H, Glaser R, Malarkey W, Loving T, Stowell J, & Kiecolt-Glaser J. (In Press). Marital Behavior, Oxytocin, Vasopressin, and Wound Healing. Psychoneuroendocrinology.

7. Kiecolt-Glaser JK, Gouin, J.P., Hantsoo, L.V. (In Press). Close Relationships and Inflammation. Neurobehavioral Review.

8. Gouin JP, Hantsoo L, Kiecolt-Glaser JK. (2010). Stress, Negative Emotions, and Inflammation. Handbook of Social Neurosciences. Edited by J.T. Caccioppo & J. Decety. John Wiley and Sons.

9. Gouin JP, Glaser R, Loving TJ, Malarkey WB, Stowell J, Houts C, & Kiecolt- Glaser JK. (2009). Attachment avoidance predicts inflammatory responses to marital conflict. Brain, Behavior, and Immunity, 23, 898-904.

10. Gouin JP, Hantsoo L, & Kiecolt-Glaser JK. (2008). Immune dysregulation and chronic stress among older adults: a review. Neuroimmunomodulation, 15: 251- 259.

11. Gouin JP, Kiecolt-Glaser JK, Malarkey WB, Glaser R. (2008).The influence of anger expression on wound healing. Brain Behavior and Immunity, 22: 699-708.

12. Christian, LM, Deichert, NT, Gouin, JP, Graham, JE, Kiecolt-Glaser, JK. (2008). "Psychological influences on neuroendocrine and immune outcomes." In Handbook of Neuroscience for the Behavioral Sciences, Edited by J.T. Cacioppo & G.G. Berntson. TBD: John Wiley and Sons.

13. Achille, MA, Rosberger, Z, Robitaille, R, Lebel, S, Gouin, JP, Bultz, BD., & Chan PTK. (2006). Facilitators and obstacles to sperm banking in young men receiving gonadotoxic chemotherapy for cancer : the perspective of survivors and health care professionals. Human Reproduction, 21: 3206-3216.

Fields of Study

Major Field: Psychology

Concentrations: Clinical Psychology Health Psychology

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Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vi

List of Tables ...... ix

List of Figures ...... xi

Chapter 1: Introduction ...... 1

Chapter 2: Method ...... 31

Chapter 3: Results ...... 45

Chapter 4: Discussion ...... 71

References ...... 97

Appendix A: Tables ...... 133

Appendix B: Figures ...... 158

Appendix C: Semi-structured interviews ...... 178

Appendix C: Self-reported questionnaires ...... 185

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List of Tables

Table 1. Sociodemographic characteristics of family dementia caregivers and noncaregiving controls...... ……… 131

Table 2. Self-reported health and medication use among caregivers and controls……. 132

Table 3. Caregiving status and inflammation…………………………………………. 133

Table 4. Psychosocial characteristics of family dementia caregivers and noncaregiving controls………………………………………………………………… 134

Table 5. Health behavior practices among caregivers and controls…………………… 135

Table 6. Daily stressors and inflammation……………………………………………..136

Table 7. Daily stressors, caregiving status, and inflammation……………….……..… 137

Table 8. Caregiving status by daily stress interaction and inflammation………...…… 138

Table 9. Daily stressors, caregiving status, depressive symptoms, and inflammation... 139

Table 10. Depressive symptoms by statin use interaction…………………..……..….. 140

Table 11. Daily stressors, depression, statin use, and inflammation………….……..…141

Table 12. Inflammation and interaction among daily stressors, caregiving status, depressive symptoms, and statin use…………………………………………....…..….142

Table 13. Cumulative stressor severity and inflammation…..….……………………... 143

Table 14. Daily stressors as a function of health behaviors…………………………….144

Table 15. Daily stressors, caregiving status, self-rated health, and usual health

ix behaviors………………………………………………………………………………..145

Table 16. Daily stressors, caregiving status, self-rated health, and recent health behaviors………………………………………………………………….………….…146

Table 17. Daily stressors by statin use interaction…………………………..………….147

Table 18. Interaction among daily stressors, caregiving status, and statin use and inflammation…………………………………………………………...……….………148

Table 19. Daily stressors by NSAID use interaction…………………………………..149

Table 20. Daily stressors by estrogen medication use interaction. …….….…….…….150

Table 21. Daily stressors by antidepressant medication use interaction……………….151

Table 22. Daily stressors by inflammation-related medication use interaction and inflammation……………………………………………………………………………152

Table 23. Interaction among daily stressors, caregiving status, and inflammation- related medication use and inflammation………………….……..….………..….…….153

Table 24. Daily stressors, caregiving status, perceived stress, hip-to-waist ratio and inflammation. ……………………………………………………….….………………154

List of Figures

Figure 1. IL-6 as a function of caregiving status...... 156

Figure 2. CRP as a function of caregiving status...... 157

Figure 3. Number of daily stressors in the past 24 hours as a function of caregiving status...... 158

Figure 4. Depressive symptoms as function of caregiving status...... 159

Figure 5. Self-reported chronic stress as a function of caregiving status...... 160

Figure 6. Sleep quality in the past month as a function of caregiving status...... 161

Figure 7. IL-6 as a function of the number of daily stressors in the past 24 hours...... 162

Figure 8. CRP as a function of the number of daily stressors in the past 24 hours...... 163

Figure 9. CRP as a function of the number of daily stressors in the past 24 hours after adjusting for caregiving status….………………………………………………………164

Figure 10. Schematic representation of the role of daily stressors as partial mediator of the relationship between caregiving stress and CRP...... 165

Figure 11. Moderation effect of statin use on the relationship between depressive symptoms IL-6...….…………………………………………………………………….166

Figure 12. Moderation effect of statin use on the relationship between depressive symptoms and CRP. ……………………………………………….…………..……….167

Figure 13. Moderating effect of statin use on the relationship between daily stressors and IL-6...... 168

Figure 14. IL-6 as a function of the interaction among daily stressors, caregiving status, and statin use...... 169

Figure 15. IL-6 as a function of the daily stressors by inflammation-related medication use…………………………………………………………………………...….………170

Figure 16 Differences in CRP among noncaregiving controls, spousal caregivers and parental caregivers...... 171

Figure 17. Schematic representation of the role of daily stressors as partial mediator of the relationship between parental caregiving and CRP...... 172

Figure 18. CRP as a function of the caregiving by marital status interaction……….....173

Figure 19. IL-6 as a function of the interaction of caregiving status and education...….174

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Chapter 1: Introduction

Caregiving stress and health

In the United States, about 10 million people are currently taking care of a relative with dementia (Alzheimer's Association, 2007). Family dementia caregivers are faced with numerous challenges associated with the care of their loved one. They must deal daily with the changes in cognitive functioning (e.g., memory loss, confusion), behaviors

(e.g., agitation, restlessness, wandering, aggression), and personality (e.g. apathy, lack of emotion) of the care recipient. In addition, they must often provide help to the patient in performing activities of daily living (e.g., bathing, dressing, shopping, housework) and in navigating through the health care system (Schulz & Martire, 2004; Schulz, O'brien,

Bookwala, & Fleissner, 1995). Other less obvious detrimental consequences of caregiving are diminished relationship quality with the care recipient, conflict with family members regarding the division of caregiving tasks, and decreased work performance because of absenteeism and tiredness (Pearlin, Mullan, Semple, & Skaff, 1990; Schulz &

Martire, 2004).

The daily challenges associated with the care of an individual suffering from a neurodegenerative disease can be burdensome. Data from the National Alliance for

Caregiving Survey showed that family dementia caregivers provide an average of 16.6 hours of care per week, and almost 25% of them spend more than 40 hours per week performing caregiving-related tasks. Seventy percent of the respondents provided care for

more than one year and 32% did so for more than 5 years (Alzheimer's Association,

2007). It is therefore not surprising that more than 25% of the caregivers consider their experience to be very stressful (Schulz & Martire, 2004). Given the sustained demands associated with the care of a relative with dementia, caregiving has been conceptualized as a model to study the impact of chronic stress on health (Grant, 1999; Kiecolt-Glaser,

Dura, Speicher, Trask, & Glaser, 1991).

A growing body of evidence suggests that caregiving stress is a risk factor for both physical and psychological disorders. Compared to age- and gender- matched noncaregiving controls, caregivers are at higher risk of experiencing depressive and anxiety disorders (Dura, Stukenberg, & Kiecolt-Glaser, 1991). In addition, caregiving appears to be hazardous for physical health. Individuals who care for a relative with dementia display a higher incidence and prevalence of hypertension (Grant, et al., 2002;

Shaw, et al., 1999) and hyperlipidemia (Vitaliano, Russo, & Niaura, 1995), and are at higher risk of developing infectious diseases (Kiecolt-Glaser, et al., 1991), diabetes

(Kolanowski, Fick, Waller, & Shea, 2004), and cardiovascular disorders (Lee, Colditz,

Berkman, & Kawachi, 2003; Schulz, et al., 1995; Shaw, et al., 2003; Vitaliano, et al.,

2002) than noncaregiving controls. Furthermore, strained caregivers had a two-fold greater risk of mortality, compared to matched community controls (Schulz & Beach,

1999).

In accord with the increase in physical illnesses, family dementia caregiving is also associated with immune dysregulation. Caregivers exhibited dysregulated cellular immunity (Esterling, Kiecolt-Glaser, Bodnar, & Glaser, 1994; Kiecolt-Glaser, et al.,

1991; Mills, Yu, Ziegler, Patterson, & Grant, 1999), weakened control over latent viruses

(Glaser & Kiecolt-Glaser, 1997; Kiecolt-Glaser, et al., 1991; Pariante, et al., 1997), impaired wound healing (Kiecolt-Glaser, 1995), and poorer responses to viral and bacterial vaccines (Glaser, Kiecolt-Glaser, Malarkey, & Sheridan, 1998; Glaser,

Sheridan, Malarkey, Maccallum, & Kiecolt-Glaser, 2000; Kiecolt-Glaser, Glaser,

Gravenstein, Malarkey, & Sheridan, 1996), compared to noncaregiving controls. Family dementia caregivers had also shorter telomere length and greater telomerase activity in peripheral blood mononuclear cells (PBMC) and T-cells than matched noncaregiving controls, suggesting an accelerated aging of the immune system (Damjanovic, et al.,

2007).

The chronic stress of caregiving is also associated with chronic low-grade inflammation. In cross-sectional studies, caregivers had a higher concentration of interleukin-6 (IL-6) serum levels compared to community-matched controls. Older women caring for a spouse with dementia exhibited higher IL-6, compared with both older women undergoing the time-limited stress of housing relocation, as well as women who were not experiencing significant life changes (Lutgendorf, et al., 1999). These results were replicated in a larger study in which caregivers also had higher IL-6 plasma levels than demographically-similar noncaregiving controls (Von Kanel, R, et al., 2006).

In addition, caregivers exhibited a shift in the balance between Th1 and Th2 cytokines, particularly an increase in interleukin-10 (IL-10), suggestive of an inflammatory state

(Glaser, et al., 2001). Notably, caregivers displayed an amplified age-related increase in inflammation, exhibiting on average a four-fold greater rate of IL-6 increases over a 6- year period, compared to control participants (Kiecolt-Glaser, et al., 2003). Chronic low- grade inflammation is a physiological mechanism that might explain the association

between the chronic stress of caregiving and the development and progression of age- related diseases and even death (Black, 2003; Ershler & Keller, 2000).

Inflammation: friend or foe?

Inflammation is an essential immune response triggered by infection and injury. It is the initial, automatic, and nonspecific reaction of the innate immune system upon exposure to an antigen -- a substance foreign to the host’s body. Inflammation promotes the destruction and clearance of pathogens and initiates wound healing. Local inflammatory reaction leads to heat, swelling, redness, and pain at the site of inflammation. This process is enacted primarily by proinflammatory cytokines, such as

IL-6 or tumor necrosis factor-α (TNF-α), which provide an intercellular signal to recruit and activate other immune cells into the affected area. In contrast, anti-inflammatory cytokines, such as IL-10, decrease the production and function of proinflammatory cytokines, thereby regulating the immune response to antigens (Parham, 2004).

Interleukin-6 is a pleiotropic molecule that has hematologic, hepatic, immune, endocrine, and metabolic actions. Among the many functions of IL-6, it promotes the differentiation of B cells, elicits secretion of corticotrophin hormone, stimulates the release of acute phase reactants, decreases serum lipid concentration, stimulates secretion of growth hormones, inhibits secretion of thyroid-stimulating hormone, promotes bone resorption, and is elevated in several inflammatory, infectious, and traumatic states. IL-6 is produced by immune cells such as monocytes, macrophages, lymphocytes, and endothelial cells, but also by non-immune cells such as osteoblasts, intestinal epithelial

cells, adipocytes, and vascular smooth-muscle cells (Papanicolaou, Wilder, Manolagas, &

Chrousos, 1998).

C-reactive protein (CRP) is an acute phase reactant produced by the liver and adipocytes and mainly stimulated by IL-6. CRP increases complement opsonization and amplifies macrophages’ phagocytosis. High CRP plasma levels are indicative of a state of inflammation (Parham, 2004).

Although inflammation is a critical response to acute infection or injury, chronic or excessive inflammation may be detrimental to health. In fact, chronic low-grade inflammation has been implicated in a number of serious medical conditions (Ershler &

Keller, 2000; Maggio, Guralnik, Longo, & Ferrucci, 2006). In epidemiological studies, high levels of circulating inflammatory markers have been associated with higher incidence of cardiovascular disorders, diabetes, certain cancers, autoimmune diseases, frailty, and even death (Ershler & Keller, 2000).

Among rheumatoid arthritis patients, interleukin-6 and its soluble receptor are elevated in plasma and are positively correlated with disease activity (Madhok, Crilly,

Watson, & Capell, 1993). Elevated circulating inflammatory markers have been related to greater markers of physical frailty and lower bone mineral density, and have been prospectively associated with the development of frailty and disability in older adults

(Cesari, et al., 2004; Ferrucci, et al., 1999; Giuliani, et al., 2001). Furthermore, high IL-6 plasma levels have been related to smaller hippocampal grey matter volume and poorer cognitive performance among healthy individuals (Marsland, Gianaros, Abramowitch,

Manuck, & Hariri, 2008).

Elevated markers of inflammation may also increase risk of certain cancers

(Aggarwal, Shishodia, Sandur, Pandey, & Sethi, 2006). In experimental and clinical studies, inflammation in the tumor area appears to promote cancer development and progression (Balkwill, Charles, & Mantovani, 2005). In humans, elevated inflammatory markers have also been associated with higher incidence of cancer-related morbidity and mortality (Collado-Hidalgo, Bower, Ganz, Cole, & Irwin, 2006; Salgado, et al., 2003).

Notably, IL-6 is a prospective risk factor for type 2 diabetes (Pradhan, Manson,

Rifai, Buring, & Ridker, 2001). Furthermore, an increasing number of studies report that inflammation is associated with atherosclerotic processes and hypertension, which in turn are related to the development of cardiovascular disorders (Amar, et al., 2006; Sesso, et al., 2003). Indeed, elevated plasma levels of IL-6 and CRP have been associated with an increased risk of myocardial infarction among apparently healthy individuals (Ridker,

Rifai, Stampfer, & Hennekens, 2000; Ridker, 2000). Circulating markers of inflammation also predict a worse prognosis following an acute coronary episode (Lindmark,

Diderholm, Wallentin, & Siegbahn, 2001). Increased low-grade circulating inflammation has even been associated with a higher risk of mortality among older adults (Ferrucci, et al., 1999; Harris, et al., 1999; Reuben, et al., 2002). The sustained overproduction of circulating inflammatory markers might therefore place caregivers at higher risk for a host of age-related diseases (Black, 2003; Ershler & Keller, 2000; Kiecolt-Glaser,

McGuire, Robles, & Glaser, 2002; Kiecolt-Glaser, et al., 2003).

Acute stress and inflammation

In addition to the increase in inflammation associated with chronic stress, numerous studies have reported an increase in plasma levels of proinflammatory cytokines following exposure to acute psychological stressors. In well-controlled animal studies, rodents who were exposed to unpredictable foot shocks secreted higher levels of plasma IL-6, compared to control animals that did not receive any shock (Zhou,

Kusnecov, Shurin, Depaoli, & Rabin, 1993). Subsequent animal studies have found that a range of stressors such as physical restraints and open field exposure also led to increased circulating IL-6 (Johnson, et al., 2002; Lemay, Vander, & Kluger, 1990). In human studies, exposure to an experimental stressor comprised of a computerized color-word interference (i.e., Stroop) task and a mirror tracing task led to a rise in IL-1β mRNA expression in mononuclear cells (Brydon, et al., 2005). Similarly, exposure to a standardized laboratory stressor, the Trier Social Stress Test (TSST) comprised of speech and mental arithmetic tasks, led to an increase in plasma IL-6 (Pace, et al., 2006). A meta-analysis of human studies on the effect of acute stress on circulating markers of inflammation suggests that an increase in plasma levels of IL-6, IL-1β, and CRP is reliably observed after exposure to a standardized psychological stressor. The increase in biomarkers of inflammation appears to be greater 30 to 120 minutes post-stress compared to immediately after the stress task (Steptoe, Hamer, & Chida, 2007). In fact, elevations in circulating markers of inflammation were observed up to 22 hours following the discussion of a marital disagreement in a well-controlled environment (Kiecolt-Glaser, et al., 2005).

Short-term naturalistic stressors also appear to augment systemic inflammation.

Academic examinations led to an increase in interferon-γ (IFN-γ), TNF-α, and IL-6 among the more anxious college students, compared to students who exhibited less examination-induced anxiety (Maes, et al., 1998). Similarly, psychiatry residents who gave an oral presentation before the members of their department exhibited an increase in

IL-1β and sICAM, a chemokine whose presence suggests an inflammatory state, as compared to control days when they listened to colleagues giving presentations (Heinz, et al., 2003). Taken together, those results provide strong evidence that rather mild stressors can elevate, at least transiently, circulating markers of inflammation.

Daily hassles as acute stressors among chronically-stressed caregivers

The daily hassles experienced by caregivers can be conceptualized as minor acute stressors whose accumulation can potentially lead to persistent elevations in inflammation and contribute to the immune dysregulation observed in this population.

Repeated exposure to a standardized stressor does not appear to lead to the habituation of the inflammatory response. Participants who participated in the TSST once a week for three weeks demonstrated reduced cortisol and systolic blood pressure reactivity during the second and third exposure to the stressor. However, the inflammatory responses remained the same across the three experimental sessions (Von Kanel, Kudielka, Preckel,

Hanebuth, & Fischer, 2005). If such lack of habituation also occurs in naturalistic settings, inflammatory responses to relatively minor but recurrent stressors in daily life may contribute to increased chronic low-grade inflammation. Daily stressors could thus

represent the repeated “hits” to the physiological systems that are thought to increase allostatic load and lead to detrimental health outcomes (McEwen, 1998).

Although studies linking acute stress response to long-term immune dysregulation are lacking, the stress reactivity hypothesis suggests that individuals who exhibit greater physiological reactivity to everyday stressors will be at higher risk of developing stress- related diseases (Cacioppo, et al., 1998). Preliminary evidence for this hypothesis was provided by studies showing that a greater inflammatory response to a laboratory stressor was associated with a larger increase in ambulatory systolic blood pressure and carotid artery stiffness over the course of a 3-year period (Brydon & Steptoe, 2005; Ellins, et al.,

2008). This suggests that the inflammatory response to the daily hassles associated with chronic stress could play a central role in the dysregulated immune responses observed among family dementia caregivers.

Psychological mechanisms linking daily stress and inflammation

Activation of the physiological stress systems is thought to underlie the increase in systemic inflammation following exposure to psychosocial stressors (Rohleder, 2009).

A direct molecular mechanism has been detailed by Bierhaus and collaborators (2003).

They showed that exposure to the TSST led to an increase in nuclear factor-κB (NF-κB) from peripheral blood monocyte cells within 10 minutes. NF-κB is a transcription factor that influences the gene expression of several inflammatory mediators (Barnes & Karin,

1997). In humans, activation of NF-κB was correlated with stress-induced catecholamine production. Animal studies specified that binding of norepinephrine, but not epinephrine, led to a downstream signaling cascade that resulted in the activation and translocation of

the NF-κB in the nucleus of the cells (Bierhaus, et al., 2003). Therefore, stress-induced increases in norepinephrine might lead to the activation of NF-κB and subsequently, to an increase in gene expression of inflammatory proteins.

Stress-induced mood disturbances is another psychobiological pathway by which acute stress might be associated with increased inflammation. As reported above, academic examination-induced anxiety was associated with higher production of serum

TNF-α, IL-6, and IFN-γ among medical students (Maes, et al., 1998). Increases in self- reported anxiety in response to a laboratory mental challenge were associated with higher

IL-1β gene expression, suggesting that mood disturbances following exposure to a stressor contributes to changes in systemic inflammation (Brydon, et al., 2005). In a similar vein, several studies have noted an elevation in the serum concentrations of IL-6 and CRP among individuals presenting with subsyndromal depressive symptomatology

(Dentino, et al., 1999; Kop, et al., 2002; Ladwig, Marten-Mittag, Lowel, Doring, &

Koenig, 2003; Suarez, Krishnan, & Lewis, 2003) and clinically depressed patients (Ford

& Erlinger, 2004; Irwin, 2002; Maes, et al., 1997; Miller, Stetler, Carney, Freedland, &

Banks, 2002), compared to less depressed individuals. A meta-analytic study confirmed that higher levels of both IL-6 (d=.15) and CRP (d=.25) were associated with greater depressive symptoms (Howren, Lamkin, & Suls, 2009). Moreover, among healthy young women, state depressive symptoms that represented a deviation from trait depressive symptoms were associated with an increase in plasma IL-6 (Rohleder & Miller, 2008). In a longitudinal study, greater depressive symptoms at baseline predicted greater increases in IL-6 over a 6-year period, providing further evidence that mood disturbances can influence peripheral inflammatory activity (Stewart, Rand, Muldoon, & Kamarck, 2009).

The relationship between inflammation and mood is bidirectional. In animal studies, central and peripheral administration of IL-1β led to sickness behavior. Sickness behavior is part of an animal model of depression comprising many depressive-like symptoms such as anhedonia, social withdrawal, lethargy, fatigue, psychomotor retardation, decreased appetite, and sleep disturbances (Dantzer, O'Connor, Freund,

Johnson, & Kelley, 2008; Dantzer, Wollman, Vitkovic, & Yirmiya, 1999). The fact that these symptoms disappeared when the administration of cytokine was interrupted, or with the administration of cytokine antagonists or anti-inflammatory compounds, supports the role of cytokines in causing these depression-like symptoms (Dantzer, et al., 2008). In humans, successful antidepressant treatment is associated with a reduction in circulating markers of inflammation among clinically depressed patients (Kim, et al., 2007).

Furthermore, administration of IFN-γ or IL-2 as treatment for certain cancers and hepatitis C has been related to the development of depressive disorders in up to 40% of the patients (Capuron & Miller, 2004). Similarly, vaccination studies have shown that individuals who were vaccinated or who were exposed to an endotoxin reported an increase in negative affect and a decrease in positive mood that was correlated with the vaccine- and antigen-induced elevation in IL-6 (Reichenberg, et al., 2001; Wright, Strike,

Brydon, & Steptoe, 2005).

Mechanistically, the impact of cytokines on mood may be mediated by their impact on the serotonergic and other monoamine systems. In rats, intraventricular injections of IFN- reduced serotonin (5-HT) brain levels (Kamata, Higuchi, Yoshimoto,

Yoshida, & Shimizu, 2000). Proinflammatory cytokines can decrease the availability of tryptophan (TRP) in the brain by activating the enzyme indoleamine-2,3 dioxygenase,

which provokes a switch from the synthesis of TRP to the synthesis of kynurenine and quinolinic acid, thereby reducing the production of 5-HT (Schiepers, Wichers, & Maes,

2005). Decreased CSF levels of TRP have been positively correlated with the development of depressive symptoms among cancer patients treated with IFN-

(Capuron, et al., 2002). In addition, several proinflammatory cytokines (TNF- , IL-1 ,

IFN- ) reduced the activity of the 5-HT transporter, which may result in a decrease in extracellular 5-HT levels (Bonaccorso, et al., 2002). Cytokines may also impact mood by influencing the synthesis and reuptake of dopamine and norepinephrine (Kitagami, et al.,

2003; Moron, et al., 2003). Furthermore, inflammation can reduce neural plasticity.

Pharmacologically-induced inflammation via LPS injection was associated with cognitive impairment, decreased hippocampal expression of brain-derived neurotrophic factor

(BDNF) and its receptor, tyrosine kinase-B, as well as reduced hippocampal neurogenesis in rats (Wu, et al., 2007).

Methodological issues in the assessment of daily stressors

Daily stressors are defined as routine challenges of day-to-day living and unexpected small occurrences that disrupt daily life. Examples of daily stressors include arguments with children, unexpected work deadlines, malfunctioning computers, or troubles commuting between work and home (Almeida, 2005). In contrast, chronic stressors, such as family dementia caregiving, represent stable events with no predictable ending that pervade someone’s life, forcing individuals to restructure their identities or social roles (Elliot & Eisdorfer, 1982).

Four main assessment methods have been used to measure daily events. The first method consists of retrospective self-reported questionnaires that investigate the frequency of different daily stressors over the course of a predetermined period of time, often one month (Kanner, Coyne, Schaefer, & Lazarus, 1981). Retrospective recall of daily events over long periods of time is often biased and may confound occurrences of daily events with general perception of stress. To overcome these biases, daily diary methodology requires the participants to fill out a short diary including questions about daily events, psychological distress, and/or physical health at the end of each day

(DeLongis, Coyne, Dakof, Folkman, & Lazarus, 1982). Daily diary studies considerably reduce the recall biases associated with retrospective questionnaires sampling longer periods of time. They also allow longitudinal analysis of daily hassles and well-being. A third approach uses an experience sampling methodology, also called momentary assessment study, that is designed to reduce recall biases associated with retrospective assessment of stressors during the day (Smyth & Stone, 2003). In these studies, participants are prompted at randomly chosen occasions during the day to provide data on the occurrence of stressors at the time of the assessment and sometimes also provide biological samples such as saliva (Jacobs, et al., 2007). This assessment method eliminates the recall bias associated with retrospective assessment. However, compliance with the recording instructions is often an issue. Unfortunately, experience sampling methodology cannot be used to investigate the relationship between inflammation and stress because the assays for these biological markers require collection of plasma that is not easily performed by the research participants.

The use of checklist measures of daily stressors in the assessment methods mentioned above can be problematic for several reasons. Items in those measures are often confounded with psychological and physical symptoms (Dohrenwend & Shrout,

1985). Although some studies have analyzed their results with and without the potentially confounding items and found similar results (Russell & Cutrona, 1991), those procedures can create content sampling issues. Indeed, often those checklist questionnaires do not provide an adequate coverage of the different categories of daily events that an individual may experience (Thoits, 1983). Furthermore, the association between chronic stress, daily hassles, and psychological and physical outcomes may be inflated by common method variance when they are all assessed by self-reported questionnaires.

In the past decade, an interview-based measure of daily events, the Daily

Inventory of Stressful Events (DISE) was developed to overcome these limitations

(Almeida, Wethington, & Kessler, 2002). This semi-structured interview evaluates the occurrence of daily stressors in the past 24 hours.The DISE allows for a better assessment of the events’ content, the identification of overlapping stressors, as well as the ability to differentiate between stressor severity and appraisal. This latter property is an appreciable advantage because the personal meaning of the event may modulate its impact on the person’s life (Hammen, 2005). The DISE allows the researcher to make judgments about the so-called “objective” severity of the stressor by taking in consideration the contextual threat associated with the events and evaluating “what one can expect the average person in that particular set of biographical circumstances to feel”. For example, the same event

“having a car accident” has a different impact if the person does not need a car to perform

his/her work or has a second car than if the person has only one car, no money to pay for the repair, and depends on the vehicle to perform his/her work.

Lazarus (1999) argues that to obtain a valid measurement of daily events both stressor exposure and appraisal must be differentiated from the underlying personality traits that affect stressor reactivity. When determining the objective severity of a daily event, the emotional reaction of the participant is not considered because it may influence event recall and interpretation. For example, among depressed patients, dysfunctional attitudes and attributional style influenced the number and severity of life events reported on self-reported measure, but not on interview-based measures (Simons, Angell, Monroe,

& Thase, 1993). A dictionary-based system based on Brown & Harris’s (1989) methodology has been developed to facilitate the evaluation of the objective severity of the daily events. The objective severity of the stressor is a reliable predictor of depressive episodes (Brown, 1989). Furthermore, interview-based measures of life events are better are better predictor of depression recurrence than self-reported checklist questionnaires

(McQuaid, Monroe, Roberts, Kupfer, & Frank, 2000). In the DISE validation study, objective and subjective severity were correlated at .36, further indicating the unique variance explained by the objective severity assessment (Almeida, et al., 2002).

Daily stress, distress, and health

Early conceptualization of stress focused on how major life events influence psychological and physical well-being (Holmes & Rahe, 1967). Subsequent studies revealed that daily stressors make an important and independent contribution to psychological adjustment and health. In several studies using self-reported

questionnaires, daily stressors appear to be a better predictor of psychological distress than major life events. For example, in an early study, daily hassles in the past month explained a greater percentage of the variance in psychological distress than major life events (Kanner, et al., 1981). Several prospective studies using a daily diary methodology also showed that the association between psychological distress was stronger with daily hassles than with major life events assessed with self-reported instruments (Delongis,

Coyne, Dakof, Folkman, & Lazarus, 1982; Holahan, Holahan, & Belk, 1984). In within- subject analyses, daily stress accounted for about 19% of the variance in daily mood

(Bolger, Delongis, Kessler, & Schilling, 1989). In momentary assessment studies, the occurrence of daily stressors was also associated with simultaneous increases in negative affect and decreases in positive affect (Jacobs, et al., 2007).

Studies using the DISE methodology confirm the association between daily stressors and distress. The DISE was administered in the National Study of Daily

Experiences, a study including respondents from a US general population sample of non- institutionalized adults aged 25 to 74 selected through random-digit procedures. The

DISE validation study was comprised of a total of 1031 participants, including 562 women and 469 men. Over the course of eight consecutive days, participants completed a telephone-based version of the DISE at the end of each day. On average, 7 out of 8 interview days were completed. Respondents reported at least one stressor on 40% of the days and multiple stressors on 10% of the days. Respondents were more likely to report psychological symptoms on days when they reported a stressor, compared to days when they experienced no daily events. Daily stressors accounted for 31% of the variance in

the same day negative mood. Higher subjective and objectively stressors severity were associated with greater psychological distress (Almeida, et al., 2002).

Self-reported somatic symptoms and overall perceived health were also related to daily events. In a daily diary study, higher daily stress score on a given day was associated with the same and the next day self-reported physical symptoms (Delongis, et al., 1982). In the DISE validation study, respondents were more likely to report physical symptoms on days when they reported a stressor, compared to days when no daily event was experienced (Almeida, et al., 2002). Those results were replicated in both healthy participants and individuals with medical conditions (Jandorf, Deblinger, Neale, & Stone,

1986). Among individuals with rheumatoid arthritis, increases in daily stress in the past week have been associated with pain and both subjective and objective increases in disease activity (Zautra, et al., 1997). Furthermore, in a study of family dementia caregivers and comparable control participants, caregiving status was associated with retrospective reports of gingival symptoms in the past 3 months. However, when daily hassles were included in the model, caregiving status was no longer associated with periodontal disease symptoms, suggesting that the effect of chronic stress on self-reported gingival symptoms was explained by an increase in daily hassles among caregivers

(Vitaliano, Persson, Kiyak, Saini, & Echeverria, 2005).

Daily stressors have also been associated with changes in objective physiological parameters. In a daily diary study, afternoon urinary cortisol levels were elevated on days when individuals experienced a greater number of stressors, compared to days when a smaller number of stressors occurred (Brantley & Jones, 1993). In an experience sampling methodology study, individuals who were experiencing a stressful event at the

time of the prompt had higher salivary cortisol, compared to period when they were not experiencing stressful events (Jacobs, et al., 2007; Van Eck, Berkhof, Nicolson, & Sulon,

1996). Using a similar methodology, Smyth et al. (1998) found that higher salivary cortisol were associated with both current stressors and anticipation of stressors, compared to stress-free sampling period. They also observed that the occurrence of several stressors at the same time led to a greater increase in cortisol, and this increase was partially mediated by the negative affect evoked by those stressful events.

Daily hassles appear to influence immune functioning. Stone and colleagues (Stone,

Cox, Valdimarsdottir, Jandorf, & Neale, 1987) asked 30 dental students to ingest rabbit albumin pills, a non-harmful protein for which the immune system does not have prior memory, daily during an 8-week period. Participants were also asked to complete a positive and negative mood checklist daily and to provide saliva sample 3 times per week to assess IgA antibody production to the rabbit albumin. Within-subject analyses showed that, on a given day, those who reported greater negative mood and lower positive mood than their average mood exhibited lower IgA antibody production (Stone, et al., 1987). A later study using a similar methodology with 96 adult men found that negative events were related to a lower antibody response on the same day while positive events on one day predicted higher antibody responses to the oral antigen for the following two days

(Stone, et al., 1994). Those results suggest that disruptive daily events can impair IgA antibody production.

Daily stressors may also impact cytokine production and inflammation. Individuals diagnosed with rheumatoid arthritis who reported more frequent interpersonal stress during a 30-day daily diary period exhibited higher IL-6 production by

lipopolysaccharide (LPS)-stimulated peripheral blood mononuclear cells, compared to individuals who reported less interpersonal stress (Davis, et al., 2008). Similarly, adolescents who reported greater daily interpersonal stressors in a two-week daily diary study had higher CRP than individuals having less social stress (Fuligni, et al., 2009).

Among college students, retrospective assessment of daily hassles in the past month was associated with a shift in the Th1/Th2 cytokine balance toward a Type 2 response

(Marshall, et al., 1998). Among community-dwelling adults, the frequency of daily hassles in the past month was associated with elevated plasma sICAM-1, while fewer positive events in the past month were associated with higher plasma IL-6 (Jain, Mills,

Von Kanel, Hong, & Dimsdale, 2007). Collectively, these studies provide evidence that daily events may impact peripheral inflammatory processes.

Conceptual models of the interaction between chronic and daily stress

Three general models have been proposed to explain how chronic and daily stressors interact to influence psychological and physical well-being (Almeida, 2005;

Bolger & Zuckerman, 1995; Serido, Almeida, & Wethington, 2004). A first model predicts that chronic stress and daily hassles have unique and independent effects on well-being. A second model suggests that chronic stress influences well-being through an increased exposure to daily stressors; chronic stress leads to the experience of a larger number of daily stressors having a cumulative, detrimental impact on psychological and physical health. A third mechanism by which chronic stress may influence well-being is through an increased reactivity to daily events; according to this model, the psychological and physiological impact of daily stressors is magnified by the fact that they are

occurring in the context of a chronic stressor. These two latter perspectives are not mutually exclusive since a chronic stressor can potentially increase both the exposure and the reactivity to daily stressors.

Support for the first model comes from studies showing that chronic stressors and daily hassles are both independent predictors of well-being. Some daily diary studies have reported that chronic and daily stress make independent contributions to the prediction of well-being, although the magnitude of the relationship tends to be higher between daily events and distress (Delongis, et al., 1982; Eckenrode, 1984). However, not all studies have found that chronic stress predicts psychological distress over and above the variance accounted for by daily hassles (Monroe, 1983).

Evidence for an exposure model is found in daily diary studies whereby major life events and chronic stress appear to exert their effects on psychological distress by increasing the number of daily stressors (Kanner, et al., 1981; Wagner, Compas, &

Howell, 1988). For example, in a longitudinal study of healthy older adults including monthly assessment of major life events and daily hassles, the number of daily events mediated the effects of major life events, even after adjusting for initial levels of depression (Russell & Cutrona, 1991). Contagion of daily stress from one life domain to another is frequent. Transmission of daily stressors across life domains appears to occur by stress spillover and stress cross-over. Stress spillover refers to the situation where the stress at home or at work results in increased stress in the other domain, while stress cross-over represents the instance where the individual’s spouse’s exposure to stress leads to increased tension for the individual (Bolger, Delongis, Kessler, & Wethington,

1989).

The relationship between daily hassles and well-being may vary as a function of the type of chronic stressor. Zautra, Reich and Guarnaccia (1990) compared the relationship of retrospectively assessed daily stressors over the last month to psychological distress among conjugally bereaved older individuals, older individuals experiencing physical disability, and healthy older adults. Among bereaved individuals, daily undesirable events were not associated with distress. However, daily strains were related to negative affect among the physically disabled older adults as well as the matched control participants, with the former exhibiting a stronger relationship between daily events and distress than the latter. In that study, a ceiling effect on the mood measure may have prevented the researchers from observing the impact of daily stressors; bereaved individuals already had highly elevated negative mood scores with little room to increase following exposure to daily stressors. Subsequently, Pillow, Zautra, & Sandler

(1996) further tested their hypothesis by studying four groups of parents: recently divorced parents, spousal bereaved parents, parents of a child diagnosed with asthma, and matched controls. Parents from each group completed a self-reported measure of daily hassles in the past 3 months as well as a measure of psychological distress. When the four groups of parents were collapsed together, the impact of major life events was mediated by an increased exposure to daily stressors. However, group differences revealed that in some situations major life events directly influence psychological distress. Among parents of children with asthma there was a direct relationship between chronic stress and distress and no mediation by daily hassles. Bereaved parents displayed both a direct and an indirect relationship between chronic stress and distress. In contrast, the effect of divorce was completely mediated by an increased exposure to daily stressors (Pillow et

al., 1996). Individuals providing routine assistance to parents experienced more psychological distress on days during which they were involved in their parents’ care, compared to days during which they reported no caregiving responsibilities (Savla,

Almeida, Davey, & Zarit, 2008). This suggests that there is a positive relationship between daily stressors and distress among individuals providing care for a parent.

Chronic stress may also impact well-being by enhancing one’s reactivity to daily hassles. From a psychological perspective, major life events and chronic stress may diminish the individual’s coping resources so that previously minor annoyances become overwhelming, frustrating, and painful. In support of this model, events leading to job disruption, such as being fired or having to take sick leave, gave rise to changes in self- esteem and sense of mastery, which in turn attenuated their buffering impact on the relationship between economic strain and depression (Pearlin et al., 1981). Furthermore, studies using a daily diary methodology provide evidence that the effects of daily stressors on physical and mental health are exacerbated by chronic stressors such as overcrowding and poor neighborhood quality (Lepore, Evans, & Palsane, 1991). For example, among people who were socially isolated or who lived in a chronically stressful environment, such as a violent neighborhood, daily stress increased the likelihood of mood disturbances for the day following the occurrence of the stressor. No such effect was found among individuals living in a safe neighborhood (Caspi, Bolger, & Eckenrode,

1987).

From a physiological perspective, stressors and depression may prime the stress systems leading to an increased physiological reactivity to subsequent stressors. Rats exposed to stressors such an inescapable tail shock or social disruption exhibited an

amplified inflammatory and sickness behavior responses to the administration of a bacterial endotoxin, LPS, compared to rats who were not exposed to stressors (Gibb,

Hayley, Gandhi, Poulter, & Anisman, 2008; Johnson, et al., 2002). Similarly, mice exposed to a social disruption stressor for several days displayed more pronounced sickness behaviors, greater plasma corticosterone and higher secretion of IL-6, TNF-α, and IL-10 in response to intraperitoneal administration of IFN-α, compared to control mice that were not exposed to the stressor (Anisman, Poulter, Gandhi, Merali, & Hayley,

2007b). Interestingly, this increased in peripheral IL-6 following exposure to stressor is paralleled by priming of central IL-6 production via microglia activation (Frank, Baratta,

Sprunger, Watkins, & Maier, 2007).

In humans, individuals with higher levels of chronic stress exhibited greater subjective distress, higher peak levels of epinephrine, and lower peak levels of β- endorphin and NK cells lysis in response to an acute mental stressor, compared to participants reporting less chronic stress (Pike, et al., 1997). Furthermore, individuals who reported more daily hassles in the past month had a greater decrease in T-cells and natural killer (NK) cells in response to a laboratory stressor (Brosschot, et al., 1994).

Similarly, greater daily hassles in the past month were associated with greater cortisol response to a laboratory stressor (Heim, et al., 2002). A meta-analytic study reported that greater life stress in the past year was associated with poorer heart rate and blood pressure recovery following exposure to stressors (Chida & Hamer, 2008).

Stress and depression also appear to sensitize the inflammatory response to stress.

Depression exacerbated the IL-6 and NF-κB responses to the TSST (Pace, et al., 2006) and also promoted sustained elevations in CRP (Miller, Rohleder, Stetler, & Kirschbaum,

2005). Non-depressed individuals had a sharper increase in CRP following exposure to a laboratory stressor, but their CRP declined to baseline during the recovery period. In contrast, depressed individuals had a smaller CRP increase following exposure to the stressor. However, during the recovery period CRP kept rising to the levels that the controls reached immediately after the stressor, suggesting that depression may be associated with delayed, but sustained stress-induced CRP elevations (Miller, et al.,

2005). Among older adults, the presence of depressive symptoms was associated with an amplified IL-6 secretion up to two weeks after immunization with an influenza vaccine

(Glaser, Robles, Sheridan, Malarkey, & Kiecolt-Glaser, 2003). Similarly, individuals who received a typhoid vaccine and performed a laboratory stressor exhibited greater IL-6 responses than participants who received the vaccine but were not involved in the TSST, than participants who did the TSST but did not receive the vaccine, as well as participants who neither received the vaccine nor participated in the TSST (Brydon, et al., 2009). The cross-sensitization between stressors and cytokines observed in these studies suggests that chronic stress might lead to increased inflammatory responses to subsequent stressors. Chronically-stressed caregivers are more susceptible to infectious illnesses, and take longer to heal wounds. Since these two conditions fuel increases in inflammation, caregivers may be more vulnerable to the behavioral and physiological effects of infection and injury (Glaser & Kiecolt-Glaser, 2005; Kiecolt-Glaser, et al., 1996; Kiecolt-

Glaser, 1995).

One biological mechanism by which chronic stress might increase inflammatory responses to stress is through enhanced glucorticoid resistance. Glucocorticoids usually have potent anti-inflammatory properties. However, prolonged exposure to cortisol

associated with chronic stress may lead to a downregulation of glucocorticoid receptors

(GR) (Engler, Engler, Bailey, & Sheridan, 2005; Miller, Pariante, & Pearce, 1999). This leads to glucocorticoid resistance, whereby immune cells are less responsive to the anti- inflammatory properties of glucocorticoids. For example, lymphocytes in family dementia caregivers exhibited a reduced inhibition of the blastogenic response to the mitogen phytohaemagglutinin (PHA) when exposed to a synthetic glucocorticoid in vitro, in comparison to the lymphocytes of noncaregiving controls (Bauer, et al., 2000).

Furthermore, this process may also promote chronic elevations in circulating markers of inflammation. For instance, women who experienced an episodic stressor in the context of chronic stress exhibited increased salivary cortisol output and CRP levels, and decreased GR mRNA, while women who reported an episodic stressor in the absence of chronic stress displayed lower cortisol output and higher GR mRNA. The decrease in GR mRNA observed during chronic stress may lead to glucocorticoid insensitivity and subsequently to increased circulating biomarkers of inflammation (Marin, Martin,

Blackwell, Stetler, & Miller, 2007).

Caregiving stress may also alter glucucoticoid sensitivity. The chronic stress of caregiving for a child with cancer has been cross-sectionally associated with diminished inhibition of LPS-stimulated IL-6 production following administration of a synthetic glucocorticoid (Miller, Cohen, & Ritchey, 2002; Wirtz, et al., 2003). In a prospective study, individuals who were caring for a family member with cancer exhibited an increase in CRP and a decrease in glucocorticoid sensitivity over the year following the family member’s first radiotherapy treatment (Rohleder, Marin, Ma, & Miller, 2009). In line with these results, genes under-expressed by family caregivers of cancer patients

involved glucocorticoid response elements and genes over-expressed by caregivers involved NF-κB response elements, compared to noncaregiving controls (Miller, et al.,

2008). In the context of chronic stress, decreased glucocorticoid sensitivity may therefore promote sustained inflammatory responses following exposure to daily stressors.

Both increased exposure and reactivity to daily stressors may link chronic stress with greater distress and inflammation. Respondents in the National Study of Daily

Experiences who were experiencing chronic stress either at home or at work reported a higher number of daily stressors both at home and at work. In addition, chronically- stressed individuals were more likely to experience mood disturbances on days when they reported daily stressors, compared to individuals who did not experience chronic stress.

Those results support both an exposure and a reactivity model whereby chronic stress influences both the occurrence of stressors as well as the response to them (Serido, et al.,

2004). However, in that study chronic stress was assessed using a self-reported measure administered several weeks before the DISE interview.

A subsample of participants in the National Study of Daily Experience were caregiving for adult children with a range of physical and psychiatric disabilities. Parents of children with disability reported a greater number of daily stressors and a greater number of days during which they experienced multiple stressors, consistent with an increased exposure to daily stress. Moreover, on average, they rated their stressors as more severe and they had greater cortisol production on days when they spent more time with their children, compared to parents who did not have such caregiving responsibilities (Seltzer, et al., 2009). This suggests that the chronic stress of caregiving

for children with disabilities may increase both the psychological and physiological reactivity to daily stressors.

Stress, health behaviors, and immunity

Contemporary models in psychoneuroimmunology stipulate that stress can influence immune functioning both directly via activation of the hypothalamic-pituitary- adrenal and the sympathetic-adrenergic-medullary axes, and indirectly via affective and behavioral changes (Kiecolt-Glaser & Glaser, 1988). Health behavior change is a major pathway by which stress may influence inflammatory processes. Stress often elicits the adoption of detrimental health behaviors, including increased smoking and alcohol use, reduced physical activity, and poorer sleep (Steptoe, Wardle, Pollard, Canaan, & Davies,

1996; Vitaliano, Scanlan, Zhang, Savage, & Hirsch, 2002). These negative health behaviors have been associated with elevated biomarkers of systemic inflammation

(Kiecolt-Glaser, Gouin, & Hantsoo, 2009).

Smoking has been reliably associated with increased circulating levels of IL-6 and CRP (Hamer & Chida, 2009; Nazmi, Oliveira, & Victora, 2008). The relationship between alcohol intake and inflammation depends on the amount of daily alcohol intake.

A small amount of daily alcohol intake (1-2 drinks) is often associated with lesser inflammation, while greater alcohol intake is related to greater inflammation (Hamer &

Stamatakis, 2008; Imhof, et al., 2004; Pai, et al., 2006; Volpato, et al., 2004).

Acute bouts of physical activity have different inflammatory consequences than regular exercise. Acute physical activity triggers transient increases in plasma IL-6 and

TNF-α. The concomitant increases in proinflammatory and anti-inflammatory cytokines

are thought to be important for muscle repair, cell turnover, and regulation of lipids

(Petersen & Pedersen, 2005). In contrast, regular physical activity has been associated with lower basal levels of circulating inflammatory markers (Elosua, et al., 2005; Ford,

2002). Similarly, greater cardiovascular fitness has been associated with lower inflammation (Plaisance & Grandjean, 2006).

Sleep disturbances can impact both circadian fluctuation and total production of proinflammatory cytokines (Vgontzas, et al., 2004). Both objectively measured and self- reported sleep disturbances have been associated with increases in circulating markers of inflammation (Mills, et al., 2007). In epidemiological studies, greater self-reported sleep disturbances were associated with greater CRP production (Liukkonen, et al., 2007).

Furthermore, objectively measured sleep efficiency was inversely related with plasma IL-

6 (Friedman, et al., 2005). Sleep deprivation and disruption in laboratory experiments also led to increases in circulating markers of inflammation the following day (Frey,

Fleshner, & Wright, 2007; Haack, Sanchez, & Mullington, 2007; Vgontzas, et al., 2004).

The conceptual model guiding the current research proposes that reliable associations exist among stress, health behaviors, and inflammation (Kiecolt-Glaser &

Glaser, 1988). Therefore, statistical adjustment for differences in health behaviors is recommended to examine whether daily stressors can directly influence the production of circulating inflammatory mediators.

The present study

Family dementia caregiving has been associated with detrimental health outcomes. The chronic low-grade inflammation observed among caregivers is thought to

at least partially mediate the increase in age-related diseases observed in this population.

Both acute and chronic stress are known to elicit elevations in circulating biomarkers of inflammation. Daily diary studies have shown that daily hassles partially mediate the relationship between chronic stress and self-reported mental and physical health. Daily stressors have also been associated with increased cortisol secretion and retrospectively with greater cytokine production. Individuals under chronic stress appear to exhibit both greater exposure and reactivity to daily stressors. Daily hassles occurring in the context of family dementia caregiving might then be conceptualized as acute stressors that repeatedly activate the inflammatory system and potentially promote amplified age- related increase in systemic inflammation. The goals of this study were to investigate whether the greater exposure and reactivity to daily stressors among family dementia caregivers fuel a sustained overproduction of biomarkers of inflammation.

The hypotheses were:

1) Caregivers will report a greater number of stressors in the past 24

hours, more depressive symptoms, and poorer health behaviors than

noncaregiving control participants.

2) Across both caregivers and controls, the number of daily stressors in

the past 24 hours will be positively associated with inflammation.

3) Caregiving status will moderate the relationship between daily

stressors and inflammation, with caregivers displaying a stronger

association between daily stressors and inflammation than controls.

4) Depressed mood will moderate the relationships between daily

stressors, caregiving status, and inflammation. Greater mood

disturbances will be associated with more pronounced inflammation

and this effect will be further amplified among caregivers compared to

controls.

5) The objective severity rating of the stressors will moderate the

relationship between daily stressors, caregiving status, and

inflammation. Greater objective stressor severity will be associated

with greater inflammation and this effect will be further amplified

among caregivers, compared to controls.

6) The relationship between daily stressors, caregiving status, and

inflammation will persist even after adjusting for differences in health

behaviors, chronic medical conditions, and medication use.

Chapter 2: Methodology

Participants

Participants were recruited as part of a larger study investigating the role of genetic factors in the association between family dementia caregiving and changes in physical, psychological, and immune functioning. Caregivers were recruited from diagnostic clinics, neurologists’ referrals, local support groups and Alzheimer

Association Newsletters. Inclusion criteria were caring for a spouse or a parent with dementia, and spending at least 5 hours per week in caring duties. Since the specific dementia diagnosis does not appear to influence the physical and psychological consequences of caregiving, caregivers of a relative with any dementia diagnosis were included in the study (Dura, Haywood-Niler, & Kiecolt-Glaser, 1990; Kiecolt-Glaser,

Glaser, et al., 1987). Noncaregiving participants were recruited through local newspapers, church groups, and senior citizens’ organizations. Control participants were excluded from the study if they were involved in any caregiving activities in the past year.

Participants aged 40-90 were included in the study. Individuals with a body mass index

(BMI) over 45 were excluded from the study. To ensure the comparability between the two groups, noncaregiving control participants were selected to approximate the sociodemographic characteristics of the caregivers, and efforts were made to ensure that there were no significant group differences in age (mean and dispersion), ethnicity, and in the proportion of participants currently working.

Since most of our participants were older adults, excluding participants taking any medication would lead to the recruitment of a biased sample of the healthiest older adults.

Therefore, only prospective participants with immunology-related diseases or using any medication that can affect the immune system were excluded from the study.

Specifically, individuals with autoimmune diseases, cancer or cancer treatment in the last

3 years, HIV, kidney disease, liver disease, lupus, multiple sclerosis and other muscle disorders, Parkinson’s disease, seizure disorder, stroke, diabetes, rheumatoid arthritis, and surgery in the past 3 months were excluded from the study. Previous studies with older adults using these selection criteria showed that non-immunology-related chronic health conditions and medication did not obscure the relationship between psychosocial factors and inflammation (Kiecolt-Glaser, et al., 2003; Von Kanel, et al., 2006).

The parent study was comprised of 291 research participants. The DISE was added to the research protocol after the 74th participant was recruited. DISE data were collected for 217 individuals. However, data of 6 participants were lost because of recording difficulties. Furthermore, 20 participants were excluded because of current diabetes or rheumatoid arthritis diagnosis, seven participants were excluded because they were either under or over the study’s age limits, and one participant was excluded because of a BMI value over 45. Twenty-five participants had missing IL-6 data because of technical difficulties with the assay, but all participants had CRP data. The final sample was thus comprised of 183 participants, including 78 family dementia caregivers and 105 noncaregiving controls.

Protocol

A cross-sectional design was used to investigate the relationship between daily stress and biomarkers of inflammation. Individuals interested in the study completed a medical screening form by mail, online, or in a phone interview. Eligible individuals were scheduled for a visit at our laboratory or for a home visit. To minimize the impact of diurnal changes in circulating cytokines, all participants were scheduled between 8 to 10

AM (Seiler, Muller, & Hiemke, 1995). Participants signed the consent form at the beginning of their visit and had their blood drawn shortly after. A nurse performed anthropometric measurements for BMI and hip-to-waist ratio calculation. Completion of self-reported questionnaires about mood, health behaviors, and sociodemographic characteristics was followed by a semi-structured interview evaluating the occurrence of daily stressors in the past 24 hours. The protocol took between 3-4 hours to complete. A monetary compensation of $40 was given to participants upon completion of the study.

Measures

Self-reported questionnaires

A sociodemographic questionnaire collected data on age, sex, ethnicity, education, and marital status. For caregivers, information about the average number of hours spent caregiving per week, the number of months since the beginning of caregiving, the location of the dementia patient (caregiver’s home vs. nursing home), and the relationship to the dementia patient were also obtained.

The Center for Epidemiological Studies Depression Scale (CESD; Radloff, 1977) is a 20-item scale assessing depressive symptomatology in the past week. Ratings are

made on the frequency of symptoms within the last week. Score ranges from 0 to 60 with higher scores indicating greater depressive symptomatology. Symptoms assessed include depressed mood, feelings of helplessness and hopelessness, guilt and worthlessness and loss of energy, sleep and appetite problems. The test-retest reliability is .54 over a 4-week interval (Radloff, 1977). A cut-off score of 16 is used to identify older individuals who are likely to be depressed (Lewinsohn, Seeley, Roberts, & Allen, 1997; Myers &

Weissman, 1980). In the current study, the scale had an internal consistency of .76.

The Trier Inventory for Chronic Stress (TICS; Schulz, 1999) is a 57-item self- reported questionnaire assessing chronic stress. Items assess the frequency of occurrence of stressful situations in 9 different domains including work overload, social overload, performance pressure, work discontent, overextended at work, lack of social recognition, social tension, social isolation, and chronic worrying. A 5-point Likert scale, ranging from never to very often, assessed the frequency of occurrence of stressful events in the past 3 months. Scores range from 0 to 228 with higher scores indicating greater chronic stress (Schultz & Schlotz, 1999). The internal consistency coefficient of the scale was

.96. A subsample of 141 participants completed this questionnaire.

The frequency of chronic medical conditions and medications use was assessed with the Physical Health section of the Older Adult Resources and Services (OARS)

Multidimensional functional Assessment Questionnaire (Fillenbaum & Smyer, 1981).

This scale measures problems with lungs, kidneys, liver, digestive system, heart, high blood pressure, migraines, hormonal conditions, thyroid, cancer, cataracts, teeth, hernia, gout, hardening of the arteries, circulatory system, prostate, ovaries or uterus, and muscle-related disorders, as well as any medication used for each condition. This scale

possesses good criterion validity with a coefficient of questionnaire-physician agreement ranging from .66 to .87 (Berkman, 1983).

Stress and depression often elicit the adoption of detrimental health behaviors, including smoking and alcohol use, reduced physical activity, poor diet choices, and less sleep (Steptoe, et al., 1996; Vitaliano, et al., 2002). In evaluating the relationship between mood, behavior, and immunological functioning, it is always important to ascertain the role of health behaviors as potential mediators of the relationship between distress and immunity (Kiecolt-Glaser & Glaser, 1988). Accordingly, health behaviors such as smoking, alcohol, or medication use in the past week were evaluated (Kiecolt-Glaser &

Glaser, 1988).

The Community Healthy Activities Model Program for Seniors Questionnaires

(CHAMPS; Stewart, et al., 2001) was designed to assess the weekly frequency and duration of various physical activities among middle-aged and older populations. The questionnaire provides an estimate of the weekly caloric expenditure in physical activity.

The scale had a 6-month test-retest reliability coefficient of .66. This measure was sensitive to changes in physical activities following a community intervention in an older adult population.

The Pittsburgh Sleep Quality Index assessed sleep quality and sleep disturbances in the past month (PSQ; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). This self- reported questionnaire comprises seven subscales evaluating subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. Using a clinical cut-off of 6, the scale shows a sensitivity of 89.6% and a specificity of 86.5% in distinguishing between good and poor

sleepers. The scale has a good internal consistency (Cronbach of 0.85) and a high test- retest reliability (r = .86) at 45 days (Backhaus, Junghanns, Broocks, Riemann, &

Hohagen, 2002). Data on the number of hours of sleep during the night prior to the study visit were also collected.

The Blessed Dementia Scale evaluated the severity of the dementia symptoms of the patients, as perceived by the caregiver. This 22-item scale measured changes in a dementia patient’s functioning across different areas: daily living, self-care, and personality. Higher scores indicate greater impairment in functioning. This questionnaire has excellent sensitivity (90%) and specificity (84%) for dementia diagnosis (Erkinjuntti,

Haltia, Palo, Sulkava, & Paetau, 1988).

Semi-structured interviews

The DISE is a semi-structured interview assessing the occurrence of daily stressors in the past 24 hours (Almeida, et al., 2002). The interview allows the researcher to collect a short narrative about each event in order to rate the stressor content, the actors involved in the event, and the objective and subjective severity of the event. The DISE was chosen because of its flexibility and sensitivity in the assessment of daily stressors. The stressors experienced by family dementia caregivers are often unique and idiosyncratic.

For example, one caregiver reported that her mother with dementia believed that her foot was not her own and tried to cut it from her leg. This kind of stressor is very unique and would not be encompassed by a checklist measure of daily stressors. In this context, the

DISE provided a more comprehensive assessment of stressors in the past 24 hours.

The DISE comprises 7 stem questions that evaluate the incidence of different types of stressful events in the past 24 hours. Specific probes are associated with each stem question to measure the respondents’ primary appraisal and subjective severity of the stressors. Stake questions following endorsement of each stem question help the interviewer to identify the biographical circumstances in which the event takes place. Collection of the short narrative about the event and the participant’s life circumstances allows the researcher to make a judgment about "what one can expect the average person in that particular set of biographical circumstances to feel", and therefore assign an “objective” severity rating to the event (Almeida et al., 2002).

Objective severity ratings are based on the Brown & Harris’s short-term rating of contextual threat associated with the degree of disruptiveness and unpleasantness of the event that an average person would experience following exposure to the stressor (Almeida, et al., 2002). The objective severity rating aims at evaluating the degree and duration of disruption, and unpleasantness that the event creates for the respondent. An electronic event dictionary comprising the rating of more than 4000 events was developed to assist in event coding. While coding a new event, the coder uses the dictionary to identify an event of the same category that matched the current respondent’s characteristics and uses it as an anchor to rate the severity of the new event (Grzywacz, Almeida, Neupert, & Ettner, 2004).

Based on a sample of 15% of the daily events coded, the kappa value for the DISE objective severity coding was .69.

The Psychiatric Epidemiology Research Interview Life Events Scale (PERI;

Dohrenwend, Krasnoff, Askenasy, & Dohrenwend, 1978) was used to assess major life events in the past year. Participants rated the occurrence of 102 possible major life events

in the past year. The PERI was administered in an interview format using a life history calendar methodology. The life history calendar method facilitates the recall of multiple events by using personally relevant time-related anchor in the past year. This methodology promotes greater recall of retrospective events than regular self-reported questionnaires (Belli, Shay, & Stafford, 2001; Caspi, et al., 1996).

Anthropomorphic measurements

Abdominal fat has been related to greater cortisol and IL-6 production in response to psychological stress (Brydon, et al., 2008; Epel, et al., 2000). Systemic inflammation was once thought to be primarily the result of the production of IL-6 by immune cells, but recent data revealed that more than one third of the circulating IL-6 originates from adipocytes (Mohamed-Ali, Pinkney, & Coppack, 1998). Obesity per se is associated with elevated circulating markers of inflammation, but adiposity below the threshold of obesity also contribute to increase inflammation (Dandona, Aljada, & Bandyopadhyay,

2004). BMI was used as a proxy for the volume of body fat. The hip-to- waist ratio was also measured for a subset of 133 participants. Waist was measured midway between the iliac crest and the lowest rib margin. Hip circumference was measured as the widest measure over the buttocks and below the iliac crest.

Cytokine and acute phase reactant assays

Serum IL-6 levels were determined using Quantikine High Sensitivity

Immunoassay kits (R&D) according to the kit instructions. Samples were run undiluted in

duplicate. The sensitivities of these kits to detect IL-6 are 0.1 pg/ml. Data for 25 participants were lost because of technical problems with the assay.

Plasma CRP was assessed using a high sensitivity rate nephelometric immunoassay using a Dade-Behring BN-100 nephelometer at the OSU General Clinical

Research Center. The sensitivity for this assay is .03 mg/L.

Guidelines from the American Heart Association and the Centers for Disease

Control and Prevention suggest that CRP plasma levels above then 10 mg/L may indicate the presence of an acute infection (Pearson, et al., 2003). However, recent evidence suggests that very high CRP levels are associated with both health behaviors and psychosocial adversity in the absence of acute infection (Alley, et al., 2006; Hamer &

Chida, 2009). Given that our participants were screened for acute illness before each visit, we retained individuals who had CRP levels greater than 10 mg/L in the statistical models.

Statistical analyses

Data transformation

Data from the National Studies of Daily Experiences showed that 52% of the respondents reported no daily event in the past 24 hours, 39.4% reported experiencing one stressor, and 10.4% of the sample experienced multiple stressors (Almeida, et al.,

2002). In accord with other studies using the DISE, the number of daily events in the past

24 hours ranged from 0 to 6 (Stawski, Sliwinski, Almeida, & Smyth, 2008). Given the nature of the distribution of the DISE data, most researchers have used a dichotomous,

(i.e. presence or absence of stressors), or categorical, (i.e. zero, one, or multiple stressors)

variables to analyze the daily stressors variable. Furthermore, in an ecological momentary assessment study, the number of stressors at a given assessment occasion had a cumulative impact on salivary cortisol production, highlighting the importance of differentiating between the occurrence of one or multiple stressors (Smyth et al., 1998). A categorical variable with 3 levels: absence of stressor, presence of one stressor, or occurrence of multiple stressors, was used as the independent variable. In subsequent analyses, the presence of multiple stressors in the past 24 hours appeared to drive the increased production of circulating markers of inflammation, in comparison to zero or one stressors. Therefore, a dichotomous variable distinguishing the occurrence of multiple stressors and the presence of one or no stressors was created. Dummy coding was used to represent the categorical daily stressors variables.

Several study variables did not have a normal distribution. Base 10 logarithmic transformations were applied to correct for skewness in the distribution of the number of depressive symptoms in the past week, the weekly caloric expenditure in physical activity, and IL-6 and CRP. The following severely skewed continuous variables were recoded into categorical variables: the number of self-reported chronic illnesses was recoded as zero, one, or multiple diseases; the weekly average alcohol consumption was recoded as none, 1-3 drinks, or 4 and more drinks; recent alcohol consumption was recoded as absence or presence of alcohol use in the past 24 hours. Since only 3 participants did not complete high school, education was recoded as at least some high school education, at least some college education, and graduate or professional education.

Statistical models

Chi-square tests and analysis of variance tests assessed group differences between caregivers and noncaregiving controls. For the first hypothesis stipulating that caregivers would report a greater number of stressors in the past 24 hours, a multinomial logistic regression model evaluated the difference in the absence, the presence of one, or the presence of multiple daily stressors reported by caregivers and control participants.

Gender, age, employment status, major life events in the past year, and marital status were entered as covariates in the model.

The second hypothesis suggested that the number of daily stressors would be associated with IL-6 and CRP. Hierarchical linear regression models were fitted with daily stressors as the independent variable, and IL-6 and CRP as dependent variables. For the third hypothesis suggesting that caregivers would exhibit greater inflammatory reactivity to daily stressors, linear regression models were fitted with caregiving status, daily stressors, major life events in the past year, and a caregiver status by daily stressors interaction as independent variables, with IL-6 and CRP as dependent variables. The caregiving status by daily stressors interaction tested the moderating effect of group membership according to the Baron & Kenny (1986) recommendations.

A mediation model in which the increased number of daily stressors experienced by caregivers partially explained the relationship between caregiving stress and chronic low-grade inflammation was tested. A bootstrapping resampling procedure computed the bias-corrected 95% confidence interval of the indirect effect in the mediation model. The indirect effect was considered statistically significant when its confidence interval did not include zero (Preacher & Hayes, 2008). The bootstrapping method was used because this

technique is more powerful than the Sobel test at detecting indirect mediation effects

(Preacher & Hayes, 2008).

To test the fourth hypothesis that stipulated that depression would moderate the relationships between daily stressors, caregiving status, and inflammation, a series of regression models were fitted with the main effects, two-way interactions, and the three- way interaction among daily stressors, caregiving status, and depression as independent variables, and with IL-6 and CRP as dependent variables. Given that there was a significant depression by statin use interaction, a model including a four-way interaction among daily stressors, caregiving status, depression, and statin use was also tested.

For the fifth hypothesis stipulating that objectively severe events would be more strongly associated with inflammatory markers than less severe stressors, linear regression models were fitted with the main effects and two-way and three-way interaction terms among the number of daily stressors, the cumulative objective severity of the stressors, and caregiving status as independent variables and IL-6 and CRP as dependent variables. As described in the results section, the models were not fitted because of severe multicollinearity problems.

For the sixth hypothesis, the linear regression models evaluating the impact of daily stressors and caregiving status on inflammation were repeated while including self- reported health, as well as usual and recent health behavior variables. The first model included the number of chronic medical conditions reported by the participants, smoking status, the average number of alcoholic drinks per week, the weekly caloric expenditure in physical activity, as well as sleep quality in the past month. A second model included

the number of alcoholic drinks in the past 24 hours, as well as the number of hours slept during the night prior to the study visit.

Another potentially confounding variable is medication use. For example, in a sample of cardiac patients, a relationship between depression and CRP was observed among individuals not taking statins, but not among participants using the lipid-lowering medication (Lesperance, Frasure-Smith, Theroux, & Irwin, 2004). Similarly, clinical studies have shown that a successful antidepressant medication led to a reduction in inflammation (Sluzewska, et al., 1995), while no differences in inflammation are observed in antidepressant users in some epidemiological studies (Penninx, et al., 2003).

Furthermore, in an experimental study, aspirin use attenuated the stress-induced increase in inflammation (Von Kanel, et al., 2008b). Taking an approach similar to Lesperance et al. (2004) we evaluated whether four classes of medication (statin, non-steroidal anti- inflammatory [NSAID], estrogen/progesterone supplement, and antidepressant) would influence the relationship between daily stressors and inflammation. Participants were considered users of any of those medications if they reported using them on a regular basis and/or over the past 24 hours. A summary variable comprising all inflammation- related medication was created by aggregating the use of statins, NSAIDs, estrogen medications, and antidepressants as described by Danese et al. (2008). Linear regression models evaluating the impact of daily stressors and caregiving status on inflammation were repeated while including main effects of each medication type and daily stressors by medication use interaction terms. It was initially planned to evaluate the impact of systemic corticosteroids use. However, no individuals using such medication met the study’s inclusion criteria.

The covariates included in all linear regression models involving IL-6 or CRP as dependent variables were gender, age, education, BMI, and marital status. Gender was included as a covariate since women tend to experience a greater number of daily stressors compared to men (Almeida & Kessler, 1998; Bolger, Delongis, Kessler, &

Schilling, 1989) and have greater IL-6 responses to psychological stress (Steptoe, Owen,

Kunz-Ebrecht, & Mohamed-Ali, 2002). Age and socio-economic status can influence both exposure and mood reactivity to daily stressors (Grzywacz, et al., 2004; Mroczek &

Almeida, 2004) and are associated with differential inflammatory reactivity to stress

(Brydon, Edwards, Mohamed-Ali, & Steptoe, 2004). BMI was included as a covariate since abdominal fat appears to influence the proinflammatory cytokine response to psychological stress (Brydon, et al., 2008). In the order to adjust for other types of chronic stress, the number of major life events in the past year was retained as a covariate in all analyses comparing caregivers and noncaregiving controls.

A two-sided .05 alpha level was used for the study. For unplanned multiple post- hoc comparisons, p-values were adjusted using the Tukey-Kramer procedure (Kramer,

1956). Tests of simple main effects using the SAS test of effect slices procedure evaluated whether the relationship between daily stressors and inflammation was significant among participants using certain types of medication or not. Throughout the analyses, unstandardized regression coefficients were reported. All analyses were conducted using SPSS 17.0 or SAS 9.0.

Chapter 3: Results

Sociodemographic characteristics of caregivers and controls.

The final sample included 78 caregivers and 105 controls with CRP data and 62 caregivers and 96 controls with IL-6 data. Table 1 presents the sociodemographic characteristics of caregivers and noncaregiving controls for the full sample. There were no significant differences in age, sex, ethnicity, education, and current employment between caregivers and controls. However, caregivers differed from controls in regard to marital status. Caregivers were more likely to be married or in a long-term committed relationship and less likely to be widowed, compared to controls. Caregivers were marginally less likely to be divorced, but did not differ from controls in the likelihood of being single. Similarly, among participants with IL-6 data there were no group differences in age, sex, ethnicity, education, and current employment, but caregivers were more likely to be married than controls. Given the fact that there were no widowed caregivers in the sample and that previously married individuals tend to have higher CRP than currently married individuals (Sbarra, 2009), current involvement in a long-term romantic relationship was included in all models to account for group differences in marital status.

Health and medication use among caregivers and controls.

Table 2 presents self-reported health and medication use among caregivers and controls. Caregivers reported significantly more chronic illnesses, compared to noncaregiving controls. In terms of medication use, there were no group differences in the use of statins, NSAIDs, estrogen or progesterone, antidepressants, and any inflammation-related medications.

Caregiving Status and inflammation

IL-6 and CRP were significantly correlated with each other, r = .47, p < .001.

Linear regression models evaluated whether caregivers differed from controls in terms of these two inflammatory mediators. Details of the statistical models are found in Table 3.

The range of raw IL-6 values was .41 to 10.00. In an unconditional model, there was no significant difference in IL-6 between caregivers (M = 1.20, SEM = .04) and controls

(M = 1.24, SEM = .03), (SE) = -.04 (.05), t(156) = .89, p = .38, R2 = .005. In a model adjusting for differences in age, sex, BMI, education, and marital status, caregiving status was still not significantly related to IL-6. Older age and higher BMI were associated with greater IL-6, while sex, education, and marital status were unrelated to the cytokine.

Figure 1 illustrates the relationship between caregiving status and IL-6.

The range of raw CRP values was from .30, the lower limit of detection of the assay, to 40.90. In an unconditional model, caregivers (M = .32, SEM = .05) had significantly higher CRP than noncaregiving controls (M = .21, SEM = .04), (SE) = .15

(.07), t(181) = 2.08, p = .04, R2 = .024. In a model including age, sex, BMI, education, and marital status as covariates, caregiving status remained significantly associated with

CRP. Women (M = .30, SEM = .04) had significantly higher CRP, compared to men

(M = .11, SEM =.07). Higher BMI was associated with greater CRP. Age, education, and marital status were not related to CRP. Figure 2 depicts the relationship between caregiving stress and CRP.

Hypothesis 1.

Caregiving status and the occurrence of daily stressors in the past 24 hours

Study participants reported an average of 1.37 (SD=1.22) daily stressors in the past 24 hours. Caregivers reported more daily stressors than noncaregiving controls, F

(1,175) =16.84, p < .001. Forty-five participants reported no daily stressors, 70 participants recalled one daily stressor, and 68 participants experienced two or more stressors in the past 24 hours. A multinomial logistic regression model evaluated whether caregiving status was related to the experience of one or multiple stressors, compared to the absence of daily stressors. A model including age, sex, employment status, marital status, the number of major life events in the past year, and caregiving status significantly predicted the number of daily stressors in the past 24 hours, 2(12) = 40.59, p < .001. A higher number of major life events in the past year increased the likelihood of experiencing one, 2(1) = 7.72, p = .005, or multiple stressors, 2(1) = 10.66, p < .001, in the past 24 hours, compared to no stressors. Caregivers were more likely to report one,

2(1) = 4.27, p = .04, and multiple stressors, 2(1) = 13.74, p < .001, in the past 24 hours, compared to no stressors. Age, sex, employment status, and marital status were not significantly related to the number of daily stressors in the past 24 hours. Figure 3 displays the number of daily stressors as a function of caregiving status.

Caregiving status, depressive symptoms, and perceived chronic stress

Linear regression models evaluated whether caregivers and controls differed in depressive symptoms, and self-reported chronic stress. Table 4 presents the psychosocial characteristics of caregivers and controls. After adjusting for age, sex, and marital status, caregivers reported significantly more depressive symptoms compared to controls, (SE)

= .30 (.07), t(178) = 4.51, p < .001, R2 = .101. Age, sex, and marital status were not significantly associated with depressive symptoms. Figure 4 depicts the relationship between caregiving status and depression.

After adjusting for differences in age, sex, and marital status, caregiving status was significantly associated with self-reported chronic stress, (SE)= 6.50 (.42), t(136)=

4.74, p < .001, R2 = .123. Older participants perceived less chronic stress than younger individuals. Sex and marital status were not related to self-reported chronic stress. Figure

5 illustrates the relationship between caregiving status and self-reported chronic stress.

Caregiving status and health behaviors

Table 5 displays health behavior practices among caregivers and controls. A series of linear and logistic regression models evaluated whether caregivers had poorer health behaviors than noncaregiving controls. Age, sex, and marital status were included as covariates in each model. The proportion of smokers was similar among caregivers and controls, 2(1) = .29, p = .59. Caregivers did not differ from controls in their average weekly alcohol intake, 2(1) = .19, p = .67, and 21) = .01, p = .91, or in their alcohol use in the past 24 hours, 2(1) = .24, p = .83. Caregivers had poorer sleep quality in the past

month than controls, (SE)= 1.48 (.46), t(178) = 5.46, p = .001, R2 = .055, but they did not differ in the number of hours of sleep during the night prior to the study visit, (SE)=

-.10 (.22), t(178) = .43, p = .67, R2 = .001. Caregivers did not differ from controls in terms of weekly caloric expenditure in physical activity, (SE)= -.001 (.06), t(178) = .02, p = .98, R2 < .001, BMI, (SE)= 1.23 (.85), t(182) = 1.38, p = .17, R2 = .01, and hip-to- waist ratio, (SE)= .03 (.02), t(135) = 1.61, p = .11, R2 = .017. Figure 6 depicts sleep quality in the past month as a function of caregiving status.

Results from this section indicate that caregivers were more likely than controls to experience one or multiple stressors, compared to no stressors in the past 24 hours.

Furthermore, caregivers reported more depressive symptoms, chronic stress, and sleep disturbances than noncaregiving controls.

Hypothesis 2.

Daily stressors and circulating biomarkers of inflammation

Linear regression models evaluated whether daily stressors were associated with inflammation. Details of the statistical models for hypothesis 2 are included in Table 6.

In an unconditional model, the number of daily stressors in the past 24 hours was not significantly associated with IL-6, (SE) = .01 (.03), t(157) = .47, p = .64, R2 = .001. In a model adjusting for age, sex, BMI, and education, daily stressors were still not related to

IL-6. Figure 7 illustrates the relationship between daily stressors and IL-6.

In an unconditional model, the number of daily stressors was significantly related to CRP, (SE) = .12 (.04), t(182) = 2.11, p = .03, R2 = .041. In a model adjusting for differences in age, sex, BMI, and education, daily stressors were still significantly related

to CRP. Post-hoc tests with Tukey-Kramer adjustment revealed that individuals who experienced multiple stressors in the past 24 hours (M=.31, SEM=.06) had greater CRP, compared to participants who reported no stressors (M=.14, SEM=.06), t = 2.06, p = .04, or only one stressor (M=.16, SEM=.06), t = 2.21 p = .03. However, CRP did not differ among participants experiencing zero or one stressors, t = .16, p = .98. Figure 8 illustrates the relationship between daily stressors and CRP. Because the presence of multiple daily stressors appeared to be driving the increased CRP, a dichotomous variable distinguishing between the occurrence of zero or one stressors and the presence of multiple stressors in the past 24 hours was used in subsequent models.

Results from this section suggest that the presence of multiple stressors in the past

24 hours was associated with increased CRP, compared to the occurrence of zero or one stressors. The number of daily stressors was not significantly related to IL-6.

Hypothesis 3.

Daily stressors, caregiving stress, and inflammation

Linear regression models evaluated whether the inclusion of a main effect of caregiving status impacted the relationship between daily stressors and inflammation.

Details of the statistical models are found in Table 7. The number of daily stressors in the past 24 hours and caregiving status were not significantly associated with IL-6.

The number of daily stressors in the past 24 hours remained significantly associated with CRP. Caregiving status became marginally related to CRP. Figure 9 illustrates the relationship between daily stressors and CRP after adjusting for caregiving status.

Does the occurrence of multiple stressors in the past 24 hours mediate the relationship between caregiving status and CRP?

All conditions for a mediation model were met, as illustrated in Figure 10. The independent variable, caregiving stress, was associated with the mediator, daily stressors, p < .001. The mediator, daily stressors, was significantly related to the dependent variable, CRP, p = .04. The independent variable, caregiving stress was significantly associated with the dependent variable, CRP, p = .02. When the mediator, daily stressors, was included in the model, the direct effect of caregiving status on CRP became marginally significant, p = .09. A bootstrapping resampling procedure tested whether the reduction in the beta weight of the caregiving stress-CRP relationship was statistically significant. The bias-corrected 95% confidence interval of the indirect effect did not include zero [.006-.09], indicating that the presence of multiple daily stressors in the past

24 hours partially mediated the relationship between caregiving stress and CRP.

Caregiving status and inflammatory reactivity to daily stressors

Linear regression models including a daily stressors by caregiving status interaction tested the hypothesis that caregivers would display an increased reactivity to daily stressors compared to controls. Details of the statistical models of this section are found in Table 8. The daily stressors by caregiving status interaction term did not predict

IL-6. Similarly, the daily stressors by caregiving status interaction was not significantly associated with CRP.

These results indicate that after adjusting for caregiving status, the number of daily stressors was still not related to IL-6, but remained significantly associated with

CRP. Furthermore, daily stressors partially mediated the relationship between caregiving status and CRP. Caregiving stress did not moderate the relationship between daily stressors and inflammation, suggesting that caregivers did not display an increased reactivity to daily stressors, compared to noncaregiving controls.

Hypothesis 4.

Depressive symptoms, caregiving status, daily stressors, and inflammation

Linear regression models evaluated whether an individual’s level of depressive symptoms impacted the relationship between daily stressors and inflammation. Details of the statistical model are presented in Table 9. After adjusting for differences in depressive symptoms, daily stressors were not associated with IL-6. Depressive symptoms did not predict IL-6. In the CRP model, the main effect of daily stressors was still significant.

Depressive symptoms did not predict CRP.

Does statin use moderate the relationship between depression and inflammation?

The absence of significant relationships between depressive symptoms and either

IL-6 and CRP was surprising given the reliable association between depression and inflammation (Howren, et al., 2009). However, some authors have reported that statin use moderated the relationship between depressive symptoms and CRP (Lesperance, et al.,

2004). Linear regression models evaluated whether the lipid-lowering medication attenuated the relationship between depression and inflammation in the present sample.

Details of the statistical models are found in Table 10. The statin use by depressive symptoms interaction was marginally associated with IL-6 (Figure 11). The test of the

simple slopes revealed that among non-statin users there was a trend for greater depressive symptoms to be associated with higher IL-6, t = 1.7, p = .09. The simple slope of the statin users was not significant, t = .31, p = .75. In the CRP model, the statin use by depressive symptoms interaction was significant (Figure 12). However, neither the simple slope of non-statin users, t = 1.38, p = .17, nor the slope of statin users was significant, t =

1.2, p = .23. Daily stressors remained a significant predictor of CRP even after adjusting for the depressive symptoms by statin use interaction.

Depression and inflammatory responses to daily stressors

Given the significant two-way interaction between depressive symptoms and statin use, a three-interaction among daily stressors, depressive symptoms, and statin use tested whether more depressed individuals exhibited greater inflammatory responses to daily stressors than less depressed individuals. Details of the statistical models are found in Table 11. In both the IL-6 and CRP models, the three-way interaction was not a significant predictor of inflammation.

Linear regression models evaluated whether the impact of depressive symptoms on inflammatory responses to daily stressors was moderated by caregiving status and statin use. Details of the statistical models are found in Table 12. The four-way interaction composed of daily stressors, caregiving status, depressive symptoms, and statin use was not significantly related to either IL-6 or CRP.

Results from this section indicate that the severity of depressive symptoms was associated with greater CRP and IL-6 among non-statin users, but not among statin users.

However, depressive symptoms did not moderate the relationships among daily stressors, caregiving status, statin use, and inflammation.

Hypothesis 5.

Daily stressors’ objective severity and inflammation

It was hypothesized that more severely-rated stressors would be associated with greater inflammatory responses than milder stressors. This hypothesis was based on the assumption that the number of daily stressors and the cumulative objective severity of the stressors would be largely independent. However, in the present sample the number of stressors and the cumulative severity of the stressors were strongly correlated, r = .90, p =

.001. This strong correlation was a function of the low prevalence of severe (N=16) or very severe (N=4) stressors in the past 24 hours. Because of this multicollinearity problem, the interaction between the number and the cumulative severity of the daily stressors was not tested. Instead, linear regression models evaluated whether the use of the daily stressors’ cumulative severity as an independent variable would change the effect size of the relationship between daily stressors and inflammation. Details of the statistical models are found in Table 13. The objective severity of daily stressors did not significantly predict IL-6 or CRP. Results from this section suggest that the cumulative severity of daily stressors was not significantly associated with inflammatory markers.

Hypothesis 6.

Self-reported health, health behaviors, daily stressors, and inflammation

Table 14 presents the associations between daily stressors in the past 24 hours and self-reported health and health behaviors. None of the health or health behavior variables

was significantly associated with daily stressors. Linear regression models evaluated whether self-reported health and health behaviors impacted the relationship between daily stressors and inflammation. The first model included self-reported health and usual health behaviors. The second model included recent health behaviors that occurred in the past

24 hours. The usual and recent health behaviors variables were run in two separate models to avoid multicollinearity problems. Details of statistical models are found in

Table 15 and 16.

In the IL-6 model adjusting for usual health behaviors, the number of daily stressors in the past 24 hours was not associated with IL-6. Smokers (M = 1.20, SEM =

.02) had higher IL-6 than non-smokers (M = 1.45, SEM = .09). There was a trend for greater alcohol consumption to be associated with lower IL-6. The number of chronic illnesses, the weekly caloric expenditure in physical activity, and sleep quality in the past month were not significantly associated with IL-6. After adjusting for recent health behavior, daily stressors in the past 24 hours were not significantly related to IL-6.

Alcohol use and the number of hours of sleep in the past 24 hours were not associated with IL-6.

In the CRP model adjusting for usual health behaviors, the number of daily stressors in the past 24 hours remained significantly associated with CRP. Greater weekly alcohol intake was marginally associated with lower CRP. The main effects of the number of chronic illnesses, smoking status, the weekly caloric expenditure in physical activity, and sleep quality in the past month were not significantly related to CRP. For the

CRP model including recent health behavior, the number of daily stressors in the past 24 hours was still significantly associated with CRP. However, a slight reduction in the

effect size of the relationship between daily stressors and CRP was observed. Alcohol use and the number of hours of sleep in the past 24 hours were not significantly associated with CRP.

Results from this section suggest that after adjusting for usual and recent health behaviors, the number of daily stressors in the past 24 hours was still not associated with

IL-6, and remained significantly related to CRP. Overall, these data indicate that daily stressors are associated with CRP, over and above differences in health behaviors.

Medication use and inflammatory responses to daily stressors

Medication use is a major confound in the relationship between stress and inflammation. Linear regression models including daily stressors by medication use interactions evaluated whether medication use moderated the relationship between daily stressors and inflammation.

Statin

Statin use was not significantly associated with IL-6, β (SE) = .007(.06), t(148) =

.13, p = .90. However, the daily stressors by statin use interaction was significantly related to IL-6. The tests of the simple main effects revealed a significant relationship between daily stressors and IL-6 among non-statin users, F(1,146)= 3.74, p = .04, but not among statin users, F(1,146)= 2.86, p = .09. As depicted in Figure 13, among non-statin users the occurrence of multiple daily stressors in the past 24 hours was associated with higher IL-6, compared to reports of zero or one stressors. However, there was no significant relationship between daily stressors and IL-6 among statin users.

Given the significant two-way interaction, a model including a three-way interaction among caregiving status, daily stressors, and statin use was tested. The three- way interaction was marginally associated with IL-6. Post-hoc tests revealed that among non-statin users, caregivers who experienced multiple stressors had higher IL-6 than caregivers who reported zero or one stressors, t = 2.14, p = .04, while controls who experienced multiple stressors did not differ from controls who reported zero or one stressors, t = .31, p =.77. This indicates that caregiving stress moderated the relationship between daily stressors and IL-6 among non-statin users. Furthermore, caregivers who experienced multiple daily stressors and used statin (N=7) had lower IL-6 levels, compared to caregivers who reported multiple daily stressors and did not use statins, t =

2.69, p = .01, caregivers who experienced zero or one stressors and used statins, t = 2.00, p =.05, controls who reported multiple stressors and did not use statins, t = 2.01, p =.05, and controls who experienced zero or one stressors and used statins, t = 2.41, p = .01.

However, after the Tukey-Kramer adjustment for multiple comparisons, none of these differences were statistically significant, all ps > .20. Figure 14 depicts IL-6 as a function of the interaction among caregiving stress, daily stressors, and statin use.

Statin use was not significantly related to CRP, β (SE) = -.04(.08), t(173) = .46, p

= .64. The daily stressors by statin use interaction was not significant. Moreover, the three-way interaction did not significantly predict CRP. Details of the statistical models are found in Tables 17 and 18.

What are the characteristics of the participants using statins?

Statin users (M=63.35, SEM=1.15) were significantly older than non-statin users

(M=70.21, SEM=2.16), F(1,175) = 7.86, p = .01. Men were more likely to use statins than women, χ2(1) = 5.48, p = .02. Caucasians participants were more likely to be using statins, compared to non-Caucasians, χ2(1) = 3.7, p = .05. Statin users reported more chronic illnesses than non-statin users, χ2(2) = 10.97, p = .01. Marital status, χ2(1) = 2.97, p = .09, education, χ2(2) = .65, p = .72, BMI, F(1,175) = 1.64, p = .20, and depressive symptoms, F(1,175) = 1.94, p = .17, were unrelated to statin use.

NSAID

NSAID use was not significantly associated with IL-6, β (SE) = -.004(.05), t(148)

= .09, p = .93. The daily stressors by NSAID use interaction was not related to IL-6.

NSAID use was not significantly associated with CRP, β (SE) = -.02(.07), t(173) = .31, p

= .76. The daily stressors by NSAID use interaction was not related to CRP. Details of the statistical models are found in Table 19.

Estrogen & Progesterone

Estrogen medication use was not significantly related to IL-6, β (SE) = .002(.07), t(148) = .03, p = .98. The daily stressors by estrogen medication use interaction was not significantly associated with IL-6. Estrogen medication use was significantly related to

CRP, β (SE) = .23(.10), t(173) = 2.26, p = .03. Participants who used estrogen medication

(M= .42; SEM=.10) had greater CRP, compared to individuals not using estrogen

medication (M=.19, SEM= .04). The daily stressors by estrogen use interaction was not significantly related to CRP. Details of the statistical models are found in Table 20.

Antidepressant

Antidepressant medication use was not significantly related to IL-6, β (SE) =

.06(.05), t(148) = 1.07, p = .29. The daily stressors by antidepressant medication use interaction was significantly associated with IL-6. The tests of the simple main effects revealed that there was no significant relationship between daily stressors and IL-6 among non-antidepressant users, F(1,146) =2.78, p= .10, and antidepressant users,

F(1,146) =2.06, p= .15. Post-hoc tests revealed that among participants who experienced zero or one stressors, those who did not use antidepressants had lower IL-6 than individuals using antidepressants, t = 2.14, p = .03. However, after the Tukey-Kramer adjustment for multiple comparisons the difference was no longer significant, p = .15.

Antidepressant medication use was not significantly related to CRP, β (SE) =

.06(.04), t(173) = 1.42, p = .16. The daily stressors by antidepressant use interaction was not significantly related to CRP. Details of the statistical models are found in Table 21.

Use of inflammation-related medication

A composite variable comprising all inflammation-related medications was created by aggregating the use of statins, NSAIDs, estrogen medications, and antidepressants as described by Danese et al. (2008). Inflammation-related medication use was not associated with IL-6, β (SE) = .04(.05), t(148) = .88, p = .16. The daily stressors by inflammation-related medication use was significantly related to IL-6. The

tests of the simple main effects revealed a significant relationship between daily stressors and IL-6 among non-inflammation-related medication users, F(1,146)= 6.92, p = .01, but not among inflammation-related medication users, F(1,146)= 2.24, p = .27. As depicted in Figure 15, among non-inflammation-related medication users the occurrence of multiple daily stressors in the past 24 hours was associated with higher IL-6, compared to reports of zero or one stressors. However, there was no relationship between daily stressors and IL-6 among inflammation-related medication users. Given the significant two-way interaction, a model including a three-way interaction among caregiving status, daily stressors, and inflammation-related medication use was tested. The three-way interaction was not significantly associated with IL-6.

Inflammation-related medication use was not significantly associated with CRP, β

(SE) = .09(.07), t(173) = 1.35, p = .18. The daily stressors by inflammation-related medication use interaction was not related to CRP. The three-way interaction among caregiving status, daily stressors, and inflammation-related medication use was not significantly associated with CRP. Details of the statistical models are found in Tables 22 and 23.

Results from this section indicate that statin use and the use of any inflammation- related medications moderated the relationship between daily stressors and inflammation.

Among non-statin and non-inflammation-related medication users, individuals who experienced multiple daily stressors in the past 24 hours had greater IL-6, compared to participants who reported fewer daily stressors. However, this relationship was not observed among statin and inflammation-related medication users. No other medications moderated the relationship between daily stressors and inflammation.

Lack of difference between caregivers and controls in IL-6: exploration of potential confounding factors

The lack of a main effect of caregiving status on IL-6 was unexpected given that this finding had been replicated in several research laboratories (Kiecolt-Glaser et al.,

2003; Lutgendorf et al., 1999; von Kanel et al., 2006). The current study differs from previous studies on three key characteristics: it included both spousal (N = 32) and parental caregivers (N = 46), it included participants younger than 55, and there was a group difference in marital status. Parental caregivers (M=56.87, SEM= 1.32) were significantly younger than spousal caregivers (M=73.44, SEM=1.60), F(1,73) = 63.53, p

<.001. A series of linear regression models evaluated the impact of these three characteristics on the relationship between caregiving status and biomarkers of inflammation.

IL-6

To evaluate the impact of the inclusion of parental caregivers in the sample, a variable distinguishing among noncaregiving controls, spousal caregivers, and parental caregivers was included in the model. This new caregiving status variable did not significantly predict IL-6, β (SE) = -.03(.03), t(151) = 1.12, p = .26, R2 = .006.

The current sample included a wide age range (40-89). A key difference between the current study and other studies that have reported a main effect of caregiving on IL-6 is the inclusion of participants younger than 55. In order to ascertain the role of age, a variable distinguishing among individuals younger than 55, between the ages of 55 and

75, and older than 75 was created. Although caregivers and controls who had IL-6 data did not differ in terms of their mean age, use of this new categorical age variable showed that the age distribution differed between groups, 2 (2) = 6.75, p = .03. Caregivers were more likely to be in the 55-75 age group and less likely to be in the 75 and older group, compared to noncaregiving controls. In prior analyses, caregiving stress was not associated with IL-6 even after adjusting for differences in age. However, it is possible that age moderated the relationship between caregiving status and inflammation. The caregiving status by age groups interaction did not significantly predict IL-6, β (SE) =

.02(.06), t(150) = .41, p = .69, R2 = .001.

In prior analyses adjusted for differences in marital status, caregiving status was not significantly related to IL-6. However, it is possible that marital status moderated the relationship between caregiving stress and IL-6. The marital status by caregiving status interaction was not significantly associated with IL-6, β (SE) = -.12(.10), t(150) = 1.14, p

= .26, R2 = .007.

CRP

In a model distinguishing among noncaregiving controls, spousal caregivers, and parental caregivers, caregiving status was significantly related to CRP, (SE)= .12 (.04), t(176) =2.97, p = .003, R2 = .038. Post-hoc tests with Tukey-Kramer adjustment revealed that parental caregivers (M=.44, SEM=.07) had higher CRP than controls (M=19,

SEM=.04), t = 3.15, p = .005, and marginally higher CRP than spousal caregivers

(M=.20, SEM=.08), t = 2.16, p = .08. However, spousal caregivers did not differ significantly from controls, t = .15, p = .98. Figure 16 depicts CRP differences among noncaregiving controls, spousal caregivers, and parental caregivers.

Linear regression models evaluated whether daily stressors were still associated with inflammation after adjusting for the caregiver’s relationship with the dementia patient. In a model distinguishing among noncaregiving controls, spousal caregivers and parental caregivers, the number of daily stressors in the past 24 hours was still significantly associated with CRP, β(SE) = .13(.07), t(174) = 1.97, p = .05, R2 = .017.

However, a slight reduction in the effect size of the relationship between daily stressors and CRP was observed. The mediation model was repeated to examine whether distinguishing parental caregivers from noncaregiving controls and spousal caregivers would impact the significance of the indirect effect. Figure 17 depicts the role of daily stressors in partially mediating the relationship between parental caregiving and increased

CRP. In a model comparing parental caregivers to spousal caregivers and controls, the occurrence of multiple daily stressors in the past 24 hours mediated the relationship between parental caregiving and CRP, β (SE) = .02(.01), 95% CI = [.002, .05].

Furthermore, the interaction of daily stressors with the variable distinguishing among noncaregiving controls, spousal caregivers, and parental caregivers was not significant, β

(SE) = .08(.08), t(176) = 1.04, p = .30, R2 = .005.

In the sample of participants with CRP data, the age group distribution among caregivers and controls was marginally different, 2 (2) = 5.77, p = .06. Linear regression models evaluated the question of whether the different age groups moderated the relationship between caregiving stress and inflammation. The interaction of caregiving status with age group was not significant, β (SE) = -.11(.09), t(176) = 1.28, p = .20, R2 =

.007.

Linear regression models tested whether marital status moderated the relationship between caregiving and CRP. The caregiving status by marital status interaction was a significant predictor of CRP, β (SE) = -.41(.14), t(1, 182) = 2.88, p = .004, R2 = .035.

Given that by definition spousal caregivers are married, post-hoc tests with Tukey-

Kramer adjustment contrasted single and married parental caregivers and controls. Single parental caregivers (M=.64, SEM=.10) had higher CRP, compared to married parental caregivers (M=.31, SEM = .08), t = 2.81, p =.05, married controls (M=.22, SEM=07), t =

2.81, p =.003, and single controls (M=.20, SEM=.05), t = 2.81, p =.002. Married parental caregivers controls did not differ from married controls, t = .87, p =.37, and single controls, t = 1.22, p =.23. Married controls did not differ from single controls, t = .30, p

=.76. Figure 18 illustrates CRP as a function of the caregiving by marital status interaction.

Are the characteristics of the caregiving experience related to inflammation?

In order to understand the lack of difference between caregivers and controls on

IL-6, a number of exploratory analyses examined potential factors that may have influenced the relationship between caregiving stress and IL-6 production. Using caregivers only, linear regression models assessed the relationship between caregiving characteristics and inflammation. The covariates included in the models were age, sex,

BMI, and education.

IL-6

The severity of the dementia symptoms was not significantly related to IL-6,

β (SE) = -.004(.005), t(56) = .75, p = .46, R2 = .007. The number of hours spent caregiving each day was not associated with IL-6, β (SE) = .001(.005), t(56) = 29, p =

.77, R2 = .001. The number of months spent caregiving was not significantly related to

IL-6, β (SE) = -.001 (.001), t(56) = 1.43, p = .16, R2 = .027. Whether the dementia patient was living in the caregiver’s home did not predict IL-6, β (SE) = -.06 (.07), t(56) = .90, p = .37, R2 = .011.

CRP

The severity of the dementia symptoms was not significantly related to CRP, β

(SE) = .001(.008), t(73) = .10, p = .92, R2 < .001. The number of hours spent caregiving each day was marginally associated with CRP, β (SE) = .01 (.008), t(73) = 1.78, p = .08,

R2 = .042. A greater number of hours spent in caregiving-related tasks each day was marginally related to higher CRP. The number of months spent caregiving was not significantly associated with CRP, β (SE) = -.001(.001), t(73) = 1.15, p = .26, R2 = .016.

Whether the dementia patient was living in the caregiver’s home was not significantly related to CRP, β (SE) = -.05 (.11), t(73) = .43, p = .67, R2 = .002.

Results from this section indicate that the perceived severity of the dementia symptoms, the average number of hours of caregiving per day, the length of caregiving, and the location of the dementia patient were not related to IL-6. There was a marginally positive correlation between the number of hours spent caregiving and CRP. None of the other caregiving characteristics were related to CRP.

Sociodemographic characteristics, caregiving status, and inflammation

Sociodemographic characteristics were assessed as potential moderators of the relationship between caregiving status and inflammation. A variable distinguishing among noncaregiving controls, spousal, and parental caregivers was used in all CRP models.

Sex

The caregiving status by sex interaction was not significantly related to IL-6, β

(SE) = -.12(.10), t(150) = 1.19, p = .24, R2 = .007. Similarly, the caregiving status by sex interaction was not significantly associated with CRP, β (SE) = -.01(.10), t(175) = .04, p

= .97, R2 < .001.

Ethnicity

Given the small number of ethnic minority participants in the sample, ethnicity was coded as Caucasians and Non-Caucasians. Ethnicity was a significant predictor of

IL-6, β (SE) = -.20 (.08), t(1,151) = .2.57, p = .01. Caucasian participants (M=.23, SEM=

.03) had lower IL-6, compared to Non-Caucasians (M=.43, SEM= .08). The caregiving status by ethnicity interaction was not significantly related to IL-6, β (SE) = .13(.12), t(150) = 1.09, p = .28, R2 = .006. Ethnicity was significantly associated with CRP, β (SE)

= -.22(.09), t(176) = 2.34, p = .02. Caucasians (M=.24, SEM=.04) had lower CRP, compared to Non-Caucasians (M=.41, SEM=.08). The caregiving status by ethnicity

interaction was not significantly related to CRP, β (SE) = -.28(.18), t(175) = 1.58, p = .13,

R2 = .010.

Education

The caregiving status by education interaction term was significantly related to

IL-6, β (SE) = .12(.05), t(150) = 2.30, p = .02, R2 = .026. Tests of the simple main effects revealed that higher education was associated with lower IL-6 among controls,

F(2,147)=3.68, p=.05, but not among caregivers, F(2,147)=.91, p=.45. Figure 19 depicts

IL-6 as a function of the caregiving status by education interaction. The caregiving status by education interaction term was not significantly related to CRP, β (SE) = -.28(.18), t(175) = .13, p = .90, R2 < .001.

Employment status

Employment status was not significantly associated with IL-6, β (SE) = -.03(.05), t(151) = .55, p = .58. The caregiving status by employment status interaction was not significantly related to IL-6, β (SE) = -.01(.09), t(150) = .10, p = .92, R2 <.001.

Employment status was not significantly associated with CRP, β (SE) = -.05(.08), t(176)

= .58, p = .56. The caregiving status by employment status interaction was not significantly related to CRP, β (SE) = -.14(.13), t(175) = 1.06, p = .29, R2 < .005.

Results from this section suggest that age, sex, ethnicity, and employment status did not moderate the relationship between caregiving stress and inflammation. However, there was a significant caregiving status by education interaction. Education was

associated with IL-6 among controls, but not among caregivers. The education by caregiving status interaction did not predict CRP.

Caregiving Status, Medication Use, and Inflammation

Medication use may confound the relationship between caregiving status and inflammation. Linear regression models evaluated whether medication use moderated the relationship between caregiving stress and inflammation. The caregiving status by estrogen use interaction was not tested because only 2 spousal caregivers were using estrogen-based medication.

Statin

The caregiving status by statin use interaction was not significantly related to IL-

6, β (SE) = -.14(.10), t(149) = 1.32, p = .19, R2 = .009. Similarly, the caregiving status by statin use interaction was not associated with CRP, β (SE) = -.05(.10), t(174) = .51, p =

.62, R2 = .001.

NSAID

The caregiving status by NSAID use interaction was not significantly related to

IL-6, β (SE) = -.02(.10), t(149) = .22, p = .83, R2 < .001. The caregiving status by NSAID use interaction was not associated with CRP, β (SE) = -.10(.09), t(174) = 1.17, p = .24, R2

= .006.

Antidepressant

The caregiving status by antidepressant use interaction was not significantly related to IL-6, β (SE) = -.20(.10), t(149) = 1.47, p = .11, R2 = .013. The caregiving status by antidepressant medication use interaction was not associated with CRP, β (SE) = -

.08(.09), t(174) = .90, p = .37, R2 = .004.

Inflammation-related medication

The caregiving status by inflammation-related medication use interaction was not significantly related to IL-6, β (SE) = -.09(.09), t(149) = 1.05, p = .30, R2 = .006. The caregiving status by inflammation-related medication use interaction did not predict CRP,

β (SE) = -.05(.07), t(174) = .64, p = .52, R2 = .002. Results from this section indicate that medication use did not moderate the relationship between caregiving status and circulating biomarkers of inflammation.

Caregiving status, self-reported chronic stress, hip waist ratio, and inflammation

A subset of participants had data on self-reported chronic stress and hip-to-waist ratio. Linear regression models evaluated whether the inclusion of these variables would impact the relationship between daily stressors, caregiving status, and inflammation.

Details of the statistical models are found in Table 24. These analyses are exploratory because the difference in sample size prevents us from directly comparing these results with prior analyses.

In the IL-6 model adjusting for self-reported chronic stress and hip-to-waist ratio, the number of daily stressors in the past 24 hours was not significantly associated with

IL-6. Older age and greater hip-to-waist ratio were associated with higher IL-6. Women had marginally higher IL-6 than men. Caregiving status, self-reported chronic stress, education, and marital status were unrelated to the cytokine.

In the CRP model, the number of daily stressors in the past 24 hours was not associated with CRP. However, the effect size was similar to the ones obtained in prior analyses. Caregiving status was not significantly related to CRP. Greater hip-to-waist ratio was associated with higher CRP. Self-reported chronic stress, age, education, and marital status were not significantly related to CRP.

Chapter 4: Discussion

The goals of this study were to evaluate whether greater exposure and reactivity to daily stressors promoted increased systemic production of inflammatory mediators among family dementia caregivers. In the present sample, caregiving stress was associated with heightened CRP, but was not significantly related to IL-6. Caregivers were more likely to experience multiple stressors in the past 24 hours, compared to noncaregiving controls. The occurrence of multiple daily stressors was associated with greater CRP, compared to reports of zero or one stressors. Exposure to multiple daily stressors in the past 24 hours mediated the relationship between parental caregiving and increased CRP. Daily stressors were related to IL-6 only among participants not using statins. Furthermore, chronic stress amplified IL-6, but not CRP responses to daily stressors among participants not using statins. The associations between daily stressors and inflammation remained significant even after adjusting for differences in health and health behaviors. These results indicate that the cumulative effect of daily stressors may promote elevations in circulating markers of inflammation.

Daily stressors and caregiving

In the current study, the prevalence of daily stressors in the past 24 hours was greater than in the DISE normalization sample (Almeida, Wethington, & Kessler, 2002).

The increased occurrence of daily stressors might be explained by the fact that the current

sample was comprised of a large proportion of family dementia caregivers. Indeed, different types of caregiving have been related to greater reports of daily stressors.

Individuals were more likely to experience multiple daily stressors on days when they were providing routine assistance to their parents, compared to days when they were not involved in such care activities (Savla, et al., 2008). Parents of adult children with physical or psychiatric disability experienced at least one stressor on 50% of the study days, while noncaregiving controls reported at least one stressor on 40% of the days

(Seltzer, et al., 2009). As predicted in hypothesis 1, family dementia caregivers were more likely than noncaregiving controls to experience one or multiple stressors in the past 24 hours, compared to no stressors. More than 50% of the caregivers in the current sample reported multiple daily stressors in the past 24 hours, as compared to 26% of the noncaregiving controls. These results are concordant with self-reported questionnaire data indicating that caregivers experienced more daily hassles than noncaregiving controls (Vitaliano, Scanlan, Krenz, Schwartz, & Marcovina, 1996).

Depression, perceived stress, and caregiving

Caregivers reported more depressive symptoms and more chronic stress than controls. Previous studies indicated that caregivers are at higher risk of experiencing both subclinical and clinical depression (Cooper, Balamurali, & Livingston, 2007; Dura,

Stukenberg, & Kiecolt-Glaser, 1990). In large epidemiological studies, family dementia caregivers reported more stress than participants who were not caregiving (Black, et al.,

2009). In a meta-analytic study, dementia caregivers had more stress, depression, and anxiety, compared to noncaregiving controls (Pinquart & Sorensen, 2003). These data are

consistent with the assumption that family dementia caregiving is an excellent model of chronic stress in humans.

Health behaviors and caregiving

Hypothesis 1 predicted that caregivers would have poorer health behaviors than noncaregiving controls. In the present study, caregivers reported more sleep disturbances in the past month, compared to controls. About two-third of all family dementia caregivers experiences some form of sleep disturbances (McCurry, Logsdon, Teri, &

Vitiello, 2007). In a study using polysomnographic recordings, longer awake times after sleep onset were associated with higher IL-6 among dementia caregivers, suggesting that sleep disturbances may fuel heightened inflammation (Von Kanel, et al., 2006). In the present study, sleep problems were not related to IL-6 and CRP. This may be due to the use of a retrospective self-reported questionnaire for the assessment of sleep disturbances, as opposed to more precise sleep recording techniques.

Caregivers did not differ from controls in the practice of other health behaviors. In previous studies, caregivers had similar alcohol and tobacco use, compared to controls

(Esterling, et al., 1994; Glaser & Kiecolt-Glaser, 1997; Glaser, et al., 2000; Kiecolt-

Glaser, et al., 1991; Kiecolt-Glaser, et al., 1987; Vitaliano, et al., 2005; Vitaliano, 2002;

Von Kanel, et al., 2006). Differences between caregivers and controls in the frequency of vigorous exercises and BMI have been observed in some (Glaser, et al., 2001; Kiecolt-

Glaser, et al., 1996; Vitaliano, et al., 2002), but not all studies (Esterling, et al., 1994;

Glaser & Kiecolt-Glaser, 1997; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1991; Kiecolt-

Glaser, Glaser, et al., 1987; Von Kanel, et al., 2006).

Daily Stressors and Inflammation

Hypothesis 2 predicted that daily stressors would be associated with IL-6 and

CRP. In accordance with the stress-induced elevations in inflammation observed after exposure to a laboratory stressor (Steptoe, et al., 2007), the number of daily stressors in the past 24 hours was significantly related to CRP. Statin use moderated the relationship between daily stressors and IL-6; daily stressors were associated with IL-6 only among participants not using statins. The experience of multiple stressors in the past 24 hours was associated with greater CRP and IL-6, but the occurrence of only one stressor did not impact the inflammatory mediators. In animal studies, stress-induced IL-6 production occurred only after a certain number of footshocks were administered, and the following

IL-6 increase was proportional to the number of times the stressor was repeated (Zhou, et al., 1993). The occurrence of multiple stressors also has a cumulative impact on certain physiological parameters in humans. For example, afternoon urinary cortisol levels were more elevated on days when the individuals experienced multiple stressors than on days when less stressors were reported (Brantley & Jones, 1993). In a study using an experience sampling methodology, participants who reported multiple stressors at randomly prompted occasions had greater salivary cortisol than individuals reporting one or no stressors (Smyth, et al., 1998). In a daily diary study, adolescents who reported more daily social stressors had higher CRP than participants who experienced less social stress (Fuligni, et al., 2009).

Why was the occurrence of one stressor in the past 24 hours not associated with

CRP? It is possible that only stressors of a certain severity or the accumulation of several milder stressors have a physiological impact lasting several hours. Kiecolt-Glaser et al.,

(2005) reported that a marital disagreement discussion led to sustained increases in IL-6 for 22 hours among couples displaying frequent hostile interactions, but not among low- hostile couples. The conflict discussion was stressful for both low- and high-hostile couples, as evidenced by the increases in negative affect in both groups. However, the interaction task appeared to be more stressful among high-hostile couples who exhibited a larger increase in negative affect than low-hostile couples. These results suggest that only stressors of certain intensity may elicit inflammatory changes lasting several hours.

In the current study, most stressors were rated as of low or medium severity. The occurrence of one mild stressor might not elicit lasting increases in inflammatory biomarkers.

Although the main effect of the number of daily stressors on CRP was statistically significant, the effect size was relatively small. In the unconditional model, daily stressors explained about 4.1% of the variance in CRP. After adjusting for caregiving status, as well as sociodemographic, and anthropometric differences, daily stressors accounted for 2.1% of the variance in CRP. This effect size was slightly greater than the effect size of the impact of acute laboratory stressors on CRP (r = .12 equivalent to R2 =

.014) reported in the Steptoe et al. (2007) meta-analysis. Key differences between the present study and the studies included in the meta-analysis might explain the disparity in effect sizes. An obvious difference is the fact that the results of the current study were driven by the occurrence of multiple stressors as opposed to the presence of zero or one stressors. Furthermore, the timing of the measurements of inflammatory mediators likely contributed to the effect size differences. In the experimental studies included in the meta-analysis, most blood samples were taken no later than 90 minutes after the

occurrence of the laboratory stressor. Greater production of inflammatory mediators was observed as the time elapsed since exposure to the stressor increased. It is therefore possible that the peak inflammatory responses may occur several hours after exposure to stressors.

Immediate increases in IL-6 in response to psychological stress are likely due to change in serum volume, increased hemoconcentration, and redistribution of NK cells in the circulation (Mischler, et al., 2005; Richlin, Arevalo, Zack, & Cole, 2004). IL-6 increases 45 minutes after the stressors are more likely to represent the biosynthesis of new cytokines (Steptoe, et al., 2007). Mechanistically, stress-induced norepinephrine production leads to the activation of NF-κB, which in turn elicits gene expression of inflammatory mediators (Bierhaus, et al., 2003). Higher anxiety following a standardized social stressor was associated with greater IL-1β mRNA production in PBMCs up to 2 hours after the stressor (Brydon, et al., 2005). The increased IL-1β mRNA production was in turn associated with heightened serum IL-6 production 2 hours after the stressor

(Brydon, et al., 2005). This suggests that stressors of greater intensity or the accumulation of milder stressors may elicit prolonged NF-κB activation and inflammatory gene expression, which may promote IL-6 production over longer periods of time.

Daily stressors were more strongly associated with CRP than IL-6. Differences in the production and the kinetics of these two inflammatory mediators may explain this result. Production of acute phase reactants, such as CRP, occurs after the expression of proinflammatory cytokines. In fact, serum IL-6 concentration is considered to be the main determinant of CRP production by the liver (Pepys & Hirschfield, 2003).

Furthermore, these two molecules have different half-lives. The CRP’s half-life in serum

is about 19 hours (Pepys & Hirschfield, 2003), while IL-6’s half-life is between 2-4 hours

(Tunon & Egido, 2004). The stress-induced production of CRP will therefore be observed in serum for a longer period than IL-6 production. Given that the blood samples were collected up to 24 hours after the occurrence of the stressors, greater stress-induced elevations in CRP are expected.

Stress, depression, and inflammation: the moderating effect of statin and inflammation- related medication use

Pharmacological agents with anti-inflammatory properties can attenuate stress- induced inflammatory mediators production. Among rheumatoid arthritis patients, the

TSST led to increased stimulated monocyte production of TNF-α, but only among participants not using TNF-α antagonists (Motivala, Khanna, Fitzgerald, & Irwin, 2008).

In a randomized, placebo-controlled study, aspirin use for 5 consecutive days attenuated stress-induced increases in IL-6 (Von Kanel, et al., 2008). Furthermore, statin use moderated the association between depressive symptoms and CRP among cardiac patients (Lesperance, et al., 2004). Greater depressive symptoms were associated with higher CRP among non-statin users, but there was no relationship between depression and inflammation among statin users (Lesperance, et al., 2004). In accord with these results, statin use moderated the relationship between depressive symptoms and inflammation. Statin use and the use of any inflammation-related medications also moderated the association between daily stressors and IL-6 production. The effect of daily stressors was only observed among non-statin users. Statins can reduce NF-κB activation (Miller & Jialal, 2002), providing a mechanism by which the lipid-lowering

medication can mitigate stress-induced increases in inflammation. In fact, regular statin use has been associated with decreased serum IL-6 and CRP (Ren, Ma, & Wang, 2009).

Although the effect of statin use on the relationship between stressors and inflammation was concordant with prior literature, it was intriguing that among statin users, there was a negative relationship between depressive symptoms and inflammation. This puzzling finding suggests that among statin users, individuals who report fewer depressive symptoms have greater inflammation than those who report more depressive symptoms.

One possibility is that denial or social desirability made some participants report no depressive symptoms in the actual presence of distress. An experimental design in which participants with varying levels of depressive symptoms are randomized to statin treatment or placebo treatment prior to the TSST would help to evaluate whether this finding is spurious.

Conceptual model linking stress and inflammation: Exposure model

The exposure model suggests that the chronic stress of caregiving is associated with enhanced circulating inflammatory markers because of the high frequency of daily stressors experienced by caregivers. Significant relationships among these three components must be met in order to test such a mediation model. Caregiving status was associated with a larger number of daily stressors in the past 24 hours and greater CRP, compared to controls. Furthermore, multiple daily stressors were significantly related to

CRP. The temporal sequencing of each component of the model is important in order to draw adequate inference from a mediation analysis. The mediator must occur after the independent variable, but before the dependent variable (Kraemer, Kiernan, Essex, &

Kupfer, 2008). Caregiving status is a condition that precedes the occurrence of daily stressors and the daily stressors in the past 24 hours happened prior to the blood draw yielding CRP data. Mediation analyses revealed that the increased occurrence of daily stressors among caregivers represented a mechanism by which caregiver stress led to elevations in CRP. Further analyses determined that this mediation effect was observed among parental caregivers, but not among spousal caregivers.

This mediation model is consistent with results from studies using self-reported questionnaire methodology. In daily diary studies, the experience of multiple stressors during the day have been associated with greater mood disturbances and greater report of physical symptoms (Almeida, et al., 2002; Delongis et al., 1982; Eckenrode, 1984) than the occurrence of zero or one stressors. Furthermore, some studies have reported that an increased number of daily hassles mediated the relationship between major life events and depression (Pillow, et al., 1996; Wagner, et al., 1988). Among family dementia caregivers, the greater occurrence of daily hassles mediated the relationship between chronic stress and self-reported gingivitis symptoms (Vitaliano, et al., 2005). These data indicate that the greater occurrence of daily stressors among caregivers may promote a host of physical and psychological symptoms.

Conceptual model linking stress and inflammation: Reactivity model

Across both caregivers and noncaregiving controls, the occurrence of multiple stressors in the past 24 hours was associated with increased CRP and IL-6. The reactivity model stipulates that chronic stress and depression amplify inflammatory responses to daily stressors. There was preliminary evidence that caregivers may have greater IL-6

production following exposure to multiple daily stressors. The three-way interaction among caregiving status, daily stressors, and statin use was marginally associated with

IL-6. Among participants not using statins, caregivers who reported multiple daily stressors had higher IL-6 than caregivers who experienced zero or one stressors. In contrast, there was no difference in IL-6 among non-statin users controls who reported zero, one, or multiple stressors. However, after adjusting for multiple comparisons none of the observed differences remained statistically significant. Nevertheless, these data point to the possibility that chronic stress may lead to physiological changes, such as immune cells’ glucocorticoid insensitivity, that promote exaggerated IL-6 responses to stressors (Miller, et al., 2002).

Neither caregiving status nor depressive symptoms exacerbated CRP responses to daily stressors. This result is in contrast with animal and human studies in which chronic stress and depression sensitized inflammatory and neuroendocrine responses to daily stressors (Anisman, Poulter, Gandhi, Merali, & Hayley, 2007; Pace, et al., 2006).

However, not all studies have found that chronic stress enhanced cardiovascular, neuroendrocrine, and immune responses to acute stressors (Cacioppo, et al., 2000;

Cacioppo, et al., 1998).

The timing of the inflammatory measurements may explain the discrepant finding. Most studies that have found support for the physiological reactivity model have measured inflammatory markers several times over the course of the 2 hours following an acute laboratory stressor (Miller, et al., 2005; Pace, et al., 2006). In one study, one hour after exposure to the stressor, CRP kept rising among depressed participants, but started to return to its baseline among control participants (Miller, et al., 2005). Because of the

lack of extended follow-up after exposure to the stressor, it is unclear how long the increased inflammatory reactivity persists. Measurement of inflammation several hours after the stressor may better reflect peak inflammatory responses to the stressors as opposed to acute increases in reactivity. Furthermore, the cumulative impact of several stressors may have overshadowed the increased inflammatory reactivity associated with chronic stress. Namely, the exposure to multiple stressors in the past 24 hours may lead to physiological changes that are larger than the increased reactivity associated with each stressor.

Contrary to hypothesis 4, greater depressive symptoms were not associated with exacerbated inflammatory responses to the daily stressors. Depressive symptoms have been associated with higher levels of circulating inflammatory biomarkers (Dowlati, et al., In Press; Howren, et al., 2009). In the present study, statin use moderated the relationship between depression and inflammation. Depressive symptoms were associated with higher IL-6 and CRP among non-statin users, while statin users displayed the inverse pattern. Because of this significant two-way interaction, a three-way interaction among caregiving status, daily stressors, and statin use tested the reactivity hypothesis. However, interaction effects are harder to detect than main effects. This phenomenon is further amplified in the context of three-way and four-way interactions, and when the effect size of the relationship of interest is small (McClelland & Judd,

1993). Therefore, it is unclear if this study was adequately powered to detect reactivity effects associated with chronic stress, depression, and statin use. In sum, these results suggest that increased exposure, but not increased reactivity to daily stressors, might promote heightened CRP among parental dementia caregivers.

Objective severity of daily stressors and inflammation

Hypothesis 5 proposed that stressors of greater objective severity would be associated with larger inflammatory responses than less severe stressors. This hypothesis was based on data showing that more severely-rated stressors have been related to greater psychological distress than less severe stressors (Almeida, et al., 2002). One of the assumptions underlying this hypothesis was that the number of daily stressors would be independent of the cumulative severity of the stressors. However, in the current sample there was a large positive correlation between the number of daily stressors in the past 24 hours and the cumulative objective severity of these stressors. This strong correlation was due to the low prevalence of severe and very severe stressors. In the DISE validation study comprising 1031 adults studied over a 7-day period, the average objective severity of all daily stressors was rated as low, indicating that even in a large sample the prevalence of severe and very severe was small.

When the daily stressors’ cumulative objective severity was used as an independent variable, the relationship with CRP became non-significant. One factor that might have contributed to the current finding is the lack of concordance in objective severity ratings between judges. The Cohen’s kappa value for inter-rater reliability was

.68, indicating that measurement error may have hindered our ability to find significant associations between the so-called objective severity ratings and inflammation.

Furthermore, data from the current study were collected over a 4-year period. Several different interviewers (N=7) administered the DISE. Although all interviewers received intensive training, variability in the choice of which daily events were explored enough to provide information allowing objective severity ratings may also have contributed to the

lack of findings. Moreover, the restricted range of objective severity ratings increased the difficulty of finding a significant effect.

Health, health behaviors, daily stressors, and inflammation

Contemporary psychoneuroimmunology models stipulate that stress influences immune function via both behavioral and physiological pathways (Kiecolt-Glaser &

Glaser, 1988). In the current study, none of the self-reported health and health behavior variables were significantly associated with daily stressors. Statistical adjustments for self-reported health and health behaviors were made to increase our confidence that the observed inflammatory changes were at least partially due to the physiological responses to daily stressors. Even after adjusting for the number of chronic illnesses, tobacco and alcohol use, weekly caloric expenditure in physical activity, and sleep quality, multiple daily stressors in the past 24 hours remained significantly associated with CRP. Only the adjustment for recent health behaviors led to a slight decrease in the effect size of the relationship between daily stressors and CRP.

In accord with epidemiological studies, tobacco use was associated with greater

IL-6 (Hamer & Chida, 2009; Nazmi, et al., 2008). Greater alcohol consumption was also marginally associated with lower IL-6 and CRP. Study participants consumed a mean of

2.61 alcoholic drinks per week. Such moderate weekly alcohol use has been associated with lower inflammatory markers (Hamer & Stamatakis, 2008; Imhof, et al., 2004; Pai, et al., 2006; Volpato, et al., 2004). One participant reported a very large daily alcohol intake

(42 drinks per week). When this participant was excluded from the analyses, the results remained the same (data not shown). Six participants had a BMI over 40 indicating

morbid obesity. When these participants were excluded from the analyses, the effect size of the relationship between daily stressors and CRP increased from .019 to .025.

Furthermore, the daily stressors by statin use interaction still significantly predicted IL-6.

This indicates that the relationship between daily stressors and inflammation might have been underestimated because of the inclusion of morbidly obese individuals in the sample. None of the other health behavior variables were significant associated with inflammation in our multivariate statistical models. Physical activity can modulate inflammation (Elosua, et al., 2005; Ford, 2002). The lack of significant relationship in the present study might be due to the confounding effects of other variables concurrently included in the model such as BMI. Furthermore, accurate measurement of physical activity in older adults is difficult and potentially contributes to the lack of significant effects in the present study (Harada, Chiu, & Stewart, 2001).

Absence of relationship between spousal caregiving and inflammation

Spousal caregiving has been associated with increased IL-6 in studies from three independent laboratories (Kiecolt-Glaser, et al., 2003; Lutgendorf, et al., 1999; Von

Kanel, et al., 2006). In a relatively small study, Lutgendorf et al. (1999) reported that female spousal dementia caregivers aged 60 and older (N=18) had greater IL-6, compared to older women undergoing the stress of housing relocating (N=17), as well as older (N=15), and younger (N=20) control women who did not report significant stressor at the time of the study. Furthermore, in a 6-year longitudinal study comprised of 119 spousal caregivers and 106 noncaregiving controls aged 55 and older, caregivers exhibited an amplified age-related increase in IL-6, compared to noncaregiving controls.

The rate of increase in IL-6 over the 6-year period was 4 times higher in caregivers than in controls. However, IL-6 did not differ between the two groups at the beginning of the study (Kiecolt-Glaser, et al., 2003). Moreover, in a cross-sectional study of 116 spousal caregivers and 54 controls aged 55 and older, caregivers had significantly higher IL-6, but not CRP, compared to noncaregiving controls. However, after statistically adjusting for differences in age and BMI, the group difference in IL-6 was no longer significant.

Interestingly, older age was significantly associated with greater IL-6 among caregivers, but not among controls (Von Kanel, et al., 2006).

Three key characteristics distinguish the present study from other studies that have reported an effect of caregiving on IL-6. The current study included parental as well as spousal caregivers, individuals younger than 55 years old, and single caregivers and controls. The inclusion of parental caregivers in the current study introduced age and marital status differences that may explain the lack of a significant association between caregiving stress and IL-6. Furthermore, parental caregivers in and of themselves may be different from spousal caregivers. Differences related to the caregiver’s relationship to the dementia patient, age, and marital status are all intertwined. Statistical analyses including two-way interactions were computed in order to tease apart the respective contribution of each of these factors. However, the unique contribution of each factor is difficult to isolate statistically. As stated above, interaction effects are hard to detect. This problem is further compounded by the fact that some cells of these interactions included a small number of participants. Only experimental control could have provided a more definitive conclusion regarding the unique contribution of each factor.

Spousal and parental caregiving and inflammation

The inclusion of parental and spousal caregivers distinguishes the current study from other studies on caregiving and inflammation. A meta-analysis of the effect of caregiving on psychological distress suggests that parental caregivers report less distress than spousal caregivers (Pinquart & Sorensen, 2003). In contrast, a meta-analysis of the effect of dementia caregiving on health reports that parental and spousal caregivers do not differ in the extent to which caregiving impacted self-reported health and objective physiological markers (Vitaliano, Zhang, & Scanlan, 2003). However, both of these meta-analyses are based on a small number of studies directly comparing parental and spousal caregivers. Furthermore, the impact of parental and spousal caregiving on inflammatory markers was not evaluated. In the current study, spousal caregivers did not differ from controls in IL-6 and CRP. However, parental caregivers had greater CRP, but not IL-6, compared to spousal caregivers and noncaregiving controls.

Stress-induced CRP may be less age dependent that IL-6. In the present study, IL-

6, but not CRP, was significantly correlated with age. Although a previous study of spousal dementia caregivers did not find an effect of caregiving on CRP (Von Kanel et al., 2006), caregivers for a family member with brain cancer had elevated CRP compared to matched controls (Rohleder, et al., 2009). Specifically, caregivers and controls were assessed four times over the course of a 12-month period. Caregivers exhibited greater

CRP, but not IL-6 increases over time, compared to controls (Rohleder, et al., 2009).

Notably, cancer caregivers (mean age= 50.5) in this study were younger than the typical spousal dementia caregivers (mean age = 72.9) (Rohleder, et al., 2009; Von Kanel, et al.,

2006). It is also possible that the unique characteristics of both parental and spousal caregiving lead to different immune outcomes.

Age, caregiving status, and inflammation

Age appears to play an important role in stress-induced IL-6 increases. The mean age in the current study was similar to other studies on caregiving stress and immune function. However, the age range was much wider and included individuals as young as

40. Furthermore, although there was no mean age difference, there was a difference in the age group distribution of caregivers and controls. Caregivers were over-represented in the

55-75 age group, while controls were over-represented in the 75 and older age group.

Because IL-6 increases with age (Ershler & Keller, 2000), the over-representation of controls in the older age group may have blurred the impact of caregiving stress on the proinflammatory cytokine. Relatedly, some studies have reported that chronic stress- induced immune dysregulation was more pronounced among older caregivers than among younger caregivers (Pariante, et al., 1997). Therefore, the inclusion of younger caregivers may have reduced the effect size of the relationship between caregiving stress and increased IL-6. However, the caregiving status by age interaction was not significantly associated with IL-6 and CRP.

Caregiving, marital status, and inflammation

The inclusion of participants who were not currently involved in a long-term romantic relationship may have impacted the relationship between caregiving stress and inflammation. A large body of evidence indicates that marital relationships have a

protective effect on health and can modulate detrimental inflammatory processes (House,

Landis, & Umberson, 1988; Kiecolt-Glaser, Gouin, & Hantsoo, 2009; Kiecolt-Glaser &

Newton, 2001). In the current study, caregivers were more likely to be involved in a long- term committed relationship than noncaregiving controls. Kiecolt-Glaser et al. (2003) observed a main effect of caregiving on IL-6 despite the fact that 36% of their noncaregiving controls were not currently involved in a committed romantic relationship.

Nevertheless, the greater proportion of not-currently-married controls in the present study

(63%) may have impeded our ability to find group differences on inflammation.

However, data indicated that marital status did not moderate the relationship between caregiving status and IL-6. In epidemiological studies among older adults, the protective effect of marriage on IL-6 was seen in men, but not in women (Ford, Loucks, &

Berkman, 2006; Loucks, Berkman, Gruenewald, & Seeman, 2006). The small proportion of males in the sample may explain why marital status was not significantly associated with IL-6.

Marital status moderated the relationship between caregiving stress and CRP.

Parental caregivers not currently involved in a committed romantic relationship had higher CRP than married, parental caregivers and married and non-married controls.

Married and non-married controls did not differ in terms of CRP. This result is consistent with epidemiological studies indicating that single individuals have poorer immune function, and greater morbidity and mortality than married individuals (Manzoli, Villari,

G, & Boccia, 2007; Seeman, Kaplan, Knudsen, Cohen, & Guralink, 1987). Furthermore, both divorce and widowhood have been associated with dysregulated immune function and poorer health outcomes (Kiecolt-Glaser, Fisher, et al., 1987; Kiecolt-Glaser, et al.,

1988; Sbarra & Nietert, 2009). In a recent population-based study, currently married men had lower serum CRP, compared to divorced or widowed men (Sbarra, 2009). The marital relationship is a major source of support among older adults. Two conceptual models describe the impact of social support on health (Cohen & Wills, 1985). The direct effect model suggests that social support has a beneficial effect on health irrespective of the occurrence of stressor. In contrast, the stress-buffering effect model postulates that social support promotes better health outcomes by attenuating detrimental stress responses. Data from the current study support the stress-buffering model because the protective effect of a marital relationship was observed among individuals undergoing the chronic stress of caregiving, but not among noncaregiving controls.

Characteristics of the caregiving experience and inflammation

The characteristics of the caregiving experience were assessed as potential confounds of the relationship between caregiving stress and inflammation. In a study that used an interview-based coding of dementia severity, participants caring for a spouse with moderate or severe dementia had marginally higher IL-6 than noncaregiving controls (Mills, et al., 2009). However, caregivers whose spouse had probable or mild dementia did not differ in IL-6 from controls (Mills, et al., 2009). In the current study, a greater number of hours spent daily in caregiving-related tasks was marginally related to higher CRP, but was not significantly associated with IL-6. However, the severity of the dementia symptoms as perceived by the caregiver, the time elapsed since the beginning of the caregiving, and the location of the dementia were not significantly related to IL-6 and CRP. In prior studies, the characteristics of caregiving were not related to blood

levels of lymphocytes and NK cells, antibody titers to latent viruses, blastogenic responses to mitogen, and vaccine responses (Glaser & Kiecolt-Glaser, 1997; Glaser, et al., 2000; Kiecolt-Glaser, et al., 1991; Kiecolt-Glaser, et al., 1996; Kiecolt-Glaser, Glaser, et al., 1987). Overall, the characteristics of the caregiving experience do not appear to have a strong impact on the relationship between caregiving stress and immune function.

Sociodemographic characteristics, caregiving, and inflammation

Sociodemographic characteristics were evaluated as potential moderators of the relationship between caregiving stress and inflammation. Sex differences in chronic stress-induced immune dysregulation have been reported (Vitaliano, et al., 1996). In the current study sex did not moderate the relationship between caregiving stress and circulating inflammatory biomarkers. This result is in accord with prior studies in which sex did not predict cross-sectional and longitudinal IL-6 levels among dementia caregivers (Kiecolt-Glaser, et al., 2003; Mills, et al., 2009). Furthermore, Non-Caucasian individuals had higher IL-6 than Caucasians. However, ethnicity did not moderate the impact of caregiving stress on inflammation. African-American and Caucasian caregivers experience similar restriction in social activities and impairment in physical health

(Haley, 1995), and ethnicity did not influence the exacerbation of age-related increases in

IL-6 among caregivers (Kiecolt-Glaser et al., 2003). Moreover, employment status did not moderate the relationship between chronic stress and inflammation. However, there was a significant interaction between caregiving status and education. Higher educational achievement was associated with lower IL-6 among controls, but there was no relationship between education and IL-6 among caregivers. Lower socioeconomic status

has been associated with greater IL-6 in several epidemiological studies (Gruenewald,

Cohen, Matthews, Tracy, & Seeman, 2009; Petersen, et al., 2008).

Medication use, caregiving, and inflammation

Because statin use moderated the association between daily stressors and inflammation, a series of linear regression analyses evaluated whether medication use moderated the relationship between caregiving status and inflammation. The use of statins, NSAIDs, antidepressants, or any inflammation-related medications did not impact the relationship of caregiving status with either IL-6 or CRP. However, the inferences that can be drawn from these analyses are limited for two main reasons. No data on adherence to the medication was collected. In randomized controlled trials there are large differences in medication’s effects between treatment adherent and treatment non- adherent individuals (Dimatteo, Giordani, Lepper, & Croghan, 2002; Simpson, et al.,

2006). Moreover, no information was collected on the dosage of each medication. The medication dosage is an obvious factor that can influence the extent to which a given pharmacological agent can influence inflammatory processes. However, it is very difficult to collect accurate dosage information from self-reported questionnaires and to integrate these data in statistical analyses when participants are not recruited based on medication use.

What are the theoretical implications and clinical relevance of the study’s findings?

Transient increases in inflammation associated with naturalistically occurring stressors are unlikely to be of clinical significance in and of themselves. However, if

inflammatory mediators remain elevated up to 24 hours after the occurrence of daily stressors, one may wonder what happens when a person experiences multiple daily stressors for several consecutive days. Repeated exposure to the same psychological stressor did not lead to the habituation of the inflammatory responses, suggesting that multiple daily stressors can have a cumulative impact on inflammation (Hamer, Gibson,

Vuononvirta, Williams, & Steptoe, 2006; Von Kanel, et al., 2006b). Furthermore, individuals suffering from chronic exhaustion exhibited sensitization rather than habituation of the salivary cortisol response following repeated exposure to the same stressor (Kudielka, et al., 2006). This indicates that the additive impact of daily stressors may persist over several days and promote immune dysregulation. Greater exposure to daily stressors might erode negative feedback processes that regulate and maintain a low level of circulating inflammatory markers. The allostatic load model suggests that repeated elevations in inflammation over several years might promote pathophysiological processes (McEwen, 1998). In fact, elevations in IL-6 and CRP have been associated with increased risk for cardiovascular diseases and a host of age-related diseases (Ershler

& Keller, 2000; Ridker, et al., 2000; Ridker, 2000). Greater exposure to daily stressors may therefore partially mediate the chronic low-grade inflammation and enhanced risk for age-related diseases among family dementia caregivers.

Limitations & future directions

The results of this study must be interpreted in light of its limitations. Although the use of the DISE represents an improvement over the use of self-reported questionnaires, the recall bias associated with retrospective assessment of daily stressors

may have diminished our ability to capture an accurate portrait of the number of stressors experienced by each participant. Ecological momentary assessment methodology provides a prospective evaluation of daily stressors that can reduce such recall bias

(Smyth & Stone, 2003). Furthermore, consecutive measurements of daily stressors and inflammatory biomarkers over several days would strengthen the hypothesis that daily hassles can promote chronic low-grade inflammation. Multiple collection of blood samples would also allow an assessment of the relationship between caregiving and inflammation that is less influenced by day-to-day variations in IL-6 and CRP. Moreover, the timing of IL-6 and CRP measurements can potentially impact the strength of the relationship between daily stressors and inflammation. It is notable that the effect of daily stressors on inflammation was observed even after a potentially restorative night of sleep.

Blood samples drawn the same day as the stressor might reveal stronger associations between daily stressors and inflammation.

In behavioral science, the strongest evidence of causation comes from experimental studies in which participants are randomized to an experimental and control groups and the independent variable is manipulated in a controlled environment. Because it would not be ethical to manipulate chronic stress in human participants, researchers have to study existing groups, such as family dementia caregivers. The lack of randomization of the participants to the caregiver and control groups implies that we cannot rule out the possibility that pre-existing differences between caregivers and controls explain their differences in exposure to daily stressors and inflammation. This issue is particularly important in the current study. The fact that caregivers and controls differed in terms of marital status and age distribution prevent us from inferring that the

increased CRP was due only to parental caregiving. Although unlikely, the possibility that the observed caregiving effects may be driven by confounding factors or other unmeasured third variable cannot be ruled out. In this context, replication of results in different independent laboratories and convergence of results using different methodologies are paramount in strengthening our confidence of reliable associations among chronic stress, daily stressors, and inflammation. In future studies, researchers should make sure to recruit caregivers and controls who are equivalent in terms of all sociodemographic characteristics including marital status.

When designing psychoneuroimmunology studies with older adults, one faces the challenge of selecting individuals who can provide interpretable immune data, while accruing a sample that is still representative of the overall population of interest. Acute and chronic diseases and their treatments can significantly modulate circulating inflammatory markers, obscuring the relationship between stress and inflammation. In order to circumvent this problem, one can exclude participants diagnosed with illnesses or undergoing treatments that have an obvious impact on immune function and record the medications taken by each participant. Results from the present study suggest that statin use may attenuate some of the stress-induced increases in inflammation. Future research should either exclude participants using statins or formulate a priori hypotheses about the moderating role of statin use. With the latter option, efforts must be made to recruit enough statin users to adequately test these hypotheses.

A number of statistical issues also impacted the interpretation of the data. The large number of statistical analyses performed in this study increased the likelihood of spurious findings (Type I error). Furthermore, some hypotheses involved three-way

interactions that are more difficult to detect when examining small effect size relationships. Future studies would benefit from planning a priori comparisons within these three-way and four-way interactions based on the results of the current study.

Specifically, this study provided preliminary evidence that caregivers not using statins may exhibit greater IL-6 production in response to multiple daily stressors than controls.

However, this relationship was no longer significant after adjusting for multiple post-hoc comparisons. Replication of this result is therefore essential before the reactivity model can be confirmed and the possibility of a spurious effect ruled out.

Conclusion

Data from the current study extend laboratory evidence by showing that acute stressors can increase inflammatory biomarkers in naturalistic settings. Across both caregivers and controls, the occurrence of multiple daily stressors was associated with elevations in CRP. Furthermore, the greater frequency of daily stressors among caregivers partially mediated the relationship between parental caregiving and heightened

CRP. However, replication of these results is essential given the group differences in age and marital status.

If stress-induced immune dysregulation promotes detrimental health outcomes, interventions attenuating these physiological stress responses may have a beneficial health impact. Both behavioral and pharmacological interventions might be useful in this endeavor. Statin medication may modulate stress-induced inflammation. Furthermore, cognitive appraisal influences inflammatory responses to a laboratory stressor (Wirtz, et al., 2007), suggesting the relevance of cognitive-behavioral interventions. Health

behaviors such as increased physical activity or polyunsaturated fatty acids intake may attenuate stress-induced inflammation (Maes, Christophe, Bosmans, Lin, & Neels, 2000;

Petersen & Pedersen, 2005). Multimodal intervention strategies are clearly warranted in this distressed population. Moreover, a better understanding of the behavioral and physiological mechanisms linking caregiving stress and poor health will allow the development of tailored, and more effective interventions.

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Appendix A: Tables

133

Caregivers Controls Total F/ 2 p-value Age 1.04 .31 Mean (SD) 63.42 65.52 64.62 (12.03) (14.88) (13.74) Minimum-Maximum 40-80 41-89 40-89 Sex .82 .37 Male 20 21 41 Female 58 84 142 Ethnicity .40 .53 Caucasian 65 91 156 Non-Caucasian 13 14 27 Education .14 .93 High school 23 33 56 College 31 39 70 Graduate/Professional 24 33 57 Employment status .01 .92 Employed 38 52 90 Unemployed 40 53 93 Marital Status 33.13 .001 Single 7 15 22 1.19 .28 Married 59 39 98 26.67 .001 Divorced 12 28 40 3.34 .07 Widowed 0 23 23 19.54 .001 Table 1. Sociodemographic characteristics of family dementia caregivers and noncaregiving controls. Data represents the number of participants in each category, except for the age variable.

134

Caregivers Controls Total F/ 2 p-value Number of chronic 3.04 .003 health conditions Mean (SD) 1.24 (1.03) .82 (.94) 1.00 (1.00) Minimum-Maximum 0-5 0-3 0-5 Medication use -Statins 19 22 41 .29 .59 -Non-steroidal anti- 29 26 55 1.28 .26 inflammatory drugs -Estrogen/progesterone 11 9 20 1.41 .24 supplements -Antidepressants 21 10 31 2.04 .15 -Inflammation-related 49 59 107 .81 .37 medications Table 2. Self-reported health and medication use among caregivers and controls. Data represent the number of participants taking each medication, except for the number of chronic health conditions variable. Chi-square tests for the medication variables were computed against the number of participants not using the medication.

135

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .22 .202 Age .007 (.002) 4.32 <.001 .003 (.002) 1.17 .25 Sex .06 (.05) 1.17 .24 .15 (.08) 2.00 .05 BMI .02 (.004) 5.08 <.001 .04 (.006) 5.87 <.001 Education -.02 (.03) .70 .49 -.05 (.04) 1.29 .20 Marital status -.06 (.05) 1.31 .19 -.11 (.07) 1.62 .11 Step 2 .002 .023 Caregiving status -.03 (.05) .62 .54 .16 (.07) 2.27 .03 Table 3. Caregiving status and inflammation.

136

Caregivers Controls Total Depression Mean (SD) 11.29 (8.54) 6.95 (8.34) 8.80 (8.67) Minimum-Maximum 0-36 0-41 0-41 Self-reported chronic stress Mean (SD) 18.90 (7.94) 12.28 (7.20) 14.64 (8.09) Minimum-Maximum 2-37 0-34 0-37 Major life events Mean (SD) 5.5 (2.42) 4.92 (2.53) 5.16 (2.49) Minimum-Maximum 0-11 0-12 0-12 Table 4. Psychosocial characteristics of family dementia caregivers and noncaregiving controls.

137

Caregivers Controls Total Smoking Status Smokers (N) 3 7 10 Non-smokers (N) 75 98 173 Weekly Alcohol intake Mean (SD) 2.36 (3.71) 2.79 (5.69) 2.61 (4.94) Minimum-Maximum 0-20 0-42 0-42 Recent Alcohol use Mean (SD) .38 (.63) .48 (1.16) .44 (.97) Minimum-Maximum 0-2 0-6 0-6 Weekly Caloric Expenditure Mean (SD) 3735.06 (2850.77) 3102.08 (2307.20) 3371.88 (2564.88) Minimum-Maximum 62.28- 12689.04 50.00-13206.14 50.00-13206.14 BMI Mean (SD) 28.34 (5.46) 27.01 (5.05) 27.58 (5.26) Minimum-Maximum 17.85-44.93 18.40-44.62 17.85-44.93 Waist/Hip ratio Mean (SD) .91(.10) .88 (.07) .89 (.08) Minimum-Maximum .69-1.23 .71-1.10 .69-1.23 Sleep disturbances Mean (SD) 6.07 (3.18) 5.07 (2.55) 5.50 (2.87) Minimum-Maximum 0-18 1-14 0-18 Recent sleep Mean (SD) 6.87 (1.45) 7.05 (1.28) 6.97 (1.35) Minimum-Maximum 2.75-9.97 3.67-11.92 2.75-11.92 Table 5. Health behavior practices among caregivers and controls. Recent alcohol use refers to alcohol consumption in the past 24 hours. Recent sleep refers to the number of hours of sleep during the night prior to the study visit.

138

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .20 Age .007(.002) 4.29 <.001 .21 .003 (.002) 1.17 .25 Sex .07 (.05) 1.44 .15 .15 (.08) 2.00 .05 BMI .02 (.004) 4.92 <.001 .04 (.006) 5.87 <.001 Education -.02 (.03) .88 .38 -.05 (.04) 1.29 .20 Step 2 <.001 .021 Daily stressors .007(.03) .24 .81 .09 (.04) 2.18 .03 Table 6. Daily stressors and inflammation.

139

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .23 .202 Age .006 (.002) 4.17 <.001 .003 (.002) 1.18 .24 Sex .07 (.05) 1.23 .22 .17 (.08) 2.12 .04 BMI .02 (.004) 5.11 <.001 .04 (.006) 6.04 <.001 Education -.02 (.03) -.73 .47 -.05 (.04) 1.21 .23 Marital status -.06 (.05) 1.21 .23 -.11 (.07) 1.54 .12 Life events -.006 (.009) .70 .49 -.01 (.01) .88 .38 Step 2 .002 .024 Caregiving status -.04 (.05) .73 .47 .12 (.07) 1.72 .09 Step 3 .003 .019 Daily stressors .04 (.05) .78 44 .14 (.07) 2.07 .04 Table 7. Daily stressors, caregiving status, and inflammation

140

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .23 .202 Age .007(.002) 4.24 <.001 .003 (.002) 1.22 .23 Sex .07(.05) 1.25 .21 .17 (.08) 2.17 .03 BMI .02(.004) 5.10 <.001 .04 (.006) 6.06 <.001 Education -.02 (.03) -.73 .47 -.05 (.04) 1.20 .23 Marital status -.06 (.05) 1.18 .24 -.10 (.07) 1.48 .14 Life events -.006(.009) .72 .47 -.01 (.01) .89 .37 Step 2 .005 .042 Caregiving status -.05(.06) .79 .43 .09 (.09) .95 .34 Daily stressors .02 (.06) .36 .72 .10 (.10) 1.02 .31 Step 3 .001 .002 Caregiving x daily .03 (.09) .33 .74 .09 (.14) .67 .50 stressors

141 Table 8. Caregiving status by daily stress interaction and inflammation.

141 Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .23 .202 Age .007 (.002) 4.22 <.001 .003 (.002) 1.19 .24 Sex .07 (.05) 1.24 .22 .17 (.08) 2.12 .04 BMI .02 (.004) 5.10 <.001 .04 (.006) 6.02 <.001 Education -.02 (.03) .72 .47 -.05 (.04) 1.19 .24 Marital status -.06 (.05) 1.17 .25 -.11 (.07) 1.52 .13 Life events -.006 (.009) .73 .47 -.01 (.01) .89 .37 Step 2 .002 .024 Caregiving status -.04 (.05) .76 .45 .12 (.08) 1.59 .11 Depression .01 (.05) .22 .83 .01(.08) .18 .86 Step 3 .003 .019 Daily stressors .04 (.05) .78 .44 .14 (.07) 2.07 .04 Table 9. Daily stressors, caregiving status, depressive symptoms, and inflammation.

142

142 Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .23 .202 Age .007 (.002) 4.34 <.001 .004 (.002) 1.56 .12 Sex .07 (.05) 1.38 .17 .18 (.08) 2.21 .03 BMI .02 (.004) 5.40 <.001 .04 (.006) 6.41 <.001 Education -.01 (.03) .53 .60 -.04 (.04) 1.00 .32 Marital status -.04 (.05) .89 .37 -.08 (.07) 1.06 .29 Life events -.006 (.009) .71 .48 -.01 (.01) .90 .37 Step 2 .002 .024 Caregiving status -.05 (.05) .88 .38 .11 (.08) 1.43 .16 Statin use .17 (.10) 1.70 .09 .24 (.15) 1.61 .11 Depression .07 (.06) 1.17 .24 .11 (.09) 1.25 .21 Step 3 .017 .015 Depression x -.22 (.12) 1.94 .05 -.38 (,17) 2.16 .03 statin use Step 4 .006 .024 Daily stressors .05 (.05) 1.05 .30 .16 (.07) 2.35 .02 Table 10. Depressive symptoms by statin use interaction and inflammation

143

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .23 .202 Age .007 (.002) 4.31 <.001 .004 (.002) 1.55 .12 Sex .09 (.06) 1.67 .10 .19 (.08) 2.36 .02 BMI .02 (.004) 5.42 <.001 .04 (.006) 6.42 <.001 Education -.02 (.03) .70 .49 -.05 (.04) 1.18 .24 Marital status -.04 (.05) .90 .37 -.06 (.07) .89 .37 Life events -.007 (.009) .76 .45 -.01 (.01) .96 .34 Step 2 .005 .043 Caregiving status -.04 (.05) .77 .44 .11 (.08) 1.43 .16 Statin use .17 (.11) 1.53 .13 .24(.18) 1.19 .24 Depression .05 (.07) .75 .46 .07(.11) .69 .49

44 Daily stressors .02 (.13) .13 .90 .10 (.17) .59 .56

Step 3 .04 .027 Depression x -.13 (.14) .93 .35 -.23 (.22) 1.02 .31 statin use Daily stressors x -.09 (.26) .33 .74 .04 (.34) .13 .90 statin use Daily stressors x .08 (.14) .59 .56 .12 (.18) .67 .50 depression Step 4 .002 .003 Daily stressors x -.15 (.27) .55 .58 -.29 (.36) .82 .42 depression x statin use Table 11. Daily stressors, depression, statin use, and inflammation.

144

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .225 .202 Age .007 (.002) 4.36 <.001 .004 (.003) 1.53 .13 Sex .10 (.06) 1.69 .09 .20 (.09) 2.29 .02 BMI .02 (.004) 5.30 <.001 .04 (.007) 6.56 <.001 Education -.02 (.03) .69 .49 -.04 (.04) 1.06 .29 Marital status -.03 (.05) .54 .59 -.05 (.07) .67 .50 Life events -.004 (.009) .43 .67 -.01 (.01) .94 .35 Step 2 .005 .043 Caregiving status -.01 (.16) .08 .94 .005 (.26) .02 .98 Statin use .22 (.13) 1.73 .09 .19(.20) .98 .33 Depression .09 (.08) 1.07 .29 .09(.13) .68 .50 145 Daily stressors .07 (.15) .51 .61 .19 (.21) .93 .35

Step 3 .040 .035 Depression x statin use -.24 (.17) .139 .17 -.15 (,27) .56 .57 Daily stressors x statin use -.14 (.27) .53 .60 .02 (.37) .05 .96 Daily stressors x depression -.06 (.17) .37 .71 -.11 (.25) .44 .66 Caregiving x depression -.08 (.16) .52 .60 .05 (.25) .20 .84 Caregiving x statin use -.14 (.26) .52 .60 .12 (.37) .32 .75 Caregiving x daily stressors -.18 (.26) .67 .50 -.11 (.34) .33 .74 Step 4 .007 .010 Daily stressors x depression x statin .14 (.38) .37 .71 -.18 (.53) .26 .80 Daily stressors x caregiving x .34 (.28) 1.20 .23 .35 (.36) .97 .33 depression Caregiving x depression x statin .27 (.30) .89 .37 -.23(.44) .53 .60 Step 5 .009 <.001 Four-way interaction -.41 (.31) 1.34 .18 -.11 (.41) .26 .80 Table 12. Inflammation and interaction among daily stressors, caregiving status, depressive symptoms, and statin use.

145

Log10 IL-6 (N=154) Log10 CRP (N=178) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .227 .200 Age .007 (.002) 4.17 <.001 .003 (.002) .90 .37 Sex .07 (.05) 1.23 .22 .18 (.08) 2.25 .03 BMI .02 (.004) 5.11 <.001 .04 (.006) 6.04 <.001 Education -.02 (.03) -.73 .47 -.05 (.04) 1.21 .23 Marital status -.05 (.05) 1.21 .23 -.13 (.07) 1.86 .07 Life events -.006 (.009) .70 .49 -.01 (.01) 1.29 .20 Step 2 .001 .030 Caregiving status -.02(.05) .43 .67 .15 (.08) 2.02 .05 Step 3 <.001 .009 Stressors Severity .-.003(.02) .13 .90 .05 (.03) 1.43 .15 Table 13. Cumulative stressor severity and inflammation. The cumulative stressors severity was not computed for 5 participants because of incomplete interview data.

146

Zero or One Multiple F/ 2 p-value stressors stressors Smoking Status .23 .63 Smoker 7 3 Non-smoker 108 65 Weekly lcohol 1.57 .46 Intake 0 drink 52 37 1-3 drinks 33 15 4 + drinks 30 16 Recent alcohol use .27 .60 Non-users 81 33 Users 50 17 Weekly caloric .01 .96 expenditure in physical activity Mean (SE) 3379.86 (240) 3358.37 (312) Sleep quality .03 .85 Mean (SE) 5.53 (.27) 5.45 (.35) Recent sleep .94 .33 Mean (SE) 6.9 (.13) 7.1 (.16) BMI .07 .80 Mean (SE) 27.50 (.49) 27.71 (64) Health conditions .90 .64 None 47 23 1 36 24 2 or more 32 21 Table 14. Daily stressors as a function of health behaviors. Recent alcohol use refers to alcohol intake in the past 24 hours. Sleep quality refers to sleep quality in the past month. Recent sleep refers to the number of hours of sleep in the night prior to the study visit.

147 Log10 IL-6 (N=155) Log10 CRP (N=180) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .22 .194 Age .006 (.002) 3.72 <.001 .001 (.003) .41 .68 Sex .04 (.05) .79 .43 .11 (.08) 1.36 .18 BMI .02 (.004) 4.92 <.001 .03 (.006) 5.26 <.001 Education -.004 (.03) .15 .88 -.05 (.04) 1.09 .28 Marital status -.03 (.05) .57 .57 -.04 (.07) .56 .58 Life events -.006 (.008) .75 .46 -.002 (.013) .17 .87 Step 2 .10 .027 Chronic illness .04 (.03) 1.48 .14 .02 (.05) .49 .63 Smoking status .29 (.09) 3.18 .002 .05 (.15) .32 .75 Alcohol use -.05 (.03) 1.84 .07 -.07 (.04) 1.84 .07 Exercise -.001 (.008) .09 .93 -.002 (.009) .07 .97 Sleep quality .005 (.008) .59 .56 .01 (.01) .86 .39 Step 3 .005 .020 Caregiving status -.06 (.05) 1.21 .23 .11 (.08) 1.49 .14 Step 4 .005 .021 Daily stressors .05 (.05) 1.03 .31 .15 (.07) 2.19 .03

148 Table 15. Daily stressors, caregiving status, self-rated health, and usual health behaviors. Three participants had missing data on usual health behavior variables. Exercise refers to weekly caloric expenditure in physical activity.

148

Log10 IL-6 (N=155) Log10 CRP (N=181) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .236 .202 Age .006 (.002) 4.13 <.001 .003 (.002) 1.06 .29 Sex .06 (.05) 1.24 .22 .15 (.08) 1.86 .06 BMI .02 (.004) 5.30 <.001 .04 (.006) 5.88 <.001 Education -.01 (.03) .15 .88 -.04 (.04) 1.02 .31 Marital status -.07 (.05) 1.42 .16 -.11 (.07) 1.75 .08 Life events -.006 (.009) .76 .45 -.01 (.01) .94 .35 Step 2 .001 .005 Alcohol use <.001 (.05) .15 .88 -.06 (.07) .85 .40 Recent sleep .02 (.16) .30 .76 .03 (.02) 1.40 .67 Step 3 .001 .026 Caregiving status -.02 (.05) .31 .75 .13 (.07) 1.80 .06 Step 4 .001 .017 Daily stressors .01 (.05) .19 .85 .14 (.07) 1.93 .05 Table 16. Daily stressors, caregiving status, self-rated health, and recent health behaviors. Three participants were missing data on 149 recent health behaviors. Recent sleep refers to the number of hours of sleep in the past 24 hours.

149

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .225 .202 Age .006 (.002) 4.14 < .001 .003 (.002) 1.26 .21 Sex .09 (.05) 1.67 .10 .19 (.08) 2.31 .03 BMI .02 (.004) 5.24 <. 001 .04 (.006) 6.13 <.001 Education -.02 (.03) .82 .41 -.05 (.04) 1.30 .20 Marital status -.06 (.05) 1.21 .23 -.10 (.07) 1.38 .17 Life events -.006 (.009) .74 .46 -.01 (.01) .83 .41 Step 2 .005 .043 Caregiving status .03 (.05) .55 .58 .13 (.07) 1.77 .08 Statin use .09 (.07) 1.36 .18 .07 (.10) .66 .51 Daily stressors .08 (.05) 1.63 .11 .20 (.08) 2.61 .01 Step 3 .025 .012 Daily stressors x .25 .2.23 .03 -.26 (16) 1.65 .10 statin use Table 17. Daily stressors by statin use interaction and statin use. 150

150

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .222 .203 Age .007 (.002) 4.23 <.001 .003 (.002) 1.32 .19 Sex .10 (.06) 1.78 .08 .18 (.08) 2.20 .03 BMI .02 (.004) 5.40 <.001 .04 (.006) 6.24 <.001 Education -.03 (.03) .97 .33 -.05 (.04) 1.21 .23 Marital status -.03 (.05) .64 .53 -.11 (.07) 1.51 .13 Life events -.003 (.009) .39 .70 -.01 (.01) .77 .43 Step 2 .005 .045 Caregiving status -.07 (.07) 1.13 .26 .13 (.10) 1.27 .21 Statin use .06 (.07) .84 .41 .13 (.12) 1.08 .28 Daily stressors .02 (.07) .31 .76 .14 (.11) 1.28 .20 Step 3 .029 .022

151 Daily stressors x .002 (.18) .01 .99 -.20 (.24) .83 .41 statin use Caregiving status .07 (.13) .52 .60 -.20 (.21) .98 .33 x statin use Daily stressors x .13 (.10) 1.32 .10 .11 (.16) .71 .48 caregiving status Step 4 .016 <.001 Daily stressors x -.43 (.25) 1.76 .08 .01 (.34) .03 .98 caregiving status x statin use Table 18. Interaction among daily stressors, caregiving status, and statin use and inflammation.

151

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .226 .202 Age .006 (.002) 4.03 < .001 .003 (.002) 1.22 .23 Sex .07 (.05) 1.27 .21 .17 (.08) 2.09 .04 BMI .02 (.004) 4.93 <. 001 .04 (.006) 5.97 <.001 Education -.02 (.03) 1.27 .21 -.05 (.04) 1.17 .24 Marital status -.06 (.05) 1.16 .25 -.10 (.07) 1.49 .14 Life events -.005 (.009) .58 .46 -.01 (.01) .80 .43 Step 2 .003 .043 Caregiving status -.04 (.05) .72 .47 .12 (.07) 1.72 .09 NSAID use .03 (.06) .49 .63 -.03(.09) .29 .77 Daily stressors .06 (.05) 1.18 .24 .14 (.08) 1.73 .09 Step 3 .005 .001 Daily stressors x -.10 (.10) .99 .32 .01 (14) .08 .94 NSAID use Table 19. Daily stressors by NSAID use interaction. 152

152

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .226 .202 Age .006 (.002) 4.11 < .001 .004 (.002) 1.61 .11 Sex .06 (.05) 1.17 .25 .13 (.08) 1.63 .10 BMI .02 (.004) 5.06 <. 001 .04 (.006) 5.83 <.001 Education -.02 (.03) .72 .47 -.05 (.04) 1.20 .23 Marital status -.06 (.05) 1.22 .23 -.10 (.07) 1.41 .16 Life events -.006 (.009) .65 .52 -.01 (.01) .90 .37 Step 2 .003 .060 Caregiving status -.04 (.05) .72 .47 .11 (.07) 1.51 .13 Estrogen use .01 (.08) .15 .88 .19 (.13) 1.40 .16 Daily stressors .04 (.05) .82 .41 .08 (.05) 1.69 .09 Step 3 <.001 .002 Daily stressors x -.04 (.13) .27 .79 .13 (20) .65 .52 estrogen use Table 20. Daily stressors by estrogen medication use interaction. 153

153

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .226 .202 Age .007 (.002) 4.36 < .001 .003 (.002) 1.22 .22 Sex .06 (.05) 1.20 .23 .16 (.08) 2.03 .04 BMI .02 (.004) 4.72 <. 001 .04 (.006) 5.50 <.001 Education -.02 (.03) .77 .44 -.05 (.04) 1.22 .22 Marital status -.05 (.05) 1.14 .26 -.10 (.07) 1.41 .16 Life events -.006 (.009) .68 .50 -.01 (.01) .87 .39 Step 2 .009 .037 Caregiving status -.04 (.05) .87 .38 .12 (.07) 1.67 .10 Antidepressant use .13 (.07) 2.04 .04 .09 (.10) .82 .42 Daily stressors .08 (.05) 1.60 .11 .10 (.05) 2.09 .04 Step 3 .020 .006 Daily stressors x -21 (.11) 2.01 .05 -.18 (15) 1.20 .23 antidepressants use Table 21. Daily stressors by antidepressant medication use interaction. 154

154

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .222 .203 Age .006 (.002) 3.87 < .001 .002 (.002) .90 .37 Sex .08 (.05) 1.53 .13 .17 (.08) 2.09 .04 BMI .02 (.004) 4.50 <. 001 .04 (.006) 5.54 <.001 Education -.02 (.03) .85 .40 -.05 (.04) 1.31 .19 Marital status -.05 (.05) 1.12 .26 -.12 (.07) 1.78 .08 Life events -.006 (.009) .69 .49 -.01 (.01) .91 .37 Step 2 .015 .052 Caregiving status -.04 (.05) .85 .40 .13 (.07) 1.77 .08 Inflammation-related .14 (.05) 2.64 .009 .09 (.09) 1.62 .11 medication use Daily stressors .17 (.07) 2.54 .01 .10 (.05) 1.99 .05 Step 3 .035 .003 Daily stressors x -.24 (.09) 2.66 .009 -.10 (12) .90 .37 Inflammation-related medication use Table 22. Daily stressors by inflammation-related medication use interaction and inflammation.

155

Log10 IL-6 (N=158) Log10 CRP (N=183) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .222 .203 Age .006 (.002) 3.68 <.001 .002 (.002) .66 .51 Sex .08 (.05) 1.50 .14 .16 (.08) 2.02 .05 BMI .02 (.004) 4.55 <.001 .04 (.006) 5.76 <.001 Education -.02 (.03) .72 .47 -.05 (.04) 1.25 .22 Marital status -.05 (.05) 1.15 .25 -.13 (.07) 1.85 .07 Life events -.008 (.009) .88 .38 -.02 (.01) 1.29 .20 Step 2 .015 .052 Caregiving status .006 (.09) .07 .94 .31 (.14) 2.22 .03 Inflammation-related .17 (.06) 2.74 .007 .25 (.10) 2.52 .01 medication use Daily stressors .19 (.09) 2.05 .04 .26 (.15) 1.69 .09

156 Step 3 .038 .014 Daily stressors x -.31 (.12) 2.48 .01 -.28 (.19) 1.42 .16 Inflammation-related medication use Caregiving status x -.11 (.11) .97 .33 -.37 (.18) 2.08 .04 Inflammation-related medication use Daily stressors x -.05 (.15) .35 .72 -.17 (.22) .78 .44 caregiving status Step 4 .004 .011 Daily stressors x .17 (.19) .92 .36 .44 (.28) 1.58 .12 caregiving status x Inflammation-related medication use Table 23. Interaction among daily stressors, caregiving status, and inflammation-related medication use and inflammation.

156

Log10 IL-6 (N=118) Log10 CRP (N=135) Predictor (SE) t p ∆R2 (SE) t p ∆R2 Step 1 .086 Age .004 (.002) 2.29 .02 .002 (.003) .51 .61 Sex .13 (.07) 1.83 .07 .27 (.08) 2.54 .01 Hip-to-waist .76 (.35) 2.21 .03 1.22 (.53) 2.33 .02 Ratio Education -.01 (.03) .40 .69 -.05 (.05) .89 .37 Marital status -.05 (.06) .74 .46 .001 (.09) .01 .99 Self-reported .001 (.004) .24 .81 .001 (.006) .22 .83 chronic stress Step 2 .002 Caregiving status -.07 (.07) .90 .27 .03 (.10) .25 .80 Step 3 .06 (.06) .020 Daily stressors .98 .33 .14 (.09) 1.67 .10

157 Table 24. Daily stressors, caregiving status, perceived stress, hip-to-waist ratio and inflammation.

157

Appendix B: Figures

158

1.6

1.4

1.2

1 Controls 0.8 Caregivers 0.6

Log10 IL-6 Log10 (pg/ml) 0.4

0.2

0 Caregiving Status

Figure 1. IL-6 as a function of caregiving status. Means were adjusted for age, sex, BMI, education, and marital status. Error bars represents the 95% confidence interval of the mean.

159

0.5

0.4

0.3 Controls

0.2 Caregivers

0.1 Log10 CRP levels (mg/L)CRP Log10 0 Caregiving Status

Figure 2. CRP as a function of caregiving status. Means were adjusted for age, sex, BMI, education, and marital status. Error bars represents the 95% confidence interval of the mean.

160

umberof

40 Zero Stressors 30 One Stressor Multiple Stressors

daily stressors daily 20

10 Percentageof the total n 0 Caregivers Controls Full Sample

Figure 3. Number of daily stressors in the past 24 hours as a function of caregiving status.

161

1.2

0.8

Controls 0.6 Caregivers

0.4

0.2

Log10 Depressive Symptoms Score Depressive Log10 0 Caregiving Status

Figure 4. Depressive symptoms as function of caregiving status. Depressive symptoms were assessed using the CESD. Means were adjusted for age, sex, and marital status.

Error bars represents the 95% confidence interval of the mean.

162

15 Controls Caregivers 10

Self-reported Chronic Stress Score Chronic Stress Self-reported 0

Caregiving Status

Figure 5. Self-reported chronic stress as a function of caregiving status. Self-reported chronic stress was assessed using the TICS. Means were adjusted for age, sex, and marital status. Error bars represents the 95% confidence interval of the mean.

163

5 Controls 4 Caregivers 3

2 Poor Sleep Quality 1

0 Caregiving Status

Figure 6. Sleep quality in the past month as a function of caregiving status. Sleep quality was assessed with the PSQ. Higher scores represent poorer sleep quality. Means were adjusted for age, sex, and marital status. Error bars represents the 95% confidence interval of the mean.

164

1.4

1.2

) 1

0.8 Zero stressors 6 (pg/ml 6 - One stressor 0.6 Multiples stressors

0.4 Log10IL 0.2 0 Number of Daily Stressors

Figure 7. IL-6 as a function of the number of daily stressors in the past 24 hours. Means were adjusted for age, sex, education, and marital status. Error bars represents the 95% confidence interval of the mean.

165

0.5

0.45

0.4 0.35 0.3 Zero Stressors 0.25 One Stressor

0.2 Multiple Stressors

Log10CRP (mg/L)

0.15 0.1 0.05 0 Number of Daily Stressors

Figure 8. CRP as a function of the number of daily stressors in the past 24 hours. Means were adjusted for age, sex, education, and marital status. Error bars represents the 95% confidence interval of the mean.

166

0.5

0.45 0.4 0.35 0.3 Zero or One Stressors 0.25 Multiple Stressors 0.2 0.15

0.1 Log10CRP levels (mg/L) 0.05 0 Number of Daily Stressors

Figure 9. CRP as a function of the number of daily stressors in the past 24 hours after adjusting for caregiving status. Means were adjusted for age, sex, education, marital status, major life events, and caregiving status. Error bars represents the 95% confidence interval of the mean.

167

Figure 10. Schematic representation of the role of daily stressors as a partial mediator of the relationship between caregiving stress and CRP. All relationships depicted are significant at a p <.05 level, except the path from caregiving stress to CRP in the mediation model, p = .09.

168

0.4

0.3

Non-Statin 0.2 Users

Statin Users Log10 IL-6 (pg/ml) Log10IL-6

0.1

0 Low Depressive Symptoms High Depressive Symptoms

Figure 11. Moderation effect of statin use on the relationship between depressive symptoms and IL-6. Low and high depressive symptoms represent one standard deviation below and above the mean respectively.

169

1.8

1.6

1.4

1.2

1 Non-Statin Users 0.8 Statin Users

0.6 Log10CRP (mg/L) 0.4

0.2

0 Low Depressive Symptoms High Depressive Symptoms

Figure 12. Moderation effect of statin use on the relationship between depressive symptoms and CRP. Low and high depressive symptoms represent one standard deviation below and above the mean respectively.

170

1.6

1.4

1.2 Zero or One Stressors 1

Multiple Stressors 6 (pg/ml) 6

- 0.8

0.6

Log10 IL Log10 0.4

0.2

0 No Statin Statin Use

Figure 13. Moderating effect of statin use on the relationship between daily stressors and

IL-6. Means were adjusted for age, sex, education, marital status, major life events, and caregiving status. Error bars represents the 95% confidence interval of the mean.

171

1.8 1.6 Controls who reported zero or one stressors

1.4

1.2 Caregivers who reported zero or one stressors

6 (pg/ml) 6 - 0.8 Controls who reported 0.6 multiple stressors

Log10 IL Log10 0.4 0.2 Caregivers who reported multiple stressors 0 Non-statin use Statin use

Figure 14. IL-6 as a function of the interaction among daily stressors, caregiving status, and statin use. Means were adjusted for age, sex, education, marital status, major life events, and caregiving status. Error bars represents the 95% confidence interval of the mean.

172

1.6

1.4

1.2 Zero or one

1 stressors 6 (pg/ml) 6 - 0.8 Multiple stressors 0.6

0.4 Log10IL 0.2 0 No Yes Inflammation-related medication use

Figure 15. IL-6 as a function of the daily stressors by inflammation-related medication use. Means were adjusted for age, sex, education, marital status, major life events, and caregiving status. Error bars represents the 95% confidence interval of the mean.

173

0.6

0.5

0.4 Noncaregiving Controls 0.3 Spousal Caregivers Parental Caregivers 0.2

Log10 CRP levels CRP Log10 (mg/L) 0.1

Figure 16. Differences in CRP among noncaregiving controls, spousal caregivers and parental caregivers. Means were adjusted for age, sex, education, marital status, major life events, and caregiving status. Error bars represents the 95% confidence interval of the mean.

174

Figure 17. Schematic representation of the role of daily stressors as partial mediators of the relationship between parental caregiving and CRP. All relationships depicted are significant at a p <.05 level, except the path from caregiving stress to CRP in the mediation model, p = .12

175

0.9

0.8 Not-Married 0.7 Married 0.6 0.5 0.4 0.3

0.2 Log10CRP levels (mg/L) 0.1 0 Controls Spousal CG Parental CG

Figure 18. CRP as a function of the caregiving by marital status interaction. The acronym

CG stands for caregivers. Means were adjusted for age, sex, education, and marital status.

Error bars represents the 95% confidence interval of the mean.

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1.5 1.4 High School Degree 1.3 College Degree 1.2 Graduate Degree

6 (pg/ml) 6 1.1 - 1 0.9

Log10IL 0.8 0.7 0.6 Controls Caregivers

Figure 19. IL-6 as a function of the interaction of caregiving status and education. Means were adjusted for age, sex, education, and marital status. Error bars represents the 95% confidence interval of the mean.

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Appendix C: S emi-structured interviews

178

Daily Inventory of Stressful Events (DISE)

The DISE is a semi-structured instrument consisting of three components: (1) a list of seven 'stem' questions which pertain to occurrences of stressful events in various life domains, (2) a series of open- ended 'probe' questions that ascertain a description of the stressful event, and (3) a list of structured 'stake' questions, inquiring about aspects of the Respondent's life that were 'at risk' because of the event. An affirmative response to the stem questions prompts the interviewer to probe for a detailed description of the event, which is followed by questions pertaining to "what was at stake" for the Respondent as a result of the event.

Stem Questions

Yes No F1. Did you have an argument or disagreement with 1 5 anyone since this time yesterday?

F2. Since (this time/we spoke) yesterday, did anything 1 5 happen that you could have argued about but you decided to let pass in order to avoid a disagreement?

F3. Since (this time/we spoke) yesterday, did anything 1 5 happen at work or school (other than what you've already mentioned, that most people would consider stressful?

F4. Since (this time/we spoke) yesterday, did anything 1 5 happen at home (other than what you've already mentioned,) that most people would consider stressful?

F5. Many people experience discrimination on the basis of 1 5 such things as race, sex, or age. Did anything like this happen to you since (this time/we spoke) yesterday?

F6. Since (this time/we spoke) yesterday, did anything happen 1 5 to a close friend or relative (other than what you've already mentioned) that turned out to be stressful for you?

F7. Did anything else happen to you since (this time/ we spoke) 1 5 yesterday that most people would consider stressful?

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Probes for Description Ask only if “yes” for following G1. Think of the most stressful disagreement or F1 argument you had since (this time/we spoke) yesterday. Who was that with?

G2. When did that happen? Was that some time ALL yesterday or today?

G3. What happened? F1, F5

G4. Think of the most stressful incident of this sort. F2 Who was the person you decided not to argue with?

G5. What happened and why did you decide not to F2 get into an argument about it?

G6. What happened and what about it would most F3, F4 people consider stressful?

G7. Think of the most stressful incident of this sort. F5 What was the basis for the discrimination you experienced -- your race, sex, age, or something else?

G8. Think of the most stressful incident of this sort. F6 Who did this happen to?

G9. What happened and what about it was stressful F6 for you?

G10. Think of the most stressful incident of this sort. F7 What happened and what about it would most people consider stressful?

G11. How does this affect your job? F3

G12. What kinds of things were said? F1, F2

G13. Have you had any problems with this in the past? All

G14. How long has this been going on? All

G15. Does this happen often? All

G16. Was there anything out of the ordinary in this? All G17. How stressful was this for you -- All

1. Very------>GO TO STAKE QUESTIONS 2. Somewhat--->GO TO STAKE QUESTIONS 3. Not very----->GO TO NEXT STEM QUESTION 4. Not at all---->GO TO NEXT STEM QUESTION 180

Stake Questions a lot/ some/ a little/ not at all H1. How much were the following things at risk in 1 2 3 4 this situation: First, how much did it risk disrupting your daily routine -- a lot, some, a little, or not at all?

H2. How much did it risk your financial situation? 1 2 3 4

H3. How much did it risk the way you feel about 1 2 3 4 yourself?

H4. How much did it risk the way other people feel 1 2 3 4 about you?

H5. How much did it risk your physical health or 1 2 3 4 safety?

H6. How much did it risk the health or well-being 1 2 3 4 of someone you care about?

H7. How much did it risk your plans for the future? 1 2 3 4

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PERI In each category listed below, please indicate if you have experienced any of the events listed in the PAST YEAR.

SCHOOL 1. Started school or a training program after not going to school for a long time: No Yes 2. Changed schools or training programs: No Yes 3. Graduated from school or training program: No Yes 4. Had problems in school or in training program: No Yes 5. Failed school or training program: No Yes 6. Did not graduate from school or training program: No Yes

WORK 7. Started work for the first time: No Yes 8. Returned to work after not working for a long time: No Yes 9. Changed jobs for a better one: No Yes 10. Changed jobs for a worse one: No Yes 11. Changed jobs for one that was no better and no worse than the last one: 12. Had trouble with boss: No Yes 13. Demoted at work: No Yes 14. Found out you were not going to be promoted at work: No Yes 15. Conditions at work got worse, other than demotion or trouble with boss: No 16. Promoted: No Yes 17. Had significant success at work: No Yes 18. Conditions at work improved, not counting promotion or other personal Issues 19. Laid off: No Yes 20. Fired: No Yes 21. Started a business or profession: No Yes 22. Expanded a business or professional practice: No Yes 23. Took on a greatly increased workload: No Yes 24. Suffered a business loss or failure: No Yes 25. Sharply reduced workload: No Yes 26. Retired: No Yes 27. Stopped working, not retirement, for an extended period: No Yes

LOVE AND MARRIAGE 28. Became engaged: No Yes 29. Engagement was broken: No Yes 30. Married: No Yes 31. Started a love affair: No Yes 32. Relations with spouse changed for the worse, without separation or divorce: No Yes 33. Separated: No Yes 34. Divorced: No Yes 35. Relations with spouse changed for the better: No Yes 36. Got together again with spouse after separation: No Yes 37. Marital infidelity: No Yes 38. Trouble with in-laws: No Yes 39. Spouse died: No Yes

HAVING CHILDREN 40. Became pregnant: No Yes 41. Birth of first child: No Yes 42. Birth of second or later child: No Yes 182

43. Abortion: No Yes 44. Miscarriage or stillbirth: No Yes 45. Found out that cannot have children: No Yes 46. Child died: No Yes 47. Adopted a child: No Yes 48. Started menopause: No Yes

FAMILY 49. New person moved into household: No Yes 50. Person moved out of household: No Yes 51. Someone stayed on in the household after he was expected to leave: No Yes 52. Serious family argument other than with spouse: No Yes 53. A change in the frequency of family get-togethers: No Yes 54. Family member other than spouse or child dies: No Yes 55. Moved to a better residence or neighborhood: No Yes

RESIDENCE 56. Moved to a worse residence or neighborhood: No Yes 57. Moved to a residence or neighborhood no better or worse than the last one: No Yes 58. Unable to move after expecting to be able to move: No Yes 59. Built a home or had one built: No Yes 60. Remodeled a home: No Yes 61. Lost a home due to fire, flood, or other disaster: No Yes

CRIME AND LEGAL MATTERS 62. Assaulted: No Yes 63. Robbed: No Yes 64. Accident in which there were no injuries: No Yes 65. Involved in a lawsuit: No Yes 66. Accused of something for which a person could be sent to jail: No Yes 67. Lost drivers license: No Yes 68. Arrested: No Yes 69. Went to jail: No Yes 70. Got involved with a court case: No Yes 71. Convicted of a crime: No Yes 72. Acquitted of a crime: No Yes 73. Released from jail: No Yes 74. Didn't get out of jail when expected: No Yes

FINANCES 75. Took out a mortgage: No Yes 76. Started buying a car, furniture, or other large purchase on an installment plan:N Y 77. Foreclosure of a mortgage or loan: No Yes 78. Reposession of a car, furniture, or other items bought on installment plan: No Yes 79. Took a cut in wage or salary without a demotion: No Yes 80. Suffered a financial loss or loss of property not related to work: No Yes 81. Went on welfare: No Yes 82. Went off of welfare: No Yes 83. Got a substantial increase in wage or salary without a demotion: No Yes 84. Did not get an expected wage or salary increase: No Yes 85. Had financial improvement not related to work: No Yes

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SOCIAL ACTIVITIES 86. Increased church or synagogue, club, neighborhood, or other organizational activities: No Yes 87. Took a vacation: No Yes 88. Was not able to take a planned vacation: No Yes 89. Took up a new hobby, sport, craft, or recreational activity: No Yes 90. Dropped a hobby, sport, craft, or recreational activity: No Yes 5551 91. Acquired a pet: No Yes 92. Pet died: No Yes 93. Made new friends: No Yes 94. Broke up with a friend: No Yes 95. Close friend died: No Yes

MISCELLANEOUS 96. Entered the armed services: No Yes 97. Left the armed services: No Yes 98. Took a trip other than a vacation: No Yes

HEALTH 99. Physical health improved: No Yes 100. Physical illness: No Yes 101. Injury: No Yes 102. Unable to get treatment for illness or injury: No Yes

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Appendix D: Self-reported questionnaires

185

Background Questionnaire

Please list your date of birth: month _ _ day _ _ year _ _ _ _

What is your gender?: Male___ Female____

Which of the following would you say is your race? (Please mark ALL that apply): White Hispanic or Latino Black or African American Asian Native Hawaiian or Other Pacific Islander American Indian, Alaska Native Other: (please specify): Don't know/Not sure

What is the highest level of education you have completed? Graduate or professional training (Masters, JD, MD, PhD, etc.) College or University Graduate Some college High School Some high school Junior high school Less than 7 years

Are you currently employed? Yes _____ No____

Health Behaviors

Do you smoke or use nicotine? No___ Yes___

How many alcoholic drinks (12 ounces of beer, 4 ounces of wine, or 1 ounce of hard liquor)do you normally consume in one week? ______

How many alcoholic drinks have you consumed in the past 24 hours? ___ What time did you get into bed last night? ______

What time did you get out of bed for the final time this morning? ______

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Older Adults Resource Center Scale (OARS) Multidimensional Functional Assessment Questionnaire Please answer the following questions regarding your health history. If you select yes for any question, please give a brief description of the condition (when you were diagnosed, current symtpoms, etc.), and any important dates in regards to the condition:

Have you had rheumatic fever or rheumatic heart disease? No / Yes; please explain and provide date:

Have you had any heart or blood vessel disease such as a heart attack or stroke? No / Yes; please explain and provide date:

Do you have or have you had a heart murmur or mitral valve prolapse? No / Yes; please explain and provide date:

Have you been told that your blood pressure is too high? No / Yes; please explain and provide date:

Have you ever had respiratory failure? No / Yes; please explain and provide date:

Have you ever had a bronchospasm? No / Yes; please explain and provide date:

Have you ever had chest pain or angina? No / Yes; please explain and provide date:

Have you been treated for a seizure disorder (convulsions or epilepsy)? No / Yes; please explain and provide date:

Have you had a tumor or disease that required x-ray, radium or cobalt treatments? No / Yes; please explain and provide date:

Have you had excessive or prolonged bleeding following a cut, tooth extraction, or injury? No / Yes; please explain and provide date:

Have you had any allergic or unusual reactions to any drugs, medications, bandages, or plastic? No / Yes; please explain and provide date:

Have you ever been hospitalized or had surgery (including chemotherapy and radiation)? No / Yes (If yes, please explain and include date)

To the best of your knowledge, do you have or have you ever had any hormone or immunological problems? No / Yes (If yes), please explain and include date.

Are you currently taking any: antibiotics? No / Yes cortisone? No / Yes medications for glaucoma? No / Yes medication for depression? No / Yes blood thinners? No / Yes tranquilizers or other medications for your nerves? No / Yes 187

Below is a short list of illnesses and physical problems. Please indicate if you have any of these illnesses. If you do, please indicate how much the illness or physical problem interferes with your day to day life, and whether or not you take medication as treatment.

Rheumatoid Arthritis N / Y (If yes) Interferes? not at all / some / a lot Medications:

Asthma N / Y (If yes) Interferes? not at all / some / a lot Medications:

Emphysema N / Y (If yes) Interferes? not at all / some / a lot Medications:

High Blood Pressure N / Y (If yes) Interferes? not at all / some / a lot Medications:

Heart Trouble N / Y (If yes) Interferes? not at all / some / a lot Medications:

Diabetes N / Y (If yes) Interferes? not at all / some / a lot Medications:

Liver Disease N / Y (If yes) Interferes? not at all / some / a lot Medications:

Kidney Disease N / Y (If yes) Interferes? not at all / some / a lot Medications:

Hormone Problems N / Y (If yes) Interferes? not at all / some / a lot Medications:

Cancer N / Y (If yes) Interferes? not at all / some / a lot Medications: 188

Stroke N / Y (If yes) Interferes? not at all / some / a lot Medications:

Thyroid Problems N / Y (If yes) Interferes? not at all / some / a lot Medications:

Muscle Disorder, e.g. MS, Post-Polio N / Y (If yes) Interferes? not at all / some / a lot Medications:

Depression N / Y (If yes) Interferes? not at all / some / a lot Medications:

Anxiety N / Y (If yes) Interferes? not at all / some / a lot Medications:

Have you ever had a skin cancer removed? N / Y

Medications Please list in the box below any other medications (either prescription or over-the-counter) that you are currently taking. If you are not currently taking medications, please write "none" below: ______

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PSQ

During the past month, when have you usually gone to bed at night? ______

During the past month, how long (in minutes) has it usually taken you to fall asleep each night?______

During the past month, when have you usually gotten up in the morning? ______

During the past month, how many hours of actual sleep did you get at night? ______(This may be different from the amount of hours you spend in bed.)

During the past month, how often have you had trouble Not Less than Once or Three or sleeping because you... during the once a twice a more times a past week week week month (a) Cannot get to sleep within 30 minutes (b) Wake up in the middle of the night or early morning (c) Have to get up to use the bathroom (d) Cannot breathe comfortably (e) Cough or snore loudly (f) Feel too cold (g) Feel too hot (h) Had bad dreams (i) Have pain (j) Other reason(s), please describe______

During the past month, how would you rate your sleep quality overall?

Very good Fairly good Fairly bad Very bad

During the past month, how often have you taken medicine (prescribed or "over the counter") to help you sleep?

Never Less than once a week once or twice a week three or more times a week

During the past month, how often have you had trouble staying awake while driving, eating meals, or engaging in social activities?

Never Less than once a week once or twice a week three or more times a week

During the past month, how much of a problem has it been for you to keep up enough enthusiasm to get things done?

Not a problem a very slight problem somewhat of a problem a very big problem

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CHAMPS This questionnaire is about activities that you may have done in the past 4 weeks. The questions on the following pages are similar to the example shown below.

INSTRUCTIONS:

If you DID the activity in the past 4 weeks: Step #1 Check the YES box. Step #2 Think about how many TIMES a week you usually did it, and write your response in the space provided. Step #3 Circle how many TOTAL HOURS in a typical week you did the activity.

In a typical week during the past 4 weeks, did you …

1. Visit with friends or family How many Less 9 or (other than those you live TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more with)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

2. Go to the senior center? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

3. Do volunteer work? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

4. Attend church or take part How many Less 9 or in church activities? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

5. Attend other club or group How many Less 9 or meetings? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

191

In a typical week during the past 4 weeks, did you …

6. Use a computer? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

7. Dance (such as square, How many Less 9 or folk, line, ballroom) (do not TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more count aerobic dance here)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

8. Do woodworking, How many Less 9 or needlework, drawing, or other TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more arts or crafts? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

9. Play golf, carrying or How many Less 9 or pulling your equipment (count TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more walking time only)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

10. Play golf, riding a cart How many Less 9 or (count walking time only)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

11. Attend a concert, movie, How many Less 9 or lecture, or sport event? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

192

In a typical week during the past 4 weeks, did you …

12. Play cards, bingo, or How many Less 9 or board TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more games with other people? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

13. Shoot pool or billiards? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

14. Play singles tennis (do not How many Less 9 or count doubles)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

15. Play doubles tennis (do How many Less 9 or not count singles)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

16. Skate (ice, roller, in-line)? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

17. Play a musical How many Less 9 or instrument? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

18. Read? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

193

In a typical week during the past 4 weeks, did you …

19. Do heavy work around the How many Less 9 or house (such as washing TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more windows, cleaning gutters)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

20. Do light work around the How many Less 9 or house (such as sweeping or TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more vacuuming)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

21. Do heavy gardening (such How many Less 9 or as spading, raking)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

22. Do light gardening (such How many Less 9 or as watering plants)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

23. Work on your car, truck, How many Less 9 or lawn mower, or other TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more machinery? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

**Please note: For the following questions about running and walking, include use of a treadmill.

24. Jog or run? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

194

In a typical week during the past 4 weeks, did you …

25. Walk uphill or hike uphill How many Less 9 or (count only uphill part)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

26. Walk fast or briskly for How many Less 9 or exercise (do not count TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more walking leisurely or uphill)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

27. Walk to do errands (such How many Less 9 or as to/from a store or to take TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more children to school (count walk a week did you 1 hours hours hours hours hours time only)? usually do it? hour YES How many TIMES a  week?_____  NO

28. Walk leisurely for How many Less 9 or exercise or pleasure? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

29. Ride a bicycle or How many Less 9 or stationary cycle? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

30. Do other aerobic machines How many Less 9 or such as rowing, or step TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more machines (do not count a week did you 1 hours hours hours hours hours treadmill or stationary cycle)? usually do it? hour YES How many TIMES a  week?_____  NO

195

In a typical week during the past 4 weeks, did you …

31. Do water exercises (do not How many Less 9 or count other swimming)? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

32. Swim moderately or fast? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

33. Swim gently? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

34. Do stretching or flexibility How many Less 9 or exercises (do not count yoga TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more or Tai-chi)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

35. Do yoga or Tai-chi? How many Less 9 or YES How many TIMES a TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more week?_____  a week did you 1 hours hours hours hours hours usually do it? hour NO 

36. Do aerobics or aerobic How many Less 9 or dancing? TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more YES How many TIMES a a week did you 1 hours hours hours hours hours week?_____  usually do it? hour  NO

37. Do moderate to heavy How many Less 9 or strength training (such as TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more hand-held weights of more a week did you 1 hours hours hours hours hours than 5 lbs., weight machines, usually do it? hour or push-ups)?  YES How many TIMES a week?_____  NO 196

In a typical week during the past 4 weeks, did you …

38. Do light strength training How many Less 9 or (such as hand-held weights of TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more 5 lbs. or less or elastic bands)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

39. Do general conditioning How many Less 9 or exercises, such as light TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more calisthenics or chair exercises a week did you 1 hours hours hours hours hours (do not count strength usually do it? hour training)?  YES How many TIMES a week?_____  NO

40. Play basketball, soccer, or How many Less 9 or racquetball (do not count time TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more on sidelines)? a week did you 1 hours hours hours hours hours YES How many TIMES a usually do it? hour week?_____   NO

41. Do other types of physical How many Less 9 or activity not previously TOTAL hours than 1-2½ 3-4½ 5-6½ 7-8½ more mentioned (please specify)? a week did you 1 hours hours hours hours hours usually do it? hour ______ __ YES How many TIMES a week?_____  NO

197

Center for Epidemiologic Studies Depression Scale (CESD) Please read each statement and then indicate how many days you felt or behaved this way in the past week by putting an X in the appropriate box:

Less 1-2 3-4 5-7 than days days days 1 day 1. I was bothered by things that usually don't bother me. 2. I did not feel like eating; my appetite was poor. 3. I felt that I could not shake off the blues even with help from my family or friends. 4. I felt that I was just as good as other people. 5. I had trouble keeping my mind on what I was doing. 6. I felt depressed. 7. I felt that everything I did was an effort. 8. I felt hopeful about the future. 9. I thought my life had been a failure. 10. I felt fearful. 11. My sleep was restless. 12. I was happy. 13. I talked less than usual. 14. I felt lonely. 15. People were unfriendly. 16. I enjoyed life. 17. I had crying spells. 18. I felt sad. 19. I felt that other people dislike me. 20. I could not get going.

198

Trier Inventory of Chronic Stress (TICS) On the following pages you will find descriptions of situations and experiences. Please answer how often each event has happened to you in the LAST 3 MONTHS, using the scale provided:

Never Rarely Some Often Very times often 1. I have to postpone much needed rest. 2. I receive too little appreciation for my accomplishments. 3. I make too many mistakes because what I have to do demands too much of me. 4. I do not have enough time to perform my daily tasks. 5. I must perform tasks that seem nonsensical to me. 6. I have differences of opinion with others that lead to tension. 7. I have work to do that involves carrying a lot of responsibility for other people. 8. I have encountered situations in which I must make an effort to win others' trust. 9. I worry that something unpleasant will happen. 10. My daily tasks are not interesting. 11. I have had times when I am lonely. 12. I have experienced situations when I must take pains to have a good relationship with others. 13. I have to perform tasks I do not enjoy. 14. I have tasks to perform during which I am being critically observed. 15. I have conflicts with others because they have different goals. 16. I have had times when I cannot suppress worrisome thoughts. 17. I have had times when so many business appointments accumulate I can barely get caught up. 18. I try in vain to gain recognition for my good work. 21. I have times when none of my tasks seem meaningful to me. 22. I have work to do that must not disappoint others. 23. I have to try to make a good impression with people. 24. I have had times when I can no longer cope with the demands of my work. 25. I have had times when my worries overwhelm me. 26. I have conflicts with others because I do not act the way they expect me to. 27. I have had times when I must work under strict deadlines. 28. I have to deal with other peoples' problems too much. 199

Never Rarely Some Often Very times often

29. I have had times when I do not have the opportunity to share my thoughts and feelings with others. 30. I have experienced situations in which it depends entirely on me if a relationship with another person develops satisfactorily. 31. Although I do my best, my work is not appreciated. 32. I have tasks to fulfill that pressure me to prove myself. 33. I have conflicts with others because they meddle too much in my affairs. 34. I have had times when I am isolated from other people. 35. I have had times when I am not able to perform as well as expected. 36. I have had times when I worry a lot and cannot stop. 37. I object to duties that I must fulfill. 45. I have arguments with people that lead to long- lasting conflicts. 46. I am not adequately rewarded for my efforts. 47. I worry that I will not be able to fulfill my tasks. 48. I must do work that does not take advantage of my abilities. 49. I have had situations in which the well being of others depends on how well I work. 50. I have too many tasks to perform. 51. I have had times when I miss having contact with others. 52. I have unnecessary conflict with others. 53. I have had times when I have no tasks that make me happy. 54. I experience having too much to do. 55. Although I try, I do not fulfill my duties as I should. 56. I have had times when I have no friends to do things with.

57. I have had times when my responsibility for others becomes a burden to me.

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Blessed Dementia Scale (BDS) The BDS is a caregiver interview during which questions about the patient are asked. The goal of the interview, is to detect how much change has occurred in the memory, behavior, and personality of the patient over the course of the disease. The Interviewer is to fill in the circle beside the number corresponding to the the description of the patient. At the end, a total score is calculated.

A. Changes in Patient Memory

1. Ability to perform household tasks: Large Some None 2. Ability to cope with small sums of money: Large Some None 3. Ability to remember a short list of items: Large Some None (for example, shopping) : Large Some None 4. Ability to find way about indoors: Large Some None 5. Ability to find way about familiar streets: Large Some None 6. Ability to interpret surroundings (Recognize whether in a hospital or at home; discriminate between people, ) : Large Some None 7. Ability to recall recent events: Large Some None 8. Tendency to dwell in the past: Large Some None

B. Changes in Patient Hygiene

9. Eating: please select one: Patient eats with proper utensils, can use knife and fork effectively Patient needs some assistance in eating (for example, needs to have food cut, etc.) Patient rarely uses utensils, eats with fingers, can only eat certain foods Patient cannot feed self, has to be fed

10. Dressing: please select one: Patient dresses self unaided Patient has problems with buttons, zippers Patient needs to have clothes laid out, forgets items (socks, underwear), puts items on in wrong order or inside out Patient unable to dress self, needs to be dressed

11. Bladder and Bowel Control: please select one: Patient has normal complete control Patient occasionally wets self (1-4 times per month) Patient frequently wets self (once a week or more) Patient lacks bladder and bowel control, wets self daily

C. Changes in Patient Personality and Interests

12. Increased rigidity, diminished flexibility (for example,patient deals with matters in a fixed or stereotyped manner) Change Present / Change Absent

13. Increased self-concern, self-focus, or self-centeredness Change Present / Change Absent

14. Impaired or diminished regard for the feelings of others Change Present / Change Absent

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15. Coarsening of emotion (being rude, rough, unrefined) Change Present / Change Absent

16. Impairment of emotional control (bouts of crying, bursts of anger, or any loss of control) Change Present / Change Absent

17. Laughing or smiling at inappropriate times Change Present / Change Absent

18. Diminished emotional responsiveness, patient reacts little or not at all emotionally (never smiles, etc.) Change Present / Change Absent

19. Sexual misbehavior or inappropriateness (undressing in front of others, making inappropriate advances or comments) Change Present / Change Absent

20. Hobbies given up because patient can no longer do them or has lost interest in them Change Present / Change Absent

21. Diminished initiative, growing apathy (sitting around, not starting or doing anything) Change Present / Change Absent

22. Purposeless hyperactivity, excessive activity Change Present / Change Absent

Questions regarding the characteristics of the caregiving experience

When was the person you are caregiving for first diagnosed with dementia? Month:_ _ Year: _ _ _ _

When did you first begin the role of a caregiver for this particular person? Month:_ _ Year: _ _ _ _

Up through the present, how long have you been caregiving for this particular person? Years: _ _ _ _ Months:_ _

Where does your loved one currently live? Your home Home of another family member Alone Nursing home Hospital

At present, how much time do you spend each day in activities related to caring for your loved one (on average)? This can include direct care, such as direct assistance with routine caregiving tasks, or indirect care, such as time spent arranging activities such as respite or other outside help? Average hours/day: _ _

Are caregiving for a spouse or a parent with dementia? Spouse Parent 202