The Association Between Measures of Inflammation and Psychological Factors Associated with an Increased Risk of Atherosclerotic Cardiovascular Disease: Hostility, Anger and Depressed Mood and Symptoms
Abstract and Keywords
Inflammation is acknowledged as a risk factor for the onset and development of cardiovascular disease (CVD). This has led some to hypothesize that inflammation is a possible mechanism that may mediate, in part, the relation of CVD to factors associated with increased CVD risk—hostility, anger, and depression. This chapter reviews the empirical evidence of the associations between biomarkers of inflammation and hostility, anger and depression, alone and in combination. Before doing so, I present a brief description and review of the role of inflammation in disease development and the methods used to measure inflammation at point-of-care and in research laboratories. Lastly, I review preliminary data suggesting that gender and adiposity may potentially mediate and moderate the relationship between depression and inflammation.
Notae vero inflammationis sunt quattour: rubor et tumour cum calore et dolore [There are four signs of inflammation—redness, swelling, heat and pain]
—Aulus Cornelius (Celsus), De Medicina
It was not long ago that atherosclerotic cardiovascular disease (ACVD) was primarily thought to be a lipid-storage disease. The build-up of atherosclerotic plaque as a result of the deposit of lipids within the artery wall was considered a key mechanism leading to atherosclerosis. As the plaque grew in size, blood supply was diminished resulting in cardiovascular events such as myocardial infarction (MI). In the last three decades, however, accumulating evidence suggests that atherosclerosis is more complex than lipid accumulation. In fact, atherosclerosis is now understood to be the result of complex interactions among lipid constituents and specific cellular and molecular responses associated with inflammation (Ross, 1986; Libby et al., 2009). These interactions result in a chronic and evolving inflammatory fibroproliferative response against harmful factors acting on the vascular wall (Ross, 1999). That inflammation plays a significant role at all stages of ACVD has led researchers to examine the degree to which traditional and emerging risk factors of ACVD are associated with markers of inflammation and how such empirical evidence can help guide medical treatment (Libby et al., 2006).
That inflammation contributes significantly to the onset and development of ACVD has led some to speculate whether inflammation is associated with key psychological factors associated with disease, such as hostility, depression, and anger. In this chapter, I review initial evidence supporting the relation of hostility, anger, and depression to various (p. 171) biomarkers of inflammation, including circulating markers associated with disease risk (Rozanski, Blumenthal, & Kaplan, 1999) and in vitro measures of cellular mediated immunity. I review published studies linking inflammatory markers to hostility, depression, and anger, alone and in combination. In so doing, I also discuss preliminary evidence suggesting that the relation of psychological factors to inflammatory biomarkers may be moderated by gender and adiposity (Shelton & Miller, 2011). To better understand the relation of psychological factors to ACVD, it is important to understand the role of inflammation in disease onset and progression.
Risk Factors of Heart Disease
Reflecting the multicausal nature of ACVD, there is an abundance of evidence identifying various factors associated with a heightened risk of heart disease. This constellation of risk factors includes both nonmodifiable factors, such as increasing age, being male, and a family history of heart disease, and modifiable factors, such as smoking, elevated cholesterol, high blood pressure, physical inactivity, obesity, and diabetes mellitus. The extent to which exposure to modifiable risk factors incurs disease risk in asymptomatic patients is significant (Greenland et al., 2003). However, there are studies that have shown that coronary heart disease (CHD) can develop in individuals who do not have elevated cholesterol, a key modifiable risk factor (e.g.,Ridker, Rifai, Rose, Buring, & Cook, 2002). These latter observations have led some to suggest that traditional risk factors account for “only 50%” of the CHD. This frequently cited percentage has been shown to be inaccurate and that a more reasonable estimate of the contributions of traditional risk factors to CHD ranges from 75% to 98% (Magnus & Beaglehole, 2001; Kannel & Vasan, 2009). Given this, the question about the significance of the contribution of psychological and behavioral stress factors to CHD risk requires careful consideration within the context of the contribution of traditional risk factors (Rozanski et al., 1999). Although appearing relatively straightforward, such an evaluation is complex, given that exposure to many of the modifiable traditional risk factors, such as smoking, sedentary lifestyle, obesity, and poor dietary habits in adulthood are predicted by the level of hostility, anger, and depression in adolescence and early adulthood (Miller, Markides, Chiriboga, & Ray, 1995; Kahler, Strong, Niaura, & Brown, 2004; Weiss, Mouttapa, Cen, Johnson, & Unger, 2011; Camacho, Roberts, Lazarus, Kaplan, & Cohen, 1991). Such findings have led some to postulate that the influence of psychological stress on disease may be via unhealthy behaviors such as smoking (Mainous et al., 2010).
It has also been proposed that sympathetic arousal may be a biologically plausible patho-physiological mechanism underlying the association between psychological factors and ACVD. Although most of this evidence stems from animal studies (Kaplan et al., 1983; Kaplan, Pettersson, Manuck, & Olsson, 1991), human studies have also shown that the magnitude of physiological responses to psychological stressors and the degree of physiological recovery are associated with markers of early ACVD. In light of this evidence, it has been suggested that psychological factors contribute to the onset and progression of cardiovascular disease via excessive and repeated episodes of sympathetic mediated hyper-responsivity to interpersonal stressors (Treiber et al., 2003; Brotman, Golden, & Wittstein, 2007). That inflammation is recognized to be a critical factor in ACVD has led to the hypothesis that psychological factors are associated with inflammation as a result of sympathoadrenalmedullary mediated responses to psychological stressors. Specifically, it is thought that excessive sympathetic arousal triggers an inflammatory response characterized by cellular and molecular phenotypic changes on peripheral leukocytes and release of pro-inflammatory cytokines that promote tethering of cellular leukocytes and subsequent leukocyte migration into the intima (Ross, 1999). This cascade of events from stress to inflammation may be particularly salient in hostile individuals where frequent bouts of anger have been shown to evoke excessive sympathetic arousal that could potentially trigger an inflammatory response (Suarez, Sasaki, Lewis, Williams, & Adams, 1996; Greeson et al., 2009).
The notion that psychological factors are associated with an immune response is not particularly novel. A number of studies have linked psychological distress to immunosuppression (Dantzer & Kelley, 1989). What is emerging, however, is that psychological distress may also evoke immune activation and specifically activation of the inflammatory response system. A number of animal studies have shown that stressful manipulations, such as immobilization, isolation, and open field, have been shown to increase circulating markers of inflammation. However, it was not until the 1990s that researchers began to examine the relation of psychosocial stress factors to biomarkers of inflammation. Initial studies showed that, among healthy adults, acute laboratory stressors alone increased (p. 172) cellular-adhesion molecules (Mills & Dimsdale, 1996). During the same time period, my laboratory showed that anger arousal during the Anger Recall Interview was associated with an increase in the expression of cytokines by peripheral blood monocytes from high-hostility, but not low-hostility, women (Suarez et al., 1996). Combined, these studies provided initial evidence that stress responses, and specifically emotional responses characterized by arousal of negative affect such as anger, could induce cellular and molecular changes consistent with an inflammatory phenotype. As reviewed in this chapter, these preliminary findings have been subsequently replicated and expanded to include other biomarkers of inflammation.
Conceptualizations of Depression, Hostility, and Anger in Cardiovascular Research
In addressing the hypothesis that psychological stress evokes an inflammatory responses it is important to define what constitutes stress. Unlike traditional risk factors such as cholesterol, blood pressure, and weight that are intrinsically well delineated and characterized, defining the concept of stress presents a greater challenge. This is due to the fact that the concept of stress encompasses diverse conceptualizations that reflect different dimensions. For researchers, stress is conceptualized as the organism's response to challenge. For the man or woman on the street, however, the term “stress” is frequently used to describe heightened emotional and mental responses to everyday events. In today's society, the phrase “I am stressed out” is the catch-all phrase to communicate to others that one is experiencing negative emotions, such as anger, tension, and anxiety, and/or changes in behaviors such as sleep, eating, and physical activities. Needless to say, the degree to which individuals report “being stressed” reflects their subjective evaluations of their emotional and physical state.
The same can be said of researchers when using the terms depression, hostility and anger. In the case of hostility and anger, researchers often used these two concepts interchangeably, but while they are related, they are distinct constructs (Smith, 1994). Similar conceptual ambiguity occurs when researchers refer to depression, especially in the cardiovascular disease literature. Review of published studies reveals that the term depression is used to refer to both a clinical diagnosis of depression and elevated levels of depressive symptoms. Differences in measurement instruments may also contribute to conceptual ambiguity. Hostility, for example has at least 26 measures used in cardiovascular studies (Matthews, Jamison, & Cottington, 1985). Like hostility, there are at least 24 measures of anger (Martin, Watson, & Wan, 2000). These self-report measures of anger purport to assess dimensions of anger that reflect affective, cognitive, and behavioral components. The confusion between the constructs of anger and hostility goes beyond mere operationalization and extends to what is being assessed even when studies used the same instrument. Specifically, in studies that have used the same questionnaire, some researchers have described the measure as tapping aggression while others have identified it as a measure of anger and hostility (Martin et al., 2000).
To circumvent the conundrum regarding the conceptualization and operationalization of the hostility and anger constructs, some researchers have used statistical modeling to examine dimensions of hostility and anger (e.g., Martin et al., 2000; Suarez & Williams, 1990). Researchers using this approach have reported two- and three-factor models. Although the three-factor model attempts to discriminate the specific dimensions of hostility and anger, the two-factor model attempts to conceptualize hostility and anger within the context of the five-factor model of personality. The two-factor model is particularly relevant because it conceptualizes and discriminates the neurotic and antagonistic dimensions of hostility while including measures of anger (Suarez & Williams, 1990), dimensions that are consistent with the five-factor model of personality (Digman, 1990). In our study, indicators of neurotic hostility included the experience of anger subscales from the Buss-Perry Hostility Inventory (BPHI) (Bernstein & Gesn, 1997), neuroticism (one of the five factors) from the NEO-Personality Inventory (PI) (Costa & McCrae, 1985), and the propensity to suppress the outward expression of anger from Spielberger's Anger Expression Scale (Spielberger et al., 1985). Indicators of antagonistic hostility, on the other hand, were BPHI physical and verbal anger expression subscales and NEO-PI agreeableness (negatively weighted) or antagonism. As described in the following sections, neurotic and antagonistic hostility appear to be differentially correlated with measures of inflammation (Suarez, Lewis, & Kuhn, 2002).
For the most part, the conceptualization of depression in most cardiovascular studies reflects categorization of level of severity based on clinical diagnosis and depressive subtypes. As described in the Diagnostic and Statistical Manual of Mental (p. 173) Disorder (DSM)—IV, clinical criteria for major depressive disorders (MDD) emphasize duration, depressed mood and anhedonia in conjunction with other symptoms (Beck, Steer, & Garbin, 1988; Brown, Shulberg, & Madonia, 1995). Specifically, MDD is defined as depressed mood and anhedonia in addition to 5 to 9 specific depressive symptoms lasting for a period of at least 2 weeks. Individuals who do not meet the full criteria for MDD often receive a diagnosis of minor depression (MinD) or subsyndromal or subthreshold depression (SSD). A diagnosis of MinD is indicated when an individual presents 2 to 4 depressive symptoms during a 2-week period and requires one of these symptoms to be either depressed mood or loss of interest or pleasure. A history of MDD excludes the possibility of MinD. Subthreshold depression, on the other hand, is defined as a depressive state having two or more symptoms of depression of the same quality as in MDD, excluding depressed mood and anhedonia (Judd, Rapaport, Paulus, & Brown, 1994). For SSD, the symptoms must be present for more than 2 weeks and be associated with social dysfunction. Lastly, another category often noted in the cardiovascular literature is dysthymic disorder. A diagnosis of dysthymia indicates a depressed mood existing for a period of at least 2 years. Although categorical diagnoses have relevance to psychiatric treatment, the use of diagnoses in CVD research may lead researchers to underestimate the effects of depression and depressive symptomatology on health (Mussselman, Evans, & Nemeroff, 1998). That the relation of depression to cardiovascular disease is linear argues for examining the risk of disease along a continuum (Rozanski, Blumenthal, Davidson, Saab, & Kubzansky, 2005). Such differences limit the generalizability of findings from studies that only examine the relation of cardiovascular risk and inflammation to diagnostic categories to a larger percentage of the population that exhibits symptoms of depression not meeting clinical criteria but that is at an elevated risk for disease. Although the issues raised in the preceding section are beyond the scope of this chapter, it is important that the reader be cognizant of the complexities of the constructs being reviewed in this chapter and how these terms may serve as a proxy for various dimensions of the psychological construct and its severity. In this chapter, I emphasize three psychological factors that have received considerable attention in the CVD literature (Rozanski et al., 1999). These factors are depression, anger, and hostility. Although other stress measures have been linked to CVD (e.g., social isolation, anxiety), the emphasis on these three factors is based on the evidence from case-control, cross-sectional and prospective cohort studies of initially healthy individuals linking hostility, anger, and depression to various cardiovascular endpoints (Smith, 1992; Everson-Rose & Lewis, 2005; Ford et al., 1998; Chang, Ford, Meoni, Wang, & Klag, 2002; Chida & Steptoe, 2009). As described in the following sections, the majority of cardiovascular studies have examined the effects of these factors to CVD in isolation. As with the clustering of traditional and emerging risk factors (Dandona, Aljada, Chaudhuri, Mohanty, & Garg, 2005), it also widely acknowledged that these psychological factors tend to cluster within individuals (Suls & Bunde, 2005). There is also evidence to suggest that trait measures of hostility and anger predict future increases in depressive symptoms (Stewart et al., 2010; Busch, 2009). These latter observations are in line with the notion that daily life stressors can precipitate increases in anger, depressive mood, and depressive symptoms, aspects that are associated with hostility as a trait. Further strengthening the likelihood of these factors clustering, emerging evidence suggests that hostility, anger, and depression may share a common underlying biological pathway—dysregulation of the serotonin system (5-HT) (Kamarck et al., 2009). Given the evidence, it is puzzling why the majority of studies have focused on a single factor in isolation (Suls & Bunde, 2005). To address this issue, I will present preliminary data to suggest that the combination of these three factors may not only be the best predictor of disease incidence and risk (Rozanski et al., 1999; Boyle, Michlek, & Suarez, 2007), but also changes in markers of inflammation (Boyle, Jackson, & Suarez, 2007).
Hostility is frequently used interchangeably with anger, and although these two concepts are distinct, the use of these terms often is confusing. For the purpose of this chapter, I will consider anger a negative emotion, whereas hostility will be conceptualized as a personality trait characterized by negative beliefs and attitudes about others, such as cynicism, mistrust, and the attribution of hostile intent (Barefoot & Lipkus, 1994). What is evident from the definition is that the construct of hostility is multidimensional, one that reflects cognitive, affective, and behavioral components (Barefoot & Lipkus, 1994). The multidimensional aspects of this (p. 174) construct are also reflected in frequently used measures—from self-report paper-and-pencil measures, such as the Cook and Medley Hostility Scale (Cook & Medley, 1954) and the Buss-Perry Hostility Inventory (BPHI) (Bernstein & Gesn, 1997), to the Structured Interview Potential for Hostility (Dembroski, MacDougall, Costa, & Grandits, 1989). Although they vary to some degree, for the most part these instruments appear to tap cognitive and behavioral dimensions of hostility. What is most important to know is that the previously mentioned questionnaires have been linked to CVD in some but not all studies (Miller, Smith, Turner, Guijarro, & Hallet, 1996).
In the literature, anger, can refer to either an emotional state or a psychological trait. When used as a trait, anger i refers to a person's predisposition to experience frequent and pronounced episodes of the emotional state of anger. As an emotional state, anger reflects a transitory subjective experience of angry affect. As with hostility, anger is often associated with aggression, but there is a distinction between these two concepts. In this chapter, aggression is defined as the physical and verbal actions resulting from anger. Not surprisingly, measures of hostility and trait anger are associated with both aspects of aggression, yet these correlations range from 0.30–0.50, suggesting that the shared variance between personality traits and aggression is moderate at best. Similarly, the shared variance among measures of verbal and physical aggression and anger expression is comparable to that between traits and aggression, suggesting that these terms encompass different aspects of anger. This is particularly the case for anger suppression, as measures of aggression do not correlate with measures of anger suppression.
In the cardiovascular literature, depression has been used to refer to an assortment of phenomena ranging from a categorical diagnosis to severity of depressive symptoms (Davidson, Rieckmann, & Rapp, 2005). As with hostility and anger, assessment of depression includes both clinical interviews and self-report scales with varying groupings of symptoms that reflect the broad use of the term depression. To guide the reader, the following sections will differentiate studies that used clinical diagnosis from those using continuous measures of symptom severity.
The Joint Effects of Psychological Factors and Cardiovascular Risk
A number of prospective studies have shown that depression, hostility, and anger independently predict the risk of coronary heart disease (CHD) (for review, see Rozanski et al., 2005; Smith & Ruiz, 2002; Yusuf et al., 2004). Moreover, the risk of CHD associated with the factors appears to be proportional to the degree of severity (e.g., Smith & Ruiz, 2002; Lesperance, Frasure-Smith, Talajic, & Bourassa, 2002). For the most part, methodological and statistical approaches employed by these studies have emphasized each factor in isolation (Suls & Bunde, 2005). As with the clustering of traditional risk factors that define the metabolic syndrome, psychological factors of hostility, anger, depression, and anxiety show high covariation, and there is the tendency for these factors to cluster within at-risk populations (e.g., Kareinen, Viitanen, Halonen, Lehto, & Laakso, 2001). Suls and Bunde (2005) have suggested that the conceptual and measurement overlap of these factors demands the development of more complex affect-disease models, models that will have direct implications for interpretation of prior studies, statistical analyses, prevention, and intervention in health psychology and behavioral medicine. In my laboratory, we have made a concerted effort to examine the joint effect of these factors not only on measures of disease (Boyle et al., 2007; Lemogne et al., 2010) but also on putative risk factors including inflammation (Suarez et al., 2002; Suarez, 2003). A similar approach was adopted by the researchers of the INTERHEART study who examined the effects of feeling irritable, perceived stress, depressive symptoms, recent stressful life events and locus of control (Rosengren et al., 2004). The case-control study was conducted in over 24,000 adults from 52 countries. Results indicated that people with myocardial infarction (MI cases) reported the experience of all stress factors and this association was independent of smoking, age, sex, ethnic group, and geographic region (Rosengren et al., 2004). Given these findings, it is reasonable to examine not only the effect of a single factor but their combined effects on health outcomes.
The notion that the presence of a mosaic of interrelated factors defines a subgroup of individuals and patients at heightened risk for new and recurring cardiac events has been reported in a number of studies. This is evident by reports from the THROMBO study of nondiabetic patients who had a documented myocardial infarction (Moss et al., 1999) suggesting that combinations (p. 175) of inflammatory and thrombotic factors identifies a subgroup of patients who are at elevated risk for recurrent events (Corsetti, Zareba, Moss, Rainwater, & Sparks, 2006). It has even been suggested that understanding the interaction between metabolic and inflammatory pathways may lead to improved therapeutic strategies (Fernandez-Real & Ricart, 2003). It is my opinion that this is also the case for psychological risk factors and potential pathophysiological and behavioral pathways contributing to ACVD. Although this chapter includes studies that report “main” effects, I will stress the importance of the interaction or joint effect among these three factors and how their interaction may be the best correlate and predictor of inflammation and CVD risk.
Inflammation and Disease
The body's primary response to infections, irritation, or injury is inflammation, a nonspecific immune response characterized by the release of an arsenal of mediators that include bioactive amines, eicosanoids, cytokines, chemokines, and growth factors (Silva, 1994). In response to this arsenal of mediators, the classic clinical symptoms of acute inflammation emerge: pain (dolore), heat (calore), redness (rubor), swelling (tumour) and loss of function (function leasa). The protective actions of inflammation are usually localized to the area of trauma or to the invading microbe and these actions aim to destroy, dilute, or wall off the injurious agent and the injured tissue. The inflammatory response guides immune system components to the site of the injury or infection by manifesting an increase in blood supply and increasing vascular permeability. These actions are also accompanied by the exudation of fluids including plasma protein, chemotatic peptides, and leukocyte migration in the inflammatory site. In tissue damage, the acute inflammatory response is a critical mechanism in isolating the damaged area, and mobilizing effector cells and molecules to the site while promoting healing. In infection, inflammatory responses protect the body by creating a barrier that prevents the pathogen from damaging the host. In these scenarios, inflammation occurs as a defensive response to trauma or invasion of the host by foreign material.
Advances in the understanding of inflammation and disease pathogenesis has led many to conclude that inflammation may contribute to many chronic diseases as diverse as diabetes, Alzheimer's disease, cataracts, cancer, and atherosclerosis. It is from this conclusion and a wealth of clinical and experimental evidence that today's view of atherosclerosis has emerged. The predominant thought is that the atherosclerotic process is fundamentally an inflammatory-fibroproliferative process (Ross, 1999), one that many now believe to be an inflammatory disease (Pearson et al., 2003). Toward understanding how inflammation contributes and characterizes atherosclerotic disease, I will briefly describe the processes contributing to atherosclerosis and subsequent clinical disease and the role of inflammation.
Inflammation, Atherosclerosis, and Atherosclerotic Lesions
The pathogenesis of atherosclerosis is believed to initiate as a result of injury to the artery wall and, specifically, to the lining of the artery or endothelium (Ross, Glomset, & Harker, 1977). It is speculated that injury can be caused by exposure to a number of different risk factors of atherosclerosis such as smoking, hypercholesterolemia, hypertension, elevations of homocysteine and other toxic factors as well as mechanical factors that occur at bifurcation sites. Although more controversial, it has been suggested that injury to the endothelium may also be infection-initiated (Saikku et al., 1992). Depending on the causative factor(s), the injury leads to inflammation (Libby & Theroux, 2005), endothelial dysfunction (Bonetti, Lerman, & Lerman, 2003), abnormal cellular interactions (Ross et al., 1977) and increases in oxidative stress and the production of reactive oxygen species (ROS) (Griendling & FitzGerald, 2003). Although the inflammatory response may be initially appropriate, if it becomes excessive and chronic it contributes to further injury and damage to the tissue. Prolonged inflammation is thought to promote further changes in the artery that advance the development of the atherosclerotic lesion that are differentiated by characteristic morphology (Stary et al., 1995). These morphological differences, however, are all characterized by inflammation.
Models of atherosclerosis in animals and humans have focused on the effects of hypercholesterolemia and the retention of atherogenic lipoprotein particles in the subendothelium (Skalen et al., 2002). It has been suggested that infiltration of these particles evokes an inflammatory response in the artery wall. Once in the endothelium, modification of the cholesterol particles leads to the release of phospholipids that can activate endothelial cells, a process that more likely occurs in lesion-prone areas of the arterial tree. As a result, monocytes and lymphocytes (p. 176) are recruited and accumulated due to the expression of specific leukocyte adhesion molecules by the vascular endothelium (Nakashima, Raines, Plump, Breslow, & Ross, 1998). The role of inflammation is not solely noted in the early stages of atherosclerosis but it also plays a role in more advanced lesions (Rosenfeld et al., 2000). In humans, inflammation is noticed in both activated plaques in patients with acute coronary syndrome but also in silent plaques.
It has been suggested that lesions of atherosclerosis (atheromata) may be arbitrarily divided into three categories: the early lesion or fatty streak, the intermediate or fibrofatty lesion, and the advanced or complicated lesion or fibrous plaque (Stary et al., 1994; Ross, 1999; Virmani, Kolodgie, Burke, Farb, & Schwartz, 2000). Models of atherosclerosis in hypercholesterolemic nonhuman primates have shown that one of the earliest events in atherosclerosis is the entrance of lipids into the intima (Faggiotto, Ross, & Harker, 1984). Soon thereafter, there is an increased adherence of monocytes and lymphocytes to the intimal surface, evidence for the initial involvement of the immune system and specifically cellular components of the inflammatory responses (Faggiotto et al., 1984; Faggiotto & Ross, 1984). The early lesion or fatty streak is characterized by a yellow discoloration on the surface of the artery that is caused by lipid accumulation in foam cells, most of which are lipid-filled macrophages making up the bulk of the lesion (Hansson, 2001; Daugherty, Rateri, & Lu, 2008). Studies of these early lesions have shown that earliest fatty streaks consist entirely of lipid-laden, monocyte-derived macrophages together with T-lymphocytes (Emeson & Robertson, 1988; Jonasson, Holm, Skalli, Bondjers, & Hansson, 1986). With progression, smooth muscle cells appear and the accumulation of macrophages and T lymphocytes are superimposed or are intermixed with the accumulations of smooth muscle and connective tissue. Extension of the proatherogenic processes contributing to early lesions leads to further thickening of the intima and subsequent intrusion of the intima into the lumen of the artery (Jonasson et al, 1986).
Assessment of Inflammation in Clinical and Research Settings
The knowledge that inflammation played an important role in atherosclerotic cardiovascular disease (ACVD) stimulated the development of more sophisticated and sensitive laboratory tests to measure inflammation. In vivo measures of immunity such as white blood cell count and fibrinogen were used in early studies of the association of inflammation to CVD (Meade et al., 1986; Kannel, Wolf, Castelli, & D'Agostino, 1987; Yarnell et al., 1991). Although useful in initially demonstrating the relationship between inflammation and CVD, these initial findings stimulated the development of other platforms to assess inflammation. The challenge was that the degree of inflammation suspected of contributing to heart disease was not associated with acute trauma or clinical diseases, but with low levels of inflammation that required highly sensitive assays. Out of this need came the development of the highly sensitive C-reactive protein (CRP) assay. Although CRP was discovered in 1930, it was not until the 1940s that CRP was described as an acute-phase reactant that was elevated among patients with conditions that were characterized by inflammation (McCarty, 1947). In the 1980s and 1990s, prospective and cross-sectional studies linked CRP to atherosclerosis (Kuller, Tracy, Shaten, & Meilahn, 1996). The challenge, however, was that the CRP levels associated with cardiovascular risk were often below the detectable levels of the available assay. This led to the development and validation of a high-sensitivity (hs) assay to measure CRP. The commercial availability of hsCRP assay allowed physicians and researchers to evaluate in vivo inflammation with greater sensitivity.
The hsCRP assay joined other in vivo tests used to examine immune-system status and responses in situ. Other assays include humoral measure of complement, alpha1-acid glycoprotein, alpha1-antitrypsin haptoglobin, and immunoglobulin plasma measures of circulating cytokines and their soluble receptors. Acute phase proteins include not only CRP and fibrinogen but also serum ferritin, serum albumins, serums amyloid A, and transferrin protein that are continually produced in the liver.
The selection of assay in the clinical setting is frequently guided by the relevance to diagnosis and the availability of the assay. In contrast, the selection of assay in research settings rests on various factors that include the specific aims of the study, characteristics of the study population (e.g., age, gender, health status, medication use), sampling rates (e.g., hours or days versus years or decades), and storage (e.g., fresh samples versus frozen). In addition to procedures used to measure humoral factors, research procedures to assess inflammations include enumerative and functional measures. Enumerative procedures are those techniques such as flow cytometry that measure the number and percentages of various immune cells by gating on specific cellular (p. 177) surface markers (e.g., CD14, CD56, CD11/CD18) or by measuring intracellular cytokine expression by specific circulating cells such as CD14+ monocytes (Suarez, Krishnan, & Lewis, 2003).
There has been a concerted effort by industry to develop commercially available assays that are appropriately standardized so as to allow comparisons of results across studies (“C-reactive protein testing,” 2009). A cursory review of articles published in recent years show that the most frequently used commercial assays to assess circulating levels of inflammatory biomarkers as they relate to CVD were: (a) soluble adhesion molecules such as E-selectin, P-selectin, intracellular adhesion molecules-1, and vascular cell adhesion molecules-1; (b) cytokines such as interleukin (IL)-1beta, IL-6, IL-8 and IL-10, and tumor necrosis factor-alpha; (c) acute-phase reactants such as fibrinogen, high sensitivity C-reactive protein, serum amyloid A; (d) white blood cell counts (e.g., total white blood cells, number of mononuclear blood cells). Although all the individual biomarkers have been associated with CVD in prospective studies (e.g., Woodward, Rumley, Tunstall-Pedoe, & Lowe, 1999; Ridker, Hennekens, Roitman-Johnson, Stampfer, & Allen, 1998; Hwang et al., 1997; Ridker & Haughie, 1998; Luc et al., 2003), the most often used has been high sensitivity C-reactive protein (hsCRP) (Ridker, 2007).
Although not exhaustive, the assays just listed allow for clinicians and researchers to assess circulating blood levels of biomarkers associated with inflammation with relative ease. Markers of inflammation such as white blood cells are simple to perform. In the case of hsCRP, commercially available assay kits also have allowed for ease of comparisons of results among research studies. What is important to understand is that, for the most part, the preceding listed measures of circulating biomarkers are nonspecific measures of inflammation. In other words, elevated levels of these inflammatory biomarkers do not reflect any specific factor that contributes to inflammation. Thus, elevated IL-6 and hsCRP may reflect the impact of factors ranging from adiposity, smoking, and exercise, to underlying inflammatory disease, to an acute infection or injury. Thus, it is important to interpret the results of any tests within the context of the individual's overall health status and vitals such as weight, age, and medications. This is particularly important in studies of patient populations in which elevations in acute phase proteins are likely to be noted.
Additional Measures of Inflammation in Research Settings
As previously described, measures of inflammation as it relates to CVD can be done using various methods that have quickly become commonly used in both clinical and research laboratories. Other measures of inflammation, however, are more complex and involve time-consuming procedures, some of which require that the test be conducted on fresh blood samples and not frozen. For the most part, the purpose of conducting these laboratory tests is to assess the complexity of the inflammatory system by quantifying cellular and molecular characteristics of the various components of the inflammatory response. The use of fluorescence flow cytometry and enzyme-linked immunosorbent assay (ELISA), alone or in conjunction with in vitro stimulation procedures, are often used to examine changes in cell populations, complexity, phenotype, and health, as well as production and expression of various cellular markers that allow for more detailed characterization of the inflammatory response.
Evidence for the Relation of Hostility, Anger, and Depression to Biomarkers of Inflammation
The preceding sections have provided a brief introduction to the concept of inflammation and a review of the most frequently examined psychological factors associated with CVD. In this section, I will review the evidence for the associations among measures of inflammation and depression, hostility and anger, alone and in combination. Although negative findings have been reported, the weight of the evidence from both cross-sectional and longitudinal studies in patient populations and healthy subjects suggests a relationship between markers of inflammation and psychological factors. What is clearly apparent is that the majority of studies have focused on depression. This may reflect the fact that researchers have long been interested in the effects of depression and immune function, and specifically the influence of depression on immune responses to infections and injury. Consistent with this notion, meta-analysis by Herbert and Cohen found that depression was significantly associated with decreases in immune parameters (Herbert & Cohen, 1993). In that same year, Smith proposed the macrophage-T-lymhocyte hypothesis of major depression (Smith, 1991). Smith posited that depression evoked immune activation and specifically inflammation, but that inflammation was a putative factor for depression (Smith, 1991). This (p. 178) novel hypothesis not only stimulated researchers to review the data already at hand (Maes, Smith, & Simon, 1995), but also to propose new studies to examine the relation of depression to biomarkers of inflammation.
In contrast to the large number of studies examining depression, far fewer studies have examined anger and hostility as they relate to inflammation. Of those studies that have assessed anger and hostility, the rationale for such assessment reflects evidence linking hostility and anger to CVD. However, as widely acknowledged, the influence of the immune system on the central nervous system (CNS) is reciprocal, in other words, bidirectional. It is not surprising that cytokines may facilitate the expression of aggressive behavior. Such a possibility reflects the fact that cytokines are present in the brain regions that are known to be associated with aggression and rage behaviors (Zalcman & Siegel, 2006). Although few in number, the results are remarkably consistent in documenting an association between inflammatory biomarkers and measures of anger and hostility, alone or in combination with depression (e.g., Suarez, 2004).
It is important to note that the majority of studies linking psychological factors to measures of inflammation have been cross-sectional in design. Few studies have examined the temporal relationship between depression and inflammation, and only one study has examined the sequential relation of anger and hostility preceding inflammation.
The notion that inflammation is associated with depression has been a topic of interest for many reasons, one of which is the biologically plausible hypothesis that inflammation may explain, in part, the depression-CVD association (Shimbo, Chaplin, Crossmna, Haas, & Davidson, 2005). This hypothesis rests on the notion that depression, via various mechanisms of action that include both biological and behavioral pathways, leads to a pro-inflammatory state characterized by enhanced cellular-mediated immunity (i.e., increases in monocytes and natural killer cells, increases in macrophage/monocyte derived cytokines, and increases in circulation complement and other acute phase proteins) (Maes, 2011), mechanisms contributing to the etiology of CVD (Shimbo et al., 2005). Although many have focused on the notion that depression precedes inflammation, others have speculated that depression is an inflammatory disease and that inflammation is a putative risk factor for the onset of depression and depressive symptoms (Maes, Smith, & Scharpe, 1995; Smith, 1991). Such a possibility leads to the notion that inflammation precedes depression and CVD, suggesting that the covariation often observed between depression and CVD is more likely an epiphenomenon (Shimbo et al., 2005). These two alternative hypotheses have been the target of considerable research efforts in determining the strength and directionality of the association between inflammation and depression.
A number of studies have examined the bidirectional nature of this association (Glover, Shaw, Williams, & Fildes, 2010; Adler, Marques, & Calil, 2008). In direct evidence for the potential impact of inflammation on depression stems from studies documenting elevated rates of depression in patients with inflammatory diseases including cardiovascular disease (Irwin, 2002). More direct evidence for a causal relation comes from studies where interferon-alpha and/or interleukin-2 were administered as part of treatment. For the most part, the evidence from those studies suggests that administration of INF-alpha or interleukin-2 evokes changes in mood and behaviors associated with depression (Capuron & Miller, 2004; Capuron, Ravaud, & Dantzer, 2000). Similar causal pathways have been demonstrated in studies of healthy adults with no history of depression or current symptoms of depression. In those studies, administration of low amounts of endotoxin and interleukin-2 reliably produced “sickness-like” behaviors that are consistent with depression (DellaGioia & Hannestad, 2010).
The temporal and relative strength of the association between inflammation and depression also underlies a third model suggesting that depression and inflammation are independent risk factors of CVD (Shimbo et al., 2005). In this case, the shared variance between depression and inflammation, even if significant, would not diminish the unique contributions of each factor to disease onset and progression. Two studies have explored the unique contributions of inflammation and depression or depressed mood in predicting future CHD (Empana et al., 2005) and CVD mortality risk (Kop et al., 2010). In both studies, measures of depression were significantly correlated with circulating inflammatory biomarkers such as CRP and IL-6. However, depression and inflammation were both significant predictors in multivariate analysis that included traditional risk factors. These results suggest that inflammation, although associated with depression, does not explain the significant relationship between depression and CVD and that depression (p. 179) does not increase the risk of CVD via inflammation alone. These preliminary findings need to be interpreted cautiously given the acknowledged limitations of the studies including the temporal relation of inflammation to episodic depression events. In both studies, depression was measured only at one time point. Interpretation of the results must also take into account that the study populations were relatively homogenous with respect to gender, race (Empana et al., 2005), and age (Kop et al., 2010), factors that are known to influence inflammation (O'Connor et al., 2009) and thus potentially moderate the relationship between depression and CHD.
A meta-analysis that included over 80 studies examined the relation of a diagnosis of depression (e.g., MDD) to approximately 40 measures of inflammation (Zorrilla et al., 2001). Using fixed-effect and random-effect modeling, these authors concluded that major depression “may be associated with immune activation reminiscent of an acute phase” (italic as used by study authors, p. 210). An acute phase response is a nonspecific and systemic immune reaction that is initiated by inflammatory processes and specifically cytokines from macrophages and circulating monocytes (Baumann & Gauldie, 1994).
As I noted in the preceding sections, studies that examine the effects of MDD cannot address whether markers of inflammation are associated with symptom severity of depression along a continuum. It is well recognized that an elevated risk of coronary disease is not restricted to a diagnosis of depression but extends to include severity of depressive symptoms that do not meet criteria for a clinical diagnosis (Wulsin & Singal, 2003). This dose-response association is similar to the associations of traditional risk factors to coronary risk and in particular, the case of low-density lipoprotein where a 1 mmol drop leads to a 25% reduction in the relative risk of vascular events (Yusuf, 2002). A similar dose-response has been shown for blood pressure, where reduction in blood pressure shows benefit across a wide range of hypertensive and nonhypertensive patients (Blood Pressure Lowering Treatment Trialist's Collaboration, 2003). Such observations lend support to the “lower is better” hypothesis (Heart Protection Study Collaborative Group, 2002). If incremental increases in depressive symptom severity are associated with increases in CVD risk, then it important to examine the relation of symptom severity to inflammation along a continuum.
There are a number of studies that have examined the cross-sectional relationship between markers of inflammation and severity of depressive symptoms along a continuum. Although various measures of severity of depressive symptoms have been used, the most often used instruments have been the Beck Depression Inventory (BDI) (Beck et al., 1988) and the Center for Epidemiologic Studies Depression (CESD) scale, both well-validated self-report scales. Other measures include the Hamilton Depression Rating Scale (HAM-D). For the most part, these studies have suggested that severity of depressive symptoms is associated with circulating levels of inflammatory biomarkers (Suarez, 2004; Hamer & Chida, 2009; Kobrosly & van Wijngaarden, 2010; Panagiotakos et al., 2004; Kop et al., 2002; Marsland, Sathanoori, Muldoon, & Manuck, 2007; Elovainio et al., 2009) in most but not all studies (Pan et al., 2008; Steptoe, Kunz-Ebrecht, & Owen, 2003). Reflecting the dose-dependent association between severity of depressive symptom and CVD (Rugulies, 2002), a recent meta-analysis suggested that inflammation increases incrementally, tracking increases in severity of depressive symptoms (Kobrosly & Wijngaarden, 2010). Studies included in the meta-analysis by Howren et al. (Howren, Lamkin, & Suls, 2009) primarily assessed CRP and IL-6, with considerably fewer studies measuring IL-1 and IL-1 receptor antagonist (ra). Using these inflammatory markers, the authors reported that depression and severity of depression were associated with inflammatory markers in both clinic- and community-based samples with the association showing a dose-related association (Howren et al., 2009). It was noted, however, that the effect size was larger for MDD than for severity of depressive symptoms.
Most published studies have used CRP and IL-6 as measures of inflammation (Howren et al., 2009). What is also important to note is that the most often used biomarkers of inflammation, CRP and IL-6, are nonspecific indicators of inflammation. Although circulating concentrations of these two biomarkers are known to predict CVD endpoints (Ridker, 2009; Ridker, Rifai, Stampfer, & Hennekens, 2000), the role of inflammation in atherosclerosis and its sequelae is thought to involve cellular and molecular responses associated with peripheral blood monocytes, monocyte-derived (p. 180) macrophages, and T lymphocytes. In atherosclerosis, it is hypothesized that the development and progression of atherosclerotic lesions involves monocyte-derived macrophages and T lymphocytes (Ross, 1999). Similarly, the basic premise of the monocyte-T-lymphocyte hypothesis of depression as proposed by Smith (1991) and expanded by Maes et al. (1995) emphasizes activation of peripheral blood monocytes and T lymphocytes as key cellular components in the development of depression. That role of peripheral monocytes and T lymphocytes in both depression and cardiovascular disease has prompted researchers to determine if depression is associated with monocyte and T lymphocyte function. In my laboratory, we have shown that severity of depressive symptoms is positively associated with stimulated expression of IL-1alpha, IL-1beta, IL-8, monocyte chemotactic protein (MCP)-1 and TNF-a (Suarez et al., 2003; Suarez, Lewis, Krishnan & Young, 2004) on peripheral monocytes from healthy controls. These initial observations have been partially replicated by Marsland et al. who showed that stimulated production of IL-8 by peripheral monocytes was positively associated with severity of depressive symptoms in a large community sample of healthy adults (Marsland et al., 2007). Together, these findings are consistent with one animal study that showed that exposure to chronic mild stress leading to mild anhedonia (operationalized as a reduction in sucrose intake with concomitant reduction in water intake) evoked increases in plasma levels of TNF-alpha and IL-1b (Grippo, Francis, Beltz, Felder, & Johnson, 2005). I note that the relationship between severity of depressive symptoms and IL-8 may be particularly important given that macrophages from atherosclerotic plaque show an enhanced capacity to produce IL-8 (Astolopoulos, Davenport, & Tipping, 1996).
The observations of an association between severity of depression and monocytic cytokines in community samples extends earlier reports of differences in monocyte function between healthy controls and patients with major depression (Schlatter, Ortuño, & Cervera-Enguix, 2004). In that study, Schlatter et al. showed that relative to healthy nondepressed controls, subjects with MDD and dysthymia showed greater stimulated production of IL-1beta and IL-6 by peripheral monocytes as well as elevated burst activity and phagocytosis (Schlatter et al., 2004). Overall, the evidence suggests that increased monocytic production of IL-1b, IL-6, and TNF-alpha reflect key and early markers of immune activation and subsequent systemic inflammation associated with MDD and severity of depressive symptom (Maes, 1995).
The evidence described in the preceding section emerges from cross-sectional studies of the relationship between inflammation and various aspects of depression. Although these studies provide positive support for this association, they do not address the issue of directionality. To best address this issue, it is necessary to examine this association in prospective studies (Howren et al., 2009). As of 2011, only a few studies have directly evaluated the temporal relationship between inflammatory biomarkers and depression (Boyle et al., 2007; Matthews et al., 2007; Stewart, Rand, Muldoon, & Kamarck, 2009; Von Känel, Bellingrath, & Kudielka, 2009; van den Biggelaar et al., 2007; Gimano et al., 2009; Kiecolt-Glaser et al., 2003; Hamer, Molloy, de Oliveira, & Demakakos, 2009) in healthy subjects and in patient populations (Shaffer et al., 2011; Wirtz, 2010).
Most of these prospective studies have examined whether depression precedes or predicts inflammation. For example, Kiecolt-Glaser (Kiecolt-Glaser et al., 2003) examined changes in IL-6 over a 6-year period in a group comparison study of older adults who were current or previous caregivers and noncaregivers. Relative to noncaregivers, caregivers showed increases in IL-6 during the 6-year follow-up period. These increases, however, were not associated with depressive symptoms as measured by the BDI (Kiecolt-Glaser et al., 2003). In contrast, Matthews et al. (2007) showed that in women going through the menopausal transition, depression predicted 5-year increases in fibrinogen even after adjusting for potential confounders including body mass index (BMI). The relation of depression at baseline to longitudinal changes in fibrinogen and CRP were also examined in adult men and women enrolled in the English Longitudinal Study of Aging (Hamer et al., 2009). Hamer et al. showed that depression, measured using the CESD, predicted 2-year changes in both CRP and fibrinogen. Using mediational analysis, Hamer et al. were able to demonstrate that the effects of depression on changes in CRP over time were both direct and indirect through behavioral risk factors. For fibrinogen, the association was completely accounted for by health behaviors. Lastly, in a large sample of healthy Vietnam veterans, Boyle et al. (2007) used the Minnesota Multiphasic Personality Inventory (MMPI) depression scale (Nelson & Cicchetti, 1991) to examine whether depression, alone and in combination with anger and hostility, predicted changes in C3 and C4 complement over a 10-year period. Results indicated that depression, both alone and in combination with anger and hostility, predicted increases in C3 complement but not C4 complement in a model adjusted for health, behavioral, and sociodemographic variables (including BMI).
Only one study has examined whether inflammation precedes changes in depression. Van den Biggelaar et al. (2007) examined the relation of depression, assessed by the 15-item Geriatric Depression Scale, and cognitive functioning, assessed by the Mini-Mental State Examination, to baseline inflammation in 85-year-old participants enrolled in a prospective population-based study of inhabitants in Leiden, the Netherlands (Leiden- 85-plus study). Measures of inflammation include both in vivo CRP and albumin, and in vitro stimulated production of IL-1beta, IL-1ra, IL-6, and IL-10. Results indicated that higher baseline CRP preceded accelerated increases in depressive symptoms. Similarly, elevated production of IL-1beta predicted increases in depressive symptoms, and IL-1ra, the natural antagonist to IL-1beta, preceded smaller increases in depressive symptoms. None of the inflammatory markers predicted changes in cognitive decline.
Lastly, the remaining studies have examined the bidirectional nature of the relationship between depression and inflammation. These studies directly tested whether depression precedes inflammation and whether inflammation precedes depression. In a study of healthy adult men and women, Stewart et al. (2009) showed that severity of depressive symptoms, assessed at entry using the BDI-II, predicted changes in IL-6, but not in CRP, over a 6-year follow-up period. In contrast, baseline IL-6 and CRP failed to predict 6-year changes in BDI-II score. Such a pattern of results suggest that depression may influence downstream inflammation, but that inflammation may not evoke increases in severity of depressive symptoms in apparently older healthy adults.
In patient populations
Two studies have examined the temporal relationship between depression and markers of inflammation in patient populations (Shaffler et al., 2011; Wirtz et al., 2010). In a sample of heart failure (HF) patients, Wirtz et al (2010) examined the relation of severity of depressive symptoms using the BDI to peripheral markers of inflammation, CRP, IL-6, and the soluble intracellular adhesion molecule-1 (sICAM-1). After controlling for baseline BDI, cardiovascular risk factors, HF severity and medications, baseline sICAM-1 predicted 12-month increases in BDI scores. C-reactive protein and IL-6 did not predict changes in BDI scores at 12-month follow-up.
Using path analysis, Shaffer et al. examined the relation of CRP to BDI score in a sample of adults with acute coronary syndrome (ACS) (Shaffer et al., 2011). Baseline measures were taken at the time of an acute cardiac event and at 1- and 3-months post cardiac event. Results indicated that baseline BDI score and the score for the cognitive-affective subscale predicted a smaller decrease in CRP from baseline to 1-month. Similar analysis failed to show that the somatic affective symptoms subscale predicts changes in CRP. Baseline CRP did not predict changes in BDI score at 1- and 3-months.
For the most part, studies examining the association between depression and inflammation are cross-sectional studies. As widely acknowledged, a cross-sectional design cannot be used to examine directionality. Only seven longitudinal-prospective studies have examined the relation of depression predicting measures of inflammation at follow-up (Boyle et al., 2007; Stewart et al., 2009; Gimeno et al., 2009; Hamer et al., 2009). In addition to study design, significant variability is noted in other methodological design features across studies. These include differences in study populations (healthy controls versus patients), measures of depression (self report and clinical ratings), and measures of inflammation (acute phase proteins, humoral inflammatory measures, cellular function). Study populations appear to fall in one of three categories: community samples of apparently healthy subjects; psychiatric populations of subjects with major depressive disorder and other depression-related diagnosis; and subject with inflammatory diseases such as CVD, chronic obstructive pulmonary disease (COPD), and arthritis. Depression measures also vary, ranging from studies using well-validated clinical and self-report measures of depression and depressive symptomatology to those that use unvalidated measures. Lastly, measures of inflammation include: (a) in vivo measures such as CRP, interleukin (IL)-6, fibrinogen, and, to a lesser extent, measures of circulating cytokines such as IL-1beta, soluble IL-2 receptor, and tumor necrosis factor (TNF)-alpha; (p. 182) and (b) in vitro techniques used to evaluate cellular and functional responses to mitogen stimulation. Regardless of these differences in methodology, the majority of studies have emphasized the directional hypothesis that depression precedes inflammation, whereas fewer studies have examined the possibility that inflammation precedes depression (Miller, Maletic, & Raison, 2009).
Hostility and Anger
Relative to studies of depression and inflammation, fewer studies have examined the relation of hostility and anger to inflammation. With the exception of two studies (Boyle et al., 2007; Elovainio et al., 2011), the studies have been cross-sectional in design and, for the most part, study populations have been healthy subjects recruited from the community. Other methodological characteristics common across studies include the use of the Cook-Medley Hostility Scale and CRP and IL-6 as inflammatory markers. Although all studies have examined hostility or anger, some have examined the joint effect of hostility with depression and in combination with anger (Suarez, 2003; Suarez, 2004; Suarez et al., 2004; Graham et al., 2006; Miller, Freedland, Carney, Stetler, & Banks, 2003; Stewart, Janicki-Deverts, Muldoon, & Kamarck, 2008). In light of these findings, the next sections will review the cross-sectional and longitudinal studies.
The initial study reporting a relationship between hostility, anger, and inflammation was conducted in my laboratory (Suarez, Lewis, & Kuhn, 2002). In that study of healthy men, we assessed hostility and anger using the Buss-Perry Aggression Questionnaire (BPAQ) (Bernstein & Gesn, 1997). The BPAQ yields a total score as well as scores on subscales of hostility, anger, verbal, and physical aggression. Inflammation was assessed using lipopolysaccharide (LPS)-stimulated expression of TNF-alpha on peripheral monocytes. Results indicated that greater stimulated production of TNF-alpha was associated with BPAQ total score as well as scores on the hostility, physical, and verbal aggression subscales (Suarez et al., 2002). These associations were observed in an analysis that controlled for demographic and biological factors known to increase inflammation. Thus, hostile men, and especially those who reported high levels of verbal aggression and physical aggression, exhibited greater in vitro production of TNF-alpha.
The 2002 Suarez study was soon followed by a study published in 2003 that showed that hostility, alone and in combination with BDI-measured severity of depressive symptom, predicted elevated concentration of IL-6 in healthy men (Suarez, 2003). In analysis that included demographic, behavioral, and biological factors, higher hostility scores were associated with higher IL-6, but only in those subjects with high BDI scores. Conversely, depressive-symptom severity was associated with higher IL-6, but only among hostile men. The observation that hostility interacts with severity of depressive symptoms to predict IL-6 was subsequently replicated by Stewart et al. (2008). Stewart et al. measured hostility using the CMHO and severity of depressive symptoms using the BDI-II. As observed in the Suarez study, hostility was associated with higher levels of CRP and IL-6, but only among those subjects who report high levels of depressive symptoms.
Although a test of the depression by hostility interaction was not conducted, Graham et al. (2006) examined the relation of hostility to changes in CRP and IL-6 over a 6-year follow-up period of older caregivers and noncaregivers. Administration of CMHO and BDI scales was conducted at yearly intervals during the 6-year follow-up period. Using structural equation modeling, Graham et al. developed and used a latent hostility factor with yearly CMHO scores as measured indicators. Graham et al. observed that hostility latent factor predicted CRP, but not IL-6 level, independent of demographic, behavioral, and biomedical factors as well as caregiver status. It was noted that inclusion of BDI score in the structural equation model attenuated the relation between hostility and CRP, even though depressed mood was associated with hostility but not with either CRP or IL-6. This pattern of observations led the authors to postulate that depressed mood may have an indirect effect on CRP via hostility, a pathway that is consistent with previous observations that hostility moderates the relationship between severity of depressive symptoms and markers of inflammation.
Not all studies have reported significant main effects for hostility or anger, and one study testing the interaction between hostility and depression resulted in a different form of the interaction. In a study of 100 healthy adults where half of the sample had a diagnosis of MDD, Miller et al. (2003) reported a significant hostility by severity of depressive-symptoms interaction predicting circulating (p. 183) levels of IL-6 and TNF-alpha. The form of the interaction, however, was dramatically different from what had been previously reported (Suarez, 2003). Using the Cook-Medley Hostility BDI, Miller et al. showed that increases in hostility were associated with higher circulating TNF-alpha and IL-6, but only among those subjects with reporting low levels of depressive symptoms. Hostility was not significantly associated with either marker in subjects reporting moderate to high levels of depressive symptoms. Differences in the form of the interaction as reported by Suarez (2003) and Miller et al. (2003) may reflect subject characteristics. In the Miller et al. study, half the subjects had previously been diagnosed with MDD, whereas none of the subjects in the Suarez study had a diagnosis of MDD. Such differences may account in part for the variation in the form of the interaction.
The previously described studies assessed not only hostility but also depression. This allowed for some, but not all, to examine the hostility-depressed mood interaction. Other studies, however, have only examined the main effect of hostility. In 2005, Coccaro (2006) examined the relation of hostility and aggression to CRP in healthy young adults meeting clinical criteria for personality disorder. Using the Buss-Durkee Hostility Inventory (BDHI), Coccaro showed that subjects with elevated CRP scored higher on the hostility and aggression subscales of the BDHI. Controlling for possible confounding variables did not eliminate the significance of the relationships. The results reported by Coccaro replicated and extended earlier work by Suarez (Suarez et al., 2002) showing that subscales of the BPHI, and specifically those measuring the dimensions of hostility and physical and verbal aggression, predicted biomarkers of inflammation.
Similarly, Shivpuri et al. (2011) examined the main effect of dimensions of anger and hostility on soluble intracellular adhesion molecule (sICAM). Subjects were apparently healthy middle-aged Mexican-American women. Anger was assessed using the Speilberger Trait Anger Scale, which yields a total anger score and scores on two subscales: anger temperament and anger reaction. Anger temperament is conceptualized as the predisposition toward quick, unprovoked or minimally provoked anger. Anger reaction, on the other hand, refers to anger aroused in response to frustration, criticism, or unfair treatment. Hostility was measured using the 6-item cynicism scale from the Cook-Medley. Results indicated no association between trait anger total score or score on the anger temperament and sICAM-1. Anger reaction was marginally associated with sICAM-1. Cynical hostility was significantly associated with sICAM-1, even after controlling for demographic, biological, and behavioral covariates. The reported analysis did not test the interaction between hostility and dimensions of anger. However, based on a request by this author to the investigators, Shivpuri et al. examined the interaction between hostility and anger. Results showed that although the hostility by total anger and the hostility by anger-reaction interactions were not significant, the hostility by anger temperament was significant in predicting sICAM. Decomposition of the significant two-way interaction revealed that hostility was not associated with sICAM for those subjects reporting low (p = .98) and moderate (p = .06) levels of angry temperament. Only among those subjects who reported that they are quick to anger, even when unprovoked or minimally provoked, was hostility significantly associated with sICAM (p = .005) (personal communication, June 30, 2011). These novel findings add to the proposed hypothesis that hostility, in conjunction with anger, predicts levels of inflammation.
In most psychoimmunological studies, the primary measures of inflammation have been CRP and IL-6. A number of studies, however, have employed more comprehensive approaches to the measurement of inflammation. For example, both Janicki-Deverts et al. (Janicki-Deverts, Cohen, & Doyle, 2010) and Mommersteeg et al. (Mommersteeg, Vermetten, Kavelaars, Geuze, & Heijnen, 2008) assessed degree of inflammation using in vitro stimulated production of pro-inflammatory and anti-inflammatory cytokines. Janicki-Deverts et al. (2010) examined the relation of hostility to stimulated production of Th1 cytokines IL-2, TNF-alpha, INF-gamma, and Th2 cytokines IL-4, IL-5, and IL-10 in a sample of healthy men and women. Hostility was assessed using the 20-item CMHO scale and severity of depressive symptoms was measured using the CESD. The primary analysis used the total CMHO score, whereas exploratory analysis examined the relation of the cynicism, hostile affect, and aggression subscales of the CMHO to inflammatory biomarkers. When controlling for confounding variables including depression, results revealed that the total CMHO score predicted both stimulated production of TNF-alpha and INF-gamma, replicating and extending the initial findings reported by Suarez et al. (Suarez, Krishnan, & Lewis, 2003). In contrast, CMHO failed to predict Th2 cytokines. Results of (p. 184) the exploratory analysis suggested that all three Th1 cytokines were associated with scores on the cynicism subscale but not on the subscales of hostile affect and aggressive responding. The authors concluded that hostility, and particularly the cognitive component of hostility, was significantly associated with greater stimulated production of inflammatory cytokines and not with a production of anti-inflammatory cytokines. Although the authors noted that the inclusion of CESD score in the model did not attenuate the strength of the associations between hostility, cynicism, and Th1 cytokines, they did not test whether the interaction of CMHO by CESD predicted inflammation.
Mommersteeg et al. (2008) also examined the relation of CMHO hostility to an array of stimulated production of pro-inflammatory and anti-inflammatory cytokines. Using a multiplex platform, Mommersteeg et al. measured stimulated levels of IL-2, TNF-alpha, INF-gamma, IL-4, IL-5 IL-10, IL-6, chemokines, MCP-1, RANTES, IP-10, and MIF in healthy male military personnel prior to combat deployment. To reduce the number of statistical tests, Mommersteeg et al. conducted a factor analysis with an oblique rotation on the array of immune markers. Results indicated a four-factor structure: a pro-inflammatory factor with IL-2, TNF-alpha, and INF-gamma as positive indicators, an anti-inflammatory factor with IL-4, IL-5, and IL-10 as negative indicators, an IL-6/chemokine factor with IL-6MCP-1, and IR10 as indicators, and a one-item factor migration inhibitory factors (MIF). Using factor scores derived from linear combinations, Mommersteeg et al. showed that hostility was positively associated with both the “pro-inflammatory” and “anti-inflammatory” factor scores and negatively associated with the IL-6/chemokine factor score. As noted by the authors, that both anti-inflammatory and pro-inflamamatory factors were positively associated with hostility was “remarkable.” Such surprising findings may reflect the difficulty in interpretation of negative loadings (Lawley & Maxwell, 1963), and in this case, the interpretation of the negative factor loadings defining the anti-inflammatory factor. As published, the anti-inflammatory factor was defined by large negative loadings for IL-4, IL-5, and IL-10. Using these loadings, larger negative factors scores reflect higher stimulated production of these cytokines. In contrast, smaller negative scores would reflect lower stimulated production. Given this interpretation of the factor score, a positive association between the anti-inflammatory factor score and CMHO scores suggests that hostile men (those with higher CMHO scores) showed lower production of anti-inflammatory cytokines (smaller negative factor scores) indicative of a blunted anti-inflammatory response to LPS. Men with low-hostility scores, on the other hand, would exhibit greater stimulated production of anti-inflammatory cytokines corresponding to a larger negative factor score. There is no doubt that negative loadings are difficult to interpret, and this is an excellent example of the conceptualization of the factors. However, this should not get in the way of what are important findings as they relate to hostility and inflammation.
The proposed alternative interpretation of the factors loadings deviates from that proposed by the study authors, suggesting that hostility was associated with “greater” production of anti-inflammatory cytokines. Given the negative loadings and the positive beta-value for the anti-inflammatory factor score predicting hostility suggests that hostile men showed “smaller” stimulated production of anti-inflammatory cytokines and low-hostility men showed larger stimulated production. This interpretation, in combination with the observation that high CMHO scores were associated with greater stimulated production of pro-inflammatory cytokines, suggests that hostile men show an imbalance in the pattern of pro-inflammatory to anti-inflammatory cytokines. Such an imbalance has been noted in cardiology studies that have used factor analysis to derive linear combinations of inflammatory and anti-inflammatory factors. As an example, an anti-inflammatory factor, defined by positive loadings on IL-10 and high-density-lipoprotein (HDL) cholesterol, was the best prospective predictor of adverse cardiac events in a one-year follow-up of patients with acute coronary syndrome (ACS) (Tziakas et al., 2007). In contrast to the Mommersteeg et al. study, Tziakas et al. defined the anti-inflammatory factor by positive loadings on IL-10 and HDL, allowing for a more straightforward interpretation of the factor.
The proposed interpretation of the factor analysis and its impact on the interpretation of the results provides new evidence that hostile persons show an imbalance between levels of anti-inflammatory cytokines and levels of pro-inflammatory cytokines. Not surprisingly, this is not the only biological imbalance noted in hostile persons. Studies have shown differences in sympathovagal balance in hostile persons that is characterized by a shift toward sympathetic dominance (Demaree & Everhart, 2004; Sloan et al., 1994). In one study, sympathovagal balance was negatively associated with (p. 185) stimulated expression of IL-6 by peripheral monocytes in women but not men (O'Connor, Motivala, Valladares, Olmstead, & Irwin, 2007). Together, these data support the general hypothesis that the increased risk of CVD associated with hostility may be due to biological imbalances across various physiological systems that include immune and nervous systems. Such an interpretation is parsimonious with previous findings suggesting that inflammatory and anti-inflammatory balance predicts the progression of the atherosclerotic lesion.
Although only measuring one cytokine, Sjogren et al. (Sjogren, Leanderson, Kristenson, & Ernerudh, 2006) examined the relation of IL-6 levels in saliva, serum, and supernatants of peripheral blood mononuclear cells (PBMC) before and after stimulation with LPS to psychosocial measures of hostility (CMHO), severity of depressive state (Major Depressive Scale), vital exhaustion (Maastricht Vital Exhaustion Scale), hopelessness (2-items from the Kuopio study), and self-esteem/coping (Pearlin's scale). Correlational analysis controlling for demographic, health, and behavioral factors revealed that serum IL-6 was positively correlated with cynicism, hostile affect, hopelessness, severity of depressed mood, and vital exhaustion. In contrast, stimulated production of IL-6 was negatively correlated with cynicism, severity of depressed mood, and vital exhaustion. The results using serum IL-6 replicate and extend previous observations of the relation of hostility and depressed mood to IL-6. However, that cynicism and depressed mood are negatively associated with stimulated production of IL-6 is in contrast to reports of positive associations among these measures. At this time, it is not clear why the pattern of correlations differs so dramatically between psychological factors and in vivo and in vitro measures of IL-6. It is possible that salivary, serum, and stimulated production reflect different regulatory mechanisms. What is apparent is that in vivo and in vitro IL-6 are not correlated and reflect different aspects of the immune system.
To date, there are two prospective studies that examined whether hostility and anger precede inflammation (Boyle et al., 2007; Elovainio et al., 2011). In a study of Vietnam male veterans, Boyle et al. (2007) showed that MMPI-derived measures of hostility and anger independently predicted 10-year increases in C3, but not C4, complement. These associations were significant even when demographic, health, and behavioral factors were included in the statistical analysis. What was also noted was that, compared to the unique association between C3 and each psychological factor, the shared variance among hostility, anger, and depression was the best predictor of C3 changes over time. These latter findings underscore the need to focus on the interaction among psychological factors that are known to cluster in individuals in light of the potential importance of the nature of these interactions regarding development of CVD (Boyle, Michalek, & Suarez, 2007).
Elovainio et al. (2011) examined the relation of cynical hostility, assessed using seven items of the cynicism scale from the CMHO, to CRP measured 9 years later. Subjects were Finnish children and adolescents participating in the Young Finns Study. Hostility was measured at baseline when subjects were 3 to 18 years and CRP was measured 9 years later when subjects were 24 to 39 years of age. After controlling for demographic, metabolic, and behavioral factors, and baseline level of CRP, cynical hostility was associated with CRP in women but not in men. In this same study, hostility only predicted aspects of the metabolic syndrome in women but not in men. Given that CRP is associated with the metabolic syndrome, the gender-disparity observed in the relationship between hostility and both factors is not surprising. Findings from Elavainio et al. extend observations from a cross-sectional study of healthy men and women who showed that hostility and anger were associated with insulin resistance, fasting glucose, and fasting insulin in women but not in men (Suarez, 2006).
Cross-sectional and prospective studies suggest that depressive mood, hostility, and anger are positively associated with both in vivo and in vitro measures of inflammation in both healthy adults and minors and in patient populations. For the most part, these associations appear to be independent of sociodemographic, behavioral, and biological factors known or suspected of being associated with CVD and inflammation. The most consistent finding among studies is that the relation of anger, hostility, and depression to measures of inflammation are complex. Moreover, the presence of more than one psychological factor is potentially synergistic. Thus, a number of studies have shown that depression and hostility interacted to predict inflammation in many cross-sectional studies. Similarly, one study showed that hostility and angry temperament predicted inflammation. Lastly, there is emerging (p. 186) evidence that hostility, anger, and depression identify a group at particularly high risk for CHD, a group that showed consistent elevations in C3 over at 10-year period.
Such complex interactions are not unexpected as related to the development and progression of ACVD. It is acknowledged that ACVD is a multicausal disease that reflects the influences of many factors, both unmodifiable factors, such as gender, age, family history, and modifiable factors, such as lipids, weight, and physical activity. For example, it is well-established that inflammation and lipoprotein constituents are independently associated with CVD risk (Ridker & Morrow, 2003). Recent studies have focused their efforts on examining the interactions among these factors with the assumption being that the presence of these two factors would identify a high-risk subgroup (Corsetti et al., 2006). The results of that study showed that high CRP and cholesterol identified a subgroup at particularly high risk (hazard ratio = 2.24, 95% CI 1.12, 4.49, p = .03) for a recurrent evident. What was surprising was that HDL, usually associated with a protective effect, was associated with increased risk in this particular subgroup. Similarly, Ridker et al. (Ridker, Hennekens, Buring, & Rifai, 2000) showed that the combination of total cholesterol and IL-6 predicted CV risk in women, with higher IL-6 having a marginal effect in women with low total cholesterol. In contrast, in women with median cholesterol, high IL-6 was associated with greater risk, and in women with high cholesterol, high IL-6 has significant effects.
The same can be said for hostility, anger, and depression. One study has shown that the shared variance of these three factors is the best predictors of incident CHD over a 20+ year follow-up period. Similarly, in the presence of depression, lack of social integration has an additive effect on cardiac events (Naqvi, Naqvi, & Merz, 2005). Thus, it may be the case that the interaction of hostility, anger, and depression is not the only one that may identify a subgroup at heightened risk, but other interactions, such as depression by social isolation, may show similar predictive power.
Future studies examining the relation of psychological factors to inflammation should adopt an approach that emphasizes tests of higher-order interactions. In the case of anger, hostility, and depression, such an approach is reasonable, given the likelihood that dysregulation of the serotonergic system may underlie anger, hostility, and depression. Aside from the possibility of a shared biological mechanism, it is well established that these factors tend to cluster in individuals. Similar to current approaches in cardiovascular research in which subgroups at heightened risk for disease are characterized by the presence of more than one risk factors, the presence of two or more psychological factors may identify a subgroup of individuals who are particularly at high risk for disease.
Potential Moderators and Mediators
Whatever approach is examined, whether researchers emphasize main effects or interaction, some have speculated that the relation of psychological factors to inflammation is moderated or mediated by traditional risk factors, such as gender, adiposity, and insulin resistance. Few studies, however, have examined these moderating effects.
Danner et al. (Danner, Kasl, Abramson, & Vaccarino, 2003) examined the relation of a lifetime history of major depressive episode to CRP in over 6,000 participants from the Third National Health and Nutrition Examination Survey. Among men, a history of depression was associated with elevated CRP, but this was not observed in women.
In another study, the Zung Self-Rating Depression Scale was used to examine the relation of depression to CRP in a sample of adult men and women free of cardiovascular disease (Panagiotakos et al., 2004). Although women scored significantly higher on the Zung Depression Scale than men, depression was similarly correlated to CRP, white blood cell count, and fibrinogen in both men and women. Differences in life styles and demographic characteristics appeared to partially be responsible for this association, however, control of these factors did significantly attenuate the relationship.
In two studies conducted in my laboratory, we have shown that BDI scores were similarly associated with stimulated levels of cytokines production by peripheral monocytes in men and women (Suarez et al., 2003; Suarez et al., 2004). The fact that these were independent studies did not allow for the BDI by gender interaction to be evaluated. Comparison of results, however, showed that BDI score predicted TNF-alpha and IL-8 in both men and women. Beck Depression Inventory predicted IL-1alpha and IL-1beta in men but not in women, suggesting that BDI may be a more robust predictor of inflammation in men.
(p. 187) Gender may also moderate the relation of anger and hostility to CVD and measures of inflammation. A recent meta-analysis (Chida & Steptoe, 2009) indicated that although there was an overall effect of hostility and anger on measures of CVD in healthy populations, this association was stronger in healthy men. Those findings contrast the results from the study by Elovainio et al., who showed that cynical hostility predicted increases in CRP over a 9-year follow-up period but only in women and not in men.
Given that women report greater severity of depressive symptoms and men report greater levels of hostility and anger, it is recommended that future studies examine gender-related interactions to confirm or exclude the possibility that the relationship between psychological factors and measures of inflammation is different or similar in men and women.
Adiposity is related to increased risk of abdominal adiposity and obesity in later life. Since adiposity is positively associated with inflammation, some have suggested that adiposity may mediate the relation of depression to future inflammation. Some, but not all, studies have reported that controlling for adiposity attenuates the relationship between depression and IL-6 and CRP. For example, Miller et al. used structural equation modeling and cross-sectional data to show that, in a sample of healthy men and women, depression was associated with increases in weight that mediated the relation of depression to CRP and IL-6 (Miller, Freedland, Carney, Stetler, & Banks, 2003). Similar observations were reported by Ladwig et al., (Ladwig, Marten-Mittag, Lowel, Doring, & Koenig, 2003) who showed that in a large sample of healthy middle-aged men, hostility was positively associated with CRP, but only in those men who were obese, defined by body mass index (BMI) equal or greater than 30 kg/m2. A similar relationship was observed in obese female patients scheduled for obesity surgery. Dixon et al. (2008) showed that the strongest predictor of elevated CRP was BMI, followed by female gender, estrogen therapy, higher BDI score, and insulin resistance. Given that all subjects were obese, no interaction was tested.
Not all studies have shown that obesity or adiposity mediates the depression-inflammation pathway. A small study of 63 obese women with and without depression showed no difference in BMI, TNF-alpha, and leptin among nondepressed, mild depression, and severe depression groups (Olszanecka-Glinianowicz et al., 2009).
It is important to note that the evidence for the mediating role of adiposity in the depression-inflammation pathway has been shown solely in studies using circulating inflammatory biomarkers and specifically CRP and IL-6. In studies that have used in vitro measures, the influence of measures of adiposity has been negligible, and the inclusion of these indicators in statistical models does not attenuate the relation of depressed mood to cytokine production (Suarez et al., 2003; Marsland et al., 2007; Suarez, Lewis, Krishnan, & Young, 2004). This is not unexpected given that levels of circulating inflammatory proteins are more strongly influenced by adiposity and factors beyond monocyte/macrophages and T lymphocytes (Shelton & Miller, 2011). Whatever the case may be, it is recommended that future studies examine the moderating and mediating effects of adiposity in the relation of depression to inflammation.
The role of adiposity has not been examined in the relation of hostility and anger to inflammation. Like depression, hostility has been related to increased weight over time (Siegler, Peterson, Barefoot, & Williams, 1992). It is possible that the relationship between hostility and inflammation is similarly mediated by adiposity. To date, no study has examined this hypothesis. Future studies should examine whether adiposity mediates the relation of hostility to inflammation.
Additional Recommendations for Future Studies
It is likely that the association between measures of inflammation and psychological factors is complex and that simple main-effect and additive models do not capture potential interactions among psychological factors as well as the potential moderating and mediating effects of biological, social, and demographic factors. With few exceptions, most studies have examined one psychological construct in isolation with post-hoc or exploratory analysis deconstructing the factor into its various components. Although such an approach is statistically sound, it fails to reflect the dynamic interplay among interrelated factors. As underscored by Suls and Bunde, there is significant construct and measurement overlap between these three factors (Suls et al., 2005). In echoing their recommendations, it is advised that future studies develop more complex models to be used in examining the relation of psychological factors to disease outcomes and putative (p. 188) mechanisms. Toward attaining this goal, the assessment of multiple psychological dimensions can provide more precise conceptualization and yield better empirical descriptions than those afforded by any single measure no matter how reliable.
One emerging hypothesis that has garnished some attention is that certain genetic polymorphisms may explain the link between psychological factors and inflammation (Almeida et al., 2009; Lotrich, Ferrell, Rabinovitz, & Pollock, 2010). For example, in hepatitis patients without MDD, the A allele in the promoter region of the TNF-alpha gene (A-308G), associated with higher plasma levels, was associated with increases in labile anger following treatment with interferon-alpha (Lotrich et al., 2010). The A allele was not associated with worsening depression. Interestingly, the serotonin transporter polymorphism did not predict labile anger.
Another study examined the polymorphisms of the CRP gene in a large sample of men and women (Almeida et al., 2009). Results from this study had already indicated that higher concentrations of CRP (〉= 3 mg/l) were associated with a twofold increase in the odds of depression. Investigators then examined whether polymorphisms of the CRP gene associated with higher basal and simulated increases in CRP (rs1130864 C 〉 T variant) and those associated with lower basal and stimulated production (rs1205 G 〉 A variant) would be associated with the severity of depressive symptoms assessed using the 15-item Geriatric Depression Scale (GDS-15). In contrast to the expected association, the odds of having higher GDS-15 scores was associated with the rs1205 G 〉 A genetic polymorphism associated with lower CRP. Although surprising given that greater severity of depressive symptoms was associated with higher CRP concentration, the result of the genetic analysis may reflect potential confounding with other factors such as age and medical co-morbidities. It may be the case that shared genetic influences between CRP and depression may be observed only in samples of medically healthier individuals.
The prevailing understanding of atherosclerosis is that it is an inflammatory disease. In light of this, studies have identified various biomarkers of inflammation that are associated with CVD risk in healthy men and women and recurrent events in CHD patients. These associations have led many to examine the relation of inflammatory biomarkers to psychological factors associated with CVD. Most studies have focused on measures of depression with the results suggesting that depression, whether operationalized as a clinical diagnosis or as a continuum of severity of depressed symptoms, is associated with biomarkers on inflammation in both cross-sectional and longitudinal studies. Although the data are equivocal, there is evidence to suggest that this association is bidirectional in that depression precedes inflammation and inflammation precedes depression.
Although fewer studies have examined the role of hostility and anger, the available evidence suggests that hostility and anger are independently associated with inflammation. What is emerging is that the interaction of depression, hostility, and anger identifies a subgroup that shows levels of inflammation higher than those with only one or two of these factors. It is recommended that future studies examine the joint effects among these factors and with others.
For more information on concepts introduced in this chapter, see also Cohen, this volume.
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