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PSYCHOLOGICAL PROFILES IN AUTOIMMUNE DISEASE:
RELATIONSHIP TO DEMOGRAPHIC, DIAGNOSTIC, DISEASE ACTIVITY
AND SOCIAL SUPPORT MEASURES
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
TABLE OF CONTENTS
LIST OF TABLES ..................................... ........ ....... iii
LIST OF FIGURES .......... ........................................... iv
ABSTRACT ................................................ ......... v
1 INTRODUCTION .................................................1
Systemic Lupus Erythematosus ................ ...................... 1
Sjogren's Syndrome, Scleroderma, and Polymyositis ................... .... 3
Antinuclear Antibody Positive Patients ................ ................. 4
Psychological Distress and Illness ................ ................... .. 5
Disease-Related Fatigue ............... ............................ 7
Disease-Related Pain .............................................. 9
Cluster Profiling ..................................... ................ 10
Illness Burden and the Immune Response .............................. 11
Social Support ..................................... ................ 13
Study Rationale .......... ........................................... 14
A im s ............. ................................ .16
Hypotheses ......... ............................................... 17
2 METHODS ....... ............................................... 19
Participants ......... ...................................... ......... 19
Procedure ........ ............................................... .20
Measures ....... ................................................ .21
Analyses ................................................... 22
3 RESULTS ........... ................................ .24
4 DISCUSSION ........ ............................................ 29
REFERENCES ....... ............................................... 36
BIOGRAPHICAL SKETCH ........................................... 43
LIST OF TABLES
2-1 Diagnostic breakdown of demographic information ................... .. 19
3-1 Description of response profiles ..................................... 25
3-2 Crosstabulation matrix of response profiles across diagnostic categories ...... 26
3-3 Values for biological markers of disease activities ....................... 27
LIST OF FIGURES
1-1 Preliminary four-cluster solution representing psychological profiles in
autoimmune disease patients .............. ................... 16
3-1 Replication four-cluster solution representing psychological profiles in
autoimmune disease patients .................................... 24
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
PSYCHOLOGICAL PROFILES IN AUTOIMMUNE DISEASE:
RELATIONSHIP TO DEMOGRAPHIC, DIAGNOSTIC, DISEASE ACTIVITY
AND SOCIAL SUPPORT MEASURES
Chair: Michael E. Robinson
Major Department: Clinical and Health Psychology
Autoimmune diseases (AD) are characterized by chronic inflammation that can
affect a variety of tissues in systemic or organ-specific forms. The challenges inherent to
managing a chronic medical illness place individuals at greater risk for psychological
distress, which could lead to deleterious effects on immune and neuroendocrine
functioning and contribute to disease progression. Relatively little is known about
variations in psychological function and the degree to which heterogeneity exists across a
variety of autoimmune diseases. Further, the differential contributions of disease-related
factors and psychological function to illness response remain unclear. Using a cluster
analytic approach, this study determined homogenous psychological subgroups in a
sample of 393 rheumatology outpatients referred to an autoimmune disease clinic for
suspected AD. Participants included individuals diagnosed with an AD as well as
individuals testing positive for anti-nuclear antibodies (ANA positive). Psychological
subgroups were determined empirically based on visual analogue measures of
depression, anxiety, anger, confusion, pain, and fatigue. Psychological response profiles
were subsequently examined in relation to demographic variables, diagnostic category,
physician rated and immune measures of disease activity, and perceived social support.
Results of the study provide support for substantial heterogeneity across in psychological
function and illness response across the AD sample and within specific diagnostic
groups. Psychological response profiles did not vary with respect to demographic
variables, diagnosis, serological markers of disease activity, or physician-rated disease
activity. Higher levels of perceived social support were associated with lower levels of
mood disturbance and symptom reporting. Results suggest that personality,
psychological, and/or social support factors may be stronger determinants of response to
Autoimmunity refers to a breakdown in the immune system's ability to maintain
self-tolerance, resulting in an immune response directed against self-components of the
body. Autoimmune diseases (AD) are characterized by chronic inflammation in which
the rate of tissue damage exceeds the body's ability to repair the damage. There is wide
variability across ADs in the tissues that are attacked and specific symptoms caused.
Although an understanding of the mechanisms responsible for maintaining tolerance
exists, the specific factors contributing to the pathogenesis of AD remain largely
unknown (Parham, 2000). It is generally accepted that the cause of any given AD is
multifactorial and that environmental and genetic factors play a role in susceptibility
Autoimmune diseases are relatively common, affecting 2% to 3% of the
population of developed countries (Parham, 2000) and 5% to 7% of adults in Europe and
North America (Tizard, 1995). Two thirds of those affected are women. ADs can
generally be divided into two types: organ-specific, where the immune response is
directed toward a target antigen that is specific to a single organ or gland, and systemic,
which involves a response directed across a broad array of organs and tissues (Kuby,
Systemic Lupus Erythematosus
Systemic lupus erythematosus (SLE) is a prototypical AD in which almost every
tissue or organ may be affected. Its diagnosis depends on multisystem involvement and
the presence of autoantibodies, which together form the diagnostic criteria for SLE (Tan
et al., 1982). Systemic lupus erythematosus is thus a syndrome, rather than single disease
entity, that exhibits considerable variation in disease manifestations between individual
patients. The course of SLE generally involves periods of intense flares and periods of
remission (Parham, 2000).
The overall prevalence of SLE varies between studies from 12.0 to 50.8 with an
average of about 40 per 100,000 individuals (Hopkinson, Doherty, & Powell, 1994). The
highest prevalence is found in African American females at a rate of 200 per 100,000.
The incidence of lupus has been reported between 2.0 and 7.6 new cases per 100,000 per
year (Johnson, Gordon, Palmer, & Bacon, 1995). It is evident that sex has a major
influence on the likelihood of developing SLE, with a 90% female predominance over
males (Rus & Hochberg, 2002). The predominance of female SLE patients is not well
understood, although hormonal factors are believed to play an important etiological role
Systemic lupus erythematosus is generally diagnosed according to the American
College of Rheumatology's revised (Hochberg, 1997) criteria for SLE. The criteria
include 11 items, 5 of which are composites of one or more abnormalities. In order to
meet criteria for a diagnosis of SLE, patients must fulfill at least 4 criteria; however, no
single criterion is essential (Wallace & Hahn, 2002). Laboratory diagnosis of SLE
focuses to a large extent on antinuclear antibodies (Kuby, 1991). The absence of a clearly
defined diagnostic marker for SLE contributes to the diagnostic challenge and can place
patients at risk for misdiagnosis.
The wide variation in disease manifestations across individuals with SLE
contributes to the challenge of developing a uniform system for assessing level of disease
activity. Consequently, the assessment of disease activity in SLE patients remains
inconsistent within the rheumatology field. Over 60 systems for assessing disease activity
exist, and agreement on a definition for SLE activity has not been achieved. In the United
States, the most commonly used assessment system for disease activity is the Systemic
Lupus Erythematosus Disease Activity Index (SLEDAI; Bombardier, Gladman, Urowitz,
Caron, & Chang, 1992), whereas in Europe, the British Aisles Lupus Assessment Group
(BILAG; Symmons et al., 1988) system predominates (Wallace & Hahn, 2002).
The economic impact of SLE has been estimated at a mean annual direct and
indirect per patient cost of $10,530 in the United States in 1991 (Gironimi et al., 1996).
Given the marked rise in health care costs in the last decade and an average prevalence of
about 40 per 100,000, this translates into considerable annual health expenditure. In
addition to growing health care costs, the economic impact of SLE is expected to rise
exponentially as the percentage of African Americans (presently at 12.9%) and Hispanics
(presently at 12.5%) continues to grow faster than the Caucasian population (U.S. Census
Sj6gren's Syndrome, Scleroderma, and Polymyositis
A number of patients referred to specialists for suspected SLE are ultimately
diagnosed with a different AD or are determined to have antinuclear antibodies (ANA)
but not meet criteria for a specific AD. Among the systemic autoimmune conditions that
share symptom overlap with SLE are Sjogren's syndrome, scleroderma, and
polymyositis. Similar to SLE, these conditions involve a response mounted by the
immune system against tissues in the body. In the case of Sjogren's syndrome, the
immune system targets the salivary and lacrimal glands, leading to dryness in the mouth
and eyes and complications including severe dental decay and corneal damage. The
female to male ratio is 9:1, and the overall prevalence for Sjogren's is 3% (Berkov,
Beers, & Burs, 1999). Scleroderma, also known as systemic sclerosis, is an autoimmune
condition involving overproduction of collagen that results in abnormal growth of
connective tissues that support the skin and internal organs. Scleroderma occurs at a rate
7-12 times greater in females than males (National Institute of Arthritis and
Musculoskeletal and Skin Diseases [NIAMS], 2001). Polymyositis is a connective tissue
disorder characterized by inflammation and degenerative changes in muscles. Over time,
this damage leads to symmetric weakness and muscle atrophy. This may be commonly
associated with severe interstitial lung disease, which can rapidly lead to death. The
female to male ratio is 2:1 with 2 to 10 new cases per one million (Berkov et al., 1999).
Antinuclear Antibody Positive Patients
Antinuclear antibody testing is one of several tests commonly ordered when a
patient in primary care settings complains of chronic low energy, arthralgias, and
myalgias (Blumenthal, 2002). Most patients referred to clinics specializing in
rheumatologic or autoimmune conditions for suspected SLE have antinuclear antibodies.
Antinuclear antibody testing is often used to screen for a diagnosis of lupus but has very
low specificity (Illei & Klippel, 1999). Conversely, the absence of a positive ANA
largely excludes the diagnosis of SLE as less than 4% of lupus patients have a negative
ANA. In addition, up to 20% of healthy, asymptomatic individuals test ANA positive
(Wallace & Hahn, 2002). Thus, many patients who test positive for ANA do not receive a
diagnosis of SLE.
Although some ANA positive patients are diagnosed with an alternate
autoimmune disorder or another condition, others receive no diagnosis. Blumenthal
(2002) reported that many patients testing positive for ANA ultimately receive a
diagnosis of fibromyalgia syndrome (FMS) or an alternative pain syndrome. He argued
that the prevalence of FMS is at least 20 times higher than that of lupus, which reduces
the likelihood that a positive ANA will result in a diagnosis of lupus. Smart, Waylonis,
and Hackshaw (1997) found that in a sample of 66 FMS patients, 20 (30%) were ANA
Al-Allaf, Ottewell, and Pullar (2002) investigated whether ANA positive FMS
patients developed other symptoms of connective disease over a 2- to 4-year follow-up
period compared with age- and sex-matched ANA negative FMS and osteoarthritis (OA)
patients. The ANA positive rates (12/137 [8.8%] in FMS and 20/225 [8.9%] in OA
patients) were similar in both groups. At final assessment, one patient from the ANA
positive FMS group was diagnosed with SLE, one patient from the ANA negative FMS
group was diagnosed with Sjogren's syndrome, and one OA patient developed
rheumatoid arthritis (RA). These results indicated that ANA status is not a good predictor
of future development of AD. The ANA positive population, in the absence of AD,
represents a unique and poorly defined group of patients. These patients range from
asymptomatic to those suffering from FMS or an alternate pain condition.
Psychological Distress and Illness
The psychological aspects of SLE have not received extensive attention and
remain poorly understood despite estimated rates for neuropsychiatric problems ranging
from 33% to 60% and affective problems from 50% to 80% (Dobkin et al., 1998).
Psychological distress, which includes depressed or anxious mood, occurs commonly in
medical disorders and has a significant effect on quality of life and coping with disease
(Adams, Dammers, Saia, Brantley, & Gaydos, 1994). Psychological distress represents
patients' interpretations of stress and their perceived impact (Ward, Marx, & Barry, 2002)
and can be considered an intermediate measure in the relationship between stress and
illness. Distress can impact health both indirectly, through health behaviors (e.g.,
compliance to medical regimens, poorer sleep, poorer nutrition) or directly through
alterations in the central nervous, immune, endocrine, and cardiovascular systems
(Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002a).
Physical health problems, particularly those that are chronic, are considered a
significant risk factor for depression (Zeiss, Lewinsohn, Rhode, & Seeley, 1996). Many
of the ADs, such as SLE, present a course of unpredictable symptom flares and
remissions. A diagnosis of AD requires adjusting to a new array of medical, social,
psychological, vocational, and financial challenges and stressors. Depression is the most
common psychiatric problem in patients with SLE. In a review of the literature, Giang
(1991) found that 31% to 52% of lupus patients who underwent a structured or semi-
structured interview were experiencing depression. The factors contributing to the
etiology and maintenance of depression in lupus patients are unclear and likely involve
numerous biopsychosocial factors (Iverson, Sawyer, McCracken, & Kozora, 2001).
Elevated levels of psychological distress have also been reported in scleroderma
and Sjogren's syndrome patients. Valtysdottir, Gudbjornsson, Lindqvist, Hallgren, and
Hetta (2000) examined levels of anxiety, depression, well-being, and symptoms in 62
Sjogren's patients compared with a group of healthy controls and a group of patient
controls with RA. The results indicated significantly higher levels of anxiety and
depression, and reduced physical and mental well-being in Sjogren's patients compared
with healthy controls. The Sjogren's patients also reported significantly more symptoms
than RA patients.
Matsura and colleagues (2003) evaluated 50 patients with scleroderma for factors
associated with depressive symptoms using the Beck Depression Inventory (BDI; Beck,
1967). Forty-six percent of the sample reported depressive symptoms ranging from mild
to severe. Regression analyses revealed that high levels of hopelessness and low sense of
coherence (coping ability and resilience in the face of stress) were the best predictors of
depressive symptoms in this sample.
Psychological distress affects morbidity in patients with comorbid medical illness
in several ways. As Iverson et al. (2001) highlighted, depression can greatly impact
functional status and degree of disability in the areas of social, occupational, educational,
and recreational functioning. Depression has also been shown to influence patients'
adherence to medical regimens. In addition to influencing overt behavioral functioning,
there is also evidence that psychological distress is directly related to disease activity
(Dobkin et al., 1998). However, it remains unknown as to whether psychological distress
leads to increased SLE activity or if disease activity leads patients to become more
depressed or anxious.
Fatigue is a commonly reported symptom in medical patients and one of the most
widely reported symptoms in SLE. Zonana-Nacach and colleagues (2000) found that
85.7% of 223 participants with SLE reported fatigue; and Krupp, LaRocca, Muir, and
Steinberg (1991) found fatigue to be reported in 80% of their SLE sample. Disabling
fatigue is also a prominent feature of primary Sjogren's syndrome (Lwin, Bishay, Platts,
Booth, & Bowman, 2003). Fatigue is a primary contributor to functional disability and
visits with health care providers. Belza, Henke,Yelin, Epstein and Gilliss (1993) reported
a significant positive association between fatigue levels in RA patients and frequency of
visits to their rheumatologists.
Numerous factors have been associated with fatigue in SLE, including level of
aerobic fitness, pain, medications, sleep problems, and clinical and laboratory features of
SLE (Zonana-Nacach et al., 2000). Depression also is related strongly to fatigue (De
Rijk, Schreurs, & Bensing, 1999) and a positive association between fatigue and
depression has been reported in patients with SLE (Krupp et al., 1991). Although fatigue
is sometimes viewed as a reflection of disease activity, it often persists despite decreases
in disease activity, indicating that additional factors likely play a role in maintaining
Studies investigating fatigue and disease activity have provided inconsistent
findings for a biologic explanation for fatigue. Bruce, Mak, Hallett, Gladmann, and
Urowitz (1999) found disease activity and damage accounted for only 4.8% and 4%,
respectively, of the variance in fatigue scores in a sample of 81 lupus patients. Several
studies (e.g., Tench, McCurdie, White, & D'Cruz, 2000; Zonana-Nacach et al., 2000)
have shown weak associations between fatigue and disease activity. In contrast, Tayer,
Nicassio, Weisman, Schuman, and Daly (2001) found that in a cross-sectional analysis of
81 SLE patients, disease status, helplessness, and depression are independently
significant predictors of fatigue. In a longitudinal design testing the same variables, only
disease status predicted future fatigue levels. Overall, these findings support the
possibility that a combination of disease and psychosocial factors are capable of
influencing fatigue levels.
Several investigations have provided support for the role of psychological distress
in the experience of fatigue in SLE. McKinley, Ouellette, and Winkel (1995)
demonstrated that while disease activity did not exert a direct effect on fatigue, it did
influence depression and sleep disruption, which may, in turn, exert a more direct effect
on fatigue. Omdal, Waterloo, Koldingsnes, Husby, and Mellgren (2003) found in a
sample of 57 SLE patients that affective and personality states as well as mental health
status were significant predictors of fatigue. Fatigue, as with pain, appears to be a
multidimensional construct with a significant psychological component.
Pain is among the most common reasons individuals seek medical care. It
accounts for substantial levels of functional disability and contributes greatly to overall
illness burden (Turk & Melzack, 1992), including visits to health care providers,
medication expense, and work-related disability. Pain has been consistently linked with
negative mood states (Robinson & Riley, 1999) and can enhance stress-related hormones
and immune dysregulation (Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002b). It
appears that this triad of symptomology involving fatigue, pain, and distress
(e.g., depression, anxiety) creates a negative spiral resulting in increasing levels of
Pain related to arthritis and arthralgias occurs in 95% of lupus patients at some
point in the course of their illness (Schur, 1996). The relationship between pain and
fatigue in chronic pain conditions has been firmly established (Belza et al., 1993; Wolfe,
Hawley, & Wilson, 1996). Coping with unpredictable and severe amounts of pain
requires additional physical and emotional endurance, which may further deplete energy
and coping resources in lupus patients. Further, reduced levels of activity could result in
muscle deconditioning, which, in turn, could contribute to increased levels of perceived
fatigue (Belza et al., 1993; Robb-Nicholson et al., 1989) and increased level of pain.
Pain is a defining symptom in polymyositis and may also be important in
scleroderma. Benrud-Larson and colleagues (2002) investigated the frequency and
impact of pain, symptoms of depression, and social network characteristics on physical
functioning and social adjustment in 142 patients with scleroderma. Sixty-three percent
of patients reported mild or greater pain, and half of the sample reported mild or greater
levels of depression. The results showed that pain was the strongest predictor of physical
function, and depressive symptoms accounted for the greatest amount of variance in
social adjustment. Their findings suggest that pain and depressive symptoms are
important determinants of quality of life in scleroderma patients.
Empirical approaches to classifying homogenous psychological subgroups have
been used extensively in chronic pain patients. This approach originated as an effort to
counter assumption that pain patients represent a homogenous group and to determine
whether treatment response could be improved by tailoring treatments to subgroups of
patients based on specific characteristics (Turk & Okifuji, 2002). Using the
Multidimensional Pain Inventory (MPI; Kerns, Turk, & Rudy, 1985), Turk and Rudy
(1988) cluster analyzed patients' responses and found three homogenous groups of pain
patients: (a) dysfunctional, (b) interpersonally distressed, and (b) active copers. This
classification system has been replicated in chronic low back pain (CLBP), head pain,
FMS, and temporomandibular disorder (TMD). Additionally, subgroup differences were
found in response to treatment, suggesting that the use of classification systems to tailor
treatment approaches to specific subgroups may improve treatment efficacy.
The Minnesota Multiphasic Personality Inventory (MMPI) has been found to be
highly consistent in identifying subgroups within a variety of chronic pain populations,
such as headache (Robinson, Geisser, Dieter, & Swerdlow, 1991), chronic
musculoskeletal pain (Riley, Geisser, & Robinson, 1999) and TMD (Velly, Philippe, &
Gornitsky, 2002). These findings provide additional evidence to suggest that within
broad categories of pain conditions, distinct subgroups exist. Identification of such
subgroups in AD could enhance our understanding of variability in responses to illness
and its subsequent treatment.
Illness Burden and the Immune Response
In AD, the relationships between emotions, psychological distress, immune and
neuroendocrine functioning, and disease manifestations are of particular interest. There is
considerable evidence to suggest that emotional states can produce alterations in the
immune response. It is currently accepted that the brain and the immune system share
bidirectional communication and exert important regulatory control over one another.
The existence of such neural-immune interactions provides a pathway by which
psychological processes can influence and be influenced by immune function (Maier,
Watkins, & Fleshner, 1994). Additionally, immunological alterations have been reported
across a wide range of psychiatric disorders (Kiecolt-Glaser et al., 2002a).
A growing body of evidence suggests a role for psychological distress in
inducing, exacerbating, and affecting outcomes in SLE (Shapiro, 1997). Depressed
immune responsiveness is characteristic of patients with SLE. Research has shown that
psychological distress further dampens the immune response via activation of the
hypothalamic-pituitary-adrenal (HPA) axis (Ader, Cohen, & Felton, 1995), which may
result in more active disease. A 1999 study by Pawlak and colleagues demonstrated a
distinct difference between the stress response of SLE patients and healthy controls
following a stressful task (public speaking). Systemic lupus erythematosus patients
showed significantly less pronounced increases in NK cell numbers compared to the
controls. Additionally, NK cell activity increased in the controls but not in the SLE
patients, indicating a blunted immune response to acute stress.
Inflammation is linked to a variety of conditions associated with aging
(Kiecolt-Glaser et al., 2002b) and is an important feature in AD. Chronic inflammation
and immune challenge associated with illness serve as physiologic stressors leading to
activation of the HPA axis. Dysregulation of inflammatory mediators is commonly
observed in ADs and has also been shown to correlate with psychological variables, such
as depression. The majority of research to date in this area has focused on pro-
inflammatory cytokines. Cytokines are low molecular weight protein substances released
by cells that serve as intercellular signals to regulate the immune response to injury and
infection (Parham, 2000). Cytokines have been proposed as the messengers between the
brain and the immune system (Maier et al., 1994).
Several studies have shown that patients with SLE display an altered cytokine
profile (Jacobs et al., 2001). One pro-inflammatory cytokine that has received increased
attention in a variety of medical and psychiatric populations is interleukin-6 (11-6).
Studies of the role of IL-6 in SLE have lead to relatively uniform results. Circulating 11-6
levels are elevated in patients with SLE, compared to those with inactive disease and
healthy controls (Lacki, Leszczynski, Kelemen, Muller, & Mackiewicz, 1997). 11-6 also
has shown a consistent relationship with depression across a number of studies. Maes and
colleagues (1995) found elevated levels of 11-6 and soluble 11-6 receptors (sIl-6R) in
patients with major depression (MD), whether active or in remission, suggesting that the
upregulated production of 11-6 may be a trait marker for MD.
Some researchers have proposed that psychological stress can instigate the
inflammatory response. According to Black (2003), the inflammatory response is
contained within the psychological stress response, which evolved later. The same
neuropeptides mediate the body's response to both stress and inflammation. Cytokines
evoked by either a stress or inflammatory response may utilize similar pathways to signal
the brain, signaling a cascade of hormones, neuropeptides, and cytokine activity.
Negative affect has been identified as a key pathway for modifying immune
processes. There is preliminary evidence that anxiety and depression enhance the
production of pro-inflammatory cytokines (Kiecolt-Glaser et al., 2002a). Immune
modulation by psychosocial stressors and/or interventions can lead to health changes.
Pro-inflammatory cytokines, such as 11-6, stimulate the release of acute phase reactants
from the liver. Acute phase proteins, such as C-reactive protein (CRP) and complement
proteins (C3 and C4) are part of the body's innate immune inflammatory response to
Berk, Wadee, Kuschke, and O'Neill-Kerr (1997) compared levels of acute phase
proteins (C3, C4 and CRP) in depressed versus nondepressed subjects according to
DSM-III-R criteria and found significant elevations in C4 and CRP in the depressed
group. These findings suggest the possibility of an underlying relationship between
depression and inflammation in autoimmune patients. Furthermore, many physicians now
believe that CRP can be used as an aid in assessing the risk of cardiovascular and
peripheral vascular disease. The relationships among psychological factors and acute
phase proteins have not been previously reported in AD populations.
The benefits of social support have been given extensive consideration throughout
the chronic illness literature. Social support is negatively correlated with psychological
distress and has been shown to influence health behaviors, such as seeking medical care
(Cohen, 1988). McCracken, Semenchuk, and Goetch (1995) found that good social
support was related to perceptions of health and that seeking social support was
associated with lower levels of pain, physical disability, psychological distress, and
depression. Furthermore, social support can serve as a buffer during both acute and
chronic stressors, thereby protecting the individual against immune dysregulation
(Kiecolt-Glaser et al., 2002a).
Two studies (Bae, Hashimoto, Karlson, Liang, & Daltroy, 2001; Sutcliffe et al.,
1999) reported that higher levels of social support were associated with better physical
and mental well-being in SLE patients. However, there are no studies to date that have
examined the role of social support as a buffer against immune dysregulation in SLE,
scleroderma, Sjogren's syndrome, polymyositis, or ANA positive patients.
The participants in this study shared common complaints and symptoms
characteristic of suspected AD. Following medical evaluation, individuals were placed
into a variety of categories ranging from ANA positive to having been diagnosed with
one of many ADs. The assumption of heterogeneity in such a diverse population might
lead to generic treatment approaches aimed at the "average" or "typical" patient. Another
pitfall might be to assume that more severe illness equals more distressed or that
adjustment to illness was similar across a variety of conditions. The purpose of this study
was to explore the possibility that homogenous subgroups exist based on specific shared
qualities. Identifying such subgroups offers the possibility to better understand variability
in response to illness and to tailor treatment to subgroups based on specific
Relatively little is known about how individuals with ADs such as SLE,
scleroderma and polymyositis and ANA positive patients vary with respect to
psychological function and adjustment to illness. The level of heterogeneity across
individuals, disease categories, and illness severity remains to be determined. To date,
there are few reports describing the psychological characteristics of ANA positive
patients. Across conditions, one might expect that those posing a greater threat to
mortality, quality of life, and function would lead to higher levels of distress and greater
coping challenges. Within disease categories, factors such as premorbid psychological
status and disease severity could play important roles in current psychological adjustment
There are few studies to date that have attempted to elucidate the relationships
among psychological factors and inflammatory mediators in ANA positive, SLE, and
related autoimmune conditions. Further, no studies have investigated the relationship
between psychosocial factors, pain, fatigue, and acute phase proteins in this population.
Finally, although physician-rated measures of disease activity have been compared to
individual psychological and self-rated symptoms, such as pain and fatigue, in SLE
patients, the relationship between physician-rated disease activity and multivariate
psychological response profiles has not been previously reported. The aim of this study
was to contribute to the understanding of these relationships using an empirical clustering
approach to examine how these variables are related.
Using a cluster-analytic approach, a pilot study was conducted to determine
whether unique patterns of psychological (depression, anxiety, confusion, and anger) and
symptom (pain and fatigue) reports exist within an AD sample. The preliminary study
was conducted on 279 participants to determine whether patients presenting in an AD
clinic could be classified into subgroups based on unique psychological response
profiles. The cluster analysis revealed a four-cluster solution. The four-cluster solution
was chosen because it provided better group separation and more parsimonious
interpretation. Figure 1-1 provides a graphical representation of the four clusters.
0.5 --Pain/ Fatigue
0 --I- Mod. Impact
-0.5 High Impact
-1 -X Low Imapct
Figure 1-1. Preliminary four-cluster solution representing psychological profiles in
autoimmune disease patients
The first cluster, "Pain/Fatigue" (n = 66), displayed moderate levels of pain and
fatigue and low levels of distress. The "Moderate Impact" cluster, comprised of 77
participants, is characterized by moderate levels of pain, fatigue, and distress with an
elevation in anxiety. The "High Impact" cluster (n = 47) displayed high distress and
symptom levels, and the "Low Impact" cluster (n = 89) demonstrated low levels of
symptoms and distress. Based on this pilot data indicating the presence of four distinct
psychological response profiles in the AD sample, several aims were developed.
The initial aim of this study involved replicating the four-cluster solution using a
larger sample. Secondly, this study sought to determine whether clusters are associated
with demographic variables, including race, age, sex, and illness duration. Next, the
concordance among psychological response profiles and diagnostic categories (ANA
positive, SLE, Sjogren's, polymyositis, and scleroderma) was examined. The relationship
between psychological response profiles and biological markers was also examined to
assess whether psychological response profiles predict serological markers of disease
activity (urinary 11-6, CRP, C3, C4, albumin, and prealbumin). A secondary objective
included determining whether psychological response profiles predict physician-rated
levels of disease activity in SLE patients. The relationship between psychological
response profiles and SLE Disease Activity Index (SLEDAI; Bombardier et al., 1992)
was examined. Finally, the relationship between psychological response profiles and
perceived social support was assessed.
The results of this study provided preliminary evidence indicating whether
psychological response profiles were determined primarily by disease process, indicating
a pathophysiological basis for psychological functioning, or by psychosocial factors and
predisposing personality traits, suggesting a psychological basis for individual responses
to physical illness. Although, in reality, the answer likely lies in the middle of these
extremes, this study provided insight into the differential contributions of these opposing
The same four-cluster solution found in the pilot study was repeated in a larger
autoimmune disease clinic sample.
Equal representation of diagnosis across clusters was supportive of the notion that
predisposing factors (personality, social support, etc.) predict psychological
response to illness. A higher frequency of diagnosed AD in the more distressed
profiles was supportive of disease severity determining psychological response.
Response profiles reflecting higher levels of pain, fatigue, and distress will be
associated with increased activation of serological markers of disease activity
o Higher levels of urinary 11-6, CRP, albumin, and prealbumin
o Lower levels of complement components (C3 and C4)
Response profiles reflecting higher levels of pain, fatigue, and distress will be
associated with higher levels of physician-rated disease activity in SLE patients,
supporting the notion that disease contributes to psychological response. Absence
of significant differences between response profiles on disease activity scores
would support the view that psychological response to illness may be independent
of specific pathological processes.
Response profiles reflecting higher levels of pain, fatigue, and distress will be
inversely associated with levels of perceived social support, thereby supporting
the notion that greater social support serves as a buffer against the harmful effects
of illness on psychological well-being.
Participants in this study were 393 rheumatology outpatients recruited from the
Autoimmune Disease Clinic at Shands Hospital in Gainesville, Florida. The mean age of
the patients was 44.3 (SD = 13.7), and the mean duration since diagnosis was 8.0
(SD = 7.6) years. Patients were predominantly female (90.1%), and the majority of
patients were Caucasian (64.1%). Patients were categorized according to primary
diagnosis, although it should be noted that many patients met criteria for more than one
autoimmune disorder. The largest proportion of patients was diagnosed with SLE (43%),
followed by patients who were ANA positive (25.7%) but did not meet criteria for an
autoimmune disease diagnosis. Diagnostic information is presented in Table 2-1.
Table 2-1. Diagnostic breakdown of demographic information
Duration Edu Age Sex Race
Diagnosis N % (yrs) (yrs) (yrs) N (% total) N (% total)
M (SD) M(SD) M(SD) F M W B O
SLE 176 45 10.5 13.5 41.3 162.0 14.0 89.0 62.0 25.0
(8.4) (2.4) (12.8) (41.2) (3.6) (22.6) (15.8) (6.4)
ANAPOS 101 26 4.0 13.7 43.8 91.0 10.0 77.0 11.0 13.0
(4.5) (2.5) (13.7) (23.2) (2.5) (19.6) (2.8) (3.3)
Sjogren's 26 7 6.8 14.0 52.8 24.0 2.0 25.0 1.0 0.0
(4.0) (2.3) (14.4) (6.1) (0.5) (6.4) (0.3) (0.0)
SSC 22 6 8.2 12.8 53.8 19.0 3.0 16.0 6.0 0.0
(6.1) (3.3) (10.4) (4.8) (0.8) (4.1) (1.5) (0.0)
Other 68 17 7.3 13.7 46.4 58.0 10.0 45.0 16.0 7.0
(8.3) (2.4) (13.8) (14.8) (2.5) (11.5) (4.1) (1.8)
Total 393 100 8.0 13.6 44.3 354.0 39.0 252.0 96.0 45.0
(7.6) (2.5) (13.7) (90.1) (9.9) (64.1) (24.4) (11.5)
Patients eligible for participation in this study were preselected based on
participation in the large study examine factors contributing to the development of
autoimmune disease (IRB#: 454). Patients were recruited by their treating physicians
during routine medical visits. Eligibility was determined by each patient's treating
physician according to necessary criteria. Eligibility criteria included being 18 years or
older, English-speaking, literate, and possessing a minimum education level of 8th grade.
Both males and females from all racial/ethnic backgrounds were included. Participation
was also contingent upon ability to provide consent. Patients with cognitive, emotional,
or physical problems believed to interfere with his or her ability to provide consent were
not permitted to participate. Written consent for research participation was obtained at
the conclusion of routine visits.
Recruitment took place during routine medical visits. Eligible participants were
approached by his or her rheumatologist or trained research staff regarding participation
in the study. The informed consent form was verbally reviewed prior to obtaining
patients' signatures to ensure complete understanding of their rights as research
participants. Participation in this study did not interfere with routine rheumatologic care.
Following their routine rheumatology visit, staff members escorted participants to
the General Clinical Research Center (GCRC) at Shands Hospital for clinical laboratory
tests. In addition to clinical laboratory tests ordered by their rheumatologist, biological
samples were obtained exclusively for research purposes. At this time, participants
completed the psychosocial questionnaire packet, consisting of a battery of self-report
paper and pencil questionnaires. Instructions for completing the questionnaire were
provided by a trained research assistant. The packet generally required 10 to 15 minutes
Standard demographic data were collected during each participant's initial
assessment. Information related to medical diagnosis and disease duration was recorded
for research purposes after patients provided informed consent to participate in the study.
The assessment of distress and symptom levels includes seven visual analogue
scales (VASs). Respondents were asked to indicate their level of functioning in each of
the assessed domains by drawing a vertical line through one point on a 100 mm linear
analogue scale. Scores were obtained by manual measurement of the VAS responses and
range from 0-100. Each domain is anchored by the following descriptors: "None" and
"Worst Imaginable." The domains assessed by VASs in this study included depression,
anxiety, anger, confusion, pain intensity, and fatigue.
The use of VASs for assessing psychological distress and symptom domains in a
brief format is not uncommon. The pain VAS (Price, McGrath, Rafii, & Buckingham,
1983) is a 100-mm line anchored by the descriptors, "No pain" to "Worst Pain
Imaginable." Adequate reliability and validity have been reported (Price et al., 1983).
Social support was measured using the Perceived Social Support Scale (PSSS;
Blumenthal, Burg, Barefoot, Williams, Haney, & Zimet, 1987). The PSSS is a 12-item
scale employing a 7-point Likert response scale ranging from 1 (Very Strongly Disagree)
to 7 (Very Strongly Agree). This scale addresses perceived support from family, friends,
and significant others. Test-retest reliability was reported as 0.85 and Cronbach's
coefficient alpha was 0.88 (Blumenthal et al., 1987).
Physician-Rated Disease Activity
The SLE Disease Activity Index (SLEDAI; Bombardier et al., 1992) is a
physician-rating scale consisting of 24 descriptors associated with nine organ systems.
Clinical and laboratory measures of SLE activity are included. Items are weighted
according to severity and life-threatening items receive greater weights. The weighted
items are summed to obtain an overall score. The range for possible scores is from 0
to 105. The SLEDAI has been validated and shown to be sensitive to changes over time
(Fortin et al., 2000; Petri, Hellman & Hochberg, 1992).
Urinary levels of IL-6 was measured using Enzyme-Linked Immunosorbent
Assay (ELISA), an established method for determining cytokine levels. High-sensitivity
CRP, C3, C4, albumin, and prealbumin will be measured by nephelometry on a BN
Prospec II (Dade Behring) nephelometer. Nephelometry is a technique that uses analysis
of light scattered by liquid to measure the size and concentration of particles in the liquid.
All data analyses were performed using SPSS for windows (Version 11).
Hierarchical cluster analysis (Ward's Linkage) was employed to identify distinct
subgroups underlying the data structure. In order to validate the stability of the cluster
solution, the overall sample was split into halves using a random selection function
within the statistical software. The four-cluster solution was replicated in both halves,
lending further support to the validity of these four response profiles across AD patients.
Following the empirical derivation of response profiles, the assigned cluster
membership value (1-4) was used in subsequent analyses. Nonparametric chi square
analyses and analyses of variance were the principle statistics used in these analyses. For
all ANOVAs, significant F-tests were followed by post-hoc analyses using Tukey's HSD
to evaluate pairwise comparisons between response profiles on the dependent variable.
Statistical significance was set at an alpha value of .05 for all analyses.
This study was based on the empirical determination of patient subgroups from
the AD clinic based on the following scores: fatigue, pain intensity, depression, anxiety,
anger, and confusion. Complete data for this analysis was available for 374 participants.
Results of the hierarchical cluster analysis revealed a four-cluster solution (Figure 3-1),
which was determined quantitatively based on the percentage change in the
0.5 40- High Impact
0 Low Impact
-0.5 ~ Pain/ Fatigue
-1 Fatigue/ Distress
Figure 3-1. Replication four-cluster solution representing psychological profiles in
autoimmune disease patients
Subgroups represent relatively unique response profiles derived from the
multivariate combination of symptom and mood measures. Table 3-1 provides a
summary of the response profiles. The "High Impact" cluster (N = 67) is characterized by
high scores across all measures with particularly high (>1 SD from mean) elevations in
pain and anxiety. The "Low Impact" cluster (N = 165), the highest frequency cluster,
reflects low levels for all mood and symptom variables. The "Pain/Fatigue" cluster (N=
80) profile reflects significant fatigue, moderate pain and relatively low levels of distress
and confusion. The "Fatigue/Distress" cluster (N = 62) is characterized by high levels of
fatigue, depression, and anxiety. The increase in sample size from the pilot study
(N = 279) to the full sample (N = 374) resulted in an altered distribution of participants
across the four clusters, whereby a larger proportion (44%) of participants fell in the
profile cluster representing low symptom and distress levels.
Table 3-1. Description of response profiles
Cluster N Description
1 67 High impact
2 165 Low impact
3 80 Pain/Fatigue
4 62 Fatigue/Distress
Following the derivation of response profiles, a series of analyses were
undertaken to examine whether profiles differed across demographic, diagnostic, and
physiological variables. First, the relationship between response profiles and
demographic variables was examined to determine whether race, sex, age, or illness
duration predict psychological response profile. Nonparametric chi square analyses were
used to examine the relationships of race and sex to response profiles. Results showed
that racial background was proportionately distributed across the four psychological
response profile membership, X2=5.76 (6), p = .450. Chi-square results for sex were
significant, X2=8.49 (3), p=.037, indicating that men and women were disproportionately
represented across clusters. Closer examination of the crosstabulation matrix revealed
that no males were present in the "Fatigue/Distress" cluster. However, given the small
number of males (9.9%, N = 39) in the sample, this finding has limited interpretive value.
One-way analyses of variance (ANOVAs) were used to compare response
profiles on age and duration since diagnosis. Response profile groups did not differ on
age (F (3,370) =1.61, p = .188) or duration since diagnosis (F (3,320) =0.81, p = .490).
Thus, current age and time elapsed since being diagnosed are not predictive of symptom
and mood response profiles.
The second hypothesis concerned the concordance among psychological response
profiles and diagnostic categories (ANA positive, SLE, scleroderma, Sjogren's, and
polymyositis, and other). The frequency of response profiles across diagnostic groups
was assessed using nonparametric chi square tests. This analysis aimed to determine
whether disease factors associated with a diagnosis of AD are associated with response
profiles reflecting greater pain, fatigue and distress. The results revealed the
proportionate distribution of diagnoses across the four response profiles, X2=5.24 (12),
p=.950. This finding suggests that response profiles do not differ significantly between
various autoimmune conditions (Table 3-2).
Table 3-2. Crosstabulation matrix of response profiles across diagnostic categories
Response profile (N) Total
Diagnosis 1 2 3 4 N (%)
SLE 28 71 36 29 164 (43.8)
ANAPOS 18 48 19 14 99 (26.5)
Sjogren's 3 12 5 5 25 (6.7)
SSC 3 10 6 2 21 (5.6)
Other 15 24 14 12 65 (17.4)
Total 67 165 80 62 374 (100.0)
The next series of analyses examined whether psychological response profiles
represent varying levels of disease activity. ANOVAs were used to test for differences in
levels of disease activity as measured by a number of biological markers. Separate
ANOVAs were conducted for urinary 11-6, hsCRP, C3, C4, albumin and prealbumin. A
descriptive overview and results for these parameters is presented in Table 3-3.
Table 3-3. Values for biological markers of disease activities
Variable N Range Mean SD F Sig.
I1-6* 78 0.01 3683.50 433.56 792.15 0.932 0.430
HsCRP* 296 0.15- 108.00 8.03 13.21 0.130 0.944
C3 297 24.10- 261.00 116.65 34.40 2.180 0.091
C4 292 3.04- 118.00 21.21 12.36 2.700 .046+
Prealbumin 221 10.40- 60.40 25.95 8.61 0.695 0.556
Albumin 250 0.00 2048.00 51.64 211.69 0.336 0.799
IV = response profile group
* Values presented are based on raw data. Nonnormal distributions were transformed
logarithmically for statistical analyses.
Response profiles did not differ on mean levels of 11-6 (F (3, 70) =0.932, p =
.430), hsCRP (F (3, 293) = .13, p = .944) or C3 (F (3, 294) = 2.18, p = .091). Significant
differences between groups were found for C4 (F (3, 289) = 2.7, p = .046). Post-hoc tests
using Tukey's HSD revealed the "Low Impact" and "Pain/Fatigue" clusters differed
significantly (p=.045) on C4 values. This finding has limited value because correcting for
multiple comparisons suggests this significant finding could be due to chance. There
were no differences between prealbumin (F (3, 218)= .695, p = .556) and albumin (F
(3, 247) = .336, p = .799) levels between response profiles.
The collection of physician-rated SLE disease activity (SLEDAI) measures is
limited to those patients carrying a diagnosis of SLE. Thus, patients diagnosed with SLE
were selected to examine whether differences in total SLEDAI scores exist between
response profile groups. The range for SLEDAI scores was 0 to 24 and the mean was
3.27 (SD=4.12). Forty-one percent of SLE patients received a SLEDAI score of greater
than or equal to four. Due to the significant positive skew of the distribution, a square
root transformation was performed to normalize the data distribution. ANOVA revealed
that SLEDAI scores did not differ significantly across response profiles
(F (3, 142) = .786,p= .504).
The final hypothesis concerned the relationship between psychological response
profiles and levels of perceived social support. Results of the ANOVA showed that
psychological response profiles were associated with varying levels of social support
(F (3, 368) = 5.13, p = .002). Post-hoc analyses revealed that the "Low Impact" cluster
reported significantly (p = .001) higher levels of perceived social support than the
Unique subgroups of patients were determined empirically within a cohort of AD
and ANA positive patients based on a similar response pattern. Psychological and
symptom reporting profiles in this sample did not vary with respect to demographic
characteristics, diagnostic categories, serological markers of disease activity, and
physician-rated disease activity. Higher levels of perceived social support were
associated with response profiles characterized by lower levels of mood and symptom
reporting. The results of this study provide support for the presence of substantial
heterogeneity in illness response and psychological functioning across a large sample of
AD and ANA positive patients as well as within specific disease groups. Further,
subgroups are independent of disease factors, including diagnosis, suggesting that
personality, psychological, and/or social support factors are a stronger determinant of
response to illness.
Variations in psychological functioning within this illness population were
expected to span the continuum from well-adjusted to highly distressed participants. The
results of this study bring attention to the sizeable number of patients who are
experiencing elevated levels of subjective distress. Eighteen percent of participants fell
within the "High Impact" cluster profile, and 38% were characterized by some elevations
in symptoms and/or distress. These results signify the role of perceived symptom and
distress in overall illness coping and quality of life. At the same time, it is important to
keep in mind that not all patients experiencing illness or symptoms reminiscent of a
diagnosable disorder report compromised psychological functioning.
Cluster profiles were compared on a number of variables, including demographic,
diagnostic, disease activity, and social support measures. Race, age, and illness duration
were evenly distributed across cluster profiles, indicating that these variables are not
associated with differences in psychological response profiles. These results are
consistent with other studies that have demonstrated a lack of association between
measures of distress and demographic characteristics. For example, in a sample of RA
patients, VanDyke and colleagues (2004) found no significant relationship between
anxiety and illness duration. Similarly, Alarcon and colleagues (2004) demonstrated in a
cohort of 364 SLE patients that age and ethnicity were not associated with the physical
and mental subscales of the SF-36. Results of this study did indicate a significant effect
for sex when compared across clusters; however, the disproportionately small number of
males in the sample limits the ability to interpret this finding.
Opposing hypotheses were presented with regard to the relationship between
psychological profiles and diagnostic category. Across conditions, one might expect that
conditions posing a greater threat to mortality, quality of life, and function would lead to
higher levels of distress and greater coping challenges, thereby supporting that
psychological response is determined to a large extent by disease severity. However,
results demonstrated that response profiles were equally distributed across diagnostic
categories, suggesting that coping profiles are independent of diagnosis. Equal
representation of diagnoses across response clusters points to predisposing factors such
as personality, social support, and coping style determining response profile membership.
This finding provides meaningful implications for the treatment of patients with
the various diagnoses under consideration in this study. For example, physicians who are
biased toward assuming that worse disease is related to poor psychological adjustment or
that a positive ANA titer in the absence of a diagnosable AD should be less
psychologically threatening to the individual are at risk for under- or over-interpreting
psychological well-being based on disease threat or severity. The results of this study
suggest that illness adjustment is not related to diagnosis. In fact, psychological
functioning and/or perceived social support might be a more accurate predictor of how
patients will respond to their illness.
In this study, it is not possible to determine the extent to which psychological
response to illness is based on premorbid factors versus factors that are activated or
influenced in the presence of illness. Support for the idea that premorbid psychological
function plays an important role in response to illness has been demonstrated through
investigations of neuroticism and self-reported adjustment. Costa and McCrae (1987)
found that patients scoring particularly high on neuroticism tend to report higher levels of
self-reported psychological and physical symptoms, without suffering from worse
clinical outcomes. Given that psychological distress influences perceived quality of life,
behaviors that can affect health outcome (e.g., exercise, diet, and use of alcohol, drugs,
and nicotine), and response to illness, subjective distress levels are important to consider
when illness is present, regardless of diagnosis.
Variations in psychological functioning were hypothesized to reflect differences
in disease status as measured by serological markers of disease activity. This hypothesis
was not supported. Response profiles characterized by higher levels of pain, fatigue, and
distress were not associated with increased activation of serological markers of disease
activity. Symptom profiles are not accounted for by underlying disease processes and
appear to be better explained by premorbid psychological factors. Statistical significance
was achieved for complement protein C4 between the "Low Impact" and "Pain/Fatigue"
groups; however, the relationship was not in the anticipated direction. This finding is
most likely the product of statistical chance due to the number of serological tests
conducted. In sum, the weight of the evidence does not support an association between
serological markers of disease activity and response profiles.
Response profiles reflecting higher levels of pain, fatigue, and distress were not
associated with higher levels of physician-rated disease activity in SLE patients,
suggesting psychological response to illness may be independent of specific pathological
processes. Another possible interpretation is that physician-ratings of disease activity are
not congruent with patients' perceptions of disease activity. Ward and colleagues (2002)
found that changes in depression and anxiety were positively correlated with
simultaneous changes in the patient global assessment of SLE activity but not with
changes in SLEDAI scores. Furthermore, studies have found assessments of disease
activity by patients and physicians are often discordant (Neville et al., 2000) and the
SLEDAI is generally not responsive to changes in patients' assessments of changes in
disease activity (Chang, Abrahamowicz, Ferland, & Fortin, 2002). Thus, it seems
possible that patients' psychological adjustment to illness may depend more heavily upon
subjective assessments of disease activity, which may not be associated with other
measures of disease activity.
The importance of interpersonal relationships in the maintenance of health has
been widely reported. Poor social support is expected to increase the burden of illness
experienced by the individual. DeVellis and colleagues (1986) reported less-supportive
atmospheres play a role in the onset and exacerbation of autoimmune diseases. In the
current study, the hypothesis that response profiles reflecting higher levels of pain,
fatigue, and distress would be inversely associated with levels of perceived social support
was supported. This finding is consistent with a large body of literature across numerous
disease populations reporting that greater social support serves as a buffer against the
harmful effects of illness on psychological well-being.
This study provided an interesting approach to grouping patients into
psychological response profiles. One of the objectives of cluster analysis is to reveal
relationships among observations that were perhaps not possible using individual
observations (Hair, Anderson, Tatham, & Black, 1998). This did not appear to be the
case for the subgroups found in this study. The subgroups did not demonstrate
meaningful relationships with expected variables, bringing into question the usefulness of
determining subgroups within AD samples.
The identification of patient subgroups is ultimately beneficial to the extent that
they can be utilized in the understanding and treatment of individuals in each subgroup.
This type of application has proven successful in the chronic pain literature (e.g., Sanders
& Brenna, 1993; Swimmer, Robinson, & Geisser, 1992). Further research investigating
treatment outcome differences across clusters is necessary to determine the possible
benefits of using clustering techniques to identify subgroups of AD and ANA positive
patients. The ability to identify patients who are highly distressed and at increased risk
for poor outcomes would allow for the implementation of interventions that are more
efficiently tailored to meet the individual's needs.
Future studies investigating whether diagnostic groups within a particular cluster
(e.g., "High Impact") are more or less amenable to improvements when a targeted
psychological intervention is applied might help to elucidate relationships between
disease and illness response. For example, if ANA patients in the "High Impact" cluster
showed greater reductions in psychological distress than SLE patients from the same
subgroup, one might suspect that high distress compounded by more severe underlying
disease processes are less responsive to psychological intervention.
The findings of this study must be considered in the context of several caveats.
First, it is necessary to point out that the cluster analysis technique is a data reduction
technique based on both objective and subjective considerations on the part of the
researcher. Therefore, if another set of variables had been chosen to include in the cluster
analysis, the results could have been quite different. Similarly, it is possible that the
variables selected to test predictive validity of the clusters were not the most appropriate
in terms of their ability to discriminate between variations in psychological responses.
For example, it is possible that the SLEDAI is not an accurate measure of disease activity
or that it is not comprehensive and does not adequately capture the component of disease
activity associated with symptom (i.e., pain and fatigue) intensity.
An additional caveat relates to the a priori decision to include individuals
representing a wide range of diagnostic groups. Although the ANA positive group
presents clinically with many of the same complaints as patients who go on to be
diagnosed with AD, it is possible that they are a distinct group with regard to response to
illness. It is possible that the heterogeneity across the sample diluted the relationship of
response profiles to disease-specific measures of function. Thus, future studies
attempting to categorize patients based on response profiles might benefit from limiting
their sample to a specific diagnostic category before broadening the scope to include
The cross-sectional design on which this study is based limits the exploration of
relationships to a single point in time. The temporal relationship between disease activity
and psychological functioning remains unclear, although some researchers have
suggested that such relationships are not based on a simultaneous pattern of flux. Thus, it
is possible that relationships between biological correlates of disease activity and
psychological function can only be elucidated when the variables of interest are observed
prospectively. Research in the area of relationships between psychosocial and immune
parameters would benefit from longitudinal designs to account for temporal relationships
that are not revealed within cross-sectional designs.
Finally, the cohort sampled in this study included a relatively small number of
men. It remains largely unknown as to whether men responded similarly to the women.
These proportions did not allow for tests of gender differences. Future research is needed
to better understand psychological response to AD in men and to test for gender
differences in symptom reporting and illness response.
Cluster profiles were not validated by demographic, diagnostic, or disease activity
measures. Response profiles were related to perceived social support, the only
psychosocial variable examined across cluster profiles, suggesting that psychosocial
variables function in synchrony. Response to illness in this study was not dependent on
disease activity or type, thereby providing greater support for the role of psychological
and social support factors in determining response to illness.
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Rebecca Jump was born February 25, 1973, as the first of four children. She grew
up on the Eastern Shore of Maryland in a small community on the Chesapeake Bay. She
graduated from St. Michaels High School in 1991 and headed to the mountains of central
Pennsylvania to attend Juniata College. As an undergraduate, she played varsity field
hockey and spent one semester in Nancy, France, as part of a study abroad program. She
graduated with a Bachelor of Science degree in biopsychology and French in 1995 and
promptly headed back across the Atlantic Ocean to further indulge herself in French
language and culture. She spent one year in a French language program for foreigners at
the Universite de Lille III in Villeneuve d'Ascq, France. Upon her return from abroad in
1996, Rebecca began a master's program in the General Experimental Psychology-
Health option at the University of Hartford in West Hartford, Connecticut. She spent an
additional year in New England working full-time at the University of Connecticut
Health Center as a research assistant for parallel studies involving women with
fibromyalgia and rheumatoid arthritis. In 2000, Rebecca headed for the sunny south to
begin her doctoral training in clinical and health psychology at the University of Florida
where she specialized in adult medical psychology with a particular focus on chronic
pain and rheumatic disease. In the next phase of her "East Coast Living" Rebecca
ventured to Augusta, Georgia, to complete her predoctoral internship at the Medical
College of Georgia/Veteran's Affairs Medical Center Training Consortium. During this
year of clinical training, Rebecca pursued an advanced specialization in the assessment
and treatment of posttraumatic stress disorder (PTSD). Following the completion of her
internship, Rebecca will return to the University of Florida to complete a postdoctoral
fellowship in the Department of Clinical and Health Psychology. Her career goal is to
obtain a position in a medical center specializing in the treatment of chronic pain/illness
and PTSD. She also hopes to devote a portion of her time to research collaboration and
supervision of trainees.