The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

Mika Jönsson, Björn Gerdle, Bijar Ghafouri, Emmanuel Bäckryd, Mika Jönsson, Björn Gerdle, Bijar Ghafouri, Emmanuel Bäckryd

Abstract

Background: Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls.

Methods: The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant.

Results: We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1).

Conclusions: Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

Keywords: Biofluids; Biomarker; Cytokines; Inflammation; Neuroinflammation.

Conflict of interest statement

The authors report no conflicts of interest.

Figures

Fig. 1
Fig. 1
Volcano plot of cytokines/chemokines according to the OPLS-DA model. The x-axis shows p(corr) for each cytokine, where a negative p(corr) indicates higher levels in patients while a positive p(corr) indicates the opposite. The y-axis shows the variable importance of projection (VIP), which signifies the importance of each variable for the model. Cut-offs of p(corr) over 0.5 and under − 0.5, and VIP ≥ 1, were used and are illustrated by dotted lines. The 17 significant inflammatory proteins are highlighted within circles
Fig. 2
Fig. 2
Box plots showing variations in cytokine concentration between patients and healthy controls for 5 inflammatory substances of significant value according to OPLS-DA. Median values are represented by horizontal lines, and the boxes represent the interquartile range. Minimum and maximum values are represented by the ends of the whiskers. MIP-1α (p = 0.017), IL-6 (p = 0.005), IL-1β (p = 0,043), IL-1RA (p = 0.008), and TSLP (p = 0.009)
Fig. 3
Fig. 3
Score plot of OPLS regression model of pain intensity (VASPI). VASPI ranged from 40–94 and patients were dichotomized into moderate and high pain intensity (VASPI 40–76 respective 77–94). The model was significant according to CV-ANOVA (p = 0.041). Each dot represents a patient and the size of the dot indicates the importance for the model

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