Database of cell signaling enzymes

This paper describes a database for cell signaling enzymes. Our web database offers methods to study, interpret and compare cell-signaling enzymes. Searching and retrieving data from this database has been made easy and user friendly and it is well integrated with other related databases. We believe the end user will be benefited from this database.
Background
A cell communicates with its neighbors and environment by sending and
receiving information in the form of chemical signals. These signals are
converted into intracellular second messenger signals that ultimately make the
cells respond appropriately by dividing, moving or even dying. The external
signals may enter the cell through enzymes, G-protein coupled receptors,
hydrophobic molecules and ion channels. When the receptor sensing the signal is
a catalyst such as enzyme, the response is amplified. Thus cell signaling
governs the basic cellular activities and coordinates the cell action. Errors
in cell signaling is the cause for many serious diseases/disorders including
cancer, autoimmune diseases, diabetes etc. By understanding cell signaling we
can treat these diseases effectively and potentially. [1] However, no attempts have been made
so far to curate and catalog the enzymes involved in cell signaling.
DCSE, the Database of Cell Signaling Enzymes covers a gamut of cell
signaling enzymes, which includes kinases, phosphatases, adenylyl cyclases,
caspases, phosphodiesterases, phospholipases, prenyltransferases etc. As
mentioned above the defect in cell signaling mechanism is the major cause for
diseases and hence the cell signaling enzymes are considered to be potential
target in rational drug design approach.
Methodology
DCSE was developed using MySQL [
2], a relational database management system, at the back-end for
storing data. The database can be regularly updated. The data for the database
were collected from SwissProt [3
], the repository of protein sequence information. PHP [4], a widely used general purpose
scripting language that is especially suited for web development, was used to
design the database interface.
The database can be accessed over the Internet. A screenshot of the home
page is shown in Figure 1.The database can
be searched by specifying keywords such as name, Enzyme Commission Number and
species. For each enzyme in the database a unique identifier called DCSE ID has
been assigned. The ID consists of two parts. The first part tells about the
enzyme class and the second part indicates the number of that enzyme in that
class. The two fields are separated by an underscore (_). Each enzyme has been
defined with its name, Enzyme commission number, and the species from which it
has been sequenced. Crossreference details to other databases are also
provided. The functional and other sequence related informations are provided
by SwissProt, the domain classification and function is provided by InterPro
[5], the protein family
classification is provided by Pfam [
6] and the structural details are provided by PDB. [
7] A hyperlink has been provided
to corresponding entry page in the abovementioned databases.
The sequence is also displayed in raw-text format. One can retrieve the
sequence in FASTA format using the ‘Retrieve in Fasta format’
option available with each entry. An advanced search can also be performed by
filling an advanced query form that takes input as DCSE id, name, EC number,
Species, other database id such as SwissProt, InterPro, Pfam and PDB. The
fields in the search form are joined together by AND operator. BLAST (Basic
Local Alignment Search Tool) [8
] is an algorithm for comparing biological sequences. Given a library
or database of sequences, a BLAST search enables a researcher to look for
sequences that resemble a given sequence of interest. A standalone BLAST has
been installed and it can be used for similarity search purpose. BLAST can be
done against SwissProt, DCSE or PD.
Usability/Accessibility
There are three ways in which a user can query the database. The first is
the ‘keyword search’ that can be done by specifying exact or
likely keywords such as name, Enzyme Commission Number and species. The second
search option is the ‘Advanced Search’ wherein the user can
fill a form by specifying the details for various fields such as DCSE id,
Enzyme name, Enzyme commission number, SwissProt accession number, InterPro
domain ID, Pfam id or PDB id. The fields are joined together by AND operator.
Alternatively the users can also browse the database by the 21 different
categories of cell signaling enzymes. Whenever the database is searched the
search returns the result with number of hits found for that query along with a
summary of details for each entry with its id, name and species. The user can
then select the appropriate hit by following the link on it. This displays a
page with all the available details of the enzyme in the database such as name,
EC number, sequence information and cross-reference to already existing
databases as shown in Figure 2. A
hyperlink has been provided to corresponding entry page in the cross-referenced
databases for easy access. The sequence is displayed in raw-text format. One
can retrieve the sequence in FASTA format using the ‘Retrieve in Fasta
format’ option available with each entry. A BLAST search can also be
done to search for similar sequences within the database or in general. A link
has been provided in the tools page. The BLAST input page and result page are
shown in figure 3 and 4.
Utility to the biological community
Since DCSE gather together all the information on biological molecules,
sequences, structures, functions, and biological reactions, which transfer the
cellular signals, this database has the potential of becoming a major hub of
resource for the biological community. This database provides a mechanism by
which researchers and students can transform information about interactions
between biomolecules into knowledge about a cellular process.
Caveats
The database needs to be updated from time to time as the data has been
obtained from other sources. Despite our efforts to collect information from
various sources and check them for consistency, the quality of the information
depends heavily on the original source.
Future development
The coiled-coil structure is important for protein interaction. Since the
cell signaling also involves protein interaction, a tool to predict coiled-coil
structure is of importance.
There are few tools for this purpose like MultiCoil. The Multicoil program
just gives the probability for a residue to be in coiled-coil region. We intend
to develop a coiled-coil prediction tool based on artificial neural networks
that could tell whether a residue is in coiled-coil region if it crosses a
threshold computed from a training set consisting of proteins with known
coiled-coil structure.

cell signaling
enzymes
web database
pblast


Are Pain Intensity and Pain Related Fear Related to Functional Capacity Evaluation Performances of Patients with Chronic Low Back Pain?

Introduction: Pain related fear and pain intensity have been identified as factors negatively influencing Functional Capacity Evaluation (FCE) performances in patients with CLBP. Conflicting results have been reported in the literature. The objective of this study was to analyze the relationships between pain intensity and pain-related fear on the one hand, and performances during an FCE on the other hand in two samples of patients with chronic low back pain (CLBP). Methods: Two cross sectional observation studies were performed with two samples of patients with CLBP (study 1: n = 79; study 2: n = 58). Pain related fears were operationally defined as the score on the Tampa Scale of Kinesiophobia in study 1, and the Fear Avoidance Beliefs Questionnaire (FABQ) in study 2. Pain intensity was measured with a Numeric Rating Scale in both studies. Avoidance behavior observed during FCE was in both studies operationally defined as the unwillingness to engage in high intensity performance levels of three different functional activities: high intensity lifting, prolonged standing in a forward bend position, and fast repetitive bending at the waist. Results: A total of 25 correlations between pain and pain related fear, and performance variables were calculated, out of which 7 were significant (p < 0.05). The strength of these significant correlations ranged from r = −0.23 to r = −0.50. Multivariate linear regression analyses revealed non-significant relationships in most instances. Pain and pain related fear contributed little if any to these models. Conclusions: The relation between pain and pain related fear and FCE performance is weak or non-existent in patients with CLBP.
Introduction
Functional Capacity Evaluations (FCEs) are batteries of tests designed to assess a person's functional capacity related to work [1–3]. To establish functional capacity, the patient is asked to perform a number of activities. Combined, these performances reflect a patient's ability to perform work-related activities. A patient's performance is determined by biological, psychological and social factors [4]. Pain related fear, also referred to as fear avoidance beliefs or fear of movement and reinjury [5], and pain [6] have been identified as examples of psychological factors influencing a patient's performance during an FCE.
Pain related fear refers to a condition in which the patient has an excessive, irrational, and debilitating fear of physical movement and activity, resulting in feelings of vulnerability to painful injury or reinjury [7, 8]. People who experience pain-related fear will avoid activities they associate with increased risk for pain or (re)injury. As such, pain-related fear should have a negative effect on the results of performance testing [5, 9]. It has been stated that ‘a valid assessment of functional capacity cannot be carried out without controlling for fear avoidance beliefs’ [8]. Several studies were performed to study the relationship between pain-related fear and different types of avoidance behavior in patients with chronic non-specific low back pain (CLBP). The strength of this relationship varied between the studies from r=0.06 to r=0.49 [8–10, 11]. The relationship between pain related fear and performance appeared stronger in studies where patients were observed under strictly controlled conditions [8, 10], and weaker in studies where patients were observed in a less controlled environment [11]. In a previous study performed in an admission phase for a pain rehabilitation program, no relationship was found in a sample of patients with CLBP, between lifting performance and pain-related fear [12]. Although the evidence from other studies did not overwhelmingly support the strength of the relationship, our results, presented in the Journal of Occupational Rehabilitation in 2003, were in contrast with other studies. It could not be determined whether the results found were weaker because of the operational definitions of pain-related fear (score on Tampa Scale for Kinesiophobia (TSK)) or avoidance behavior (maximum lifting performance). It was recommended that future studies should use additional operational definitions for pain-related fear and avoidance behaviors.
In a recent review pain intensity has been identified to consistently associate negatively with patient performances during FCEs [6]. The strength of the significant associations between pain intensity and FCE performances in patients with chronic pain vary from r = −0.25 to r = −0.56 [6]. In the study mentioned above, the relation between pain intensity and lifting performance was (somewhat) weaker and non-significant (r = −0.21) [12].
Aim of this study was to further analyze the relationships between pain intensity and pain-related fear on the one hand, and avoidance behaviors on the other hand. Two studies were performed with separate samples of patients with CLBP. Pain related fears were operationally defined as the score on the TSK in study 1, and the Fear Avoidance Beliefs Questionnaire (FABQ) in study 2. Avoidance behavior was in both studies operationally defined as the unwillingness to engage in high intensity performance levels of three different FCE tests: high intensity lifting, prolonged standing in a forward bend position, and fast repetitive bending at the waist. Non-significant or weak relationships were considered falsifications of the hypotheses that pain and pain-related fears would have clinically relevant impact on FCE performances in patients with CLBP.
Materials and methods
Patients
Two separate cohorts of patients who were referred to an outpatient rehabilitation program of the Center for Rehabilitation of the Groningen University Medical Center, The Netherlands, were included. Both study samples consisted of patients diagnosed with CLBP, aged between 18 and 60 years, with symptoms lasting longer than 3 months. Excluded were patients with comorbidity with negative consequences for functioning (i.e. severe depression needing a psychiatric referral), and patients with specific pathology related to the lumbar spine (i.e. disc herniations, tumors, spondylolisthesis grade 3 or 4, etc.). Patients were not selected based on (high or low) levels of pain or pain related fears. Selection process of both study samples were equal, with the exception that patients in study sample 1 explicitly agreed to participate in a larger study in addition to regular clinical care [13]. The patients of sample 2 received regular clinical care only.
Procedures
Prior to the medical intake patients filled out a TSK [7, 8], a numeric rating scale to assess pain intensity (NRS), a Roland Morris Disability Questionnaire [14] (RMDQ) to assess disability, and a study consent form. The patients underwent the FCE approximately 2 weeks after the medical intake. Prior to the FCE, all patients were asked whether their pain and functional status was different compared to their status during medical intake. In study 2, patients filled out the FABQ [15] prior to the FCE. For study 1, approval was granted by the medical ethics committee of the University Medical Center Groningen, The Netherlands. The data of the patients of study sample 2 were collected as part of regular clinical procedures, for which institutional approval was received.
Measures
Patients rated their current pain intensity on a NRS ranging from 0 to 10; 0 being no pain at all and 10 being the worst pain imaginable. Self reported disability was assessed by the RMDQ [14]. Scores on the RMDQ can range from 0 to 24; 0 indicating no disability and 24 indicating severe disability. The psychometric properties of the RMDQ are good [14, 16].
Pain related fear was assessed with a different questionnaire for each sample. In study 1, pain related fear, called fear of movement and reinjury, was assessed by the TSK [8]. The TSK consists of 17 items. Each item is provided with a 4-point Likert scale with scoring alternatives ranging from “strongly disagree” to “strongly agree.” Although the scale consists of 2 subscales, activity avoidance and somatic focus, a total score is usually presented. The total score ranges from 17 to 68. The reliability of the TSK is fair [8]. Criterion validity was established from correlations with other self-reported measures of fear (of bodily injury), anxiety, depression, and catastrophizing [8].
In study 2, pain related fear, called fear avoidance beliefs, was assessed by the FABQ [10, 15]. The FABQ consists of 16 items. Each item is provided with a 7-point Likert scale with scoring alternatives ranging from 0 to 6. The FABQ consists of 2 scales. The activity scale consists of 4 items (total score 0–24), the work scale consists of 6 items (total score 0–36). The remaining 4 items are not used. The reliability of the FABQ is good in patients with acute and subacute back pain [17], but has not been established in patients with CLBP.
Avoidance behavior was assessed during an FCE. The patients were asked to perform 14 different activities to their maximum abilities (3 types of lifting, carrying, pushing, pulling, overhead work, stooping, crouching, kneeling, standing, walking, sitting, stair climbing), according to the protocols of Isernhagen Work Systems [18]. Selection of activities was based on the Dictionary of Occupational Titles (DOT), thus assuming construct validity with regard to work [19]. Three activities tested during the FCE were selected for this study, because they might be potentially harmful for patients with CLBP [20]. These activities were lifting, static bending and dynamic bending. Lifting was assessed by means of a standardized lifting task consisting of lifting a receptacle with incremental weights from a table (74 cm) to the floor and vice versa. ‘Ergonomically correct’ body mechanics were not emphasized [21]. The patient's maximum was reached in four to five increments. The maximal amount lifted five times within 90 seconds was recorded (kilograms). Test-retest reliability of the lifting task is good in patients with CLBP [22–24]. During lifting, heart rate was measured using a heart rate monitor. In study 2, the intensity level of lifting was observed by the evaluator, and recorded by means of a Borg CR-10 scale. The reliability and validity of this procedure is good [25]. These observations were not recorded in study 1. During the static bending capacity test the patient was asked to stand as long as possible with his / her trunk 30–60° flexed forward, while performing a simple manipulation task. The time this position was held was recorded (seconds). Test-retest reliability of this test in patients with CLBP is good [22]. For the dynamic bending test, the patient is asked to bend at the hips and back as fast as possible, pick up a small object from the floor, and to remove this object to a shelf at crown level (top of head). This was repeated 20 times. The number of repetitions completed and the time needed to complete this activity was recorded (seconds). Test retest reliability of this test in patients with CLBP is moderate to good [22]. The construct validity of the tests has been established [26, 27]. Before testing, patients were instructed regarding termination of the test. Testing was terminated when one of the following occurred: the patient stated verbally that he/she wished to terminate the activity, the heart rate reached 85% of the age-related maximum, indicated by a heart rate monitor, or the evaluator deemed further testing to be unsafe. No verbal reassurance was given during the testing procedures. The evaluator was blinded to the questionnaire scores.
Analysis
Descriptive statistics were used to describe the study samples. All variables of interest were analyzed at interval level. A student t-test was used to analyze differences between males and females. When differences were significant, further analyses were performed separately for males and females. Pearson product moment correlations were calculated to express linear associations between pain intensity and measures of pain related fear on the one hand and performance variables on the other hand. Correlations were interpreted as follows: 0.25 or less little if any relationship, 0.26–0.49 poor relationship, 0.50–0.69 moderate relationship, 0.70–0.89 strong relationship, 0.90–1.00 very strong relationship [28]. Pain intensity and pain related fear, as well as gender, age and self-reported disability were entered as predictor variables in a multivariate linear regression model (method: enter) and performances as outcome variables. A p-value of <0.05 was considered statistically significant for all analyses.
Results
The characteristics of the patients participating in study 1 and 2 are presented in Table 1. All patients declared that their pain and functional status had not changed between medical intake and the FCE. Scores on the TSK subscale in study 1 are: activity avoidance males mean 19.1 (SD 3.8), females mean 18.9 (3.3); somatic focus males mean 9.7 (2.5), females mean 9.2 (2.6). The 20 repetitions of the dynamic bending test were not completed by 10 (of 79, 13%) patients in study 1 and by 6 (of 58, 10%) patients in study 2. To correct for differences in number of repetitions, the time needed to complete one repetition was calculated and presented (time/number of repetitions). The observed level of intensity of the maximal lifting performances in study 2 were for males mean 7.2 (SD 2.2) and for females 7.4 (SD 1.6) (Borg CR-10 scale). Observational data was unavailable in study 1. Within the study samples, differences between males and females were non-significant, with the exception of the maximum lifting performance. Between the study samples, none of the differences were significant.
Table 1Age, pain intensity, self-reported disability, pain related fear (TSK or FABQ), and performance variables of 2 samples of patients with chronic low back painStudy 1 (n = 79)Study 2 (n = 58)Males (n = 49)Females (n = 30)Males (n = 39)Females (n = 19)Mean (SD)Mean (SD)Mean (SD)Mean (SD)Age (years)37.8 (9.0)37.8 (8.8)40.4(8.6)35.6 (8.3)Pain intensity (NRS; 0–10)4.7 (2.3)5.0 (1.6)4.5 (2.4)4.9 (2.2)Self-reported disability (RMDQ; 0–24)12.5 (4.4)12.4 (4.7)11.6 (4.4)11.2 (5.9)TSK (17–68)37.5 (5.3)36.0 (5.6)N/AN/AFABQ activity scale (0–24)N/AN/A13.4 (4.1)13.2 (5.6)FABQ work scale (0–36)N/AN/A19.3 (9.5)15.1 (11.6)Lifting performance (kg)*31.7 (14.7)18.9 (8.1)32.3 (3.8)20.5 (6.3)Lifting maximum heart rate (BPM)126.7 (15.8)129.7 (23.5)128.2 (14.5)128.8 (19.3)Static forward bend (sec)187.4 (148.9)230.6 (146.3)188.0 (115.8)237.3 (154.8)Dynamic forward bend (sec/rep)2.8 (0.7)3.2 (1.3)3.2 (1.6)2.9 (0.8)SD: standard deviation; NRS: Numeric Rating Scale; RMDQ: Roland Morris Disability Questionnaire; TSK: Tampa Scale for Kinesiophobia; FABQ: Fear Avoidance Beliefs Questionnaire; BPM: beats per minute; N/A: not assessed. *: Difference between males and females significant in both studies (p < 0.05).Note. Differences between the study samples were non-significant (p < 0.05).
A total of 25 correlations between pain intensity, TSK (study 1) and FABQ (study 2) and performance variables are presented in Table 2. The strengths of the correlations were moderate in one occasion and otherwise weak/poor or non-significant. Pain intensity explained 25% of the variance of the performances of male lifting performance in study 2 (r = −0.50). In all other analyses over both studies, the explained variance between pain intensity and performance variables was 8% (r = −0.29) or less. The FABQ work scale explained at best 13% of the variance of the performances (male lifting performance in study 2; r = −0.37). The correlation between either TSK subscales with any of the performance variables was non-significant. No differences were observed between patients who did or did not complete 20 repetitions of the dynamic bending test concerning their relationship with the TSK or the FABQ (all non-significant). The correlation between observed intensity of lifting and FABQ work scale was r = −0.27 (significant). The correlation between observed lifting intensity and FABQ activity scale was r = −0.07 (not significant).
Table 2Relationships between performance variables and pain intensity, the Tampa Scale for Kinesiophobia (TSK, study 1; n=79) and the Fear Avoidance Beliefs Questionnaire (FABQ, study 2; n = 58) in patients with chronic low back pain. All relationships are expressed in Pearson's r (95% CI)Study 1Study 2PainTSKPainFABQ activity scaleFABQ work scaleLifting performance males (kg)−0.26 (−0.57 to 0.11)−0.12 (−0.46 to 0.25)−0.50* (−0.71 to −0.22)−0.13 (−0.43 to 0.19)−0.37* (0.06 to 0.61)Lifting performance females (kg)−0.06 (−0.34 to 0.23)−0.17 (−0.43 to 0.12)0.09 (−0.52 to 0.38)−0.07 (−0.51 to 0.40)−0.03 (−0.48 to 0.43)Lifting maximum heart rate (BPM)0.05 (−0.17 to 0.27)−0.10 (−0.31 to 0.12)−0.27* (−0.49 to −0.01)−0.24 (−0.47 to 0.02)−0.24 (−0.47 to 0.02)Static forward bend (sec)0.18 (−0.04 to 0.39)−0.23* (−0.43 to −0.01)−0.29* (−0.51 to −0.03)−0.33* (−0.54 to −0.08)−0.25 (−0.48 to 0.01)Dynamic forward bend (sec/rep)−0.10 (−0.31 to 0.12)0.14 (−0.08 to 0.35)0.03 (−0.23 to 0.29)0.07 (−0.19 to 0.32)0.30* (0.05 to 0.52)Note. TSK: Tampa Scale for Kinesiophobia; FABQ: Fear Avoidance Beliefs Questionnaire; BPM: beats per minute; 95% CI: 95% confidence Interval.*p < 0.05.
Analyses were performed on the subgroups of patients scoring in the highest quartile of the pain intensity spectrum (study 1, NRS ≥ 6, n = 22; study 2, NRS ≥ 7, n = 17), the TSK (score ≥ 40, n = 22), and the FABQ (activity scale ≥ 17, work scale ≥ 25, n = 16). Analyses involving lifting performances were performed for males and females separately. Correlation coefficients between pain intensity or pain related fears and performance variables ranged between r = 0.00 and r = 0.23 in study 1, and between r = 0.00 and r = 0.26 in study 2. All correlations were non-significant. Scatter plots of the distribution of pain intensity or pain related fears and performance variables were created to visually analyze patterns in the datasets. As an example, the distribution of pain related fear (TSK) and lifting performances is presented in Fig. 1. Other distributions between pain intensity or pain related fears (TSK or FABQ) and performance variables were similar, but they are not presented.Fig. 1Plot of maximum lifting performance and scores on the Tampa Scale for Kinesiophobia (TSK) in 79 patients with chronic low back painTable 3Multivariate linear regression models predicting variance in performance in patients with chronic low back painβ (95% CI)Betapr2 changeModel r2Study 1 (n = 79)Dependent variable: lifting •Gender (female = 0, male = 1)14.45 (7.36 to 21.54)0.48<0.0010.230.28 •Constant17.01 (−9.60 to 43.62)–0.205–Dependent variable: static forward bend •Model r2 = 0.11; None of the independent variables contributed significantly to the regression equationDependent variable: dynamic forward bend •Model r2 = 0.12; None of the independent variables contributed significantly to the regression equationStudy 2 (n = 58)Dependent variable: lifting •Gender (female = 0, male = 1)14.94 (6.15 to 23.73)0.480.0010.230.37 •Pain intensity−1.83 (−3.65 to −0.01)−0.290.0490.08 •Constant (kg)45.70 (25.67 to 65.73)–<0.001–Dependent variable: static forward bend •Constant (sec)549.15 (336.09 to 762.21)–<0.001–0.27 •None of the other independent variables contributed significantly to the regression equationDependent variable: dynamic forward bend •Model r2 = 0.18; None of the independent variables contributed significantly to the regression equation
Results of the multivariate regression analyses are presented in Table 3. Study 1: Gender was the only variable that independently contributed significantly to the regression equation to predict lifting performance. The explained variance was 28%. Both models to predict the other two outcome variables, static forward bend and dynamic forward bend, were non-significant. Study 2: Gender and pain intensity (to a lesser extend) contributed significantly to the regression equation to predict lifting performances. The explained variance was 37%. In the regression analysis to predict static forward bend performances, none of the independent variables contributed significantly to the regression equation (explained variance 27%). In the regression analysis to predict dynamic forward bending performances none of the independent variables contributed significantly to the regression equation. Collinearity diagnostics were performed for the independent variables. In both studies the average variance inflating factors (VIF) were greater than 1, and none of the VIF-values were greater than 10. Specifically, the relationship between pain intensity and measures of pain related fear were analyzed. In study 1, the strength of the relation between pain and the TSK was r = −0.04 (ns). In study 2, the strength of the relation between pain intensity and FABQ activity scale was r = 0.07 (ns), and between pain intensity and FABQ work scale was r = 0.20 (ns).
Discussion
This study has demonstrated that the associations between pain intensity and pain related fears on the one hand and FCE performances on the other hand were generally weak or non-significant. Correlations between pain intensity and pain related fears and performances were significant in only 7 out of 25 analyses (Table 2). The strength of these significant relationships ranged from r = −0.23 to r = −0.50, explaining 5 to 25% of the variance (r2). Multivariate regression analyses were non-significant in 3 out of 6 instances. When significant, pain intensity or pain related fear contributed little if any to these models. Relationships are consistent throughout the intensity spectra (upper quarter analyses and Fig. 1). Thus, overseeing all the results of this study, it appears that the relationships between pain and pain related fear and performances in an FCE are generally weak or non-existent. Although we realize that interpretations of these results are open for discussion, we interpret the magnitude of the relationships between pain and pain related fears on the one hand and performances in an FCE on the other hand of limited clinical relevance. In comparison to our previous study [12], we have now used two new cohorts, two questionnaires instead of one to measure a wider range of pain related fears, and three performance tests instead of one to measure a wider range of avoidance behaviors. Additionally, we have added heart rate and observations as indices for effort level in lifting. Both pain intensity and pain related fear were poorly associated with indices for effort, explaining 7% or less of the variance. The results of the 2 new studies presented here are generally consistent with our previous study, which adds to the robustness of the results.
There may be several explanations for the weakness or non-existence of the associations between pain intensity and pain related fears on the one hand, and avoidance behaviors on the other hand. Pain intensity and pain related fears might have been inappropriately operationally defined by the NRS, the TSK and the FABQ, avoidance behavior might have been inappropriately operationally defined by the three performance measures, our study samples differ from samples reported elsewhere, or the variables were appropriately operationally defined, but the relationship was mediated by one or more currently unknown variables. Additionally, a combination of the above may explain our findings.
The NRS, TSK and the FABQ may be considered among the standards to measure pain intensity and pain related fears. The reliability and the validity of the measures for avoidance behavior and pain related fears have been established. Both the performance measure ‘heavy lifting’ and a postural tolerance test have been used previously in different studies as measures for avoidance behavior [8, 9, 12]. The activities selected are potentially harmful for patients with CLBP [20]. Thus, it seems unlikely that the operational definitions were inappropriate for the constructs measured. Both our study samples are similar to samples reported in other studies reporting on patients with CLBP concerning age, pain intensity, self-reported disability, and amount of fear avoidance beliefs [8, 10]. This similarity may rule out the plausibility the results of our study are caused by the selection of study samples. While the TSK and the FABQ may be considered among the consented standards to measure pain related fears, it may be possible that the domain measured might be too large and general to be of value in predicting specific behaviors such as the ones used in this study. This in itself could explain the findings of this study and the lack of consistent findings in previous research. However, if the measures are considered valid representations of the constructs pain intensity, pain related fear, and avoidance behavior, then a different explanation should be considered for our findings.
Other variables may serve as a mediator between pain variables and avoidance behavior. As suggested in recent pain literature, two variables should be considered as mediators or confounders: motivation [29] and acceptance [30]. Within the motivational models [29, 31, 32], it is suggested that the value of the goal and the patient's belief in his ability (self-efficacy) to attain this goal is predictive for his behavior. When applied to the results of this study, patients were apparently willing to perform the tests regardless of their pain related fears. They were willing to perform, because they may have been convinced that this would have helped them to reach a valued goal (for example return to work). Contrarily, patients without pain related fears may perform poorly on the tests, because they do not see it to be of any value. Alternatively, a poor performance could be explained if the patient's goal would be to use the test results for financial gain (disability allowance) [33]. In that case, pain related fears would be of little importance to determine a patient's behavior. Within the acceptance model [30], it is suggested that patients may be able to accept their pain or fears as an inevitable part of their lives. Patients would report pain or have cognitions such as pain related fears, but are able to function normally with their pain or fears. Some empirical evidence is found to support the applicability of this model within chronic pain [34, 35]. If a mediating effect or confounding of one or more different variables is present, then poor or non-existent associations between pain intensity, pain related fears and test performance are very well imaginable. Future studies should be conducted to find empirical evidence for the proposition that constructs as motivation and acceptance are more important predictors for functional capacity than pain or pain related fears. Additionally, other currently unknown variables may have played a mediating role in the relationship between pain variables and avoidance behaviors.
Both self-report and performance measures assess effort-related performance [36]. As such, they cannot be defined independently of the person's behavior. Pain-related disability is a matter of human performance, whether it is observed/measured or reported [36] and this may also apply to patient's behavior in an admission phase of a rehabilitation program. In this stage patients need not only reveal their cognitions and perception of disability, but at the same time present an image to the practitioner [37], to justify the need for treatment [38, 39] and perhaps to justify the fact that they are off work due to CLBP. The scores on the questionnaires may thus be an overrating of the ‘real’ pain or pain related fears. An FCE is a measure of demonstrated ability. Pain behaviors may prevent the patients to perform to their maximal physical capacity. The ratings of effort level indicate a sub maximal performance, suggesting that this behavior has occurred in study 2 (and probably also in study 1, but data were unavailable). Without these pain behaviors, the FCE results might have been higher. On average, the performance-based scores presented in this study may be considered an underrating of the physical abilities of the patients. Consequently, clinical interpretation of the outcomes of effort related assessments should be made with care. We propose that the scores on the questionnaires are interpreted as ‘the patient reports that … (he is fearful), instead of ‘the patient is/feels/perceives … (fearful/fear). For the FCE, we propose that the scores are interpreted as the patient's performance, instead of the patient's capacity (FPE: Functional Performance Evaluation). The performance of a patient depends on his capacity and his willingness to produce [25]. The current study and our previous study show that report of pain and pain related fears explain little of the patient's performances during FCE. Statements that ‘a valid assessment of functional capacity cannot be carried out without controlling for fear avoidance beliefs [8] and ‘pain related fear is more disabling than pain itself’10 are not supported by the results of this study. The results of this study may not be generalizable to other FCEs or other groups of patients. From the results of the current study it should be concluded that future research aimed at unrevealing ‘determinants of performance’ in chronic pain should not be restricted to pain and pain related fears only, but include other avenues such as self efficacy [40, 41], motivation and acceptance as well.

fear avoidance beliefs questionnaire
lifting
rehabilitation
tampa scale for kinesiophobia
pain cognitions
disability assessment


Peroxiredoxin 6 in human brain: molecular forms, cellular distribution and association with Alzheimer’s disease pathology

Peroxiredoxin 6 is an antioxidant enzyme and is the 1-cys member of the peroxiredoxin family. Using two-dimensional electrophoresis and Western blotting, we have shown for the first time that, in human control and brain tissue of patient’s with Alzheimer’s disease (AD), this enzyme exists as three major and five minor forms with pIs from 5.3 to 6.1. Using specific cellular markers, we have shown that peroxiredoxin 6 is present in astrocytes with very low levels in neurons, but not detectable in microglia or oligodendrocytes. In control brains, there was a very low level of peroxiredoxin 6 staining in astrocytes that was confined to a “halo” around the nucleus. In AD, there were marked increases in the number and staining intensity of peroxiredoxin 6 positive astrocytes in both gray and white matter in the midfrontal cortex, cingulate, hippocampus and amygdala. Confocal microscopy using antibodies to Aβ peptide, tau and peroxiredoxin 6 showed that peroxiredoxin 6 positive astrocytes are closely involved with diffuse plaques and to a lesser extent with neuritic plaques, suggesting that plaques are producing reactive oxygen species. There appeared to be little astrocytic response to tau containing neurons. Although peroxiredoxin 6 positive astrocytes were seen to make multiple contacts with tau positive neurons, there was no intraneuronal colocalization. In brain tissue of patients with AD, many blood vessels exhibited peroxiredoxin 6 staining that appeared to be due to the astrocytic foot processes. These results suggest that oxidative stress conditions exist in AD and that peroxiredoxin 6 is an important antioxidant enzyme in human brain defenses.
Introduction
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the progressive decline in memory, language, cognition and motor functions. The neuropathological hallmarks of AD are the accumulation of extracellular amyloid plaques containing the amyloid beta peptide (Aβ) and intraneuronal neurofibrillary tangles containing hyperphosphorylated microtubule-associated protein tau. Although the initiating molecular event(s) in AD are not known, oxidative stress produced by the generation of reactive oxygen species (ROS) appears to be a fundamental process contributing to the neuropathophysiology [10]. Whether oxidative stress has a role in the cause or affect in AD has been debated for many years, but recent research has suggested that it occurs prior to the onset of symptoms [35]. The histopathological evidence for oxidative stress in AD includes lipid, protein and DNA oxidation. There is evidence to suggest that oxidative stress is generated peripherally [26] with the brain being particularly susceptible, but there is also a body of evidence to suggest that amyloid β peptide (Aβ42) may be linked to lipid peroxidation [5, 6], which may then be linked to the phosphorylation of tau [24]. Two reactive products of Aβ42-induced lipid peroxidation are the production of four hydroxynonenal and acrolein [5], which have been shown to disrupt membrane asymmetry [7] and may be involved in the conformational changes to tau, promoting the formation of neurofibrillary tangles [24].
Oxidative damage occurs as part of the aging process, but it occurs at a much greater rate in most neurodegenerative diseases [3]. The hallmark of protein oxidation is the generation of protein carbonyls, which are markedly elevated in AD [3, 16]. There is overwhelming evidence to implicate oxidative stress in the pathogenesis of AD, and this is most likely an early event in the disease process. Less is known however of how the brain responds to oxidative insults. There are a number of cellular antioxidant defenses whose primary role is to convert the ROS into unreactive compounds. These defense mechanisms include superoxide dismutase, which works together with selenium-dependent glutathione peroxidase, catalase and more recently the peroxiredoxin family. These enzymes transform the superoxide radical to hydrogen peroxide, which in turn is converted to water. The distribution and changes in expression of the antioxidant enzymes have not been well studied as the other aspects of the disease process.
Cellular selenium-dependant glutathione peroxidase (GPx-1) has long been regarded as the major cellular antioxidant enzyme, but there are conflicting reports regarding the changes in activities of this enzyme in AD. Early reports suggest that there is no change in GPx-1 activities in AD brains [15, 20], while other researchers suggest that there is a decrease in GPx-1 activity [8, 27, 32]. There are no reports indicating compensatory increase in GPx-1 activity; however, cell models overexpressing GPx-1 are more resistant to Aβ-mediated toxicity, suggesting that increased levels of GPx-1 is protective [2].
There is considerable evidence to implicate Aβ toxicity and the generation of ROS and in particular hydrogen peroxide as key steps in contributing to the neurotoxicity in AD. We have previously reported that peroxiredoxin 6, also termed 1 Cys-peroxiredoxin or nonselenium glutathione peroxidase, is upregulated in Parkinson’s disease (PD) [30]. This enzyme has no amino acid homology to any known sequence of the glutathione peroxidase enzymes and has both antioxidant and phospholipase A2 activity and can reduce hydroxyperoxides [9]. Given that the brain is particularly vulnerable to oxidative damage due to its high energy and oxygen requirements, high levels of transition metals and peroxidizable fatty acids suggests that peroxiredoxin 6 could play a key antioxidant role in neuroprotection.
There are six members of the peroxiredoxin family at this stage with peroxiredoxins 1–5 being the 2-Cys members while peroxiredoxin 6 is the sole 1-Cys member [36]. This enzyme is bifunctional with 80% of its function as a peroxidase and the remainder as a phospholipase A2. Peroxiredoxin 6 has not specifically been examined in brain tissue of AD patients, and the aims of this study are to examine the molecular forms, cellular distribution and association with AD pathology. This study focused on the cingulate, amygdala, hippocampus and cortical regions in six AD and six control brains.
Materials and methods
Antibodies
Primary antibodies
Peroxiredoxin 6 (rabbit) was obtained from Antibody Technology Australia Pty, Ltd, Adelaide, Australia. Tau (mouse) was obtained from Novacastra Laboratories Ltd, Newcastle, UK. Tau (goat) was obtained from Santa Cruz Biotechnology Inc., California, USA. Amyloid beta (mouse) was obtained from Novacastra Laboratories Ltd, Newcastle, UK. Glial fibrillary acidic protein (GFAP) (mouse) was obtained from Novacastra Laboratories Ltd, Newcastle, UK. Human neuronal protein HuC/HuD (mouse) was obtained from Molecular Probes Inc., Eugene, USA. Human HLA (MHC2) (mouse) was obtained from Dakocytomation, Denmark A/S. Myelin basic protein (MBP) (mouse) was obtained from Novacastra Laboratories Ltd, Newcastle, UK.
Secondary antibodies
Donkey anti sheep Cy3 was obtained from Jackson Immunoresearch, West Grove, USA. Donkey antirabbit Cy3 and Cy5 were obtained from Jackson Immunoresearch, West Grove, USA. Goat antimouse Alexa 488 was obtained from Molecular Probes Inc., Eugene, USA. Goat antirabbit alkaline phosphatase was obtained from Sigma Chemical Company, St Louis, USA.
Brain tissue
The brain tissue was obtained from the National Health and Medical Research Council South Australian Brain Bank. Brains were removed at autopsy from clinically diagnosed AD cases and confirmed pathologically postmortem. The control cases were obtained from people who died of unrelated causes without diagnosed neurological pathology that was confirmed postmortem. The brains were bisected and one half snap-frozen at −80°C and the other immersion fixed in buffered formaldehyde. Tissue blocks were processed and embedded in paraffin and 8 μm sections were cut and mounted onto gelatin-coated slides. Table 1 provides a list of the brain regions examined and the case details.
Table 1List of cases used in cell countingCase numberSexAge (years)DiagnosisRegionPMI (h)Braak stageNIA ReaganAD1M65ADC, A, H, MC 35/6HighAD2F84ADC, A, H, MC 165/6HighAD3M63ADC, A, H, MC 335/6HighAD4M59ADC, A, H, MC205/6HighAD5F81ADC, A, H, MC195/6HighAD6F69ADC, A, H, MC385/6HighC1F79ADLC, A, H, MC4LowC2M69UnkC, A, H, MC48LowC3F61CFC, A, H, MC8LowC4F84SSC, A, H, MC15LowC5F86DMC, A, H, MC17LowC6F86HVDC, A, H, MC6LowPMI postmortem index, AD Alzheimer’s disease, NIA Reagan high or low likely-hood of AD, M male, F female, C cingulate, A amygdala, H hippocampus, MC midfrontal cortex, C control, ADL adeno lung carcinoma, Unk unknown, CF cardiac failure, SS septic shock, DM disseminated malignancy, HVD hypertensive vascular disease
Brain homogenate
Frozen tissue from midfrontal cortex of three AD brains and three control cases were homogenized in 50 mM Tris, 5 mM EDTA, 0.1% sodium azide, 1 μl/ml pepstatin and leupeptin and 0.3 mM of phenylmethylsulfonyl fluoride. Five milliliters of homogenization buffer was used per gram of brain tissue and homogenized using six passes of a motorized Wheaton Teflon pestle tissue grinder. Homogenates were centrifuged (500×g) for 10 min to remove particulate matter and snap-frozen at −80°C. Table 2 provides the details of these cases.
Table 2List of cases used for 2D PAGE and Western blotsCase numberSexAge (years)DiagnosisRegionPMI (h)Braak stageNIA ReaganAD7F69ADMC225/6HighAD8F79ADMC185/6HighAD9F76ADMC235/6HighC6F86HVDMC6LowC7F73ABDMC24LowC8M63SSMC38LowPMI postmortem index, AD Alzheimer’s disease, F female, M male, MC midfrontal cortex, C control, HVD hypertensive vascular disease, ABD anoxic brain damage, SS septic shock
Sample preparation
Aliquots (100 μl) of Alzheimer’s and control brain homogenates underwent a 2D clean up as per the Amersham Biosciences protocol. The samples were then resolubilized in 300 μl of TUC pH 4–7 buffer containing 7 M urea, 2 M thiourea, 4% CHAPS, 0.5% dithiothreitol, 0.5% IPG buffer pH 4–7. Samples were then assayed for total protein content. Prior to rehydration of the immobilized pH gradient strips, trace amounts of bromophenol blue was added to samples. Table 2 provides a list of cases used in the 2D electrophoresis.
2D polyacrylamide gel electrophoresis (PAGE)
Isoelectric focusing
Isoelectric focusing was performed on 13 cm immobilized pH gradient strips (pH 4–7NL, Amersham Pharmacia) using an IPGphor (Amersham Pharmacia) isoelectric focusing unit. Samples were applied in gel rehydration (50 V, overnight) using 7 M urea, 2 M thiourea, 4% CHAPS, 0.5% dithiothreitol, 0.5% pharmalyte pH 4–7 with a trace of bromophenol blue as the re-swell buffer with a total loading volume of 280 μl including sample. Stained gels received a 400 μg protein load and Western blotting gels received a 200 μg load. IEF was performed for ∼60 kV h (500 V 30 min, 1,000 V 30 min, 1,000–8,000 V 30 min, 8,000 V 5–7 h, 1,000 V hold to end).
SDS PAGE
SDS PAGE was performed using 12.5% acrylamide gels. Immobilized pH gradient strips were incubated in SDS equilibration buffer containing 1% w/v dithiothreitol for 15 min followed by SDS equilibration buffer containing 4% w/v iodoacetamide for 15 min. Strips were then placed above the second dimension gels and overlaid with 1% low melting point agarose in Tris glycine gel running buffer. Electrophoresis was performed at 350 V for 2.5–3 h at 10°C. Protein gels were visualized using SYPRO ruby fluorescent stain (incubated for 2 h) and scanned using a Typhoon 9400 Variable Mode Imager (Amersham Biosciences).
Western blotting
Separated proteins were transferred onto low fluorescence PVDF membrane by semi-dry electroblotting at 0.8 mA/cm2 for 2 h. After blocking, the membranes were incubated in affinity-purified rabbit antiperoxiredoxin 6 antibodies (1/2,000) overnight at 4°C followed by goat antirabbit alkaline phosphatase antibodies (1/10,000) (Sigma) for 1 h and visualized using ECF substrate and a Typhoon 9400 Variable Mode Imager.
Immunohistochemistry
Localization of peroxiredoxin 6
Immunohistochemical staining of AD and control tissue was carried out as previously described [30]. The cellular distribution of peroxiredoxin 6 was compared with the staining obtained with antibodies to specific cellular markers. Antibodies to GFAP (astrocytes), HLA (MHC2) (microglia), MBP (oligodendrocytes) and HuC/HuD (neurons) were used to map the distribution of peroxiredoxin 6.
Colocalization of peroxiredoxin 6, Aβ and Tau
Brain sections were incubated with rabbit antiperoxiredoxin 6, mouse anti Aβ peptide and goat anti tau antibodies overnight and detected with secondary antibodies conjugated to fluorescent fluorophores Cy5, Alexa 488 and Cy3, respectively. Sections were examined using a Bio-Rad Confocal laser scanning microscope and Bio-Rad software package, as previously described [30].
Cell counting
Cell counting was carried out blind on masked sections, and peroxiredoxin 6 positive astrocytes were counted at 20× magnification with a graticule eye piece (0.0625 mm2) using an Olympus HO-2 microscope on brain tissue from six AD and six control cases. In each case, five regions were randomly selected to give a uniform representation of the white and gray matter in the cingulate, hippocampus, amygdala and midfrontal cortex. The number of positive cells from each of the five areas was averaged and divided by the area of the graticule eyepiece and expressed as cells/mm2. The data was then analyzed using a one-way ANOVA that examined the interactions listed in Table 3. The significance was determined at 0.01 level of probability.
Table 3Interactions examined in cell counting dataInteractions examinedF valueCritical F (P = 0.01)Significance (>0.01)Are there differences in AD (C, A, H, MC) gray matter cell counts?1.004.94NSAre there differences in AD (C, A, H, MC) white matter cell counts?1.234.94NSAre there differences in control (C, A, H, MC) gray matter cell counts?0.624.94NSAre there differences in control (C, A, H, MC) white matter cell counts?0.254.94NSAre there differences between gray and white matter cell counts in AD tissue?89.37.21SAre there differences between gray and white matter cell counts in control tissue?14.57.21SAre there differences in gray matter cell counts between control and AD tissue?2997.21SAre there differences in white matter cell counts between control and AD tissue?28.07.21SAD Alzheimer’s disease, NS not significant, S significant, C cingulate, A amygdala, H hippocampus, MC midfrontal cortex
Results
Molecular forms of peroxiredoxin 6 in human brain
We have previously shown that this antibody is specific for a 26 kDa monomer in rat lung and human brain, and the N-terminal sequence is identical to peroxiredoxin 6 [30, 31]. Two-dimensional PAGE and Western blotting of AD and control brain homogenates indicated that this protein exists as three major and five minor forms as shown in Fig. 1a–d. Although the loadings were consistent, the five minor forms could not be detected on all gels. For comparison, the spots were labeled 1–8 (Fig. 2).
Fig. 1a, b A two-dimensional PAGE gel and blot of AD brain tissue (Case AD7) and a corresponding control brain gel and blot panels (c, d) (Case C7). pI 4–6 is indicated between the gel and blots and the molecular weight of the standards (Std) in kilo Daltons is shown on the Y-axis. The gels were stained with SYPRO ruby fluorescent stain. The box indicates the location of the molecular forms of peroxiredoxin 6. Some spots are shown on the blots to indicate the range of peroxiredoxin 6 pIsFig. 2A composite figure at higher magnification showing the range of molecular forms in three cases of AD tissue (a AD7, AD8 and AD9) and three cases of control tissue (b C7, C6 and C8). The range of molecular forms 1–8 is shown, but not all forms are present in all cases. Although there are slight variations between blots, the respective pIs are as follows: 1 (5.3), 2 (5.4), 3 (5.6), 4 (5.75), 5 (5.85), 6 (5.9), 7 (5.95), 8 (6.1)
The respective pIs of the eight forms are 5.3, 5.4, 5.6, 5.75, 5.85, 5.9, 5.95 and 6.1. The three major forms were 3, 6 and 7 and the average pIs of these were 5.59, 5.88 and 5.96 for AD tissue and 5.59, 5.80 and 5.86 for control tissue. These major forms were consistent in all homogenates. The intensity of the three major forms was analyzed using the Typhoon imager and the intensity of spots 6 and 7 was reversed in the AD and control homogenates (Fig. 2). Minor forms 1 and 2 were present in AD homogenates but absent in the control tissue, while minor form 8 was present in control tissue but absent in AD tissue (Fig. 2).
Cellular distribution of peroxiredoxin 6 in control and AD brain tissue
Using immunohistochemical markers for specific human brain cell types, we have shown that peroxiredoxin 6 is abundant in reactive astrocytes (Fig. 3a–c) in AD tissue and present in very low levels in neurons (Fig. 3j–l). We were not able to detect peroxiredoxin 6 in either microglia (Fig. 3d–f) or oligodendrocytes (Fig. 3g–i).
Fig. 3Confocal localization of peroxiredoxin 6 (P6) with GFAP (astrocyte marker) (a P6, Cy2-green; b GFAP, Cy3-red; c merged image; bar 25 μM), MHC2 (microglia marker) (d P6, Cy2-green; e MHC2, Cy3-red; f merged image; bar 25 μM), MBP (oligodendrocyte marker) (g P6, Cy2-green; h MBP, Cy3-red; i merged image; bar 10 μM), Hu (neuronal marker) (j P6, Cy2-green; k Hu, Cy3-red; l merged image; bar 25 μM). Cell localization was performed using AD tissue
There was a marked contrast between the staining of control and reactive astrocytes in AD. Reactive astrocytes in AD were hypertrophied with abundant staining of peroxiredoxin 6, while in control astrocytes the staining was confined to a “halo” around the nucleus (Fig. 4). The increased staining of peroxiredoxin 6 was seen in all regions examined and in both gray and white matter, but the staining was not evenly distributed and some regions on the same section were heavily labeled but adjacent regions showed little staining (Fig. 5).
Fig. 4Oil immersion image (×1,000) of typical astrocyte seen in AD cortex (a) and in control cortex (b)Fig. 5Light immunohistochemistry (DAB substrate) of peroxiredoxin 6 staining of astrocytes in AD gray matter (a), AD white matter (b), control tissue gray matter (c) and control tissue white matter (d). All images at ×200 magnification
Cell counting
In AD and control tissue, peroxiredoxin 6 was colocalized with GFAP, indicating that the upregulation is occurring in astrocytes, which is similar to what was found in PD and dementia with Lewy bodies [30]. Cell counting was conducted on midfrontal cortex, cingulate, amygdala and hippocampus brain regions and in gray and white matter to determine if specific areas or matter type were specifically affected. These are summarized in Fig. 6. A one-way ANOVA was performed comparing the cell counts between AD and control tissue in different regions by tissue and matter type. The results of the interactions examined are shown in Table 3.
Fig. 6A bar chart showing the range of cell counts in different brain regions in gray and white matter in control tissue and in AD tissue. WM white matter, GM gray matter, Cing cingulate, Amy amygdala, MFC midfrontal cortex, Hip hippocampus. Counts are shown as mean ± SE, N = 6 in all cases
Colocalization of peroxiredoxin 6 and AD pathology
The major defining pathology in AD is the presence of extracellular plaques and intracellular hyperphosphorylated tau in the form of tangles. Plaques are present in brain tissue at different stages of development, ranging from diffuse plaques to fibrillar and neuritic plaques. It is generally considered that diffuse plaques do not contain tau, but neuritic plaques contain both Aβ and tau. Colocalization of peroxiredoxin 6 and the Aβ peptide, a major component of plaques, and tau was carried out to determine if peroxiredoxin 6 positive astrocytes were associated with AD pathology.
Tau pathology
Tau deposits in the form of neurofibrillary tangles were observed in many neurons and neurites, and were often surrounded by peroxiredoxin 6 astrocytes, which made multiple contacts (Fig. 7a–c). Although astrocytes made many contacts with neurons, most neurons had very low levels of peroxiredoxin 6 and it did not appear to be upregulated or to be colocalized with tau in AD.
Fig. 7a–c Confocal localization of tau within neurons (a Cy2-green) and peroxiredoxin 6 positive astrocytes (b Cy3-red) and (c) the merged image in AD brain tissue (bar = 10 μM). d–f Confocal localization of Aβ peptide (d Alexa 488, green) and peroxiredoxin 6 positive astrocytes (e Cy3-red) and (f) the merged image in a diffuse plaque in AD brain tissue (bar = 50 μM). g–j Confocal localization of Aβ peptide (Alexa 488, green) and peroxiredoxin 6 positive astrocytes (Cy5-light blue) in four different plaques in AD brain tissue (bar = 50 μM). k Immunohistochemical staining (DAB substrate) showing peroxiredoxin 6 staining of astrocytes and astrocyte foot processes in the walls of blood vessels in the midfrontal cortex in Alzheimer’s disease (×400). Arrows indicate peroxiredoxin 6 positive astrocytic processes
Plaques
Diffuse plaques contained strongly stained aggregations of Aβ peptide. Peroxiredoxin 6 staining was confined to astrocytes and their projections that were in close proximity to plaques (Fig. 7d–h). Some astrocytes were observed within plaques, but generally the astrocyte cell bodies were on the periphery with their processes projecting into the plaques. Some plaques contained the beginnings of a dense core of amyloid and took on a “wagon wheel” appearance (Fig. 7i, j). Diffuse plaques and those with Aβ beginning to aggregate appeared to elicit a stronger peroxiredoxin 6 astrocytic response than those with a dense core of Aβ.
Peroxiredoxin 6 staining of blood vessels
In many regions of the AD brain, there appeared to be strong peroxiredoxin 6 staining in the walls of many blood vessels, which could be seen to have multiple contacts with strongly staining activated astrocytes (Fig. 7k).
Discussion
Peroxiredoxin 6, also known as 1-Cys peroxiredoxin or non-selenium glutathione peroxidase, is a human brain antioxidant enzyme that is abundant in activated astrocytes and present in low levels in neurons. On exposure to hydrogen peroxide, the N-terminal peroxiredoxin 6 corresponding to Cys 47 is readily oxidized, but as yet its redox partner has not been identified. Glutathione, lipoic acid and cyclophilin have all been implicated [13, 23, 29]. In addition to its peroxidase activity, peroxiredoxin 6 also has phospholipase A2 activity that might be significant in the brain, which has a high lipid content [9]. Peroxiredoxin 6 staining of astrocytes is markedly elevated in many brain regions in PD and dementia with Lewy bodies [30]. We now show that it is upregulated in the astrocytes in AD and describe for the first time the range of molecular forms of peroxiredoxin 6 in human brain in both control and AD tissue.
Three major variants and five minor forms were identified with pIs ranging from 5.3 to 6.1. There were three major differences in the peroxiredoxin 6 variants between control and AD tissue. The AD tissue had the two more acidic forms 1 and 2 that were not present in control tissue; the intensity of the major forms 6 and 7 were reversed between AD and control tissue and minor form 8 was absent in AD tissue. We can only speculate on the significance of these findings at this stage, but different forms may have different affinities for different substrates that might be more abundant under various pathological conditions or the more acidic forms are the result of oxidation [11]. At this stage, we cannot determine whether these differences are true isoforms or post-translational modifications of the same protein, but would suspect the latter. These findings are in agreement with two previous proteomic studies in an AD mouse model and in human brain AD tissue, which reported elevated levels of peroxiredoxin 6 [33, 34]. A more recent study using PC12-resistant and PC12 cells treated with Aβ also reported increased levels of peroxiredoxin 6 [11]. Aβ-treated cells contained an extra acidic form, which was suggested to be the result of oxidation of the catalytic cysteine to a sulfenic, sulfinic or sulfonic derivative. We found two minor acidic forms in AD tissue, which is also consistent with this report.
Tissue selection is also a variable that needs to be considered. While the AD tissue had all the hallmarks of AD pathology and was easy to define, age-matched control tissues are somewhat more variable. All the control tissue was from people without clinically defined AD, but most brains in this age group usually contain some neurodegenerative pathology. The age span of the two groups was similar, 59–84 years for the AD group and 61–86 years for the control group, but the mean age for the control group was 7 years older. We feel confident that, although some control tissue may have had some elements of subclinical AD pathology, the reduced level of astrocyte activation and the clear differences in the cell counting would indicate that all the control tissues were not experiencing high levels of oxidative stress as compared to the AD tissue.
Previously, we had shown that peroxiredoxin 6 was abundant in astrocytes in PD and dementia with Lewy body tissue [30]. We have now used cellular markers to other brain cell types to show that in addition to astrocytes there is a low level in the cytoplasm of most neurons. Unlike astrocytes, neurons do not appear to have increased peroxiredoxin 6 staining in AD tissue. We could not detect any peroxiredoxin 6 staining in microglia or oligodendrocytes.
Cell counting of peroxiredoxin 6 positive astrocytes indicated that there was a marked increase in both the gray and white matter in AD tissue compared with control tissue. Gray matter had more peroxiredoxin 6 positive astrocytes than white matter in both AD and control tissue, and this difference was more pronounced in AD tissue with F values of 89.0 and 14.5, respectively. Considering that the major pathology (plaques and tangles) is located in the gray matter, it would be expected that oxidative stress is more pronounced in this area. Cell counting indicated that the peroxiredoxin 6 positive astrocyte numbers were elevated in all regions examined, suggesting that the insult activating astrocytes is wide spread and not confined to any specific region in either the gray or white matter. Indeed, extracellular plaques were observed in all regions examined in AD tissue. Although all regions examined showed astrocyte activation, this was not uniformly distributed through all areas, with some areas showing extensive astrocyte activation while adjacent areas were similar to controls.
There are two defining pathologies in AD. These are the presence of extracellular plaques comprising abundant deposits of Aβ peptides, which later aggregate to form amyloid [25] and intracellular hyperphosphorylated tau aggregations in the form of paired helical filaments, which are termed tangles [14, 22]. The two well-described forms of plaques have been termed diffuse plaques and neuritic or senile plaques [12, 18], although other intermediate forms such as fibrillar plaques have been described [12]. Diffuse plaques are spherical structures of less dense Aβ deposits and are considered an early stage of plaque formation. In diffuse plaques, aggregations of Aβ exists as nonamyloid deposits, but as the plaque progresses, the Aβ and other protein aggregations change from the normal conformation into a β-sheet structure, termed amyloid, and take up specific dyes like Congo red and thioflavin [4]. These plaques do not generally contain tau; however, in many confocal images, diffuse plaques contain focal points of tau, which are probably enlarged neurites. Neuritic or senile plaques contain a dense core of amyloid enriched with Aβ peptide with a corona of less dense amyloid. The diffuse plaques appeared to elicit a stronger astrocytic response based on the level of peroxiredoxin 6 staining than the neuritic plaques. Our images suggest that once the Aβ forms amyloid and condenses, it is less stimulating toward astrocytes and perhaps the formation of amyloid is a protective mechanism to inactivate the toxicity of soluble Aβ. These observations are consistent with the findings of Kawaguchi-Niida et al., who reported that the highly reactive carbonyl crotonaldehyde generated during lipid peroxidation is localized to reactive astrocytes, microglia and diffuse plaques but undetectable in amyloid cores [19].
Although the activation of astrocytes and upregulation of peroxiredoxin 6 would be considered a protective response, in cell culture, soluble Aβ has been shown to stimulate nitric oxide synthetase and the production of nitric oxide, which is very damaging to neurons [17]. This response is further upregulated by other inflammatory cytokines such as interleukin 1β and tumour necrosis factor-α, which are reported to be released from astrocytes [1]. At this stage, we are not sure of the net benefit to neural tissue of activated astrocytes with elevated levels of peroxiredoxin 6, concomitant with the increased production of nitric oxide.
Neurofibrillary tangles comprising hyperphosphorylated tau were observed in many regions in AD tissue. Two antibodies were used to localize tau, a monoclonal antibody (Novacastra) and one specific for paired helical filaments (Santa Cruz) and both produced a similar staining pattern. Neurons containing tau were not surrounded by activated astrocytes as were the diffuse plaques and to a lesser extent the neuritic plaques, suggesting that these cells are not secreting toxic or activating products that are stimulating to astrocytes. In PD, Lewy bodies contained a dense core of peroxiredoxin 6 staining suggesting that it was trying to detoxify oxidative stress produced by Lewy bodies, but we did not observe any interaction between tau and peroxiredoxin 6 in AD tissue. Although there is considerable evidence to implicate oxidative stress in the hyperphosphorylation of tau, this does not appear to upregulate peroxiredoxin 6 in tau positive neurons.
In some regions, the walls of blood vessels exhibited strong peroxiredoxin 6 staining, which also elicited a strong astrocytic response in surrounding astrocytes. These activated astrocytes had many processes in contact with the endothelial cells. Although vascular amyloid deposits were seen in some sections and is a key feature of AD [28], the upregulation of peroxiredoxin 6 appeared to occur in regions without distinct Aβ staining. Aβ has been shown to induce vascular vasoconstriction in rat skin, which can be reversed by superoxide dismutase and catalase, indicating that Aβ is capable of generating ROS within blood vessels [21]. The blood brain barrier consists of the capillary endothelial cells, and the foot processes of the astrocytes and the fused basal lamina of both cells. The peroxiredoxin 6 staining within the capillaries appears to be in the foot processes of the astrocytes. Whether this increased staining is due to ROS generated from within neural tissue or reflects a more systemic pathology derived from the circulation is unclear.
In conclusion, we have shown for the first time the range of peroxiredoxin 6 variants, which are either isoforms or post-translational modifications in human brain tissue. We have also shown that peroxiredoxin 6 is primarily an astrocytic enzyme with very low levels in neurons and is not detectable in microglia or oligodendrocytes. This enzyme is markedly elevated in astrocytes in both white and gray matter in AD. Strongly staining peroxiredoxin 6 astrocytes appear to be involved in the detoxification of diffuse plaques and to a lesser extent in neuritic plaques, but is not associated with tau aggregations within neurons. From this work and previous work on PD and dementia with Lewy bodies, we suggest that peroxiredoxin 6 is a major antioxidant enzyme in human neural tissue.

peroxiredoxin 6
molecular forms
alzheimer’s disease
astrocytes
oxidative stress


Quality of life in couples living with Huntington’s disease: the role of patients’ and partners’ illness perceptions

Research suggests that chronically ill patients and their partners perceive illness differently, and that these differences have a negative impact on patients’ quality of life (QoL). This study assessed whether illness perceptions of patients with Huntington’s disease (HD) differ from those of their partners, and examined whether spousal illness perceptions are important for the QoL of the couples (n = 51 couples). Partners reported that their HD-patient spouses suffered more symptoms and experienced less control than the patients themselves reported. Illness perceptions of patients and partners correlated significantly with patient QoL. Partners’ beliefs in a long duration of the patients’ illness and less belief in cure, were associated with patient vitality scores. Suggestions for future research emphasize the importance of qualitative research approaches in combination with cognitive-behavioural approaches.
Quality of life research is increasingly being directed at both the impact of the social environment on the quality of life (QoL) of chronically ill individuals, and at the toll the illness exacts on the QoL of close family members [1] One of the most influential members of the social network of chronically ill individuals is the spouse. Research conducted in chronically ill individuals and their healthy spouses revealed that the spouses’ role problems [2, 3], their unsupportive behaviour [4–6], and the marital relationship itself [7] are some of the factors influencing patients’ QoL.
Studies examining adaptation in caregivers have identified negative social support [8], patients’ cognitive impairment [9, 10], and the caregivers’ overestimation of the patient’s functional disabilities [11] as some of the factors influencing the partners’ QoL.
Recent findings suggest that patients’ QoL is also dependent on beliefs fostered by the social environment regarding the patient’s illness. Illness beliefs or illness perceptions can be subdivided into five components: identity (the label the patient places on the illness and the symptoms he/she experiences), cause (the personal ideas patients have about the cause of their illness), timeline (the duration of the illness according to the patient), consequences (the expected effects and outcome of the illness), and cure/control (the curability or controllability of the illness according to the patient) [12]. In the area of physical health problems, illness perceptions have been found to be related to the ways patients react and cope with their illness. A strong illness identity combined with a perceived long duration and perceived more severe consequences of the disease for daily life have been associated with poor physical and psychological well-being. Stronger beliefs regarding the curability or controllability of a disease have been found to have positive effects on patients’ QoL [13, 14].
Research shows that illness beliefs of spouses significantly affect patients’ beliefs, the coping mechanisms they adopt to deal with their disease, and ultimately their functioning and well-being [15, 16]. Heijmans and colleagues found that dissimilarities between the illness perceptions of chronically ill patients (patients with Addison’s disease and patients with Chronic Fatigue Syndrome) and those of their healthy spouses were associated with higher impairments in predicted patients’ adaptive outcome [15]. In another study, Figueiras and Weinman explored whether the degree of similarity in patients’ and spouses’ illness perceptions was related to recovery following myocardial infarction [16].
Few studies have focused on the role of illness perceptions for caregiver outcomes, and none have examined the role of patients’ illness perceptions on the QoL of their healthy partners. Barrowclough et al. [17] and Fortune et al. [18] studied illness perceptions in carers of schizophrenia patients. Their findings suggest that carer perceptions about identity, consequences and control may have important implications for carer outcomes in schizophrenia. In this study we will focus on Huntington’s disease (HD) which is an inherited neurodegenerative disorder, characterized by clearly defined clinical features such as involuntary movements and hypokinesia, dementia, and personality changes. The first symptoms of HD typically manifest between the ages of 35 and 45, and the disease has a mean duration of 16 years [19, 20]. At present, there is no cure for HD.
Little empirical data has been gathered on the psychosocial aspects of living with HD, but some research findings suggest that HD contributes to marital breakdown [21–23]. One of the most important reasons for this is believed to be the changes HD brings about in the relationship. Spouses of HD patients often consider their partners to be lost [23], particularly when dementia and changes in personality and behaviour develop [22, 24]. In addition, over time the spouse takes on an increasing nursing role, which creates a psychological distance between the patient and his/her spouse [22]. HD greatly impacts the patients’ physical and psychosocial well-being [25, 26], and places a heavy burden on families [27–29]. Interestingly, some research has indicated that there is a discrepancy between the aspects of HD the patient finds most disturbing and those the spouse or partner finds most disturbing. Partners are most disturbed by mental and personality changes in the patients [30, 31].
In all studies examining the role of spousal illness perceptions for patient outcomes, researchers have concentrated on the degree of similarity / dissimilarity in patients’ and spouses’ illness perceptions as a predictor of patient outcome, thereby ignoring alternative possible relationship patterns between spouses’ illness beliefs and patient outcome. Results from Figueiras and Weinman [16] showed that not only similar positive perceptions in couples, but also conflicting representations were predictive of lower levels of disability (as compared to similar negative perceptions). This suggests that it might be more important that at least one member of a couple has positive perceptions. However, the results obtained in the Heijmans et al. [15] study show that better patient adjustment in Addison’s Disease is related to spouses’ negative perceptions about timeline, while better patient adjustment in Chronic Fatigue Syndrome is related to spouses’ positive perceptions of a short illness duration. From these studies it seems that both contrasting and concordant perceptions in couples can be related to better patient adjustment, and further study is needed to evaluate the extent to which spouses’ illness perceptions can influence outcomes which have been found to be related to the patient’s own perceptions.
Thus, the first aim of this study was to examine if patients and partners hold similar views about HD. Our second aim was to compare the relationship patterns between patients’ and partners’ own illness perceptions and QoL. The third aim was to contribute to the understanding of the cognitive factors related to QoL in couples dealing with HD, by examining which spousal illness beliefs are important in both patients’ and partners’ quality of life.
Method
Sample and procedure
Participants in this study were 51 couples (HD patients and their partners) who were recruited from the outpatient clinic of the Department of Neurology of the Leiden University Medical Centre (LUMC; n = 14 couples), and the Dutch Huntington Association (n = 37 couples). HD patients from the LUMC were selected if they had received a clinical diagnosis of HD at least 1 year prior to commencement of the study, and if they were capable (mentally, emotionally, and physically) of participating in an interview lasting approximately 2 h. The selected patients (n = 75) and their spouses were invited by post to participate. A total of 36 patients and 19 spouses agreed to participate. The main reason for not participating were the verbal communication difficulties that patients were experiencing, and having participated in other research projects before. Non-participants did not differ from participants with respect to age, sex, or duration of HD. HD patients and their spouses recruited from the Dutch Huntington Association were invited to participate by means of a letter sent to all the members of the association (n = 1,450). The selection criteria for participation were the same as those applied to the patients selected from the Department of Neurology of the LUMC. A total of 41 patients and 71 spouses agreed to participate. No information could be gathered on non-participants due to the fact that members of the Dutch Huntington Association are not registered on the basis of their patient status. Members include HD patients and their partners, as well as their family members, friends, and researchers in the field of HD. A total of 51 couples (married or living together) were identified. Patients and their partners were interviewed separately at their homes by a psychologist.
Measures
Demographic variables—HD patients and their partners were asked their age, sex, marital status, the duration of their relationship, number of children, their employment status, and the duration of HD.
Unified Huntington Disease Rating Scale [32]—We used the motor section of the UHDRS to assess HD patients’ motor performance. The motor section is composed of 20 items rating ocular motor function, dysarthria, chorea, dystonia, gait, and postural stability. The Total Motor Score (TMS) is the sum of all the individual items, higher scores indicating worse motor performance (maximum score = 124).
Mini-Mental State [33]—We used this scale to assess HD patients’ cognitive performance. It comprises 11 items covering a number of cognitive domains including: orientation, registration, attention, memory, language and visuo-constructional abilities. The maximum score is 30, lower scores indicating worse performance. Scores of 20 or less have been associated with dementia, delirium, schizophrenia or affective disorder [33].
The Illness Perception Questionnaire [34]—This questionnaire was used to assess the illness perceptions of HD patients and those of their partners (not yet validated in Dutch). The latter were interviewed by means of a partner-version of the IPQ [35]. The IPQ consists of the following five subscales: “Identity”, “Timeline”, “Cause”, “Consequences”, and “Cure/Control”. In this study we divided the last subscale into “Cure” and “Control”, thus creating a sixth scale. This is in line with current revisions of the IPQ in which these scales are generally separated [36]. The “identity” scale was composed of 24 items, each corresponding to a symptom commonly reported in HD. Patients are asked to rate whether or not they have experienced each symptom since their illness began, and if they believe the symptom to be specifically related to their illness (yes or no). Partners were asked whether or not their partner (the patient) had experienced each symptom since onset of their illness, and to report if they believed the symptom to be specifically related to their partners’ illness (yes or no). The summed yes-rated items on the second questions were divided by the number of items to form the illness identity scale, with higher scores indicating a stronger belief that the experienced symptoms are part of the patient’s illness. Cronbach’s alpha for patients and partners was 0.88 and 0.82, respectively.
For the remaining scales, patients and their partners were asked to indicate whether they agreed with statements on a five-point scale, ranging from “strongly agree” to “strongly disagree”. The “Timeline” scale contained two statements about the perceived duration of the disease, with high scores indicating strong beliefs in a chronic long-term disease. Cronbach’s alpha for patients and partners was 0.45 and 0.72, respectively. “Consequences” consisted of five items assessing beliefs about the impact of HD on everyday life. High scores indicate stronger beliefs in serious consequences of the disease. Cronbach’s alpha for patients and partners was 0.69 and 0.61, respectively. High scores on the “Cure” scale (two items) indicate strong beliefs in the effectiveness of treatment. Cronbach’s alpha for patients and partners was 0.77 and 0.66, respectively. “Control” contained two items pertaining to the degree to which patients and partners believe they have the ability to influence the course of illness. Cronbach’s alpha for patients and partners was 0.85 and 0.83, respectively. The weight of all the items per scale were summed, and divided by the number of items, with the exception of those of the “Cause” subscale. Each item in this subscale was considered individually because each causal item represents a specific causal belief.
The Medical Outcome Study 36-item Short Form Health Survey [37]—We assessed the QoL of HD patients, and the QoL of partners by means of seven subscales of the MOS SF-36 (“Physical functioning”, “Role functioning-physical”, “General health”, “Vitality”, “Social functioning”, “Role functioning-emotional”, and “Mental health”). The raw scores are transformed in order to obtain a 0–100 scale, with higher scores indicating a better outcome.
Analysis plan
First, the samples of HD patients and partners from the outpatient clinic and the Dutch Huntington Association involved in this study were compared for demographic and illness related variables (age, sex, marital status, duration of the relationship, number of children, employment status, duration of HD, patients’ TMS, and patients’ scores on the MMS) by means of t-tests. To examine the extent of (dis)agreement that HD patients and their partners held with regard to their beliefs about HD, Pearson correlations and paired sample t-tests were computed.
To assess whether spousal illness perceptions are related to patients’ and partners’ QoL, we conducted hierarchical regression analyses with scores on the MOS SF-36 as criteria. For patients, illness related variables (disease duration, TMS, and MMS scores) were entered as control variables, prior to the steps containing the patients’ and partners’ scores on the IPQ (step 2 and 3, respectively). For partners, parallel analyses were conducted, but no control variables were entered. The variables for the regression analyses were selected based on an examination of bivariate correlations between the illness perception dimensions and the QoL scales.
Results
Sample characteristics
Couples recruited from the Department of Neurology of the LUMC did not differ significantly in demographic or illness related variables from couples from the Dutch Huntington Association. Further analyses were thus conducted on the combined data.
In total, 51 HD patients (28 males, 23 females) and their partners (23 males, 28 females) were included in this study. The mean age of both patients and partners was 51 years (SD = 10). Forty-seven couples were married and four were living together. The mean duration of the relationship was 25 years (range: 2–48 years), and the mean number of children was 2 (range: 0–6). Eight couples did not have children. Thirty-nine patients (76.5%) were unemployed, of which 28 (54.9%; 19 males, 9 females) stopped working because of HD, whereas 10 (19.6%; 6 males, 4 females) were still working, and 2 (3.9%; males) were retired and receiving pension. Thirty-four partners (66.7%; 19 males, 15 females) were employed, 15 (29.4%) were unemployed (for different reasons including marriage and childbearing; 5 females had never had gainful employment), and 2 (3.9%; males) were retired and receiving a pension. The mean duration of HD was 7 years (SD = 5; range: 1–24). Patients’ mean Total Motor Score (TMS) was 37.8 (SD = 29.5), and their mean score on the MMS was 23.5 (SD = 3.9).
Patient-partner differences in illness perceptions
HD patients and their partners did not differ significantly on the “Timeline”, “Consequences”, “Cure” or “Cause” subscales of the IPQ (see Table 1). Both groups of respondents reported perceiving HD as having a long duration and having many consequences for their daily lives, without either of them believing in a cure for HD. Patients and partners (with the exception of four patients and one partner) attributed HD to genetic causes. Six HD patients (11.8%) reported believing that stress was an important contributing factor to the development of the symptoms of HD they were experiencing.
Table 1Means, standard deviations (SD), correlations and t-test statistics comparing Huntington’s Disease (HD) patients and their partners on the Illness Perception Questionnaire (IPQ) IPQ subscalesHD patientsPartnersPatient-partner correlation coefficientaPatient-partner differencebMean (SD)Mean (SD)tIdentity.44 (.22).55 (.22).57***−3.59***Timeline4.70 (.63)4.50 (.81).101.44Consequences3.60 (1.19)3.82 (1.01).29*−1.16Cure1.72 (1.77)1.69 (1.69).90***.27Control3.11 (1.72)2.36 (1.58).30*2.73**aPearson correlation. b Paired t-test* P < .05; ** P < .01; *** P < .001
Partners reported that the HD patients were suffering from significantly more symptoms (on the “Identity” subscale) of HD than the patients did themselves, and reported experiencing significantly less control over HD than the patients did.
Correlations between illness perceptions and QoL
Tables 2 and 3 display the bivariate correlations between the study variables. As expected, examination of the correlations between patients’ and partners’ illness perceptions and patients’ QoL (presented in Table 2) shows that patients’ QoL was most strongly associated with their own illness perceptions. A higher QOL correlated with a less strong illness identity, a longer perceived illness (and thus life) duration, less perceived consequences, more control, and less belief in treatment. With regard to partners’ perceptions, the same associations (albeit fewer) were found, with the exception of partners’ cure perceptions. Partner, but not patient, belief in cure through treatment was significantly related to patients’ vitality and social functioning ratings. Also, partners’ identity and consequences perceptions were not significantly related to patients’ vitality, social functioning, and mental health ratings. Except for the MOS subscales “Role Functioning - emotional” and “Mental Health”, illness perceptions of both patients and partners correlated significantly with patient QoL. Thus, regression analyses were conducted on the remaining MOS subscales, including only the illness perceptions dimensions that correlated significantly with QoL.
Table 2Pearson correlations between Illness Perceptions (IPQ) and patients’ QoL (MOS SF-36)IPQ patientsIPQ partnersIdentityTimelineConsequencesControlCureIdentityTimelineConsequencesControlCureMOS subscalesPhysical functioning−.51***.48***−.31*.37**−.33*−.43**.28*−.31*.39**−.39**Role functioning (physical)−.31*−.29*General health−.39**−.31*−.31*Vitality−.44**.34*−.35*.33*−.35*Social functioning−.32*−.38**−.31*Role functioning (emotional)Mental health−.40**−.39**Note: Only significant correlations are depicted* P < .05; ** P < .01; *** P < .001
Bivariate correlations between illness perceptions and partners’ QoL are presented in Table 3. The quality of life of partners was associated with their own beliefs about the patients’ illness, with a stronger belief in a long duration of the patients’ illness being related to better physical functioning and more vitality, and less perceived consequences being related to better physical role functioning, more vitality, and to better mental health. Partners’ vitality and mental health were also associated with illness perceptions of patients. Stronger patient beliefs in control over the illness, and less serious perceived consequences were related to better partner QoL. Interestingly, the only significant correlations between partners’ ratings of general health and emotional role functioning, and illness perceptions were correlations with patient beliefs in control over the illness.
Table 3Pearson correlations between Partners’ and Patients’ Illness Perceptions (IPQ), and Partners’ QoL (MOS SF-36)IPQ partnersIPQ patientsIdentityTimelineConsequencesControlCureIdentityTimelineConsequencesControlCureMOS subscalesPhysical functioning.29*Role functioning (physical)−.38**General health.28*Vitality.33*−.29*.30*Social functioningRole functioning (emotional).33*Mental health−.40**−.33*Note: Only significant correlations are depicted* P < .05; ** P < .01; *** P < .001
Importance of spousal illness perceptions for patients’ QoL
To determine the relative extent that partners’ illness perceptions are predictive of patients’ QoL, hierarchical stepwise multiple regression analyses were conducted for patients’ physical functioning, role functioning (physical), general health, vitality and social functioning. The results of the regressions are summarized in Table 4. After controlling for patients’ illness related variables and their own scores on the IPQ, partners’ IPQ scores added a significant amount of explained variance (13%) to patient scores on the “Vitality” subscale only. A stronger partner belief in a long duration of the patients’ illness (ß .30, P < .05), and less belief in cure through treatment (ß −.33, P < .05) both added to the prediction of higher patient vitality.
Table 4Hierarchical regressions examining if spousal illness perceptions explain additional variance in patients’ QoLStep and variablesAdj. R²R² changeF for R² changeMOS physical functioning1. Control variablesa.49.5217.05***2. Illness perceptions (patient): identity, timeline, consequences, control, cure.59.143.29*3. Illness perceptions (partner): identity, timeline, consequences, control, cure.64.082.06MOS role functioning (physical)1. Control variablesa.01.071.232. Illness perceptions (patient): cure.11.095.83*3. Illness perceptions (partner): cure .09.000.01MOS general health1. Control variablesa.10.152.84*2. Illness perceptions (patient): identity, consequences.18.113.24*3. Illness perceptions (partner): identity.16.000.08MOS vitality1. Control variablesa.12.183.35*2. Illness perceptions (patient): identity, timeline, consequences.29.204.65**3. Illness perceptions (partner): timeline, cure.41.13 5.62**MOS social functioning1. Control variablesa.01.050.832. Illness perceptions (patient): identity, consequences.11.154.18*3. Illness perceptions (partner): cure.16.063.51aDisease duration, TMS, and MMS scores * P < .05; ** P < .01; *** P < .001
The variance in patients’ scores on the other subscales of the MOS SF-36 was explained mainly by patients’ own IPQ scores, with the amount of added explained variance ranging from 9 to 20%. Less perceived consequences, a less strong illness identity, more control, and less belief in treatment contributed significantly to better QoL.
Importance of patients’ illness perceptions for partners’ QoL
To determine the relative extent that patients’ illness perceptions are predictive of partners’ QoL, hierarchical stepwise multiple regression analyses were conducted for partners’ vitality and mental health. The results of the regressions are summarized in Table 5. Patients’ IPQ scores added a significant amount of explained variance (8%) to partner scores on the “Vitality” subscale only. Stronger patient beliefs in control over the illness (ß .29, P < .05) added to the prediction of higher partner vitality.
Table 5Hierarchical regressions examining if spousal illness perceptions explain additional variance in partners’ QoLStep and variablesAdj. R²R² changeF for R² changeMOS vitality1. Illness perceptions (partner): timeline, consequences.17.216.26**2. Illness perceptions (patient): control.24.08 5.05*MOS mental health1. Illness perceptions (partner): consequences.14.169.04**2. Illness perceptions (patient): consequences.17.053.11* P < .05; ** P < .01; *** P < .001
Most variance was explained by partners’ own IPQ scores, with amount of explained variance ranging from 16 to 21%. A longer perceived illness (and thus life) duration, and less perceived consequences contributed significantly to better QoL.
Discussion
Our results indicate that HD patients and their partners did not differ significantly in their beliefs regarding the duration, consequences, causality, and curability of the disease. HD was attributed, in all but four HD patients and one partner, to a genetic cause. There were however, significant differences between the illness identity of HD patients and that of their partners. Partners attributed significantly more symptoms to HD than patients. In addition, HD patients and their partners differed significantly in the degree of control they believed they had over the disease process. HD patients perceived their disease as being more controllable than their partners. In general, as was the case in couples dealing with Addison’s disease [15], patients held more positive beliefs about HD than their partners did.
Examining the relationships between spousal illness perceptions and patients’ QoL, the results indicate that none of the partners’ perceptions (whether consonant or dissimilar) are relevant in being associated with patients’ quality of life in the areas of mental health and emotional role functioning. Partners’ identity and consequences perceptions (whether consonant or dissimilar) are not associated with patients’ quality of life in the areas of social functioning and vitality. Partners’ consequences perceptions (whether consonant or dissimilar) are not relevant for patients’ quality of life in the area of general health. Also, less partner (but not patient) belief in cure through treatment was related to higher patients’ vitality and social functioning ratings. Thus, our results suggest that except for patients’ physical functioning, it is of limited use to investigate (only) dissimilarities in illness perceptions of patients and partners as determinants of patient outcomes in HD.
With regard to the third aim of this study, although patients’ and partners’ own illness perceptions explained the largest amount of variance in QoL, spousal illness perceptions were related to patients’ and partners’ quality of life, but only to their vitality ratings. After controlling for patients’ illness related variables and patients’ own beliefs, a stronger partner belief in a long duration of the patients’ illness and less belief in cure through treatment both added to the association with higher patient vitality. The results are in line with those of Heijmans et al. [15], who found positive relationships between spousal maximization of illness duration and patients’ vitality ratings in Addison’s disease. Stronger patient beliefs in control over the illness added to higher vitality scores in partners. From both our results and those of Heijmans et al., it appears that it is most beneficial to have a spouse who is realistic (albeit negative) about the possibilities for cure, and who expects the illness to be long-lasting. Research on how realistic optimism may impact on vitality in healthy persons and patient samples supports these findings [38, 39].
The results obtained in this study must be regarded in the light of some limitations. The lack of information on the non-response rate in the sample of members of the Dutch Huntington Association makes selection bias likely. Also, the patients involved in this study were in the early to middle stages of HD, as shown by their scores on the UHDRS and MMS. This prevents extrapolation of our results to other samples of patients with Huntington’s disease. On the other hand, our results indicate that the patients in our study did not differ significantly from those patients with Huntington’s Disease involved in other studies with regard to important disease related characteristics such as motor and cognitive functioning [40]. Our results could, therefore, be instrumental in future research on quality of life in patients (and their partners) who are in these stages of HD. The relatively small number of couples included and the relatively large number of variables entered into the regression analyses must be taken into account as well.
More research is justified on the unique contributions of the spouses’ perceptions of the illness that may be associated with outcome in chronically ill patients and their partners. Examining these associations further and studying which psychological mechanisms may be involved, for instance by interviewing pairs in whom these associations are clearly discernable, are areas for future research. From a clinical perspective, an understanding of the cognitive factors that are related to quality of life in couples dealing with chronic illness will help to guide family interventions. Given the relative paucity of research on biopsychosocial aspects of Huntington’s disease, qualititative research on quality of life may be helpful in future research, e.g., in the work by Brouwer-Dudokdewit et al. [41], where qualitative research in a case-study format that was carefully embedded in a theoretical framework helped explore quality of life issues in pre-symptomatic testing for HD. These researchers emphasize the relevance of adding an existential and/or spiritual approach in exploring quality of life issues in HD patients [42, 43]. Empirical studies in other neurological disorders support these suggestions: Hodgson et al. [44], for example, describe the lives of 10 couples living with Parkinson’s disease, and outline how these couples preferred a multidisciplinary approach to their treatment and believed in taking an active role in their health care. Qualitative approaches to assessing QoL in Huntington’s disease spousal carers are described in a recent paper by Aubeeluck & Buchanan [29], where visual representations of QoL were gathered by using ‘Photovoice’: spousal carers photographed and described elements of their life. Finally, cognitive-behavioural approaches in patients with dementia and their caregivers were recently shown in a randomized controlled trial to result in improvements in patients’ daily functioning and reduced burden in the caregiver. These studies and ours, illustrate possible directions for future research in patients (and their partners) who experience an extreme negative impact on quality of life [45].

couples
huntington’s disease
illness perceptions
patient qol
partner qol