Plasma proteins from several components of the immune system differentiate chronic widespread pain patients from healthy controls – an exploratory case-control study combining targeted and non-targeted protein identification : Medicine

Journal Logo

Research Article: Observational Study

Plasma proteins from several components of the immune system differentiate chronic widespread pain patients from healthy controls – an exploratory case-control study combining targeted and non-targeted protein identification

Gerdle, Björn MD, PhDa,*; Wåhlén, Karin PhDa; Gordh, Torsten MD, PhDb; Bäckryd, Emmanuel MD, PhDa; Carlsson, Anders Engineer Studenta; Ghafouri, Bijar Chemist PhDa

Author Information
doi: 10.1097/MD.0000000000031013

Abstract

1. Introduction

Chronic widespread pain (CWP), including fibromyalgia (FM), has a high population prevalence (5%–10%) with a female predominance.[1–3] CWP is often associated with depressive and anxiety symptoms (e.g., insomnia) as well as their consequences such as personal suffering and high socioeconomic costs.[4–6] Nociplastic pain is a new pain mechanistic descriptor (IASP definition: “Pain that arises from altered nociception despite no clear evidence of actual or threatened tissue damage causing the activation of peripheral nociceptors or evidence for disease or lesion of the somatosensory system causing the pain.” Source: https://www.iasp-pain.org/Education/Content.aspx?ItemNumber=1698#Nociplasticpain), which classifies FM (a subgroup of CWP) as a nociplastic pain condition.[7]

Clinical management, including interventions, are hampered by lack of valid biomarkers.[8,9] Omic methods are increasingly applied in pain research as emerging insights indicate that pain involves complex patterns of molecular changes.[10] The proteome of a tissue regulates biological processes and integrates the effects of genes with, for example, age, behaviors, comorbidities, drugs, and environmental factors.[10–12] In 2015, we reported significant differences in the trapezius muscle proteome between CWP (mainly FM) and controls.[13] Later, we and others have found significant differences in plasma/serum proteome between CWP/FM and healthy controls.[14–17] Proteins obtained from proteomic analysis are generally at nano and micro molar levels, whereas inflammation-related substances (e.g., cytokines and chemokines) are typically found at pico molar levels. Also, targeted omics-like methods using panels of 70 to 90 inflammation-related proteins [mainly cytokines and chemokines (Hereafter labeled Inflammatory panel.)] have been applied when investigating CWP/FM; proteins from such panels can significantly differentiate CWP/FM patients from controls.[18,19]

We recently published two studies of the same subjects aimed at differentiating CWP from control (CON) using plasma biomarkers.[14,18] In the first study, proteomic analyses (two-dimensional gel electrophoresis [2-DE]: number of proteins: 325) were applied and a highly significant differentiation was obtained with 22 significant proteins (e.g., acute phase proteins, complement factors, and coagulation factors) (R2 = 0.84, Q2 = 0.60).[14] In the second study, an inflammation panel of 92 proteins was used, and 11 proteins (e.g., 3 chemokines, 1 cytokine, and 2 growth factors) significantly differentiated the two groups (R2 = 0.58, Q2 = 0.37). Thus, proteins identified using proteomic analysis were somewhat more important than the proteins from the inflammatory panel for group differentiation. As the important proteins identified in the two studies are mainly part of the host defence/immunity system, it is reasonable to assume tight interactions between the different components of this system. However, the relative importance of the proteins from these two protein sources for group differentiation is not known. Understanding this may be important for the design of larger biomarker studies of CWP/FM and in the long perspective for management and treatment of CWP/FM.

Based on the two previous studies,[14,18] we assumed that it will be possible to obtain a significant regression differentiating the two groups of subjects using a broad pattern of plasma proteins from pico to micro molar levels. Hence, this exploratory study investigates the relative importance of proteins obtained from the two sources—i.e., a targeted inflammatory panel and proteins obtained from proteomics (untargeted analysis)–for group differentiation (CWP and CON) and whether a significant protein-protein association network exists among the important proteins. In addition, this exploratory study maps the biological processes involved according to the gene ontology (GO).

2. Subjects and Methods

2.1. Subjects

The two groups of subjects have been described in detail elsewhere.[20] All subjects received a standardized clinical examination to confirm the individual eligibility. As this project was initiated in 2009, the American College of Rheumatology (ACR) criteria from 1990 was used to classify FM/CWP.[6] None of the subjects used any anticoagulatory, opioid, or steroidal medication. Exclusion criteria were medical history record of bursitis, tendonitis, capsulitis, postoperative conditions in the neck/shoulder area, previous neck trauma, disorder of the spine, neurological disease, rheumatoid arthritis or any other systemic diseases, metabolic disease, malignancy, severe psychiatric illness, pregnancy, and difficulties understanding Swedish. A total of 19 CWP and 24 CON were initially recruited, and, as previously reported, 15 CWP patients and 23 CON subjects were included in the final cohorts[18,21]; for a flow chart see.[22]

The study was approved by the Regional Ethical Review Board in Linköping, Sweden (Dnr. M10–08, M233–09, Dnr. 2010/164–32) and followed the guidelines in the Declaration of Helsinki. All participants signed a written consent before the start of the study and after receiving verbal and written information about the objectives and procedures of the study.

2.2. Clinical variables

2.2.1. Age and body mass index.

Age (years), weight (kg), and height (m) were registered at the clinical examination. Body mass index (BMI) was calculated as weight/height2 (kg/m2). All subjects answered a brief health questionnaire and these data have been presented elsewhere[21,23] and here some of these variables (Patient Reported Outcome Measures [PROM]) are used as brief clinical descriptions of the two groups (Table 1). Descriptions of these PROM measures are given below; for details, see.[21,23]

Table 1 - Background and PROM variables (Mean and SD together with Median and Range) for CON and CWP; data have been published elsewhere.[21,23]
Group CON n = 23 CWP n = 15 Statistics
Variables Mean SD Median Range Mean SD Median Range P value
Age (yr) 41.0 10.2 42.0 29.0 49.2 8.9 50.0 31.0 .014
BMI (kg/m2) 24.0 2.8 23.2 12.4 26.0 5.0 23.8 15.6 .185
NRS-whole body 0.0 0.0 0.0 0.0 4.9 2.0 6.0 7.0 <.001
HADS 3.3 2.8 2.0 9.0 14.0 5.3 13.0 17.0 <.001
QOLS 93.1 9.7 94.0 37.0 82.5 13.1 85.0 46.0 .013
BMI = body mass index, CON = healthy control group, CWP = chronic widespread pain group, HADS = Hospital Anxiety and Depression Scale, NRS = Numeric Rating Scale, PROM = Patient Reported Outcome Measures, QOLS = Quality-of-Life Scale.

2.2.2. Pain intensity.

The subjects rated their pain intensity on the whole body using an 11-grade (0–10) Numeric Rating Scale with two endpoints: zero indicating no pain at all and 10 indicating worst possible pain.[24]

2.2.3. Psychological distress.

The Hospital Anxiety and Depression Scale measures symptoms of anxiety and depression using 14 items.[25] A total score of Hospital Anxiety and Depression Scale (score range: 0–42) was used to indicate psychological distress.[26]

2.2.4. Quality-of-Life.

The Quality-of-Life Scale is composed by 16 items measured on seven-point satisfaction scales (total score: 16–112).[27] A higher total score reflects higher satisfaction.

2.3. Protein analyses

Venous blood samples were collected in a EDTA vacutainer and centrifuged (1000 × g, 15 minutes) and plasma was collected. 2-DE in combination with mass spectrometry (MALDI-TOF MS and nLC-MS/MS) was used for the proteomic analysis as described previously.[13,14,28] Briefly, depleted plasma proteins were separated first according to pI (isoelectric focusing) in the first dimension then according to the molecular weight in the second dimension. The separated proteins were stained fluorescently by SYPRO Ruby® (Bio-Rad Laboratories, Hercules, CA) and visualized/analyzed as digitizing images using a charge-coupled device camera (VersaDoc™ Imaging system 4000 MP; Bio-Rad) in combination with a software system designed for evaluation of 2-DE and quantification of protein spots (PDQuest Advanced v. 8.0.1, Bio-Rad). The protein spots of interest were excised from the gels. After enzymatic digestion by trypsin, the samples were subjected to mass spectrometer. A database search of proteins was used to identify the obtained peptide peak list from mass spectrometer as previously described.[14,29]

A multiplex proximity extension assay (PEA) was used to analyze a panel (n = 92) of inflammation related proteins (mainly cytokines, chemokines, and growth factors; see Supplementary Digital Content text file #1, https://links.lww.com/MD/H551). This targeted PEA was conducted using Proseek® Multiplex Inflammation I (Olink Bioscience, Uppsala, Sweden); for details, see previous publication.[18]

2.4. Statistics

For comparison, background and PROM variables are reported mean ± SD together with median and range. For group comparisons of values of background and PROM variables, Student’s t test was applied using IBM SPSS (version 27.0; IBM Corporation, Route 100 Somers, New York, NY). P < .05 was considered significant in all analyses. Analysing omics data and a large number of variables in relation to number of subjects (i.e., broad, and short data tables) requires advanced statistical techniques such as Multivariate Data Analysis (MVDA).[30,31] Here, we have use the recommendations for MVDA of omics data.[30] We have investigated which proteins can be used to differentiate CWP from CON (i.e., group belonging) with Orthogonal Partial Least Squares discriminant analysis (OPLS-DA) using SIMCA-P + (version 17.0; Sartorius Stedim Biotech, Umeå, Sweden).[32] As this procedure has been described in detail elsewhere,[13,14,23,29] we only give a summary. All variables were mean centered, scaled to unit variance (UV-scaling), and log-transformed if necessary. No multivariate outliers were identified according to a principal component analysis. Variables with variable influence on projection (predictive) value (VIPpred) > 1.0 (combined with jack-knifed 95% confidence intervals in the regression coefficients plot not including zero) and with absolute p(corr) ≥ 0.40 were considered significant; p(corr) is the loading of each variable scaled as a correlation coefficient and therefore results in a standardized range from −1 to +1.[30] Number of predictive and orthogonal components are also reported. The OPLS-DA was made in two steps. In the first step, all identified proteins from 2-DE and inflammatory panel analysis (several hundred) were included in the analysis. In the second step, the proteins with VIPpred > 1.5 were used in a new OPLS-DA provided that the first analysis resulted in a significant component according to the internal rules used in SIMCA-P+.[32] When investigating the possible influences of age and BMI, we added the two variables to the final OPLS-DA regression of group membership and analyzed whether they were significant. Furthermore, an OPLS regression was made using the t score of the predictive component of the second OPLS-DA as a dependent variable and with age and BMI as independent variables. R2 describes the goodness of fit and Q2 describes goodness of prediction.[32] Cross validated analysis of variance (CV-ANOVA) with a P ≤ .05 was used to validate the obtained models.

2.5. Network analysis

The protein-protein association network of the important proteins for group differentiation was analyzed using the online database Search Tool for Retrieval of Interacting Genes/Proteins (STRING; version 11).[33] Protein accession numbers (UniProt) for the important significant proteins (i.e., proteins with VIPpred ≥ 1.5) were entered in STRING’s search engine (multiple proteins) with the following parameters: species set to Homo sapiens; maximum number of interactions for query proteins only; and interaction score set to minimum required interaction score of medium confidence (0.400). For the obtained network, PPI enrichment P value and average local clustering coefficient were reported. In the network figure, each protein is represented by a coloured node, and protein-protein interaction and association are represented by an edge visualized as a line. Higher combined confidence scores are represented by thicker lines/edges. Using Protein ANalysis THrough Evolutionary Relationships (PANTHER; version 16.0), the Cellular Component, Molecular Function, and Biological Processes according to the Gene Ontology (GO; http://geneontology.org/docs/ontology-documentation/) were listed.[34] The following settings were used in PANTHER: Organism: Homo sapiens; Analysis: complete; Test type: Fisher’s Exact; and Correction: Bonferroni correction for multiple testing.

3. Results

3.1. Clinical variables

The two groups have been presented earlier and some clinical measures are reported in Table 1.[21,23] Hence, the CON group was younger, had a higher quality of life, and had significantly lower levels of psychological distress. The CWP group had considerable pain intensities, whereas the CON group was pain free.

3.2. Regression analysis of group belonging

Using all proteins (proteomic analysis: n = 325; measurable proteins from the inflammation panel: n = 72), we obtained a significant OPLS-DA with the following characteristics: R2 = 0.996; Q2 = 0.529; and CV-ANOVA P value = .0044 (one predictive and three orthogonal components). When only the most important proteins were included (i.e., VIPpred > 1.5) (Table 2), the significant regression had the following characteristics: R2 = 0.829; Q2 = 0.644; CV-ANOVA P value = 7.0e-07 (one predictive and one orthogonal component). In total, 58 proteins (including proteoforms) had a VIPpred > 1.5.

Table 2 - OPLS-DA regression of group membership (CWP or CON). The most important plasma proteins (i.e., VIPpred > 1.5) are presented in alphabetical order. Numbers of proteoforms are reported together with mean VIPpred and mean p(corr). A positive p(corr) indicates higher levels in CWP. Characterisation is in a pain-immune-nociception context. Proteins in bold are from the panel of 92 inflammation-related proteins.
Accession number Protein Abbreviation No. of proteoforms Mean VIPpred Mean p(corr) Characterisation
Q13541 Eukaryotic translation initiation factor 4E-binding protein 1 EIF4EBP1/4E-BP1 1 1.59 0.46 Control of protein translation & sensitivity of neurons
P01009 Alpha-1-antitrypsin SERPINA1 2 1.62 0.43 Acute phase protein
P08697 Alpha-2-antiplasmin SERPINF2 2 1.60 0.43 Acute phase protein
P02765 Alpha-2-HS-glycoprotein AHSG 1 2.33 0.62 Immune response
P01023 Alpha-2-macroglobulin A2M 2 1.55 0.41 Acute phase protein
P02647 Apolipoprotein A-I APOA1 2 1.69 −0.45 Antiinflammation
O15169 AXIN1 AXIN1 1 2.05 0.58 Downregulates Wnt signaling pathway
Q14790 Caspase-8 CASP8 1 1.51 0.43 Modulate IL-1b & inflammation; Regulation of NF-κB
P78556 C-C motif chemokine 20 CCL20 1 1.94 0.53 Chemokine
P00450 Ceruloplasmin CP 10 1.85 0.49 Acute phase protein
P05160 Coagulation factor XIII B chain F13B 1 1.77 0.47 Coagulation factor
P00736 Complement C1r subcomponent C1R 7 1.90 0.51 Complement factors
P01024 Complement C3c alpha chain fragment 2 C3 1 2.42 0.64 Complement factors
P00751 Complement factor B CFB 1 1.66 0.44 Complement factors
P05156 Complement factor I CFI 1 2.42 0.64 Complement factors
P02679 Fibrinogen gamma chain FGG 3 1.93 0.51 Acute phase protein
O75636 Ficolin-3 FCN3 1 1.75 0.47 Pattern recognition molecule—activate complement
P06396 Gelsolin GSN 2 1.75 0.47 Anti-inflammatory
P00738 Haptoglobin HP 1 1.73 0.46 Acute phase protein
P02790 Hemopexin HPX 1 1.50 0.40 Acute phase protein
P01877 Ig alpha-2 chain C region IGHA2 1 1.73 −0.46 Immunoglobulin
P42702 Leukaemia inhibitory factor receptor LIFR 1 1.56 −0.39 Activates JAK/STAT and MAPK signaling pathways
P00747 Plasminogen PLG 5 1.87 0.50 Acute phase protein
P02787 Serotransferrin TF 1 1.77 −0.47 Acute phase protein
Q8IXJ6 NAD-dependent protein deacetylase sirtuin-2 SIRT2 1 1.74 0.50 Anti-inflammatory; Regulation of NF-κB
O95630 STAM-binding protein STAMPB 1 1.62 0.47 regulation of endosomal-lysosomal degradation pathway
P01135 Protransforming growth factor alpha TGFA 1 1.59 0.46 Restoring function in neurodegenerative disorders
P02766 Transthyretin TTR 1 1.79 −0.48 Acute phase protein
O43508 Tumour necrosis factor ligand superfamily member 12 TNFSF12 1 1.65 0.44 Cytokine
P02774 Vitamin D-binding protein GC 3 1.78 0.47 Immunity
AXIN1 = axin-1, CON = healthy control group, CWP = chronic widespread pain group, GC = vitamin d-binding protein, IL = interleukin, JAK/STAT = Janus kinase-signal transducer and activator of transcription pathway, MAPK = mitogen-activated protein kinase signaling pathway, NF-κB = nuclear factor kappa-light-chain-enhancer of activated b cells pathway, OPLS-DA = Orthogonal Partial Least Squares discriminant analysis, VIPpred = variable influence on projection (predictive) value, TF = serotransferrin, TGFA = protransforming growth factor alpha, TNFSF12 = tumour necrosis factor ligand superfamily member 12.

Both proteins determined from the 2-DE analysis and from the inflammation panel were important for the group differentiation although the majority belonged to the proteins from the proteome (Table 2). The four most important proteins for the group differentiation (i.e., highest absolute p(corr) and VIPpred value) were two complement factors (Complement C3c alpha chain fragment 2 and Complement factor I), alpha-2-HS-glycoprotein, and AXIN1.

Several of the important proteins had proteoforms; most proteoforms were noted for ceruloplasmin (n = 10), complement C1r subcomponent (n = 7), and plasminogen (n = 5).

Most proteins were increased in CWP (i.e., a positive p(corr)) but apoliprotein A-I, Ig alpha-2 chain C region, leukaemia inhibitory factor receptor [LIFR], serotransferrin, and transthyretin were higher in the CON (i.e., negative p(corr)) (Table 2).

3.3. Possible influences from age and BMI

When age and BMI were added as regressors to the second OPLS-DA regression of group membership, none of these two variables were significant regressors (i.e., VIPpred < 1.0). We also regressed the t score of the predictive component obtained from the second OPLS-DA using age and BMI as regressors, but the model was not significant. Hence, age and BMI did not significantly influence the important proteins differentiating the CWP from the CON.

3.4. The network and enrichment analysis together with GO analysis

The network and enrichment analysis of the important proteins differentiating CWP from CON (n = 58) (Table 2) identified a significantly enriched protein-protein interaction network (note that proteoforms cannot be included in STRING) (Fig. 1). This means that the proteins included in the analysis have more interactions among themselves than what would be expected for a random set of proteins of similar size drawn from the genome. Thus, the proteins are at least partially biologically connected as a group. When scrutinizing Figure 1, it was found that five of nine proteins from the inflammation panel did not show interactions: SIRT2, AXIN1, LIFR, EIF4EBP1 (4E-BP1), and STAMPB.

F1
Figure 1.:
Network analyses of important proteins differentiating CWP from CON. The network had the following characteristics: number of nodes: 29; number of edges: 129; average node degree: 8.9; average local clustering coefficient: 0.62; expected number of edges: 10; PPI enrichment P value: < 1.0e-16. Note that Ig alpha-2 chain C region is not included in STRING. A2M = alpha-2-macroglobulin, AHSG = alpha-2-HS-glycoprotein, APOA1 = apolipoprotein A-I, AXIN1 = axin-1, C1R = complement C1r subcomponent, C3 = complement C3, CASP8 = caspase-8, CCL20 = C-C motif chemokine 20, CFB = complement factor B, CFI = complement factor I, CP = ceruloplasmin, EIF4EBP1 = eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), F13B = coagulation factor XIII B chain, FCN3 = ficolin-3, FGG = fibrinogen gamma chain, GC = vitamin D-binding protein, GSN = gelsolin, HP = haptoglobin, HPX = hemopexin, LIFR = leukaemia inhibitory factor receptor, PLG = plasminogen, SERPINA1 = alpha-1-antitrypsin, SERPINF2 = alpha-2-antiplasmin, SIRT2 = NAD-dependent protein deacetylase sirtuin-2, STAMBP = STAM-binding protein, TF = serotransferrin, TGFA = protransforming growth factor alpha, TNFSF12 = tumour necrosis factor ligand superfamily member 12, TTR = transthyretin.

Biological Processes according to the GO analysis (PANTHER) identified in the enriched network included acute phase response, regulation of complement activation, complement activation, regulation of endopeptidase activity, fibrinolysis, and innate immune response (see Supplementary Digital Content Table 1, https://links.lww.com/MD/H552). Detailed results from the GO analyses are presented in the Supplementary Digital Content Tables 1–3, https://links.lww.com/MD/H552: https://links.lww.com/MD/H553: https://links.lww.com/MD/H554; for molecular function and cellular component see Supplementary Digital Content Tables 2–3, https://links.lww.com/MD/H553: https://links.lww.com/MD/H554.

4. Discussion

As expected, a broad pattern of mainly interacting immune- and inflammation-related plasma proteins from pico to micro molar levels differentiated CWP from CON. Proteins from the plasma proteomics were somewhat more important than proteins from the inflammation panel. The pattern of interacting proteins was neither age- nor BMI-dependent. The protein-protein interaction network was significantly enriched, and the identified biological processes included acute phase response, aspects of complement activation, and innate immune response. The broad involvement of the immune system peripherally supports suggestions that systemic low-grade inflammation is present in CWP.

Both proteins identified from the proteome and from the inflammatory panel contribute to group differentiation (CWP vs. CON). Most of the important proteins were obtained from the plasma proteome although proteins from the inflammation panel also contributed (Table 2). Our results broaden the perspective and clearly suggest that proteins from pico to micro molar levels must be considered to obtain a more complete understanding of the plasma alterations in CWP/FM. Due to the intimate relationships between the immune system and nociception, the present protein alterations differentiating CWP from CON might indicate a more continuous input (immune and nociceptive) to the CNS maintaining central alterations and clinical presentations.

Currently, there are four plasma/serum proteome studies of CWP/FM versus controls.[14–17] These studies indicate that peripheral immune proteins are involved in differentiating CWP/FM from controls. Several studies have investigated peripheral (blood) cytokines and chemokines and their roles in FM, but recent systematic reviews (SRs) have not found clear evidence (except for IL-6) as they disagree about which cytokines were altered in FM.[35,36] One SR reported significantly higher levels of IL-6, IL-17A, and IL-4 in FM.[35] Another SR reported higher IL-6, IL-8, TNF-α, and chemokine eotaxin levels, and decreased IL-10 in FM.[36] The present study did not identify any of the cytokines/chemokines from the SRs as important among proteins with VIPpred > 1.5 (Table 2) or among those with VIPpred > 1.0 (data not shown). Most of the studies included in the SRs investigated relatively few proteins. Using broad plasma inflammatory panels (mainly cytokines and chemokines) in combination with MVDA, two studies report that CWP/FM can be differentiated from controls[18,19]; several of the significantly differentiating proteins were similar in the two studies (i.e., STAMPB, SIRT2, AXIN1, and 4e-BP1). However, this does not exclude that the cytokines/chemokines identified in the SRs are important in the initiation of chronic pain or for certain pain variables. Moreover, recent studies of CWP/FM have reported altered plasma levels of other substances (e.g., glutamate, lactate, NGF, BDNF, and lipid mediators),[20,37–39] findings that also advocate for a broad approach to understanding the peripheral contribution in CWP/FM.[40]

The highly significant enriched protein-protein interaction network (Fig. 1) indicated biologically meaningful interactions. The identified biological processes according to the GO analysis agreed with earlier proteomic studies (e.g., acute phase response, regulation of complement activation, complement activation, and innate immune response).[14–17] These are parts of the host defence/immunity system and support interpretations of low-grade inflammation in CWP/FM. Scrutinising the important proteins in Table 2 in more detail in an immune-nociception-pain context revealed that several of the proteins were acute phase proteins (n = 10), complement related factors (n = 5), chemokine/cytokine (n = 2), other inflammation-related proteins (n = 3), coagulation factor (n = 1), and immunoglobulin factor (n = 1). Furthermore, three proteins that affect pathways—e.g., the Wingless-related integration site pathway (AXIN1), Janus kinase-signal transducer and activator of transcription pathway (JAK/STAT) (LIFR), mitogen-activated protein kinase signaling pathway (MAPK) (LIFR), and the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway (SIRT2)–were also present. These pathways are involved in the induction of cytokines and the inflammatory effects of cytokines.[41–45] JAK-STAT regulates durations and magnitude of pro- and anti-inflammatory cytokines and is involved in homeostasis and inflammation.[46,47]

Several proteins identified by 2-DE had proteoforms—i.e., the acute phase proteins ceruloplasmin (n = 10) and plasminogen (n = 5), and one complement factor (Complement C1r subcomponent; n = 7). Thus, future large studies should characterize in more detail the types, functions, and patterns of the proteoforms and how they are altered in CWP. Because the network analysis in STRING does not handle proteoforms, other methods are required to consider these alterations.

The aetiopathogenesis including pathophysiological alterations, diagnostic, and classification criteria of CWP/FM is still a matter of debate.[48] In the literature, CWP/FM has been associated with molecular, morphological, and functional (e.g., central sensitization) alterations both in the central nervous system (CNS) and in the periphery (e.g., systemic low-grade inflammation, autoreactive IgG, nociceptor impairment, and muscle mitochondrial disturbance).[40,49–62] The circulatory system is vital for the immune-system-based host defence mechanisms and tissue homeostasis.[63,64] Nociceptors have neuro-immune and neural-vascular interactions,[65] and bidirectional signaling pathways exist between immune and nervous systems at peripheral and central levels.[66–71] Furthermore, the CNS exerts top-down effects on peripheral inflammatory activity via neuroendocrine and autonomic mechanisms.[67,72] Information on peripheral inflammatory activity is transmitted via peripheral nociceptors and humoral and neuronal pathways to the CNS, leading to, for example, sickness behavior, decreased endogenous pain inhibition, and neuroinflammation.[66,71,73–75] Altered blood-brain barrier permeability due to influences from cytokines and complement factors facilitates this communication to the CNS,[76–78] and in FM and CWP signs of CNS neuroinflammation have been found.[19,29] These and the present findings indicate complicated interactions between peripheral and central alterations and processes in CWP/FM.

Generalised hyperalgesia for pressure is a criteria for FM according to the 1990 ACR criteria and are, as in the present patient cohort, generally found in CWP.[23] The generalized hyperalgesia is often attributed to central sensitization and is associated with activation of certain parts of the CNS.[79] However, it has also been proposed that neuroinflammation in the peripheral and central nervous system and/or dys-regulation in the immune-nociception signaling might be associated with or drives of central sensitization, which drives CWP/FM.[77,80] Other authors suggest that different peripheral stimuli/stressors including pro-inflammatory cytokines can lead to long-term potentiation, “wind up”, and/or central sensitization.[81,82]

4.1. Strength and limitations

Limitations of this study were the small sample size, the cross-sectional design, and including only women. Larger studies of both sexes with repeated measures are important for validation. Traditional statistical analyses are challenging with few subjects and many probably highly intercorrelated proteins. In these situations, MVDA is the backbone as pointed out elsewhere and applied in this study.[31] The necessary removal procedure of large abundant proteins (i.e., albumin and IgG) could have removed other low abundant proteins that could be of interest. Research using peripheral proteomics in CWP/FM is in an initial exploratory stage and larger studies are required to understand what changes are specific to the pain condition. Immunological and inflammatory biological processes have also been reported for depression, obesity, and aging.[83–85]

5. Conclusions

This study contributes to the emerging knowledge that plasma proteins can differentiate CWP, including FM from healthy controls. Proteins involved in the host defence/immune system are generally identified and give support to the interpretation that low-grade inflammation is present in CWP/FM. Focusing on traditional cytokines/chemokines might not be sufficient to grasp the peripheral biological processes that maintain CWP/FM. Our results indicate that other parts of the immune and inflammation systems are also important and that a combination of different methods need to be used to identify proteins involved in these processes. Unconditional exploratory studies with a wide range of proteins and other substances investigating interactions and biological processes are necessary. Within such efforts it will be important to determine the primary biological and molecular processes in CWP/FM. When such an understanding has been achieved, it will be fruitful to focus on key proteins for identification of clinical plasma biomarkers.

Acknowledgments

This study was supported by a grant to Björn Gerdle from the Swedish Research Council (2018-02470). The sponsor 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.

Author contributions

All authors have read and agreed to the published version of the manuscript.

Conceptualization: Björn Gerdle, Bijar Ghafouri, Emmanuel Bäckryd, Karin Wåhlén, Torsten Gordh.

Data curation: Björn Gerdle, Bijar Ghafouri, Karin Wåhlén, Anders Carlsson.

Formal analysis: Björn Gerdle, Karin Wåhlén.

Funding acquisition: Björn Gerdle, Bijar Ghafouri.

Investigation: Björn Gerdle, Bijar Ghafouri.

Methodology: Björn Gerdle, Bijar Ghafouri, Emmanuel Bäckryd, Karin Wåhlén, Torsten Gordh, Anders Carlsson.

Project administration: Björn Gerdle.

Resources: Bijar Ghafouri, Torsten Gordh.

Supervision: Björn Gerdle, Bijar Ghafouri.

Visualization: Björn Gerdle.

Writing – original draft preparation: Björn Gerdle.

Writing – review & editing: Björn Gerdle, Bijar Ghafouri, Karin Wåhlén, Emmanuel Bäckryd, Anders Carlsson, Torsten Gordh.

    References

    [1]. Mansfield KE, Sim J, Jordan JL, et al. A systematic review and meta-analysis of the prevalence of chronic widespread pain in the general population. Pain. 2016;157:55–64.
    [2]. Cimmino MA, Ferrone C, Cutolo M. Epidemiology of chronic musculoskeletal pain. Best Pract Res Clin Rheumatol. 2011;25:173–83.
    [3]. Bergman S, Herrstrom P, Hogstrom K, et al. Chronic musculoskeletal pain, prevalence rates, and sociodemographic associations in a Swedish population study. J Rheumatol. 2001;28:1369–77.
    [4]. Perez de Heredia-Torres M, Huertas-Hoyas E, Maximo-Bocanegra N, et al. Cognitive performance in women with fibromyalgia: a case-control study. Aust Occup Ther J. 2016;63:329–37.
    [5]. Aparicio VA, Ortega FB, Carbonell-Baeza A, et al. Fibromyalgia’s key symptoms in normal-weight, overweight, and obese female patients. Pain Manag Nurs. 2013;14:268–76.
    [6]. Wolfe F, Smythe HA, Yunus MB, et al. The American College of Rheumatology 1990 criteria for the classification of fibromyalgia. Report of the multicenter criteria committee. Arthritis Rheum. 1990;33:160–72.
    [7]. Kosek E, Cohen M, Baron R, et al. Do we need a third mechanistic descriptor for chronic pain states? Pain. 2016;157:1382–6.
    [8]. Tracey I, Woolf CJ, Andrews NA. Composite pain biomarker signatures for objective assessment and effective treatment. Neuron. 2019;101:783–800.
    [9]. Galvez-Sanchez CM, Reyes Del Paso GA. Diagnostic criteria for fibromyalgia: critical review and future perspectives. J Clin Med. 2020;9:1219.
    [10]. Gomez-Varela D, Barry AM, Schmidt M. Proteome-based systems biology in chronic pain. J Proteomics. 2019;190:1–11.
    [11]. Niederberger E, Geisslinger G. Proteomics in neuropathic pain research. Anesthesiology. 2008;108:314–23.
    [12]. Emilsson V, Ilkov M, Lamb JR, et al. Co-regulatory networks of human serum proteins link genetics to disease. Science. 2018;361:769–73.
    [13]. Olausson P, Gerdle B, Ghafouri N, et al. Protein alterations in women with chronic widespread pain – an explorative proteomic study of the trapezius muscle. Sci Rep. 2015;5:11894.
    [14]. Wåhlén K, Olausson P, Carlsson A, et al. Systemic alterations in plasma proteins from women with chronic widespread pain compared to healthy controls: a proteomic study. J Pain Res. 2017;10:797–809.
    [15]. Han CL, Sheng YC, Wang SY, et al. Serum proteome profiles revealed dysregulated proteins and mechanisms associated with fibromyalgia syndrome in women. Sci Rep. 2020;10:12347.
    [16]. Wåhlén K, Ernberg M, Kosek E, et al. Significant correlation between plasma proteome profile and pain intensity, sensitivity, and psychological distress in women with fibromyalgia. Sci Rep. 2020;10:12508.
    [17]. Ramirez-Tejero JA, Martinez-Lara E, Rus A, et al. Insight into the biological pathways underlying fibromyalgia by a proteomic approach. J Proteomics. 2018;186:47–55.
    [18]. Gerdle B, Ghafouri B, Ghafouri N, et al. Signs of ongoing inflammation in female patients with chronic widespread pain: a multivariate, explorative, cross-sectional study of blood samples. Medicine. 2017;96:e6130.
    [19]. Backryd E, Tanum L, Lind AL, et al. Evidence of both systemic inflammation and neuroinflammation in fibromyalgia patients, as assessed by a multiplex protein panel applied to the cerebrospinal fluid and to plasma. J Pain Res. 2017;10:515–25.
    [20]. Gerdle B, Larsson B, Forsberg F, et al. Chronic widespread pain: increased glutamate and lactate concentrations in the trapezius muscle and plasma. Clin J Pain. 2014;30:409–20.
    [21]. Wåhlén K, Ghafouri B, Ghafouri N, et al. Plasma protein pattern correlates with pain intensity and psychological distress in women with chronic widespread pain. Front Psychol. 2018;9:2400.
    [22]. Gerdle B, Wahlen K, Gordh T, et al. Thermal pain thresholds are significantly associated with plasma proteins of the immune system in chronic widespread pain-an exploratory pilot study using multivariate and network analyses. J Clin Med. 2021;10:3652.
    [23]. Gerdle B, Wåhlén K, Ghafouri B. Plasma protein patterns are strongly correlated with pressure pain thresholds in women with chronic widespread pain and in healthy controls-an exploratory case-control study. Medicine. 2020;99:e20497e20497.
    [24]. Ferreira-Valente MA, Pais-Ribeiro JL, Jensen MP. Validity of four pain intensity rating scales. Pain. 2011;152:2399–404.
    [25]. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67:361–70.
    [26]. LoMartire R, Äng B, Gerdle B, et al. Psychometric properties of SF-36, EQ-5D and HADS in patients with chronic pain. Pain. 2020;161:83–95.
    [27]. Burckhardt CS, Anderson KL. The Quality of Life Scale (QOLS): reliability, validity, and utilization. Health Qual Life Outcomes. 2003;1:60.
    [28]. Gorg A, Drews O, Luck C, et al. 2-DE with IPGs. Electrophoresis. 2009;30(Suppl 1):S122–32.
    [29]. Olausson P, Ghafouri B, Bäckryd E, et al. Clear differences in cerebrospinal fluid proteome between women with chronic widespread pain and healthy women - a multivariate explorative cross-sectional study. J Pain Res. 2017;10:575–90.
    [30]. Wheelock AM, Wheelock CE. Trials and tribulations of ‘omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine. Mol Biosyst. 2013;9:2589–96.
    [31]. Gerdle B, Ghafouri B. Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses. Expert Rev Proteomics. 2020;17:483–505.
    [32]. Eriksson L, Byrne T, Johansson E, et al. Multi- and Megavariate Data Analysis: Basic Principles and Applications. 3rd ed. Malmö, Sweden: MKS Umetrics AB, 2013.
    [33]. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.
    [34]. Mi H, Ebert D, Muruganujan A, et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 2021;49:D394–403.
    [35]. Andres-Rodriguez L, Borras X, Feliu-Soler A, et al. Peripheral immune aberrations in fibromyalgia: a systematic review, meta-analysis and meta-regression. Brain Behav Immun. 2020;87:881–9.
    [36]. O’Mahony LF, Srivastava A, Mehta P, et al. Is fibromyalgia associated with a unique cytokine profile? A systematic review and meta-analysis. Rheumatology (Oxford). 2021;60:2602–14.
    [37]. Jablochkova A, Backryd E, Kosek E, et al. Unaltered low nerve growth factor and high brain-derived neurotrophic factor levels in plasma from patients with fibromyalgia after a 15-week progressive resistance exercise. J Rehabil Med. 2019;51:779–87.
    [38]. Stensson N, Ghafouri N, Ernberg M, et al. The relationship of endocannabinoidome lipid mediators with pain and psychological stress in women with fibromyalgia: a case-control study. J Pain. 2018;19:1318–28.
    [39]. Stensson N, Ghafouri B, Gerdle B, et al. Alterations of anti-inflammatory lipids in plasma from women with chronic widespread pain – a case control study. Lipids Health Dis. 2017;16:112.
    [40]. Coskun Benlidayi I. Role of inflammation in the pathogenesis and treatment of fibromyalgia. Rheumatol Int. 2019;39:781–91.
    [41]. Shu Q, Chen L, He WJ, et al. A review of inflammatory signaling pathway regulated by acupuncture. World J Acupunct-Mox. 2016;26:63–9.
    [42]. Zhang Y, Huang Z, Liu S, et al. WNT signaling underlies the pathogenesis of neuropathic pain in rodents. J Clin Invest. 2013;123:2268–86.
    [43]. Liu F, Zhang YY, Song N, et al. GABAB receptor activation attenuates inflammatory orofacial pain by modulating interleukin-1beta in satellite glial cells: role of NF-kappaB and MAPK signaling pathways. Brain Res Bull. 2019;149:240–50.
    [44]. Dihm K, Ek M, Roth B, et al. Plasma AXIN1 expression exhibit negative correlations with inflammatory biomarkers and is associated with gastrointestinal symptoms in endometriosis. Biomed Rep. 2020;12:211–21.
    [45]. Sun J, Nan G. The Mitogen-Activated Protein Kinase (MAPK) signaling pathway as a discovery target in stroke. J Mol Neurosci. 2016;59:90–8.
    [46]. Busch-Dienstfertig M, Gonzalez-Rodriguez S. IL-4, JAK-STAT signaling, and pain. JAKSTAT. 2013;2:e27638.
    [47]. Moresi V, Adamo S, Berghella L. The JAK/STAT pathway in skeletal muscle pathophysiology. Front Physiol. 2019;10:500.
    [48]. Sarzi-Puttini P, Giorgi V, Atzeni F, et al. Fibromyalgia position paper. Clin Exp Rheumatol. 2021;39(Suppl 130):186–93.
    [49]. Sluka KA, Clauw DJ. Neurobiology of fibromyalgia and chronic widespread pain. Neuroscience. 2016;338:114–29.
    [50]. Schrepf A, Harper DE, Harte SE, et al. Endogenous opioidergic dysregulation of pain in fibromyalgia: a PET and fMRI study. Pain. 2016;157:2217–25.
    [51]. Üçeyler N, Sommer C. Small nerve fiber pathology. Häuser W, Perrot S, eds. In: Fibromylagia Syndrome and Widespread Pain – From Construction to Relevant Recognition. Philadelphia, PA: Wolters Kluwer, 2018:204–14.
    [52]. Gerdle B, Larsson B. Muscle. Häuser W, Perrot S, eds. In: Fibromyalgia Syndrome and Widespread Pain – From Construction to Relevant Recognition. Philadelphia, PA: Wolters Kluwer, 2018:215–31.
    [53]. Albrecht DS, Forsberg A, Sandström A, et al. Brain glial activation in fibromyalgia – a multi-site positron emission tomography investigation. Brain Behav Immun. 2019;75:72–83.
    [54]. Jensen KB, Kosek E, Petzke F, et al. Evidence of dysfunctional pain inhibition in Fibromyalgia reflected in rACC during provoked pain. Pain. 2009;144:95–100.
    [55]. Littlejohn G, Guymer E. Neurogenic inflammation in fibromyalgia. Semin Immunopathol. 2018;40:291–300.
    [56]. van Ettinger-Veenstra H, Lundberg P, Alfoldi P, et al. Chronic widespread pain patients show disrupted cortical connectivity in default mode and salience networks, modulated by pain sensitivity. J Pain Res. 2019;12:1743–55.
    [57]. Goubert D, Danneels L, Graven-Nielsen T, et al. Differences in pain processing between patients with chronic low back pain, recurrent low back pain, and fibromyalgia. Pain Physician. 2017;20:307–18.
    [58]. Evdokimov D, Frank J, Klitsch A, et al. Reduction of skin innervation is associated with a severe fibromyalgia phenotype. Ann Neurol. 2019;86:504–16.
    [59]. Fasolino A, Di Stefano G, Leone C, et al. Small-fibre pathology has no impact on somatosensory system function in patients with fibromyalgia. Pain. 2020;161:2385–93.
    [60]. Sawaddiruk P, Paiboonworachat S, Chattipakorn N, et al. Alterations of brain activity in fibromyalgia patients. J Clin Neurosci. 2017;38:13–22.
    [61]. Goebel A, Krock E, Gentry C, et al. Passive transfer of fibromyalgia symptoms from patients to mice. J Clin Invest. 2021;131:e144201.
    [62]. Jung YH, Kim H, Lee D, et al. Dysfunctional energy metabolisms in fibromyalgia compared with healthy subjects. Mol Pain. 2021;17:17448069211012833.
    [63]. Yurkovich JT, Hood L. Blood is a window into health and disease. Clin Chem. 2019;65:1204–6.
    [64]. Bruderer R, Muntel J, Muller S, et al. Analysis of 1508 plasma samples by capillary-flow data-independent acquisition profiles proteomics of weight loss and maintenance. Mol Cell Proteomics. 2019;18:1242–54.
    [65]. Baral P, Udit S, Chiu IM. Pain and immunity: implications for host defence. Nat Rev Immunol. 2019;19:433–47.
    [66]. Karshikoff B, Tadros M, Mackey S, et al. Neuroimmune modulation of pain across the developmental spectrum. Curr Opin Behav Sci. 2019;28:85–92.
    [67]. Kraynak TE, Marsland AL, Wager TD, et al. Functional neuroanatomy of peripheral inflammatory physiology: a meta-analysis of human neuroimaging studies. Neurosci Biobehav Rev. 2018;94:76–92.
    [68]. Iwata M, Ota KT, Duman RS. The inflammasome: pathways linking psychological stress, depression, and systemic illnesses. Brain Behav Immun. 2013;31:105–14.
    [69]. Verma V, Sheikh Z, Ahmed AS. Nociception and role of immune system in pain. Acta Neurol Belg. 2015;115:213–20.
    [70]. Pavlov VA, Tracey KJ. Neural circuitry and immunity. Immunol Res. 2015;63:38–57.
    [71]. Eisenberger NI, Moieni M, Inagaki TK, et al. In sickness and in health: the co-regulation of inflammation and social behavior. Neuropsychopharmacology. 2017;42:242–53.
    [72]. Vichaya EG, Dantzer R. Inflammation-induced motivational changes: perspective gained by evaluating positive and negative valence systems. Curr Opin Behav Sci. 2018;22:90–5.
    [73]. Karshikoff B, Jensen KB, Kosek E, et al. Why sickness hurts: a central mechanism for pain induced by peripheral inflammation. Brain Behav Immun. 2016;57:38–46.
    [74]. Lampa J, Westman M, Kadetoff D, et al. Peripheral inflammatory disease associated with centrally activated IL-1 system in humans and mice. Proc Natl Acad Sci USA. 2012;109:12728–33.
    [75]. Kempuraj D, Thangavel R, Selvakumar GP, et al. Brain and peripheral atypical inflammatory mediators potentiate neuroinflammation and neurodegeneration. Front Cell Neurosci. 2017;11:216.
    [76]. Palada V, Ahmed AS, Finn A, et al. Characterization of neuroinflammation and periphery-to-CNS inflammatory cross-talk in patients with disc herniation and degenerative disc disease. Brain Behav Immun. 2019;75:60–71.
    [77]. Ji RR, Nackley A, Huh Y, et al. Neuroinflammation and central sensitization in chronic and widespread pain. Anesthesiology. 2018;129:343–66.
    [78]. Heurich M, Focking M, Mongan D, et al. Dysregulation of complement and coagulation pathways: emerging mechanisms in the development of psychosis. Mol Psychiatry. 2021;online:1–14.
    [79]. Pujol J, Macia D, Garcia-Fontanals A, et al. The contribution of sensory system functional connectivity reduction to clinical pain in fibromyalgia. Pain. 2014;155:1492–503.
    [80]. Lacagnina M, Heijnen C, Watkins L, et al. Autoimmune regulation of chronic pain. Pain Rep. 2021;6:e905.
    [81]. Eller-Smith OC, Nicol AL, Christianson JA. Potential mechanisms underlying centralized pain and emerging therapeutic interventions. Front Cell Neurosci. 2018;12:35.
    [82]. Goubert D, Meeus M, Willems T, et al. The association between back muscle characteristics and pressure pain sensitivity in low back pain patients. Scand J Pain. 2018;18:281–93.
    [83]. Silva-Costa LC, Carlson PT, Guest PC, et al. Proteomic markers for depression. Adv Exp Med Biol. 2019;1118:191–206.
    [84]. Santos-Lozano A, Valenzuela PL, Llavero F, et al. Successful aging: insights from proteome analyses of healthy centenarians. Aging (Albany NY). 2020;12:3502–15.
    [85]. Cominetti O, Nunez Galindo A, Corthesy J, et al. Obesity shows preserved plasma proteome in large independent clinical cohorts. Sci Rep. 2018;8:16981.
    Keywords:

    2-DE; biomarker; chemokine; cytokine; fibromyalgia; immune; inflammation; proteomics; widespread pain

    Supplemental Digital Content

    Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.