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Comorbidity is associated with pain-related activity limitations in multiple sclerosis

Multiple Sclerosis and Related Disorders, Volume 4, Issue 5, September 2015, Pages 470 - 476

Abstract

Background

Comorbidities are common in multiple sclerosis (MS). The high prevalence of pain in MS is well-established but the influence of comorbidities on pain, specifically, pain-related interference in activity is not.

Objective

To examine the relationship between comorbidity and pain in MS.

Methods

We recruited 949 consecutive patients with definite MS from four Canadian centres. Participants completed the Health Utilities Index (HUI-Mark III) and a validated comorbidity questionnaire at 3 visits over 2 years. The HUI's pain scale was dichotomized into two groups: those with/without pain that disrupts normal activities. We used logistic regression to assess the association of pain with each comorbidity individually at baseline and over time.

Results

The incidence of disruptive pain over two years was 31.1 per 100 persons. Fibromyalgia, rheumatoid arthritis, irritable bowel syndrome, migraine, chronic lung disease, depression, anxiety, hypertension, and hypercholesterolemia were associated with disruptive pain (p<0.006). Individual-level effects on the presence of worsening pain were seen for chronic obstructive pulmonary disease (odds ratio [OR]: 1.50 95% CI: 1.08–2.09), anxiety (OR: 1.49 95% CI: 1.07–2.08), and autoimmune thyroid disease (OR: 1.40 95% CI: 1.00–1.97).

Conclusion

Comorbidity is associated with pain in persons with MS. Closer examination of these associations may provide guidance for better management of this disabling symptom in MS.

Highlights

 

  • The incidence of pain in MS was 31.3 per 100 persons over two years.
  • The prevalence of disruptive pain was 40.5 per 100 persons.
  • Chronic lung disease, anxiety and autoimmune thyroid disease worsened pain over time.

Keywords: Multiple sclerosis, Pain, Comorbidity, Quality of life, Cohort.

1. Introduction

Pain is one of the most common symptoms of multiple sclerosis (MS) ( Archibald et al., 1994 ). Pain can be described according to its origin and mode of transmission. Nociceptive pain can originate in soft tissue, organs, or bones where nerve fiber transmission to the brain is normal. Neuropathic pain may occur when the stimulation of nerve fibers associated with pain transmission to the brain is abnormally sustained. Multiple etiologies may lead to pain in MS ( Brola et al., 2014 ). The experience of pain is highly subjective and can be quantified in numerous ways including intensity, location, and characteristics. However, the impact of pain on activity (function) ( Ehde et al., 2003 ) is considered to be the most relevant outcome in clinical trials of pain treatment ( Jensen and Karoly, 2001 ).

Estimates of the prevalence of pain in MS vary between 20% and 90% (Ehde et al, 2003, Boneschi, 2008, and Foley et al, 2013). Such variability may reflect differences in the focus on the type of pain (neuropathic vs. nociceptive, acute vs. chronic), cause of the pain, differences in classifying pain severity, and failure to account for comorbid factors. In addition to widely varying prevalence estimates of pain in MS, changes in pain over time are poorly understood. Two studies of pain in MS have followed relatively small groups of individuals (n=68 and 74) for at least two years, using a single ordinal scale to measure pain (Brochet et al, 2009 and Khan et al, 2013). However, as these studies excluded people with certain comorbidities, such as a history of psychiatric illness or medically unstable conditions, their ability to characterize the pain experience of many people with MS is limited. These studies did not explore changes in pain within individuals over time.

A recent systematic review and meta-analysis of pain in adults with MS also concluded that there is a dearth of information on the incidence and temporal profile of pain ( Foley et al., 2013 ). In MS, pain is associated with greater disability ( Brochet et al., 2009 ), reduced employment ( Shahrbanian et al., 2013 ), individual and societal costs, reduced quality of life and impaired activities of daily living ( Piwko et al., 2007 ). However, factors associated with activity-related impacts of pain in MS remain poorly understood, including the potential roles of comorbid conditions. Physical and psychiatric comorbidities are highly prevalent in MS, affecting more than 50% of individuals ( Goldman Consensus Group, 2005 ) and negatively affecting quality of life, treatment outcomes ( Finlayson et al., 2013 ), and mortality ( Krokki et al., 2014 ). Several comorbidities that are common among persons with MS are themselves, directly associated with pain, including arthritis, migraine, and fibromyalgia ( Marrie and Hanwell, 2013 ). However, these and other comorbidities may affect pain in MS by leading to further damage of the central nervous system, augmenting inflammation, or dysregulating pain responses (O’Connor et al, 2008, Osterberg et al, 2005, Herman et al, 1992, and Von Korff et al, 2005). If comorbidity is associated with greater pain-related limitations in activity this would be relevant to the treatment of pain and of MS. Further identification of specific comorbidities that are associated with pain might highlight the need to examine the bidirectional impact of pain and comorbidity in MS. As a first step to evaluating the association between comorbidity and pain in MS we aimed to characterize the impact of comorbidity on pain-related interference in activity (hereinafter referred to as pain for simplicity) in MS. Specifically, we aimed to determine (1) the incidence of pain in an MS population and changes in pain severity over time; and (2) which comorbid conditions are associated with pain in MS and with changes in pain severity over time.

2. Materials and methods

2.1. Study population

From July 2010 to March 2011, consecutive patients attending routine visits at four participating MS Clinics across Canada (British Columbia, Alberta, Manitoba, and Nova Scotia) were recruited. Participants were approached by a trained research coordinator using a standardized script. Inclusion criteria were a confirmed diagnosis of MS according to the prevailing diagnostic criteria at the time of diagnosis (Von Korff et al, 2005, McDonald et al, 2001, Polman et al, 2010, Polman et al, 2005, and Poser et al, 1983), age ≥18 years, adequate knowledge of the English language to be able to complete the questionnaires, and ability to provide informed consent.

2.2. Clinical information

Demographic and clinical information were obtained from medical record reviews using a standardized data abstraction form including sex, date of birth, race, age of MS symptom onset, clinical course ( Lublin and Reingold, 1996 ), Expanded Disability Status Score (EDSS) ( Kurtzke, 1983 ) as recorded the day of recruitment and at follow-up visits by the treating neurologist, MS status (stable, acutely relapsing, progressing not in the context of a relapse) per the treating neurologist, and use of current disease-modifying therapy.

2.3. Self-reported measures

Each participant completed questionnaires at three visits over two years (baseline, year one, and year two). These included a generic multi-attribute utility measure of health-related quality of life: the Health Utilities Index (HUI, Mark III version) ( Horsman et al., 2003 ), an alcohol dependence measure (assessed by the 4-item CAGE (Cutting down, Annoyance by Criticism, Guilty Feeling, Eye-Openers) ( Mayfield et al., 1974 ), and a validated comorbidity questionnaire capturing physical and psychiatric comorbidities (such as diabetes and depression), including comorbidities that could be considered secondary to MS (i.e. a consequence of having MS, such as osteoporosis) ( Horton et al., 2010 ). The examined comorbidities were depression, hypertension, migraine, hyperlipidemia, anxiety, chronic obstructive pulmonary disease (COPD), irritable bowel syndrome (IBS), autoimmune thyroid disease, osteoporosis, cataracts, diabetes, rheumatoid arthritis, fibromyalgia, heart disease, inflammatory bowel disease (IBD), glaucoma, bipolar disorder, seizure disorder, peripheral vascular disease, lupus, and psychosis. The HUI-III is composed of 8 domains, one of which assesses pain via a composite utility-weighted ordinal scale that measures the amount of impairment (pain) a person is experiencing. For this study, pain was assessed using the pain single-attribute classification system of the HUI-III, which includes scale items 8 and 15. From these ratings of pain and discomfort in the previous two weeks is derived a 5-point ordinal scale, reflecting the degree of disruption of normal activities (from 1 to 5; 5 being the worst).

2.3.1. Pain definitions

Pain was initially considered using the HUI-III classification which was dichotomized as follows: (a) ‘any pain’ i.e. presence of pain at any level (≥ a score of 2 vs <2); (b) ‘disruptive pain’ i.e. pain that prevented or disrupted normal activities (≥ a score of 3 vs. <3); (c) ‘worsening pain’ i.e. the pain score increased between any of the assessments (yes vs. no), which was evaluated between baseline and year one, year one and year two, and overall. Second, pain was considered using the HUI-III classification score as an ordinal scale (i.e. 1–5), referred to as ‘overall pain’ to distinguish it from the dichotomized score.

2.3.2. Data analyses

We calculated descriptive statistics (means, medians), along with the accompanying measure of variability (standard deviation (SD), interquartile range (IQR)) and participants with and without pain were compared using the appropriate tests (t-tests, Wilcoxon Rank-Sum tests, or tests for proportions). We report odds ratios (OR) and 95% confidence intervals (95% CI) for all logistic models; univariate OR are presented as OR, and multivariable adjusted OR as aOR. The linearity assumptions of the logistic model were checked using a Box–Tidwell model transformation ( Box and Tidwell, 1962 ). For all multivariable models described below, covariates included time since symptom onset (continuous, in years), disability (EDSS score, categorized in three groups: 0.0–3.0 (mild disability); 3.5–5.5 (moderate disability); 6.0–9.0 (severe disability)), age (continuous, in years) and sex.

2.3.2.1. Pain prevalence

Characteristics of those with and without disruptive pain at baseline were compared.

2.3.2.2. Pain incidence

The incidence of disruptive pain at years 1 and 2 was calculated by dividing the number of people who developed disruptive pain at that visit by the number of people at risk to develop incident disruptive pain (i.e. no disruptive pain at any previous visit). Incident comorbidities were calculated in a similar manner (using different risk sets). Risk factors for incident disruptive pain at any point during the study, including age, alcohol dependence, presence of disease modifying therapy, and disease course, were analyzed using univariate logistic regression.

2.3.2.3. Comorbidities

Comorbidities included in the multivariable analyses were those that had a prevalence of at least 5% in the sample (depression, hypertension, migraine, hypercholesterolemia, anxiety, COPD, IBS, autoimmune thyroid disease, and osteoporosis). Based on their prevalence, these conditions were deemed relevant at the population level, and had enough individuals affected to provide stable estimates in our sample.

2.3.2.4. Pain and comorbidities over time

In univariate analyses, we compared the frequency of worsening pain among participants with a specific incident comorbidity as compared to those who had not newly developed that comorbidity (but could already have the comorbidity and be in the comparison group), using an unadjusted ratio of proportions. To determine the effect of comorbidities on ‘overall pain’ over time we used ordinal generalized estimating equation (GEE) models with an independent correlation structure to account for repeated measures within individuals. The full ordinal pain scale was utilized for the GEE models (i.e. overall pain). The analyses were also repeated for disruptive pain. GEE models generate odds ratios that are population averages of within-subject and between-subject effects, but tend to be dominated by between-subject effects (that is, the comparison of individuals with and without comorbidity). Time by comorbidity interaction terms were included to determine the effect that developing each comorbidity had on pain. Finally, change scores for pain between visits were modeled using logistic GEE to better determine within-person effects over time (that is the effect of newly developing comorbidity in an individual), using methodology recommended by Twisk (2003 ). Thus the dependent variable was worsening pain compared to pain that was stable or improved. Independent time-varying covariates were also modeled in the logistic GEE of worsening pain as change scores (e.g. change in EDSS between visits; incident comorbidity).

3. Results

3.1. Study participants

Of 1632 patients who were pre-screened for inclusion and scheduled for a clinic visit during the study period, 1144 met criteria, of whom 949 consented to participate (82.6%). Twenty-nine participants were lost to follow-up at year one, and 22 were lost to follow-up at year two, for a total of 94.6% participants with complete follow-up. No baseline differences were found in the age, prevalence of disruptive pain, prevalence of any pain, EDSS score, and the prevalence of comorbidity between those who completed and those who were lost to follow-up at either assessment (data not shown).

Most participants were female and white, with a mean (SD) age of 48.6 (11.4) years. Over two-thirds of participants had at least some post-secondary education. The mean (SD) time since symptom onset was 15.4 (10.2) years, and 687/949 (72.4%) had relapsing-onset MS ( Table 1 ).

Table 1 Demographic characteristics at baseline (n=949).

Variable Sample
Age, mean (SD a ) 48.6 (11.4)
Age of MS symptom onset, mean (SD) 33.2 (10.0)
Time since symptom onset (years), mean (SD) 15.4 (10.2)
Sex, N (%)  
 Female 714 (75.2)
 Male 235 (24.8)
Race, N (%)  
 White 810 (85.4)
 Non-White 46 (4.8)
 Missing 93 (9.8)
Education, N (%)  
 High school or Less 258 (30.1)
 Any post-secondary and higher 574 (67.1)
 Other 24 (2.8)
EDSS b , median (p25–p75 c ) 2.5 (1.5–5.0)
Clinical course, N (%)  
 RRMS d 687 (72.4)
 SPMS e 193 (20.3)
 PPMS f 60 (6.3)
HUI-III g global index, mean (SD) 0.5 (0.3)
HUI-III pain scale, median (IQR) 2 (2)
Comorbidities  
 Depression, n (%) 274 (29.0)
 Hypertension, n (%) 168 (17.8)
 Migraine, n (%) 164 (17.3)
 Hyperlipidemia, n (%) 118 (12.4)
 Anxiety, n (%) 109 (11.5)
 Chronic obstructive pulmonary disease, n (%) 93 (9.8)
 Irritable bowel syndrome, n (%) 75 (7.9)
 Thyroid, n (%) 74 (7.8)
 Osteoporosis, n (%) 57 (6.0)
 Cataracts, n (%) 42 (4.4)
 Diabetes, n (%) 38 (4.0)
 Rheumatoid arthritis, n (%) 35 (3.7)
 Fibromyalgia, n (%) 34 (3.6)
 Heart disease, n (%) 28 (3.0)
 Inflammatory bowel disease, n (%) 18 (1.9)
 Glaucoma, n (%) 14 (1.5)
 Bipolar disorder, n (%) 13 (1.4)
 Seizure disorder, n (%) 11 (1.2)
 Peripheral vascular disease, n (%) 14 (1.0)
 Lupus, n (%) 9 (1.0)
 Psychosis, n (%) 1 (0.1)

a SD=Standard Deviation;

b EDSS= Expanded Disability Status Scale;

c p= percentile;

d RRMS= relapsing remitting multiple sclerosis;

e SPMS= secondary progressive multiple sclerosis;

f PPMS= primary progressive multiple sclerosis;

g HUI-III= Health Utilities Index- Mark III.

At baseline, 41.5% of participants had at least one comorbidity of which the most common were depression, hypertension, migraine, hyperlipidemia, and anxiety ( Table 1 ). The incidence of any comorbidity was 18.3 per 100 persons at year 1, 21.2 per 100 persons at year 2, and 28.3 per 100 persons over the entire study period.

3.2. Prevalence and severity of pain at baseline

The prevalence of any pain at baseline was 74.1% (95% CI: 71.2–76.8), while the prevalence of disruptive pain at baseline was 40.5% (95% CI: 37.4–43.7). The proportion of participants reporting disruptive pain was highest in those with SPMS, followed by PPMS, and RRMS ( Table 2 ). Participants with a progressive course of MS had 1.86 times the unadjusted odds of having disruptive pain, relative to those persons with RRMS (95% CI: 1.39–2.49). For every one unit increase in EDSS, the OR of disruptive pain increased by 1.46 (95% CI: 1.25–1.72).

Table 2 Clinical and demographic characteristics according to disruptive pain status at baseline.

  With disruptive pain Without disruptive pain P-value
Age, mean (SD a ) 50.2 (10.9) 47.6 (11.3) 0.0005
Sex, n (%)
 Male 83 (35.5) 151 (64.5) 0.08
 Female 300 (42.2) 411 (57.8)
EDSS b , Median (p25–p75) c 3.0 (2.0–6.0) 2.0 (1.5–4.0) < 0.0001
Education, n (%)
 High school or less 107 (41.6) 150 (58.4) 0.06
 Greater than high school 221 (38.7) 350 (61.3)
 Other 15 (62.5) 9 (37.5)
Time since symptom onset, mean (SD) 15.9 (9.9) 15.1 (10.4) 0.24
Clinical course, n (%)
 Relapsing remitting 251 (36.6) 434 (63.4) < 0.0001
 Secondary progressive 102 (53.4) 89 (46.6)
 Primary progressive 28 (46.7) 32 (53.3)

a SD – Standard Deviation.

b EDSS – Expanded Disability Status Scale.

c p – Percentile.

Note: Some missing data exist and clinically isolated syndrome and unknown disease course (n=2) were excluded due to low numbers.

3.3. Pain and comorbidities at baseline

Of those reporting a comorbidity, 54.5% experienced disruptive pain compared with 30.7% of participants without a comorbidity (OR: 2.70; 95% CI: 2.07–3.54). The percentage of people reporting disruptive pain was higher in those with more comorbidities ( Fig. 1 ). Participants with fibromyalgia, peripheral vascular disease, and rheumatoid arthritis at baseline were most likely to report disruptive pain ( Table S-1 ).

gr1

Fig. 1 Percentage of those with disruptive pain by the number of comorbidities at baseline.

3.4. Pain incidence

The incidence of any pain and of disruptive pain, at one year and at two years, and the incidence during the two years of follow-up, is presented in Table 3 . At baseline, older age (OR: 1.03; 95% CI: 1.01–1.04), alcohol dependence (OR: 2.74; 95% CI: 1.30–5.78), lack of a disease modifying therapy (OR: 1.57; 95% CI: 1.09–2.27), and a progressive course of MS (OR: 1.60; 95% CI: 1.04–2.48) were associated with a greater risk for incident disruptive pain at any point in the study.

Table 3 Incidence of pain at years 1 and 2.

  Year 1 a Year 2 a Overall
  Number at risk Number at risk Number at risk
  Incidence per 100 (95% Confidence Interval) Incidence per 100 (95% Confidence Interval) Cumulative Incidence per 100 (95% Confidence Interval)
Any pain 245 253 245
  32.7 35.2 45.7
(27.0–38.8) (29.5–41.3) (39.5–52.0)
Disruptive pain 562 542 562
  19.6 20.5 31.3
(16.5–23.1) (17.3–24.1) (27.6–35.3)

a Year 1 is one year after baseline, year 2 is two years after baseline.

3.5. Pain and comorbidities over time

Among people with peripheral vascular disease, depression, hypertension, migraine, hypercholesterolemia, anxiety, COPD, IBS, autoimmune thyroid disease, and osteoporosis, the ratio of worsening pain at year 1 was elevated only in those with peripheral vascular disease (ratio of proportions (PR) 2.42; 95% CI: 1.22–4.80). At year 2, the ratio of worsening pain was higher in those with diabetes (PR: 2.42; 95% CI: 1.25–4.68), osteoporosis (PR: 2.30; 95% CI: 1.49–3.54), and anxiety (PR: 1.49; 95% CI: 1.05–2.12).

When analyses were conducted using an ordinal multivariable model, that included adjustment for time since symptom onset, disability, age, sex, and the presence of comorbidities (i.e. those with a prevalence of at least 5% in our sample: depression, hypertension, migraine, hypercholesterolemia, anxiety, COPD, IBS, autoimmune thyroid disease, and osteoporosis), comorbidity was associated with disruptive pain. Specifically, fibromyalgia, inflammatory bowel disease, rheumatoid arthritis, IBS, migraine, COPD, depression, and hypertension were all associated with increased odds of pain, that persisted over time (all p<0.006) ( Table S-2 ).

When worsening pain was modeled (as change scores) using logistic GEE, adjusted for time since symptom onset, disability, age, sex, and the presence of the previously defined comorbidities, there were individual-level effects of COPD (aOR: 1.50; 95% CI: 1.08–2.09), anxiety (aOR: 1.49; 95% CI: 1.07–2.08), and autoimmune thyroid disease (aOR: 1.40; 95% CI: 1.00–1.97) on the presence of worsening pain ( Table S-3 ).

4. Discussion

This study examined the longitudinal relationship between pain-related interference with activities and comorbidities in MS. Over two years, the incidence of comorbidity was 28.3% and the incidence of disruptive pain was 31.3%. Persons with disruptive pain were more likely to have comorbidity, greater neurologic disability, and a progressive course of MS. Persons who developed COPD, anxiety, and autoimmune thyroid disease were more likely to develop more pain over the study period.

The prevalence of any pain and prevalence of disruptive pain that we observed are consistent with previous studies. A recent meta-analysis reported the prevalence of any pain to be 63% ( Foley et al., 2013 ), which is similar to our baseline prevalence of 74%. Ehde and colleagues reported a prevalence of pain that interfered with daily activities of 49% in a mail survey of 442 persons with MS ( Ehde et al., 2003 ), similar to the 40% prevalence of disruptive pain in the current study. A later study by this group also reported a prevalence of any pain of 73% and of disruptive pain, 40%; these findings are almost identical to those we report ( Alschuler et al., 2013 ). As pain is a subjective experience, differences in the prevalence of pain between studies may be due at least in part to differences in how pain is operationalized, including whether the focus is on the characteristics of the pain or on the impact of the pain. In addition, differences in the characteristics of the cohorts studied, such as the proportion of those with progressive MS and differences in neurologic disability across samples, may all contribute to different reports of pain prevalence (Douglas et al, 2008, Grau-Lopez et al, 2011, and Stenager et al, 1995). We found that persons with a progressive course of MS were more likely to have disruptive pain than those with a relapsing-remitting course, and higher levels of disability were associated with greater odds of disruptive pain.

Over a relatively short 2-year period we observed a high incidence of new disruptive pain of 31.3 per 100 persons, highlighting the importance of inquiring about this symptom at each health care encounter. We were unable to find any comparable longitudinal studies of pain incidence in the MS population ( Foley et al., 2013 ). While Brochet et al. (2009 ) had previously reported a trend toward decreased pain prevalence over 2 years, their sample of persons with MS was less than 10% of the size of our study which found a relatively stable prevalence of pain over time. Regardless of their differences however, both studies highlight the general chronicity of pain in the MS population and the importance of efforts to develop more effective interventions.

There is a paucity of information regarding the influence of comorbidities on the impact of pain in persons with MS despite the fact that comorbidities were present in nearly half of our sample. The comorbidities most prevalent in persons with disruptive pain at baseline were fibromyalgia, peripheral vascular disease, and rheumatoid arthritis, all prevalent conditions that are themselves associated with chronic and potentially disabling pain (Benbow et al, 1994, Walsh and McWilliams, 2012, and Walker et al, 2014). At baseline, 82% of those with fibromyalgia, 71% of persons with peripheral vascular disease, and 69% of those with rheumatoid arthritis reported disruptive pain. Fifty percent of those with migraine and depression also reported disruptive pain. In a survey of 673 individuals, migraine was more common in participants with neuropathic pain and vice versa ( Moisset et al., 2013 ). Comorbid migraine and neuropathic pain were also associated with more intense headache pain, more intense neuropathic pain, and greater disruption of normal activities ( Moisset et al., 2013 ). We found that depression was associated with 1.58-fold increased odds of pain. Similarly, in a study of 161 persons with MS who completed the Patient Health Questionnaire-9, 67–77% of those with depression had at least moderately severe pain, and those with depression were 1.196–2.070 times more likely to have pain than those without depression ( Alschuler et al., 2013 ). Despite this, depression has not been associated with greater pain-related treatment utilization in MS ( Alschuler et al., 2012 ). Studies of other chronic health conditions also provide some findings analogous to ours and suggest that the influence of comorbid health conditions on the experience of pain is not solely due to pain that is directly attributable to the comorbid condition itself. For example, in a study of persons with chronic spinal pain the presence of other comorbid conditions were found to exacerbate the disability experienced in addition to pain ( Von Korff et al., 2005 ). Medical and psychiatric comorbidity also predict moderate and severe pain in persons undergoing total knee replacement at two and five years post-surgery ( Singh and Lewallen, 2013 ). Moreover, pain related to osteoarthritis may be associated with changes in central pain processing) Sofat et al. (2011) .

Though research on the mechanisms of pain in MS is limited, attempts have been made to characterize the underlying pathophysiology of pain in MS ( O'Connor et al., 2008 ). Lesions in the periventricular white matter, the lateral and medial thalamic regions, and the spinal cord are related to central neuropathic pain and may represent one mechanism of pain in MS ( Osterberg et al., 2005 ). GABA receptor system dysfunction has been another hypothesized mechanism for pain in MS, as the administration of intrathecal baclofen reduced extremity pain in a small case series ( Herman et al., 1992 ). Both peripheral and brain inflammation are associated with both depression and pain ( Walker et al., 2014 ); as inflammation is characteristic in MS, this may be another mechanism by which they are related. Our findings contribute to the established body of studies indicating the high prevalence of pain in persons with MS ( Foley et al., 2013 ), but also demonstrate that pain is even more prevalent in those who have additional comorbid conditions. Interestingly, comorbidities not typically associated with pain, such as autoimmune thyroid disease, COPD and hypertension, were also associated with worsened pain experience in persons with MS. The systems modulating blood pressure and pain overlap, representing a feedback loop which maintains blood pressure levels in the presence of painful stimuli; chronic pain may alter this homeostatic function, dysregulating the pain response in persons with hypertension ( Bruehl and Chung, 2004 ). Increased pain related to MS may also result in the development of hypertension in these individuals; persons with chronic pain are more likely to have hypertension than those seen for other health concerns ( Bruehl et al., 2005 ). These findings have clear implications for disease management, suggesting that monitoring and treating comorbidity may be an avenue for improving pain in MS while also identifying comorbidities that should be considered for MS patients presenting with incident pain.

This study has important strengths. We examined a previously unreported association between pain and comorbidities in MS in a large multi-site study of a well-described sample. This cohort was recruited consecutively, and a large number completed the entire study, suggesting a representative sample. Two of the recruitment sites deliver the only specialized MS care in their respective geographic regions, suggesting that our sample is also highly generalizable to the broader population of persons with MS. Unlike previous studies investigating pain in MS, our study followed the same group of persons over 2 years, allowing us to investigate incident pain. We employed a previously validated measure of comorbidity, though this may not have captured every comorbidity that could have been associated with pain. We used a generic measure (HUI-III) of the impact of pain on activities, but did not capture the characteristics of pain. However, the use of a generic measure allows our findings to be compared to other health conditions and the general population. In addition, this allowed us to establish that comorbidities are associated with the clinically relevant issue of impact on daily activities, and thus the need to further investigate this area. Future studies should further describe the association between comorbidity and pain, where pain is characterized with respect to type, quality, location, duration and intensity. Another potential limitation is our use of a clinic-attending sample as this may under-represent severely disabled and progressive patients; however this would suggest our data underestimate the severity of the problem of pain in MS. Finally, the incidence of most comorbidities was low, reducing power to detect the impact of incident comorbidity on pain.

Our findings indicate that while pain is a concern for all persons with MS, it is more so for those with comorbidities. Closer examination of these associations may provide guidance for better management of these disabling symptoms and improved quality of life for persons with MS.

Funding acknowledgment

This work was supported by the Canadian Institutes of Health Research (CIBG 101829); the Rx & D Health Research Foundation; and by the Multiple Sclerosis Society of Canada (through a Don Paty Career Development Award to RAM). The funding sources had no involvement in the study design; the collection, analysis and interpretation of data; in writing the article; or in the decision to submit this article for publication.

Conflicts of interest statement

Kirsten Fiest has no conflicts of interest to declare.

John Fisk is the Director of the endMS Atlantic Regional Research and Training Centre which is funded by the Multiple Sclerosis Society of Canada. He receives research funding from the Canadian Institutes of Health Research (CIHR) and in the past has received grants, honoraria and consultation fees from AstraZeneca, Bayer, Biogen-Idec Canada, Heron Evidence Development Limited, Hoffmann-La Roche, MAPI Research Trust, Novartis, Sanofi-Aventis, Serono Canada, and QualityMetric Incorporated.

Scott Patten was a member of an advisory board for Servier, Canada. He has received honoraria for reviewing investigator-initiated grant applications submitted to Lundbeck and Pfizer and has received speaking honoraria from Teva and Lundbeck. He is the Editor-in-Chief for the Canadian Journal of Psychiatry and a member of the editorial board of Chronic Diseases and Injuries in Canada. He is the recipient of a salary support award (Senior Health Scholar) from Alberta Innovates, Health Solutions and receives research funding from CIHR, the Institute of Health Economics and the Alberta Collaborative Research Grants Initiative.

Helen Tremlett receives funding from the Multiple Sclerosis Society of Canada [Don Paty Career Development Award]; US National MS Society [#RG 4202-A-2 (PI)]; CIHR [MOP: #190898 (PI) and MOP-93646 (PI)]; Michael Smith Foundation for Health Research (Scholar award) and the Canada Research Chair program. She has received speaker honoraria and/or travel expenses to attend conferences from: the Consortium of MS Centres, US National MS Society, Swiss Multiple Sclerosis Society, the University of British Columbia Multiple Sclerosis Research Program, Teva Pharmaceuticals and Bayer Pharmaceutical (honoraria declined) and ECTRIMS. Unless otherwise stated, all speaker honoraria are either donated to an MS charity or to an unrestricted grant for use by her research group.

Christina Wolfson receives research funding from the Multiple Sclerosis Society of Canada, the National Multiple Sclerosis Society, CIHR, and the Canada Foundation for Innovation. She has received a speaker honorarium from Novartis Pharmaceuticals.

Sharon Warren receives research funding from CIHR.

Kyla McKay has no conflicts of interest to declare.

Lindsay Berrigan has no conflicts of interest to declare.

Ruth Ann Marrie receives research funding from: CIHR, Public Health Agency of Canada, Manitoba Health Research Council, Health Sciences Centre Foundation, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Rx & D Health Research Foundation, and has conducted clinical trials funded by Sanofi-Aventis.

Acknowledgments

CIHR Team in the Epidemiology and Impact of Comorbidity on Multiple Sclerosis (by site): University of Manitoba (James Blanchard MD, PhD; Patricia Caetano, PhD; Lawrence Elliott, MD, MSc; Ruth Ann Marrie, MD, PhD; Bo Nancy Yu, MD, PhD) Dalhousie University (Virender Bhan, MBBS; John D. Fisk, PhD), University of Alberta (Joanne Profetto-McGrath, PhD; Sharon Warren, PhD; Larry Svenson, BSc); McGill University (Christina Wolfson, PhD); University of British Columbia (Helen Tremlett, PhD); University of Calgary (Nathalie Jette, MD, MSc, Scott Patten, MD, PhD).

Appendix A. Supplementary materials

 

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Supplementary Material

 

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Supplementary Material

 

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Footnotes

a Department of Internal Medicine, College of Medicine, Faculty of Health Sciences, University of Manitoba, 820 Sherbrook Street, Winnipeg, Canada R3A1R9

b Departments of Psychiatry, Medicine, Psychology & Neuroscience, Dalhousie University, 6299 South Street, Halifax, Canada B3H4R2

c Departments of Psychiatry & Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, Canada T2N4Z6

d Division of Neurology, Faculty of Medicine, University of British Columbia, 2329 West Mall, Vancouver, Canada V6T1Z4

e Departments of Epidemiology & Biostatistics, Occupational Health, & Medicine, McGill University, 1020 Pine Avenue West, Montreal, Canada H3A 1A2

f Faculty of Rehabilitation Medicine, University of Alberta, 116 Street & 85 Avenue, Edmonton, Canada T6G2R3

g Department of Experimental Medicine, University of British Columbia, 2329 West Mall, Vancouver, Canada V6T1Z4

h Department of Psychology, St. Francis Xavier University, Antigonish, Canada B0H1X0

i Department of Community Health Sciences, College of Medicine, University of Manitoba, 820 Sherbrook Street, Winnipeg, Canada R3A1R9

Correspondence to: Health Sciences Centre, GF 543-820 Sherbrook Street, Winnipeg, MB, Canada R3A 1R9. Fax: +1 204 787 1486.


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  • Prof Timothy Vartanian

    dsc_0787_400x400.jpg Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College,...
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    headshotcsr1_185x250.jpg Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia...
  • Dr Rebecca Farber

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