Abstract
It has been suggested that alterations in inflammation molecules maintain chronic pain although little is known about how these factors influence homeostatic and inflammatory events in common chronic pain conditions. Nonpharmacological interventions might be associated with alterations in inflammation markers in blood. This study of patients with chronic pain investigates whether an interdisciplinary multimodal rehabilitation program (IMMRP) was associated with significant alterations in the plasma pattern of 68 cytokines/chemokines 1 year after rehabilitation and whether such changes were associated with clinical changes. Blood samples and self-reports of pain, psychological distress, and physical activity of 25 complex chronic pain patients were collected pre-IMMRP and at 12-month follow-up. Analyses of inflammatory proteins (cytokines/chemokines/growth factors) were performed directly in plasma using the multiplex immunoassay technology Meso Scale Discovery. This explorative pilot study found that 12 substances, mainly pro-inflammatory, decreased after IMMRP. In two other relatively small IMMRP studies, four of these proinflammatory markers were also associated with decreases. The pattern of cytokines/chemokines pre-IMMRP was associated with changes in psychological distress but not with pain or physical activity. The present study cannot impute cause and effect. These results together with the results of the two previous IMMRP studies suggest that there is a need for larger and more strictly controlled studies of IMMRP with respect to inflammatory markers in blood. Such studies need to consider responders/non-responders, additional therapies, involved pain mechanisms and diagnoses. This and the two other studies open up for developing biologically measurable outcomes from plasma. Such biomarkers will be an important tool for further development of IMMRP and possibly other treatments for patients w ith chronic pain.
1 Introduction
One-fifth of the European population has moderate to severe chronic pain [1]. The prevalence of local chronic pain conditions is high – e.g. chronic neck-shoulder pain CNSP is 8% and non-neuropathic low back pain CLBP is 19.5% [2], [3], [4]. These conditions are associated with psychological distress, low health, sick leave, and high socioeconomic costs [5]. CNSP and CLBP can gradually become more easily triggered and spread to most of the body (i.e. chronic wide spread pain [CWP]) with a 5–10% prevalence [6]. According to the ACR criteria, fibromyalgia (FM) has a prevalence of 2–4% and is a subgroup of CWP with generalised hyperalgesia [7], [8].
Few mechanism-based diagnoses exist for chronic pain; one exception is neuropathic pain. Typically, diagnoses according to ICD10 (International Statistical Classification of Diseases and Related Health Problems – Tenth Revision) are based on duration and anatomical locations such as CLBP. Hence, a certain clinical diagnosis may mechanistically combine different phenotypes [9]. The lack of blood biomarkers makes diagnostic procedures problematic [10].
Several decades ago, the above chronic pain conditions were considered to be of peripheral origin. Imaging techniques have challenged the peripheral origin hypothesis and provide evidence for altered central (CNS) nociceptive/pain processing and morphology [11], [12], [13], [14], [15], [16], [17]. Therefore, some researchers have characterised these chronic pain conditions as central pain conditions [18], [19].
Understanding of the relative roles of peripheral and central factors is fundamental for developing treatments. The CNS alterations may be driven by peripheral nociception generators to produce the clinical presentations [20], [21]. This suggestion is indirectly supported by observations that CNS alterations in CLBP or chronic hip osteoarthritis are normalised after effective peripheral treatment – e.g. facet joint injections or surgery [22], [23], [24]. Studies have uncovered direct support for a peripheral muscle involvement in chronic pain conditions such as increased muscle levels of serotonin, glutamate, pyruvate, and lactate in CNSP and CWP/FM and lowered concentrations of ATP and PCr in FM [25], [26], [27], [28]. However, only a few molecules have been investigated and it is unclear if important pathophysiological mechanisms have been targeted.
Both the immune system and the pain signalling system acutely and highly coordinate response to threats [29], [30]. After tissue injury, tissue substances are released that can stimulate and sensitise nociceptors. Immune cells such as cytokines, histamine, and serotonin are activated and released [31]. Nociceptors produce as well as respond to cytokines and chemokines [32]. Moreover, antidromic firing of nociceptors releases substance P, calcitonin gene-related peptide (CGRP), glutamate, and neurokinin A [32], [33], [34], all contributors to inflammation. Local inflammation can range from mild homeostatic reactions to full-scale inflammation [35]. Cytokines in the periphery and in the CNS show bi-directional relationships [36], [37], [38], [39]. Information on peripheral inflammatory activity is transmitted to the CNS resulting in, for example, sickness behaviour, decreased endogenous pain inhibition, and neuro-inflammation [39], [40], [41], [42].
It has been suggested that alterations in inflammation molecules maintain chronic pain. Although more than 200 cytokines and chemokines have been described, little is known about how these factors influence homeostatic and inflammatory events in common chronic pain conditions [43]. Human studies have generally been restricted to a few mainly pro-inflammatory molecules [44]. The literature is not consistent for CWP/FM with respect to muscle and blood [45], [46]. A review of CLBP vaguely concluded that “classic” pro-inflammatory cytokines (IL-6, TNF-α, IL-8, and IL-1β) are often elevated [10]. Hence, a heterogenous picture exists. In amputees with and without chronic residual limb pain, 11 of 37 inflammatory markers in blood showed significant differences [47]. In a study using a panel of 92 cytokines/chemokines, we found that 11 substances in plasma significantly differentiated between CWP/FM and controls [48].
When choosing treatments, chronic pain patients are largely managed using a trial-and-error approach [49]. Different activated neurobiological mechanisms such as the extent of peripheral biochemical alterations in the patients may explain the small to moderate effect sizes of common non-pharmacological interventions [50], [51], [52], [53]. Interdisciplinary multimodal pain rehabilitation programs (IMMRP) are bio-psycho-social interventions that continue over several weeks with common goals in combination with individualised goals of the patient [54], [55], [56], [57]. Swedish IMMRPs include pain education, physical exercise, return to work strategies, and cognitive behavioural therapy (CBT) coordinated by a team [54], [55], [56], [57]. Systematic reviews (SRs) conclude higher efficacy of IMMRP compared with single-treatment or treatment-as-usual programs in chronic pain patients [55], [57], [58], [59], [60], [61], [62]. Wang et al. investigated whether the serum level of cytokine IL-8 changed after IMMRP in FM and reported a significant decrease in IL-8 [63]. Very recently Hysing et al. from a group of patients with mixed severe chronic pain conditions reported that participation in IMMRP was associated with significant reductions in seven inflammatory biomarkers [64]. In addition, two components of IMMRP – physical exercise and CBT – might be associated with alterations in peripheral inflammation markers [65], [66], [67], [68], [69].
This explorative pilot study investigates whether IMMRP is associated with significant alterations in the plasma pattern of 68 cytokines and chemokines 12 months after IMMRP and whether such changes are associated with changes in clinical aspects.
2 Subjects and methods
2.1 Study design
This longitudinal study investigates chronic pain patients before IMMRP and at a 12-month follow-up.
2.2 Subjects
The subjects were referred to the Pain and Rehabilitation Centre, University Hospital, Linköping, Sweden by other healthcare services (mainly primary healthcare) for complex chronic non-malignant pain conditions. The only exclusion criterium was not being of working age (18–67 years).
The Pain and Rehabilitation Centre is associated with the Swedish Quality Registry for Pain Rehabilitation (SQRP), which is recognized by the Swedish Association of Local Authorities and Regions. All relevant clinical departments/centres within specialist care throughout Sweden deliver data to SQRP [70]. The SQRP is based on questionnaires (i.e. PROM data); a detailed description of the SQRP is reported elsewhere [71]. The patients complete the SQRP questionnaires during their first visit to the Pain and Rehabilitation Centre. During their first visit, all the patients were given a medical examination (see below). After the patients provided informed consent to participate in the study and signed a consent form, which was in accordance with the Declaration of Helsinki, they committed to answer selected questions from SQRP immediately after (post-IMMRP) as well as at a 6-month and 12-month follow-up (12-m FU). In the present explorative pilot study, we compared data from the pre-IMMRP and the 12-m FU. In addition to the questionnaires in the SQRP, three blood samples were drawn from the patients. After final recruiting, there were 25 patients (17 women and eight men) with a mean age (SD) of 47.3 (10.5).
The study was approved by the Regional Ethics Committee in Stockholm (Dnr: 2014/953-31/1).
2.3 Clinical examination
All patients were given a standard routine clinical examination performed by a MD. The examination consisted of routine neurological examination, including registrations of systolic and diastolic blood pressures and auscultation of heart and lungs. All subjects had their weight (kg) and height (m) registered and these data were used to calculate their body mass index (BMI; kg/m2). Each patient’s diagnosis was determined using International Statistical Classification of Diseases and Related Health Problems – Tenth Revision, Swedish version (ICD10-SE). The different diagnoses of the patients can be seen in Table 1.
Diagnoses (ICD-10-SE) of the 25 patients.
| Diagnose code | Denotation | Number of patients |
|---|---|---|
| M35.7 | Hypermobility syndrome | 1 |
| M53.1 | Cervicobrachial syndrome | 2 |
| M54.4 | Lumbago with sciatica | 2 |
| M54.5 | Low back pain | 2 |
| M54.6 | Pain in thoracic spine | 1 |
| M54.8 | Other dorsalgia | 1 |
| M79.1 | Myalgia | 3 |
| M79.7 | Fibromyalgia | 5 |
| R51.9 | Headache | 1 |
| R52.2A | Chronic pain, nociceptive | 3 |
| R52.2B | Chronic pain, neuropathic | 1 |
| R52.2C | Other chronic pain | 1 |
| R52.9 | Pain, unspecified | 2 |
2.4 Patient reported outcome measures (PROM)
We retrieved selected data from the Swedish Quality Registry for Pain Rehabilitation (SQRP) for the patients. The PROM data retrieved from SQRP are briefly described below.
2.4.1 Pain intensity (NRS-7days)
Data collected included average pain intensity the previous week (numeric rating scale; 0=no pain and 10=worst possible pain).
2.4.2 Hospital Anxiety and Depression Scale (HADS)
HADS measures anxiety and depression [72] and consists of an anxiety subscale (HADS-A) and a depression subscale (HADS-D). Both subscales have seven items, scoring range between 0 and 21; lower score indicates lower possibility of anxiety or depression. HADS is frequently used in clinical practice as well as in research and has good psychometric characteristics [72], [73]. HADS has the following clinical cut-offs: 0–6, no symptoms; 7–10, probably symptomatic; and ≥11, severely symptomatic [72]. Hospital Anxiety and Depression Scale (HADS) is validated in its Swedish translation [74].
2.4.3 Physical activity and inactivity
The following three indicator items, which were developed by the Swedish National Board of Health and Welfare, were used to register physical activity and inactivity.
FYS1: During a typical week, how much time do you spend a week on physical exercise that makes you breathless, such as running, exercise gymnastics, or ball sports: 0=0–30 min; 1=30–60 min; 2=60–90 min; 3=90–120 min; or 4=>120 min?
FYS2: During a typical week, how much time do you spend a week on everyday exercise such as walking, cycling, or gardening: 0=0–30 min; 1=30–60 min; 2=60–90 min; 3=90–150 min; 4=150–300 min; or 5≥300 min?
FYS3: How long do you sit for a normal day (not including sleep): 0=always, 1=13–15 h; 2=10–12 h; 3=7–9 h; 4=4–6 h, 5=1–3 h; and 6=0 h/never?
2.4.4 IMMRP
Medical assessments and decisions to offer IMMRP were performed by senior physicians, primarily from specialists in rehabilitation medicine or similar specialties, or by specialists in training under the supervision of a senior colleague. Most patients were also assessed by a psychologist, an occupational therapist, and a physiotherapist. The following inclusion criteria for IMMRP were used: (i) disabling chronic pain (on sick leave or experiencing major interference in daily life due to chronic pain); (ii) age between 18 and 67 years; (iii) no further medical investigations needed; (iv) written consent to participate and attend IMMRP; (v) and agreement not to participate in other parallel treatments. General exclusion criteria included severe psychiatric morbidity, abuse of alcohol and/or drugs, diseases that did not allow physical exercise, or presence of red flags. Red flags are clinical indicators of possible serious underlying conditions requiring further medical intervention (i.e. other than IMMRP).
The IMMRP was conducted in groups of six to nine participants for 6 weeks (at least 20 h per week of group-based activities) and was based on CBT principles and included physical exercise, occupational therapy ergonomics (including training in coping strategies), and pain education. In addition, lectures in basic pain science and pain management were offered for both patients as well as for relatives, friends, and colleagues. The program also included work-related advice and support, and individually-tailored sessions with team members were available if necessary. Individual sessions might also be required for a few weeks following the program. The patients were encouraged to take an active part in goal setting.
2.4.5 Blood samples
Blood samples were collected before the IMMRP (pre-IMMRP) and at the 12-month follow-up (12-m FU). The two blood samples were centrifuged for removal of red blood cells and the plasma fraction was transferred to new tubes, aliquoted in small portions, and saved at −86 °C.
2.4.6 Chemical analyses
Analyses of inflammatory proteins (cytokines/chemokines/growth factors) were performed directly in plasma using the multiplex immunoassay technology Meso Scale Discovery (MSD) (Rockville, MD, USA). Using the manufacturer’s protocols for MSD, we analysed up to 68 custom kit-panel inflammatory substances with the electro-chemo-luminescence method. Data were collected and analysed using MESO QUICKPLEX SQ 120 instrument equipped with DISCOVERY WORKBENCH® data analysis software (Meso Scale Diagnostics, Rockville, MD, USA). On the day of analysis, samples were thawed, blinded, and randomly mixed. The lower and upper limits of detection (LLOD-ULOD), expressed in pg/mL, were different for each substance (Supplementary Table S1).
2.5 Statistics
Statistical analyses were made using IBM SPSS (version 24.0; IBM Corporation, Armonk, NY, USA) and SIMCA-P+ (version 15.0; Sartorius Stedim Biotech, Umeå, Sweden) and p≤0.05 was used as level of significance in all analyses. Data are presented as mean±one standard deviation (±1SD) together with median. Non-parametric tests were used for comparisons within the patient group (i.e. Friedman’s Two-way Analysis of Variance by Ranks) for clinical variables and inflammatory substances over time. Spearman’s rank correlation test was used for bivariate correlation analysis. Effect sizes (ES; Cohen’s d) for within group analysis were computed using a calculator when appropriate (https://webpower.psychstat.org/models/means01/effectsize.php).
Traditional univariate statistical methods can quantify level changes of individual substances but disregard interrelationships and thereby ignore system-wide aspects. Moreover, traditional statistical methods (e.g. multiple and logistic regression) have obvious problems handling data sets with more variables than subjects (i.e. short and broad data sets). Therefore, we used advanced multivariate data analysis by projection (MVDA) using SIMCA-P+. When applying MVDA, we followed the recommendations concerning proteomics data presented by Wheelock and Wheelock [75]. Variables were mean centred and scaled for unified variance (UV-scaling). An unsupervised principal component analysis (PCA) was used to detect whether moderate or strong outliers existed. No multivariate outlier was detected. PCA displays systematic variation in the data matrix. All variables were log transformed before the statistical analyses if data were skewed. A cross validation technique was used to identify nontrivial components (p). Variables loading on the same component p were correlated, and variables with high loadings but with opposing signs were negatively correlated. Obtained components are per definition not correlated and are arranged in decreasing order with respect to explained variation. The loading plot reports the multivariate relationships between variables. A corresponding plot reporting the relationships between subjects (i.e. t-scores) can also be used (score plot) and each subject obtained a score (t) for each of the significant components.
In order to reduce the risk for false positive results when investigating possible changes in the panel of 68 inflammatory biomarkers in relatively few subjects, a PCA was made using data from pre-IMMRP and from the 12-month FU. That is, each patient had two values for each substance. If a significant PCA will be obtained the t-scores for each component and each patient are selected for the two time points. If significant differences in t-scores were found this indicated that differences in the inflammatory panel existed over time in a multivariate perspective. Only then it will be justified to identify substances associated with significant changes using traditional non-parametric tests (i.e. Friedman’s Two-way Analysis of Variance by Ranks).
Orthogonal Partial least squares (OPLS) regression was used when regressing the clinical variables using the inflammatory markers as regressors (X-variables). Regressors with regression coefficients with a jack-knifed 95% confidence interval not including 0 and the variable of importance (VIP) value exceeding 1 were considered important. The OPLS analyses were made in two steps. First, from the analysis all the proteins, we selected proteins with VIP>1.3 combined with the jack-knifed confidence intervals in the coefficients plot not including zero. Second, these proteins were used in a new regression, which is presented in the results. The tables also present p(corr) for each significant protein: the loading of each variable scaled as a correlation coefficient and thus standardising the range from −1 to +1; p(corr) is stable during iterative variable selection and comparable between models. An absolute p(corr)>0.4–0.5 is generally considered significant [75]. R2 describes the goodness of fit – the fraction of sum of squares of all the variables explained by a principal component. Q2 describes the goodness of prediction – the fraction of the total variation of the variables that can be predicted by a principal component using cross validation methods. R2 should not be considerably greater than Q2; if R2 is substantially greater than Q2 (a difference>0.3) [76], the robustness of the model is poor, implying overfitting [75]. Moreover, Analysis of Variance of Cross-Validated predictive residuals (CV-ANOVA), which is a SIMCA-P+ diagnostic tool for assessing model reliability, was also computed. CV-ANOVA provides a familiar p-value metric for the model [75].
3 Results
3.1 Clinical variables
Blood pressures, BMI, and PROM data at the two time points are shown in Table 2; no significant changes occurred over time in these variables according to omnibus testing (p-values: 0.088–0.959).
Clinical variables and PROM data before IMMRP (pre-IMMRP) and at 12-month follow-up (12-m FU).
| Time points |
Pre-IMMRP |
12-m FU |
Statistics |
||||
|---|---|---|---|---|---|---|---|
| Variables | Mean | SD | Median | Mean | SD | Median | p-Value |
| Blood pressure diastolic (mmHg) | 82.8 | 11.1 | 80.0 | 80.2 | 8.5 | 77.5 | 0.088 |
| Blood pressure systolic (mmHg) | 127.2 | 18.6 | 120.0 | 121.2 | 15.8 | 115.0 | 0.145 |
| BMI (kg/m2) | 26.9 | 3.5 | 25.2 | 26.9 | 4.0 | 24.6 | 0.627 |
| NRS-7days | 6.7 | 1.4 | 7.0 | 6.2 | 2.2 | 6.5 | 0.353 |
| HADS-A | 7.8 | 5.3 | 7.5 | 8.8 | 6.2 | 5.0 | 0.864 |
| HADS-D | 7.6 | 3.9 | 8.5 | 8.1 | 4.8 | 6.0 | 0.876 |
| FYS1 | 1.8 | 1.5 | 1.5 | 2.1 | 1.8 | 1.5 | 0.959 |
| FYS2 | 2.6 | 1.3 | 2.5 | 2.6 | 1.6 | 2.0 | 0.751 |
| FYS3 | 3.6 | 1.0 | 4.0 | 3.4 | 1.5 | 4.0 | 0.334 |
-
Mean, SD and Median are reported. Furthest to the right are the statistical comparisons between the two time points. IMMRP=interdisciplinary multimodal pain rehabilitation program; BMI=body mass index; NRS-7days=average pain intensity previous week according to a numeric rating scale; HADS=Hospital Anxiety and Depression Scale; HADS-A=the anxiety subscale of HADS; HADS-D=the depression subscale of HADS; FYS1=amount of heavy physical exercise (see Methods for details); FYS2=amount of every day exercise (see methods for details); FYS3=amount of sitting (see Methods for details).
When scrutinising the individual patterns over time (pre-IMMRP vs. 12-m FU), 11 patients reported decreased pain intensity, seven patients reported no changes, and seven reported deterioration. HADS revealed similar symptom patterns for anxiety and depression: for anxiety, seven improved, two were unchanged, and eight worsened; and for depression, seven improved, one was unchanged, and nine worsened. Both these variables had missing data for eight patients.
3.2 Inflammatory substances
In total, 60 of 68 proteins were detected in at least 50% of the samples in each group and were included in the statistical analysis; data pre-IMMRP and 12-m FU are presented in Supplementary Table S2. To investigate possible effects of IMMRP on the panel of 60 inflammatory biomarkers, a PCA was made using data from pre-IMMRP and from the 12-month FU. That is, each patient had two values for each substance. A significant PCA with three components was obtained (R2 cumulative=0.41, Q2 cumulative=0.20). From this analysis, we obtained the t-scores for each component and each patient for the two time points. These results were compared with respect to the two time points. The scores of the first and third components differed significantly between pre-IMMRP and the 12-month follow-up (t1 pre-IMMRP vs. t1 12-m FU: −0.40±4.18 vs. 0.28±3.57, p=0.042; t2 pre-IMMRP vs. t2 12-m FU: −0.16±2.76 vs. 0.21±2.40, p=0.201; t3 pre-IMMRP vs. t3 12-m FU: −0.33±2.00 vs. 0.48±1.79, p=0.019). Hence, in a multivariate perspective differences in the inflammatory panel existed over time. These results justified trying to identify substances associated with significant changes. In this explorative pilot study, we performed Wilcoxon signed rank tests to identify substances that differed between pre-IMMRP and the 12-m FU (Table 3). Twelve substances showed significant alterations (i.e. pre-IMMRP vs. 12-m FU); IL-8 showed a borderline significant alteration (p=0.054) (Table 3). All substances including IL-8 showed decreases from pre-IMMRP to 12-m FU except Eotaxin-3/CCL26, which increased significantly.
Substances with significant changes between before IMMRP (pre-IMMRP) and the 12-month follow-up (12-m FU).
| Time points |
Pre-IMMRP |
12-m FU |
Statistics |
ES |
||||
|---|---|---|---|---|---|---|---|---|
| Substances | Mean | SD | Median | Mean | SD | Median | p-Value | Cohen’s d |
| ENA-78/CXCL5 | 66.71 | 48.22 | 40.58 | 30.92 | 23.94 | 24.23 | <0.001 | 0.86 |
| IL-7 | 0.69 | 0.52 | 0.54 | 0.40 | 0.37 | 0.29 | <0.001 | 0.78 |
| TARC/CCL17 | 19.03 | 11.59 | 17.48 | 13.13 | 5.03 | 13.96 | <0.001 | 0.68 |
| Gro-α/CXCL1 | 24.31 | 10.04 | 25.40 | 17.82 | 9.2 | 14.10 | 0.001 | 0.62 |
| IL-16 | 100.37 | 40.52 | 89.68 | 90.09 | 30.98 | 79.58 | 0.016 | 0.51 |
| SDF-1α/CXCL12 | 617.51 | 239.78 | 588.94 | 552.48 | 199.21 | 484.90 | 0.028 | 0.49 |
| VEGF-A | 9.06 | 6.31 | 7.10 | 7.05 | 4.03 | 6.29 | 0.030 | 0.47 |
| IP-10/CXCL10 | 139.48 | 142.23 | 110.76 | 97.18 | 63.29 | 77.71 | 0.011 | 0.42 |
| IL-8 | 1.5 | 0.65 | 1.38 | 1.31 | 0.49 | 1.32 | 0.054 | 0.39 |
| IL-33 | 2.34 | 4.27 | 0.80 | 0.94 | 1.28 | 0.23 | 0.030 | 0.37 |
| Eotaxin-2/CCL24 | 519.19 | 406.93 | 474.74 | 485.85 | 404.74 | 389.84 | 0.035 | 0.35 |
| Eotaxin-3/CCL26 | 11.51 | 21.59 | 3.72 | 18.7 | 37.05 | 10.58 | 0.023 | −0.35 |
| TPO/MGDF | 153.93 | 57.75 | 148.66 | 148.37 | 55.77 | 138.71 | 0.021 | 0.17 |
-
Mean, SD and Median are reported. Furthest to the right statistical are the comparisons between the two time points and the within-group effect sizes (Cohen’s d). IMMRP=interdisciplinary multimodal pain rehabilitation program.
We found no significant relationships between the inflammatory substances pre-IMMRP and changes in NRS-7days, BMI, blood pressure, and FYS1-3. However, the pattern of inflammatory substances pre-IMMRP correlated with changes in HADS-A and HADS-D (Table 4), but the number of subjects was reduced (n=17) in these analyses due to missing data in HADS at 12-m FU. When these analyses were re-run using the substances with VIP>1.3, significant regressions were obtained for the changes in HADS-A (R2=0.75, Q2=0.63, CV-ANOVA p-value<0.001) and in HADS-D (R2=0.57, Q2=0.39, CV-ANOVA p-value=0.032).
OPLS regressions of changes in HAD-A and HAD-D, respectively using the pre-IMMRP values of the inflammatory substances as regressors (X-variables).
| Changes in HADS-A |
Changes in HADS-D |
||||
|---|---|---|---|---|---|
| Variables | VIP | p(corr) | Variables | VIP | p(corr) |
| IL-15 | 2.85 | 0.89 | Fractalkine/CX3CL1 | 2.38 | 0.65 |
| SDF-1α/CXCL12 | 2.47 | 0.77 | MCP-4/CCL13 | 2.17 | 0.59 |
| IL-10 | 2.38 | 0.75 | MDC/CCL22 | 1.99 | 0.54 |
| IL-4 | 2.33 | 0.73 | MIP-3β | 1.88 | 0.51 |
| MCP-4/CCL13 | 2.07 | 0.65 | SDF-1α/CXCL12 | 1.68 | 0.46 |
| MDC/CCL22 | 1.63 | 0.51 | IL-15 | 1.63 | 0.44 |
| IL-22 | 1.62 | 0.51 | IL-23 | 1.61 | −0.44 |
| IL-12p70 | 1.56 | 0.49 | IL-10 | 1.58 | 0.43 |
| TNF-β | 1.56 | 0.49 | IL-17E/IL-25 | 1.50 | −0.41 |
| IL-17F | 1.40 | −0.44 | IP-10/CXCL10 | 1.43 | 0.39 |
| MIF | 1.34 | −0.42 | IL-17F | 1.41 | −0.38 |
| TPO/MGDF | 1.29 | 0.40 | IL-2Ra | 1.31 | 0.36 |
| Fractalkine/CX3CL1 | 1.08 | 0.34 | EPO | 1.29 | −0.35 |
| CTACK/CCL27 | 1.05 | 0.33 | IL-7 | 1.28 | 0.35 |
| IFN-γ | 1.00 | 0.31 | IL-4 | 1.28 | 0.35 |
| TARC/CCL17 | 1.12 | 0.30 | |||
| MCP-3/CCL7 | 1.09 | 0.30 | |||
| R2 | 0.96 | R2 | 0.99 | ||
| Q2 | 0.37 | Q2 | 0.41 | ||
| CV-ANOVA p-Value | 0.49 | CV-ANOVA p-value | 0.89 | ||
| n | 17 | n | 17 | ||
-
Only substances with VIP>1.0 are shown. Substances in bold type (i.e. VIP≥1.30) were used in a second regression (see text). For each protein with VIP>1.0 (VIP>1 is considered significant), VIP, and p(corr), a positive sign indicates that improvements in HADS were associated with high pre-IMMRP levels for the cytokines/chemokines The three bottom rows report R2, Q2, CV-ANOVA (p-value), and number of subjects (n). HADS=Hospital Anxiety and Depression Scale; HADS-A=the anxiety subscale of HADS; HADS-D=the depression subscale of HADS.
We found no significant relationships between changes in NRS-7d, HADS-A, BMI, and the changes in the 13 substances shown in Table 3. However, a PCA including changes in the clinical variables and changes in the 13 substances resulted in one significant component, indicating positive correlations between changes in FYS3 and changes in TARC/CCL17, ENA-78/CXCL5, IL-7, and IL-16. Using Spearman’s rank correlation tests, we found the relationship between changes in FYS3 and TARC/CCL17 was significant (ρ=−0.45); that is, improvements in FYS3 were associated with decreases in TARC/CCL17. The PCA also indicated positive correlations between changes in HAD-D and Gro-α/CXCL1, IL-7, SDF-1α/CXCL12, and Eotaxin-3/CCL26, but no significant correlations were obtained in bivariate correlation analyses (i.e. Spearman’s rank correlation tests). It was not possible to significantly regress (OPLS) changes in FYS3 or HAD-D using the 13 substances in Table 3 as regressors.
4 Discussion
4.1 Major findings
This explorative pilot study has two major findings:
Participation in IMMRP was associated with significant changes in 12 substances: the majority of these were pro-inflammatory and showed decreases from pre-IMMRP to 12-m FU.
The pattern of cytokines and chemokines pre-IMMRP was associated with changes in HADS-A and HADS-D.
There were statistically significant decreases in 12 substances at the 12-m FU. The majority of these substances were pro-inflammatory and decreased over time except Eotaxin-3/CCL26, which increased. In addition, substances characterised as growth factors (TPO/MGDF) and associated with angiogenesis (VEGF-A and IP-10/CXCL10) showed significant decreases.
4.2 Other relevant studies
We are aware of only two other studies investigating if participating in IMMRP is associated with changes in inflammatory mediators. Wang et al. investigating whether one cytokine (IL-8) in serum changed as a result of a three-week in-patient IMMRP for FM patients, found a significant decrease [63]. In a recent study, Hysing et al. investigated the plasma inflammatory pattern before and 12 months after an IMMRP over 6 months (n=28) in a group of mixed severe chronic pain conditions using a panel of 92 inflammatory substances [64]. They reported a significant decrease in overall inflammatory activity and found significant alterations in seven proteins at 12-month follow-up. Although these studies are small, their findings generally agree with respect to the effects of IMMRP on inflammatory substances in blood. However, these results from more or less explicitly stated pilot studies need to be confirmed in larger and better controlled IMMRP studies. In addition, for the individual components of IMMRP there is some support for effects on such biomarkers. Hence, a SR of non-pharmacological interventions in FM concluded that exercise interventions might act as anti-inflammatory treatment especially with respect to pro-inflammatory cytokines [69]. Other studies report that components of IMMRP (e.g. physical exercise, CBT, and mindfulness) might be associated with reductions in peripheral inflammation markers [64], [65], [66], [67], [68], [69].
4.3 IL-8
In this study, the biomarkers exhibiting changes possibly associated with IMMRP partially agree with the two earlier IMMRP studies [63], [64]. Hence, in our study, IL-8 tended to decrease (p=0.054), a finding that is in agreement with Wang et al.’s study of FM patients [63]. Our study had no controls, but increased IL-8 levels were found in FM [63], [77], in patients with lumbar radicular pain of severe intensity compared to patients with low pain intensity [78], in patients with sciatica [79], and in CRPS patients [80]. However, a recent review notes a lack of consensus among the literature concerning IL-8 levels in FM patients [81]. In contrast, lower levels in sera were found in patients with lumbar disc herniation [82]. Presently, it remains unclear whether it is appropriate to decrease IL-8 generally in patients with chronic pain conditions or whether this approach is restricted to certain diagnoses. Hysing et al. investigating a group of mixed chronic pain patients, found no significant change in IL-8 associated with participation in IMMRP. However, IL-8 levels decreased in FM patients after 4 months of a pool-aquatic programming [83] and after 6 months of analgesic treatment in CRPS 1 patients [84]. Hence, most studies indicate that IL-8 can be lowered using common interventions for chronic pain.
4.4 ENA-78/CXCL5, Gro-α/CXCL1, and IL-7
In agreement with the study by Hysing et al. [64], we found that three pro-inflammatory substances (ENA-78/CXCL5, Gro-α/CXCL1, and IL-7) had decreased significantly after IMMRP.
The presence of the protein ENA78/CXCL5, a pro-inflammatory substance, together with other pro-inflammatory cytokines associated with BMI possibly indicate low grade inflammation [85]. Furthermore, increased levels were found in prostatic secretions in chronic prostatitis [86]. In the present study, this substance had the highest effect size (0.86; i.e. moderate) of the substances with significant alterations (Table 3).
Some studies report increased blood levels of IL-7 in CRPS patients, in FM patients, and in a group of severe chronic pain patients [64], [77], [80], [87]. We found a significant decrease in the pro-inflammatory IL-7 over time with an effect size of 0.78 (second highest; i.e. moderate). This result agrees with Hysing et al. who also found a significant decrease associated with IMMRP.
The significant decrease in Gro-α/CXCL1 was associated with the fourth highest effect size (0.62). Gro-α/CXCL1 is a pro-inflammatory substance. Increased levels in blood have been found in patients with lumbar radicular pain of severe intensity compared to patients with low pain intensity [78], patients with FM [77], and a mixed group of patients with severe chronic pain [64].
4.5 Other pro-inflammatory substances
Moreover, we found that other pro-inflammatory substances were altered at the 12-m FU: TARC/CCL17, IL-16, SDF-1α/CXCL12, IP-10/CXCL10, IL-33, Eotaxin-2, and Eotaxin-3/CCL26.
TARC/CCL17 is elevated in many inflammatory conditions and in synovial fluid in OA [88]. In an animal model of OA, TARC/CCL17 blockade was associated with decreased pain [88]. In the present study, this substance was associated with a moderate effect size (0.68) among the significantly altered biomarkers, but we found no associations between changes in TARC/CCL17 (or the other substances reported in Table 3) and pain intensity.
IL-16 plays a crucial role in the inflammatory process as it acts as a chemoattractant for peripheral immune cells and has been linked to various inflammatory diseases such as asthma, Crohn’s disease, and rheumatoid arthritis [89]. Elevated plasma level has been found in acute myocardial infarction [89]. IL-16 had a moderate effect size (0.51) in the present study.
Recent animal studies indicate that SDF-1α/CXCL12 mediates the transition from acute pain to tonic pain state and thus contributes to the development and maintenance of persistent pain [90]. In the peripheral and central nervous systems, both SDF-1α/CXCL12 and their receptor chemokine C-X-C motif receptor 4 (CXCR4) are expressed in various kinds of nociceptive structures. This CXCL12/CXCR4 axis possesses pronociceptive properties involving both neuronal and glial mechanisms [91]. In intact animals, a single intrathecal injection of SDF-1α/CXCL12 produced transient mechanical allodynia [91].
For IP-10/CXCL10, a pro-inflammatory substance involved in angiogenesis, we found a significant decrease. Increased IP10/CXCL10 level was found in patients with lumbar radicular pain of severe intensity [78], in FM patients [92], and in untreated early rheumatoid arthritis patients [93].
IL-33, which boosts inflammation and pain [94], signals through the ST2 receptor and promotes inflammation by activating downstream pathways culminating in the production of pro-inflammatory mediators such as IL-1β, TNF-α, and IL-6 in an NF-κB-dependent manner [95]. IL-33 appears to worsen diseases by potentiating inflammatory and nociceptive stimuli [94]. However, significantly lower serum levels in FM have been reported [46]. We are not aware of any other study of relevant chronic pain conditions reporting decreases associated with participation in IMMRP or interventions part of IMMRP.
Elevated blood levels of Eotaxin-1/CCL11 and/or Eotaxin-2/CCL24 have been found in FM patients [92], [96], [97]. Moreover, Eotaxin-1/CCL11 is increased in patients with lumbar radicular pain of severe intensity and in primary dysmenorrhea [78], [98]. In the present study, Eotaxin-2/CCL24 decreased while Eotaxin-3/CCL26 increased (Table 4). We have no explanation for the different patterns and the results must be confirmed in larger studies.
4.6 Factors associated with growth and angiogenesis
In addition, we found significant decreases for one growth factor and one factor associated with angiogenesis.
TPO/MGDF is a humoral growth factor and involved in the regulation of platelet production. Elevated TPO/MGDF levels have been reported in clinical conditions such as haematological diseases usually associated with thrombocytopenia, in critical diseases such as acute coronary syndromes and sepsis, and in smoking [99]. Infections and inflammation can trigger a rise in platelet count (i.e. reactive thrombocytosis), which has been linked to increased TPO/MGDF production [100]. Hence, the decreases in several inflammatory substances including TPO/MGDF may also explain reduction in number of platelets (not shown, data will be published elsewhere).
VEGF-A increases healing, regeneration, and revascularisation and it is increased in patients with lumbar radicular pain of severe intensity compared to patients with low pain intensity [78]. A decrease in this substance in blood was observed in patients with disc herniation after epidural steroid injection [101]. The decrease in the present study may indicate more favourable peripheral circumstances possibly due to the physical exercise component of IMMRP.
4.7 Clinical variables vs. inflammatory mediators
On the group level, we observed no significant changes in the clinical variables (pain intensity, psychological distress, and physical activity). However, some patients reported improvements and others reported no change or deteriorations. Such mixed results have been found also in considerably larger studies of IMMRP [102]. Most available evidence according to SRs suggests that IMMRP is effective (see Introduction) although these effects are small to moderate. We found significant relationships between the pattern of pro-inflammatory substances pre-IMMRP and changes in HADS-A and HADS-D at 12-m FU (Table 4). Aspects of psychological distress are important outcomes of IMMRP according to the outcome domains presented by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) [103], [104] and the Validation and Application of a Patient-relevant Core Set of Outcome Domains to assess Multimodal PAIN Therapy (VAPAIN) [105] initiatives. A drawback with the analyses of changes in the two HADS subscales was missing data for a minor but substantial part of the patients; therefore, our results need to be confirmed in larger studies. However, both pro-inflammatory (IL-15, fractalkine/CX3CL1, and SDF-1α/CXCL12) and anti-inflammatory (IL-10, IL-4, IL-22, and IL-23) substances pre-IMMRP were associated with improvements with respect to anxiety and depression.
It was not possible to multivarietly correlate the inflammatory pattern pre-IMMRP with changes in pain intensity. Wang and colleagues found that pain intensity decreased at a six-month follow-up [63]. Before IMMRP, no correlation between IL-8 and pain intensity was found, but at the six-month follow-up they found negative correlations between pain intensity and IL-8 levels. Hysing et al. did not present any correlations between inflammatory biomarkers and clinical outcomes, but their patients had overall significant improvements associated with IMMRP [64]. In future larger and better controlled studies, it will be important to investigate changes in clinically important outcomes of IMMRP in relation to the pattern and changes of inflammatory biomarkers. To gain a complete understanding and interpretation of such analyses it will be important to include control groups.
4.8 Strengths and limitations
A major strength of this study is that it adds information about possible alterations in inflammatory proteins associated with participation in IMMRP; a field with only few studies. Our results together with the results of the two earlier IMMRP studies suggest that larger and more strictly controlled studies are needed. Another strength is that this study consists of real patients referred to the Pain and Rehabilitation Centre at a university hospital and not primarily recruited for a research project. However, the diagnoses of the patients contributing to this explorative pilot study as well as the recent study by Hysing et al. [64] were a mix of nociceptive, neuropathic, and nociplastic pain conditions of patients suffering from complex chronic pain. Hence, the involved pain mechanisms may differ and studies of more homogenous groups of chronic patients are warranted. These differences make it problematic to generalise the results to a specific group or diagnosis of chronic pain conditions. Our study also had a low number of patients and a lack of balance with respect to gender/sex, which could bias the results. A strength is that we used MVDA to determine whether changes on a general level occurred in the pattern of blood biomarkers; based on this result, we performed the analyses comparing pre-IMMRP and 12-m FU data. This approach diminishes but does not eliminate the risk for false positive results. Against our results can be argued that factors other than participating in IMMRP per se such as different amount of individual treatments in addition to IMMRP, change in pharmacological treatments and smoking habits may be responsible for the significant changes in the inflammatory mediators found at the 12-m FU. Other important limitations were that we only investigated a few of the immense number of inflammatory markers, we used subjective measurements, and we did not include healthy controls (for normalised concentrations of substances).
4.9 Conclusions and Implications
Patients with chronic pain conditions participating in IMMRP for 6 weeks had significantly lower concentrations of 12 mainly pro-inflammatory substances at the 12-month follow-up. There were some indications that the pattern of biochemical biomarkers pre-IMMRP correlated with changes in psychological distress variables. It is important to note that the present study cannot impute cause and effect. These results together with the results of the two previous IMMRP studies suggest that there is a need for larger and more strictly controlled studies of IMMRP with respect to inflammatory markers in blood. Such studies need to consider responders/non-responders, additional therapies, involved pain mechanisms and diagnoses.
The present and the two previous studies taken together open up for developing biologically measurable outcomes from plasma. Such biomarkers will be an important tool for further development of IMMRP and possibly other treatments for patients with chronic pain.
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Authors’ statements
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Research funding: This study was supported by grants from the Swedish Research Council, County Council of Östergötland (Research-ALF), the Magnus Bergvall Foundation, the Åke Wiberg Foundation, and AFA Insurance. AFA Insurance, a commercial founder, is owned by Sweden’s labour market parties: The Confederation of Swedish Enterprise, the Swedish Trade Union Confederation (LO), and The Council for Negotiation and Co-operation (PTK). These parties insure employees in the private sector, municipalities, and county councils. AFA Insurance does not seek to generate a profit, which implies that no dividends are paid to shareholders. The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit for publication. The authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
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Conflict of interest: The authors report no conflicts of interest.
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Informed consent: All participants received written information about the study and gave their written consent.
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Ethical approval: The study was conducted in accordance with the Helsinki Declaration and Good Clinical Practice and approved by the Regional Ethics Committee in Stockholm (Dnr: 2014/953-31/1).
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Author contributions: Conceptualization, Björn Gerdle, Emmanuel Bäckryd, Torkel Falkenberg and Bijar Ghafouri; Data curation, Erik Lundström and Bijar Ghafouri; Formal analysis, Björn Gerdle, Erik Lundström and Bijar Ghafouri; Writing – original draft, Björn Gerdle; Writing – review & editing, Björn Gerdle, Emmanuel Bäckryd, Torkel Falkenberg, Erik Lundström and Bijar Ghafouri.
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Data availability statement: The datasets generated and/or analysed in this study are not publicly available as the Ethical Review Board has not approved the public availability of these data.
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The online version of this article offers supplementary material (https://doi.org/10.1515/sjpain-2019-0088).
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