Physiologic Alterations in Bodily Systems in Response to Disease Processes

Physiologic Alterations in Bodily Systems in Response to Disease Processes

In your discussion, be sure to apply knowledge of the physiologic alterations in bodily systems in response to disease processes Case Study Assignment Requirements 1. Make sure all of the topics in the case study have been addressed. 2. Cite at least three references in your case study paper; this may include peer-reviewed journal articles, textbooks, or evidence-based practice websites to support the content. 3. All reference sources must be within 5 years. 4. Do not use sources such as Wikipedia or UpToDate as a reference. 5. Assignments must have at least four full pages of analytic content, double-spaced (the cover and reference pages do not count in the page count, but must be included with the assignment), and follow APA 7th edition format. Case Study 4: Disorders of Blood Flow and Blood Pressure Regulation Deborah is 56 years old, smokes a half a pack of cigarettes a day, and is overweight. Her friend wants her to come to a local women’s fitness class she attends once a week. She knows Deborah’s dad had died of an acute myocardial infarction when he was 56, and she fears, seeing Deborah’s lifestyle, the same fate awaits her friend.  Physiologic Alterations in Bodily Systems in Response to Disease Processes.What she did not know was that Deborah had also been to her doctor for her annual physical where she was told her LDLs was 180 mg/dL, HDLs were 36 mg/dL, and cholesterol was 239 mg/dL. 1. What are Deborah’s known risk factors for coronary heart disease? 2. Deborah’s doctor referred her to a dietician for strict dietary therapy, hoping the intervention would raise her HDL and lower her LDL and cholesterol levels. Why is diet modification necessary to control and moderate the lipids indicated? 3. Deborah’s doctor also gave her pamphlets describing strategies to stop smoking and a list of exercise ideas she might want to try. How is smoking thought to contribute to atherosclerotic plaque formation? Why would exercise have a positive effect on Deborah’s lipid profile? 4. Atherosclerosis is thought to be an inflammatory disorder. What is the role of macrophages in the formation of atherosclerotic plaques? What is the significance of elevated serum hs-CRP levels in at-risk individuals? Textbook for course for 1 reference: Norris, T. L. (2019). Porth’s pathophysiology: Concepts of altered health states (10th ed.). Philadelphia, PA: Wolters Kluwer.

The present report meta-analyzes more than 300 empirical articles describing a relationship between psychological stress and parameters of the immune system in human participants. Acute stressors (lasting minutes) were associated with potentially adaptive upregulation of some parameters of natural immunity and downregulation of some functions of specific immunity. Brief naturalistic stressors (such as exams) tended to suppress cellular immunity while preserving humoral immunity. Chronic stressors were associated with suppression of both cellular and humoral measures. Effects of event sequences varied according to the kind of event (trauma vs. loss). Subjective reports of stress generally did not associate with immune change. In some cases, physical vulnerability as a function of age or disease also increased vulnerability to immune change during stressors. Physiologic Alterations in Bodily Systems in Response to Disease Processes.

Since the dawn of time, organisms have been subject to evolutionary pressure from the environment. The ability to respond to environmental threats or stressors such as predation or natural disaster enhanced survival and therefore reproductive capacity, and physiological responses that supported such responses could be selected for. In mammals, these responses include changes that increase the delivery of oxygen and glucose to the heart and the large skeletal muscles. The result is physiological support for adaptive behaviors such as “fight or flight.” Immune responses to stressful situations may be part of these adaptive responses because, in addition to the risk inherent in the situation (e.g., a predator), fighting and fleeing carries the risk of injury and subsequent entry of infectious agents into the bloodstream or skin. Any wound in the skin is likely to contain pathogens that could multiply and cause infection (Williams & Leaper, 1998). Stress-induced changes in the immune system that could accelerate wound repair and help prevent infections from taking hold would therefore be adaptive and selected along with other physiological changes that increased evolutionary fitness.


Modern humans rarely encounter many of the stimuli that commonly evoked fight-or-flight responses for their ancestors, such as predation or inclement weather without protection. However, human physiological response continues to reflect the demands of earlier environments. Threats that do not require a physical response (e.g., academic exams) may therefore have physical consequences, including changes in the immune system. Indeed, over the past 30 years, more than 300 studies have been done on stress and immunity in humans, and together they have shown that psychological challenges are capable of modifying various features of the immune response. In this article we attempt to consolidate empirical knowledge about psychological stress and the human immune system through meta-analysis. Both the construct of stress and the human immune system are complex, and both could consume book-length reviews.  Physiologic Alterations in Bodily Systems in Response to Disease Processes.Our review, therefore, focuses on those aspects that are most often represented in the stress and immunity literature and therefore directly relevant to the meta-analysis.

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Conceptualizing Stress

Despite nearly a century of research on various aspects of stress, investigators still find it difficult to achieve consensus on a satisfactory definition of this concept. Most of the studies contributing to this review simply define stress as circumstances that most people would find stressful, that is, stressors. We adopted Elliot and Eisdorfer’s (1982) taxonomy to characterize these stressors. This taxonomy has the advantage of distinguishing among stressors on two important dimensions: duration and course (e.g., discrete vs. continuous). The taxonomy includes five categories of stressors. Acute time-limited stressors involve laboratory challenges such as public speaking or mental arithmetic. Brief naturalistic stressors, such as academic examinations, involve a person confronting a real-life short-term challenge. In stressful event sequences, a focal event, such as the loss of a spouse or a major natural disaster, gives rise to a series of related challenges. Although affected individuals usually do not know exactly when these challenges will subside, they have a clear sense that at some point in the future they will. Chronic stressors, unlike the other demands we have described, usually pervade a person’s life, forcing him or her to restructure his or her identity or social roles. Another feature of chronic stressors is their stability—the person either does not know whether or when the challenge will end or can be certain that it will never end. Examples of chronic stressors include suffering a traumatic injury that leads to physical disability, providing care for a spouse with severe dementia, or being a refugee forced out of one’s native country by war. Distant stressors are traumatic experiences that occurred in the distant past yet have the potential to continue modifying immune system function because of their long-lasting cognitive and emotional sequelae (Baum, Cohen, & Hall, 1993).Physiologic Alterations in Bodily Systems in Response to Disease Processes.  Examples of distant stressors include having been sexually assaulted as a child, having witnessed the death of a fellow soldier during combat, and having been a prisoner of war.

In addition to the presence of difficult circumstances, investigators also use life-event interviews and life-event checklists to capture the total number of different stressors encountered over a specified time frame. Depending on the instrument, the focus of these assessments can be either major life events (e.g., getting divorced, going bankrupt) or minor daily hassles (e.g., getting a speeding ticket, having to clean up a mess in the house). With the more sophisticated instruments, judges then code stressor severity according to how the average person in similar biographical circumstances would respond (e.g., S. Cohen et al., 1998; Evans et al., 1995).

A smaller number of studies enrolled large populations of adults who were not experiencing any specific difficulty and examined whether their immune responses varied according to their reports of perceived stress, intrusive thoughts, or both. Other studies have examined stressed populations, in which a larger range of subjective responses may be detected. This work grows out of the view that people’s biological responses to stressful circumstances are heavily dependent on their appraisals of the situation and cognitive and emotional responses to it (Baum et al., 1993; Frankenhauser, 1975; Tomaka, Blascovich, Kibler, & Ernst, 1997).

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Overview of the Immune System

As many behavioral scientists are unfamiliar with the details of the immune system, we provide a brief overview. For a more complete treatment, the reader is directed to the sources for the information presented here (Benjamini, Coico, & Sunshine, 2000; Janeway & Travers, 1997; Rabin, 1999). Critical characteristics of various immune components and assays are also listed in Table 1.

Table 1

Immune Parameters Reported and Critical Characteristics

Parameter Arm of immune system Function Cell surface marker
 Leukocytes Natural All white cells
 Granulocytes Natural Inflammation
 Neutrophils Natural Inflammation, phagocytosis
 Eosinophils Natural Inflammation
 Monocytes/macrophages Natural Inflammation, phagocytosis
 Lymphocytes Specific All lymphocytes CD2
 T lymphocytes Specific Cellular immunity CD3, CD45RA (naive)
 T-helper lymphocytes Specific Cellular (Th1) or humoral (Th2) immunity CD4
 T-cytotoxic lymphocytes Specific Cellular (Th1) immunity CD8
 B lymphocytes Specific Humoral (Th2) immunity CD19, CD20
 Activated B lymphocytes Specific Humoral (Th2) immunity CD23, CD30
 Natural killer cells Natural Cellular (Th1) immunity CD16, CD56, CD57
 IgA, IgG, IgM Specific Humoral (Th2) immunity
 Anti-EBV IgG Specific Index of EBV replication/activation
 Anti-HSV IgG Specific Index of HSV replication/activation
 Anti-influenza IgG postimmunization Specific Humoral (Th2) immunity
 Interleukin-1β Natural Inflammation, T cell activation
 Interleukin-2 Specific T cell activation (Th1)
 Interleukin-4 Specific B cell activation, antibody production (Th2)
 Interleukin-6 Natural Inflammation
 Interleukin-10 Specific Inhibits T cell activation (Th2)
 Interferon-γ Natural and specific Macrophage, natural killer cell, and T cell activation (Th1)
 Tumor necrosis factor-α Natural Inflammation
 Complement Natural Increases effectiveness of natural immunity C3
Functional assay
 Neutrophil superoxide release Natural Inflammation
 Natural killer cell cytotoxicity Natural Cellular (Th1) immunity
 Proliferation to ConA Specific Cellular (Th1) immunity (T cell proliferation)
 Proliferation to PHA Specific Cellular (Th1) immunity (T cell proliferation)
 Proliferation to PWM Specific Cellular (Th1) and humoral (Th2) immunity (T and B cell proliferation)

Note. Th1 = cells that direct a response to intracellular pathogens; Th2 = cells that direct a response to extracellular pathogens; IgA = immunoglobulin A; IgG = immunoglobulin G; IgM = immunoglobulin M; EBV = Epstein-Barr virus; HSV = herpes simplex virus; ConA = concanavalin A; PHA = phytohemagglutinin; PWM = pokeweed mitogen. Physiologic Alterations in Bodily Systems in Response to Disease Processes.

Components of the Immune System

There are several useful ways of dividing elements of the immune response. For the purposes of understanding the relationship of psychosocial stressors to the immune system, it is useful to distinguish between natural and specific immunity. Natural immunity is an immune response that is characteristic not only of mammals but also lower order organisms such as sponges. Cells involved in natural immunity do not provide defense against any particular pathogen; rather, they are all-purpose cells that can attack a number of different pathogens1 and do so in a relatively short time frame (minutes to hours) when challenged. The largest group of cells involved in natural immunity is the granulocytes. These cells include the neutrophil and the macrophage, phagocytic cells that, as their name implies, eat their targets. The generalized response mounted by these cells is inflammation, in which neutrophils and macrophages congregate at the site of injury or infection, release toxic substances such as oxygen radicals that damage invaders, and phagocytose both invaders and damaged tissue. Macrophages in particular also release communication molecules, or cytokines, that have broad effects on the organism, including fever and inflammation, and also promote wound healing. These proinflammatory cytokines include interleukin(IL)-1, IL-6, and tumor necrosis factor alpha (TNFα). Other granulocytes include the mast cell and the eosinophil, which are involved in parasitic defense and allergy.

Another cell involved in natural immunity is the natural killer cell. Natural killer cells recognize the lack of a self-tissue molecule on the surface of cells (characteristic of many kinds of virally infected and some cancerous cells) and lyse those cells by releasing toxic substances on them. Natural killer cells are thought to be important in limiting the early phases of viral infections, before specific immunity becomes effective, and in attacking self-cells that have become malignant.

Finally, complement is a family of proteins involved in natural immunity.Physiologic Alterations in Bodily Systems in Response to Disease Processes.  Complement protein bound to microorganisms can up-regulate phagocytosis and inflammation. Complement can also aid in antibody-mediated immunity (discussed below as part of the specific immune response).

Specific immunity is characterized by greater specificity and less speed than the natural immune response. Lymphocytes have receptor sites on their cell surfaces. The receptor on each cell fits with one and only one small molecular shape, or antigen, on a given invader and therefore responds to one and only one kind of invader. When activated, these antigen-specific cells divide to create a population of cells with the same antigen specificity in a process called clonal proliferation, or the proliferative response. Although this process is efficient in terms of the number of cells that have to be supported on a day-to-day basis, it creates a delay of up to several days before a full defense is mounted, and the body must rely on natural immunity to contain the infection during this time.

There are three types of lymphocytes that mediate specific immunity: T-helper cells, T-cytotoxic cells, and B cells. The main function of T-helper cells is to produce cytokines that direct and amplify the rest of the immune response. T-cytotoxic cells recognize antigen expressed by cells that are infected with viruses or otherwise compromised (e.g., cancer cells) and lyse those cells. B cells produce soluble proteins called antibody that can perform a number of functions, including neutralizing bacterial toxins, binding to free virus to prevent its entry into cells, and opsonization, in which a coating of antibody increases the effectiveness of natural immunity. There are five kinds of antibody: Immunoglobulin (Ig) A is found in secretions, IgE binds to mast cells and is involved in allergy, IgM is a large molecule that clears antigen from the bloodstream, IgG is a smaller antibody that diffuses into tissue and crosses the placenta, and IgD is of unknown significance but may be produced by immature B cells.

An important immunological development is the recognition that specific immunity in humans is composed of cellular and humoral responses. Cellular immune responses are mounted against intracellular pathogens like viruses and are coordinated by a subset of T-helper lymphocytes called Th1 cells. In the Th1 response, the T-helper cell produces cytokines, including IL-2 and interferon gamma (IFNγ). These cytokines selectively activate T-cytotoxic cells as well as natural killer cells. Humoral immune responses are mounted against extracellular pathogens such as parasites and bacteria; they are coordinated by a subset of T-helper lymphocytes called Th2 cells. In the Th2 response, the T-helper cell produces different cytokines, including IL-4 and IL-10, which selectively activate B cells and mast cells to combat extracellular pathogens. Physiologic Alterations in Bodily Systems in Response to Disease Processes.

Immune Assays

Immune assays can quantify cells, proteins, or functions. The most basic parameter is a simple count of the number of cells of different subtypes (e.g., neutrophils, macrophages), typically from peripheral blood. It is important to have an adequate number of different types of immune cells in the correct proportions. However, the normal range for these enumerative parameters is quite large, so that “correct” numbers and proportions can cover a wide range, and small changes are unlikely to have any clinical significance in healthy humans.

Protein production—either of antibody or cytokines—can be measured in vitro by stimulating cells and measuring protein in the supernatant or in vivo by measuring protein in peripheral blood. For both antibody and cytokine, higher protein production may represent a more robust immune response that can confer protection against disease. Two exceptions are levels of proinflammatory cytokines (IL-1, IL-6, and TNFα) and antibody against latent virus. Proinflammatory cytokines are increased with systemic inflammation, a risk factor for poorer health resulting from cardiac disease, diabetes mellitus, or osteoporosis (Ershler & Keller, 2000; Luster, 1998; Papanicoloaou, Wilder, Manolagas, & Chrousos, 1998). Antibody production against latent virus occurs when viral replication triggers the immune system to produce antibodies in an effort to contain the infection. Most people become infected with latent viruses such as Epstein-Barr virus during adolescence and remain asymptomatically infected for the rest of their lives. Various processes can activate these latent viruses, however, so that they begin actively replicating. These processes may include a breakdown in cellular immune response (Jenkins & Baum, 1995). Higher antibody against latent viruses, therefore, may indicate poorer immune control over the virus.

Functional assays, which are performed in vitro, measure the ability of cells to perform specific activities. In each case, higher values may represent more effective immune function. Neutro-phils’ function can be quantified by their ability to migrate in a laboratory assay and their ability to release oxygen radicals. The natural killer cytotoxicity assay measures the ability of natural killer cells to lyse a sensitive target cell line. Physiologic Alterations in Bodily Systems in Response to Disease Processes. Lymphocyte proliferation can be stimulated with mitogens that bypass antigen specificity to activate cells or by stimulating the T cell receptor.

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Pathways Between Stress and the Immune System

How could stress “get inside the body” to affect the immune response? First, sympathetic fibers descend from the brain into both primary (bone marrow and thymus) and secondary (spleen and lymph nodes) lymphoid tissues (Felten & Felten, 1994). These fibers can release a wide variety of substances that influence immune responses by binding to receptors on white blood cells (Ader, Cohen, & Felten, 1995; Felten & Felten, 1994; Kemeny, Solomon, Morley, & Herbert, 1992; Rabin, 1999). Though all lymphocytes have adrenergic receptors, differential density and sensitivity of adrenergic receptors on lymphocytes may affect responsiveness to stress among cell subsets. For example, natural killer cells have both high-density and high-affinity β2-adrenergic receptors, B cells have high density but lower affinity, and T cells have the lowest density (Anstead, Hunt, Carlson, & Burki, 1998; Landmann, 1992; Maisel, Fowler, Rearden, Motulsky, & Michel, 1989). Second, the hypothalamic–pituitary–adrenal axis, the sympathetic–adrenal–medullary axis, and the hypothalamic–pituitary–ovarian axis secrete the adrenal hormones epinephrine, norepinephrine, and cortisol; the pituitary hormones prolactin and growth hormone; and the brain peptides melatonin, β-endorphin, and enkephalin. These substances bind to specific receptors on white blood cells and have diverse regulatory effects on their distribution and function (Ader, Felten, & Cohen, 2001). Physiologic Alterations in Bodily Systems in Response to Disease Processes. Third, people’s efforts to manage the demands of stressful experience sometimes lead them to engage in behaviors—such as alcohol use or changes in sleeping patterns—that also could modify immune system processes (Kiecolt-Glaser & Glaser, 1988). Thus, behavior represents a potentially important pathway linking stress with the immune system.

Maier and Watkins (1998) proposed an even closer relationship between stress and immune function: that the immunological changes associated with stress were adapted from the immunological changes in response to infection. Immunological activation in mammals results in a syndrome called sickness behavior, which consists of behavioral changes such as reduction in activity, social interaction, and sexual activity, as well as increased responsiveness to pain, anorexia, and depressed mood. This syndrome is probably adaptive in that it results in energy conservation at a time when such energy is best directed toward fighting infection. Maier and Watkins drew parallels between the behavioral, neuroendo-crine, and thermoregulatory responses to sickness and stress. The common thread between the two is the energy mobilization and redirection that is necessary to fight attackers both within and without.

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Models of Stress, the Immune System, and Health

Conceptualizations of the nature of the relationship between stress and the immune system have changed over time. Selye’s (1975) finding of thymic involution led to an initial model in which stress is broadly immunosuppressive. Early human studies supported this model, reporting that chronic forms of stress were accompanied by reduced natural killer cell cytotoxicity, suppressed lymphocyte proliferative responses, and blunted humoral responses to immunization (see S. Cohen, Miller, & Rabin, 2001; Herbert & Cohen, 1993;Kiecolt-Glaser, Glaser, Gravenstein, Malarkey, & Sheridan, 1996, for reviews). Diminished immune responses of this nature were assumed to be responsible for the heightened incidence of infectious and neoplastic diseases found among chronically stressed individuals (Andersen, Kiecolt-Glaser, & Glaser, 1994; S. Cohen & Williamson, 1991).

Although the global immunosuppression model enjoyed long popularity and continues to be influential, the broad decreases in immune function it predicts would not have been evolutionarily adaptive in life-threatening circumstances. Dhabhar and McEwen (1997, 2001) proposed that acute fight-or-flight stressors should instead cause redistribution of immune cells into the compartments in which they can act the most quickly and efficiently against invaders. In a series of experiments with mice, they found that during acute stress, T cells selectively redistributed into the skin, where they contributed to enhancement of the immune response. In contrast, during chronic stress, T cells were shunted away from the skin, and the immune response to skin test challenge was diminished (Dhabhar & McEwen, 1997). On the basis of these findings they proposed a biphasic model in which acute stress enhances, and chronic stress suppresses, the immune response. Physiologic Alterations in Bodily Systems in Response to Disease Processes.

A modification of this model posits that short-term changes in all components of the immune system (natural and specific) are unlikely to occur because they would expend too much energy to be adaptive in life-threatening circumstances. Instead, stress should shift the balance of the immune response toward activating natural processes and diminishing specific processes. The premise underlying this model is that natural immune responses are better suited to managing the potential complications of life-threatening situations than specific immune responses because they can unfold much more rapidly, are subject to fewer inhibitory constraints, and require less energy to be diverted from other bodily systems that support the fight-or-flight response (Dopp, Miller, Myers, & Fahey, 2000; Sapolsky, 1998).

Even with this modification of the biphasic model, neither it nor the global immunosuppression model sufficiently explains findings that link chronic stress with both disease outcomes associated with inadequate immunity (infectious and neoplastic disease) and disease outcomes associated with excessive immune activity (allergic and autoimmune disease). To resolve this paradox, some researchers have chosen to focus on how chronic stress might shift the balance of the immune response. The most well-known of these models hypothesizes that chronic stress elicits simultaneous enhancement and suppression of the immune response by altering patterns of cytokine secretion (Marshall et al., 1998). Th1 cytokines, which activate cellular immunity to provide defense against many kinds of infection and some kinds of neoplastic disease, are suppressed. This suppression has permissive effects on production of Th2 cytokines, which activate humoral immunity and exacerbate allergy and many kinds of autoimmune disease. This shift can occur via the effects of stress hormones such as cortisol (Chiappelli, Manfrini, Franceschi, Cossarizza, & Black, 1994). Th1-to-Th2 shift changes the balance of the immune response without necessarily changing the overall level of activation or function within the system. Because a diminished Th1-mediated cellular immune response could increase vulnerability to infectious and neoplastic disease, and an enhanced Th-2 mediated humoral immune response could increase vulnerability to autoimmune and allergic diseases, this cytokine shift model also is able to reconcile patterns of stress-related immune change with patterns of stress-related disease outcomes (Marshall et al., 1998).

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Who Is Vulnerable to Stress-Induced Immune Changes?

If the stress response in the immune system evolved, a healthy organism should not be adversely affected by activation of this response because such an effect would likely have been selected against. Although there is direct evidence that stress-related immunosuppression can increase vulnerability to disease in animals (e.g., Ben Eliyahu, Shakhar, Page, Stefanski, & Shakhar, 2000; Quan et al., 2001; Shavit et al., 1985; Sheridan et al., 1998), there is little or no evidence linking stress-related immune change in healthy humans to disease vulnerability. Even large stress-induced immune changes can have small clinical consequences because of the redundancy of the immune system’s components or because they do not persist for a sufficient duration to enhance disease susceptibility. In short, the immune system is remarkably flexible and capable of substantial change without compromising an otherwise healthy host.

However, the flexibility of the immune system can be compromised by age and disease. As humans age, the immune system becomes senescent (Boucher et al., 1998; Wikby, Johansson, Ferguson, & Olsson, 1994). As a consequence, older adults are less able to respond to vaccines and mount cellular immune responses, which in turn may contribute to early mortality (Ferguson, Wikby, Maxson, Olsson, & Johansson, 1995; Wayne, Rhyne, Garry, & Goodwin, 1990).Physiologic Alterations in Bodily Systems in Response to Disease Processes.  The decreased ability of the immune system to respond to stimulation is one indicator of its loss of flexibility.

Loss of self-regulation is also characteristic of disease states. In autoimmune disease, for example, the immune system treats self-tissue as an invader, attacking it and causing pathology such as multiple sclerosis, rheumatoid arthritis, Crohn’s disease, and lupus. Immune reactions can also be exaggerated and pathological, as in asthma, and suggest loss of self-regulation. Finally, infection with HIV progressively incapacitates T-helper cells, leading to loss of the regulation usually provided by these cells. Although each of these diseases has distinct clinical consequences, the change in the immune system from flexible and balanced to inflexible and unbalanced suggests increased vulnerability to stress-related immune dysregulation; furthermore, dysregulation in the presence of disease may have clinical consequences (e.g., Bower, Kemeny, Taylor, & Fahey, 1998).

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The Present Analysis

We performed a meta-analysis of published results linking stress and the immune system. We feel that this area is in particular need of a quantitative review because of the methodological nature of most studies in this area. For practical and economic reasons, many psychoneuroimmunology studies have a relatively small sample size, creating the possibility of Type II error. Furthermore, many studies examine a broad range of immunological parameters, creating the possibility of Type I error. A quantitative review, of which meta-analysis is the best example, can better distinguish reliable effects from those arising from both Type I and Type II error than can a qualitative review. Physiologic Alterations in Bodily Systems in Response to Disease Processes.

We combined studies in such a way as to test the models of stress and immune change reviewed above. First, we examined each stressor type separately, yielding separate effects for stressors of different duration and trajectory. Second, we examined both healthy and medical populations, allowing comparison of the effects of stress on resilient and vulnerable populations; along the same lines, we also examined the effects of age. Finally, we examined all immune parameters separately so that patterns of response (e.g., global immunosuppression vs. cytokine shift) would be clearer.

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Article Identification

Articles for the meta-analysis were identified through computerized literature searches and searches of reference lists. MEDLINE and PsycINFO were searched for the years 1960 –2001. Following the example of Herbert and Cohen (1993), we used the terms stresshassles, and life events in combination with the term immune to search both databases. The reference lists of 11 review articles on stress and the immune system (Benschop, Geenen, et al., 1998; Biondi, 2001; Cacioppo, 1994; S. Cohen & Herbert, 1996; S. Cohen et al., 2001; Herbert & Cohen, 1993; Kiecolt-Glaser, Cacioppo, Malarkey, & Glaser, 1992; Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002; Maier, Watkins, & Fleshner, 1994; O’Leary, 1990; Zorrilla et al., 2001) were then searched to identify additional articles.

We selected only articles that met a number of inclusion criteria. The first criterion was that the work had to include a measure of stress. This criterion could be met if a sample experiencing a stressor was compared with an unstressed control group, if a sample experiencing a stressor was compared with itself at a baseline that could reasonably be considered low stress, or if differing degrees of stress in a sample were assessed with an explicit measure of stress. This criterion was not met if, for example, anxiety—an affective state—was used as a proxy for stress, or it seemed likely that a “baseline” assessment occurred during periods of significant stress. The second criterion was that the stressor had to be psychosocial.Physiologic Alterations in Bodily Systems in Response to Disease Processes.  Stressors that included a significant physical element such as pain, cold, or physical exhaustion were eliminated (e.g., Antarctic isolation, space flight, military training). The third criterion was that the work had to include a measure of the immune system. This criterion was met by any enumerative or functional in vitro or in vivo immune assay. However, clinical disease outcomes such as HIV progression or rhinovirus infection did not meet this criterion. Finally, we eliminated articles from which a meaningful effect size could not be abstracted. For example, when between- and within-subjects observations were treated as independent, the reported effect was likely to be inflated. In a few cases, effects of stress and clinical status were confounded—that is, a stressed clinical group was compared with an unstressed healthy group—and hence these studies were excluded from the meta-analysis.

Stressor Classification

We coded stressors in the articles into five classes: acute time-limited, brief naturalistic, event sequence, chronic, and distant. The most difficult distinctions among event sequence, chronic, and distant stressors were based on temporal and qualitative considerations. Event sequences included discrete stressors occurring 1 year or less before immune assessment and could be of any severity. These were most often normative stressors such as bereavement. Chronic stressors were ongoing stressors such as caregiving and disability. Distant stressors were severe, traumatic events that could meet the stressor criterion for posttraumatic stress disorder (American Psychiatric Association, 1994), such as combat exposure or abuse, and had happened more than 1 year before immune assessment. Most stressors in this category occurred 5 to 10 years before immune assessment. Disagreements in stressor classification were resolved by consensus. Subgroups for moderator analyses were similarly decided. Physiologic Alterations in Bodily Systems in Response to Disease Processes.

The Meta-Analysis

Overview of procedures

Meta-analysis is a tool for synthesizing research findings. It proceeds in two phases. In the first, effect sizes are computed for each study. An effect size represents the magnitude of the relationship between two variables, independent of sample size. In this context it can be viewed as a measure of how much two groups, one experiencing a stressor and the other not, differ on a specific immune outcome. In the second phase, effect sizes from individual studies are combined to arrive at an aggregate effect size for each immune outcome of interest.

We used Pearson’s r as the effect size metric in this meta-analysis. Effect sizes for individual studies were computed using descriptive statistics presented in the original published reports. When these statistics were not available, we requested them from authors. This strategy was successful in most circumstances. To compute Pearson’s r from descriptive statistics in between-subjects designs, we subtracted the control group mean from the stressed group mean and divided this value by the pooled sample standard deviation. The value that emerged from this computation, known as Cohen’s d, was then converted into a Pearson’s r by taking the square root of the quantity d2/(d2 + 4). (See Rosenthal, 1994.) To compute Pearson’s r from descriptive statistics in within-subjects designs, we subtracted the group mean at baseline from the group mean during stress and divided this quantity by the sample standard deviation at baseline. This d value was converted into a Pearson’s r by taking the square root of the quantity d2/(d2 + 4). In cases in which descriptive statistics were not available, Pearson’s r was computed from inferential statistics using standard formulae (Rosenthal, 1994). These formulae had to be modified slightly for studies that used within-subjects designs because effect sizes are systematically overestimated when they are calculated from repeated measures test statistics (Dunlap, Cortina, Vaslow, & Burke, 1996). In these situations we derived effect size estimates using the formula d = tc[2 (1 − r)]1/2, where tc corresponds to the value of the t statistic for correlated measures, and r corresponds to the value of the correlation between outcome measures at pretest and posttest (Dunlap et al., 1996). Because very few studies reported the value of r, we used a value of .60 to compute effect sizes in this meta-analysis. Physiologic Alterations in Bodily Systems in Response to Disease Processes. This represents the average correlation between pre-stress and poststress measures of immune function in a series of studies performed in our laboratories. To ensure that the meta-analytic findings were robust to variations in r, we conducted follow-up analyses using r values ranging from .45 to .75. Very similar findings emerged from these analyses, suggesting that the values we present below are reliable estimates of effect size. If anything, they are probably conservative estimates, because the pre–post correlation between immune measures often is substantially lower than .60.

The effect size estimates from individual studies were subsequently aggregated using random-effects models with the software program Comprehensive Meta-Analysis (Borenstein & Rothstein, 1999). The random-effects model views each study in a meta-analysis as a random observation drawn from a universe of potential investigations. As such, it assumes that the magnitude of the relationship between stress and the immune system differs across studies as a result of random variance associated with sampling error and differences across individuals in the processes of interest. Because of these assumptions, random-effects models not only permit one to draw inferences about studies that have been done but also to generalize to studies that might be done in the future (Raudenbush, 1994; Shadish & Haddock, 1994). It also bears noting that in the population of studies on stress and immunity there is likely to be a fair amount of nonrandom variance, as researchers who examine ostensibly similar phenomena may still differ in terms of the samples they recruit, the operational definition of stress they use, and the laboratory methods they utilize to assess a specific immune process.

Separate random-effects models were computed for each immune outcome included in the meta-analysis. Prior to computing the random-effects model, r values derived from each study were z-transformed by the software program, as recommended by Shadish and Haddock (1994), to stabilize variance. The z values were later back-transformed into r values to facilitate interpretation of the meta-analytic findings. In the end, each random-effects model yielded an aggregate weighted effect size r, which can be interpreted the same way as a correlation coefficient, ranging in value from −1.00 to 1.00. Each r statistic was weighted before aggregation by multiplying its value by the inverse of its variance; this procedure enabled larger studies to contribute to effect size estimates to a greater extent than smaller ones. Weighting effect sizes is important because larger studies provide more accurate estimates of true population parameters (Shadish & Haddock, 1994). After each aggregate effect size had been derived, we computed 95% confidence intervals around it, assessed whether it was statistically significant, and computed a heterogeneity coefficient to determine whether the studies contributing to it had yielded consistent findings. Physiologic Alterations in Bodily Systems in Response to Disease Processes. Following convention, aggregate effect sizes were considered statistically different from zero when (a) their corresponding z value was greater than 1.96 and (b) the 95% confidence intervals around them did not include the value zero (Rosenthal, 1991; Shadish & Haddock, 1994).

To determine whether the studies contributing to each aggregate effect size shared a common population value, we computed the heterogeneity statistic Q (Shadish & Haddock, 1994). This statistic is chi-square distributed with k – 1 degrees of freedom, where k represents the number of independent effect sizes included. When a statistically significant heterogeneity test emerged, we searched for moderators (characteristics of the participants, stressful experience, or measurement strategy) that could explain the variability across studies. The first step in this process involved estimating correlations between participant characteristics (e.g., mean age, percentage female) and immune effects to examine whether the strength of effects varied according to demographics. When it was possible to do so, we then stratified the studies according to characteristics of the stressful experience (e.g., duration, quality) or the measurement strategy (e.g., interview, checklist), and computed separate random-effects analyses for each subgroup.

Handling missing data

Occasionally authors of studies failed to report the descriptive or inferential statistics needed to compute an effect size. In some of these cases, the authors noted that there was a significant difference between a stressed and control group. When this occurred, we computed effect sizes assuming that p values were equivalent to .05. This represents a conservative approach because the actual p values were probably smaller. In other cases, the authors noted that a stressed and control group did not differ with respect to an immune outcome, but failed to provide any further statistical information. When this occurred, we computed effect sizes assuming that there was no difference at all between the groups (r = .00). Because there is seldom no difference at all between two groups, this also represents a conservative strategy. Imputation was used in less than 7% of cases. Physiologic Alterations in Bodily Systems in Response to Disease Processes.


Handling dependent data

The validity of a meta-analysis rests on the assumption that each value contributing an aggregate effect size is statistically independent of the others (Rosenthal, 1991). We devised a number of strategies to avoid violating this independence assumption. First, in studies that assessed stimulated-lymphocyte proliferation at multiple mitogen dosages, we computed the average effect size across mitogen dosages, and we used this value to derive aggregate indices. We used an analogous strategy for studies that assessed natural killer cell cytotoxicity at multiple effector:target cell ratios. Second, in studies that utilized designs in which multiple laboratory stressors were compared with a control condition, the average effect size across stressor conditions was computed and later used to derive aggregate indices. Because this averaging procedure in most cases yielded an effect size that was smaller than that of the most potent stressor, we also computed meta-analyses using the larger of the effect sizes from each study rather than the average. Doing so did not alter any of the substantive findings we report. Third, in studies in which immune outcomes were assessed on multiple occasions during a stressful experience, the average effect size across occasions was used to derive aggregate indices. Note that we did not conduct meta-analyses of recovery effects, that is, immune values after a stressor had ended. Although such an analysis would answer interesting questions about the stress-recovery process, there were not enough studies that included similar immune outcomes assessed at similar time points after stress to permit a complete analysis. Fourth, because some data were published in more than one outlet, we contacted authors of multiple publications to determine sample independence or dependence.

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Preliminary Findings

The meta-analysis is based on effect sizes derived from 293 independent studies. These studies were reported in 319 separate articles in peer-reviewed scientific journals (see Table 2). A total of 18,941 individuals participated in these studies. Their mean age was 34.8 years (SD = 15.9). Although the studies collectively included a broad range of age groups (range = 5–78 years), most focused heavily on younger adults. More than half of the studies (51.3%) had a mean age under 30.0 years, and more than four fifths (84.8%) had a mean age under 55.0 years. Slightly more than two thirds of the studies (68.5%) included women; in the average study almost half (42.8%) of the participants were female. The vast majority of studies (84.8%) focused on medically healthy adults.2 Of those that included medical populations, most focused on HIV/AIDS (k = 18; 38.3%), arthritis (k = 6; 12.8%), cancer (k = 5; 10.6%), or asthma (k = 4; 8.5%). Physiologic Alterations in Bodily Systems in Response to Disease Processes.