Topical review – salivary biomarkers in chronic muscle pain

Hajer Jasim

doi:10.1515/sjpain-2022-0112

Article Publicly Available

Topical review – salivary biomarkers in chronic muscle pain

Hajer Jasim

Published/Copyright: October 14, 2022

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From the journal Scandinavian Journal of Pain Volume 23 Issue 1

Abstract

Background and aims

Muscle related temporomandibular disorders (myogenous TMD), one of the most common orofacial pain conditions, is characterized by facial pain and often accompanied by jaw movement limitations. Although the underlying biological mechanisms are still unclear, a cluster of proteins and peptides is assumed to be involved in the pathophysiology. These proteins and peptides may be measured in a simple non-invasive saliva sample. This work investigated whether saliva can be used to sample algogenic substances that can serve as molecular biomarkers for TMD myalgia.

Methods

Saliva and blood samples were collected from healthy individuals (n=69) and patients diagnosed with TMD myalgia (n=39) according to the Diagnostic Criteria for TMD. Unstimulated and stimulated whole, parotid, and sublingual saliva were analysed. The protein profiles were investigated using two-dimensional gel electrophoresis followed by identification with liquid chromatography tandem mass spectrometry. Levels of nerve growth factor (NGF), calcitonin gene-related peptide (CGRP), and brain derived neuro-tropic factor (BDNF) were determined using western blotting based technology and multiplex electro-chemiluminescence assay panel. Glutamate, serotonin, and substance p (SP) were determined using commercially available methods.

Results

Different saliva collection approaches resulted in significant differences in the protein profile as well as in the expression of NGF, BDNF, CGRP, SP, and glutamate. Stimulated whole saliva showed least variability in protein concentration (35%) and was correlated to plasma levels of glutamate. Unlike SP and glutamate, NGF and BDNF expressed a rhythmic variation in salivary expression with higher levels in the morning (p<0.05). Patients with a diagnosis of TMD myalgia had significantly higher levels of salivary glutamate but lower salivary NGF and BDNF compared to controls; in addition, the lower NGF and BDNF levels correlated to psychological dysfunction. The quantitative proteomics data revealed 20 proteins that were significantly altered in patients compared to controls. The identified proteins are involved in metabolic processes, immune response, and stress response. Dissimilarities in protein profile and clinical variables were observed between TMD myalgia and myofascial pain.

Conclusions

The work highlights the importance of consistency in saliva collection approaches, including the timing of the collection. It displayed significant changes in pain specific mediators and protein profile in TMD myalgia and furthermore dissimilarities between subclasses indicating different pathophysiology. After extensive validation, potential salivary biomarkers can be combined with clinical features to better understand and diagnose TMD myalgia.

Keywords: biomarkers; proteomics; saliva; TMD

Introduction

Chronic masticatory muscle pain, i.e., temporomandibular disorder (TMD) myalgia, affects approximately 10% of the adult population, and is three times more frequent in women [1, 2]. The disorders are characterized by clinical features involving muscle and/or joint pain, joint noises, and limited jaw movements with alteration in the mandibular movement pattern [1, 3]. TMD causes a great deal of suffering in the community and is a widespread problem in clinical practices.

The aetiopathogenesis behind TMD myalgia remains unknown, but there is evidence for factors that predispose, initiate, and perpetuate the pain. These factors are biological, behavioural, and/or psychosocial [4], [5], [6]. Since the nociceptive mechanisms that underlie TMD pain are still not fully understood, the clinician must rely on subjective measures such as patients’ anamnesis, questionnaires, and semi-objective findings such as muscle palpation or assessment or pressure pain threshold (PPT). According to the well-established diagnostic criteria for TMD (DC/TMD), there are three sub diagnoses of myalgia which differ only regarding the presence of pain spread upon palpation, but the pathogenesis underlying these diagnoses may not be the same. As pain is a subjective experience, semi-objective methods have limited sensitivity and correlate weakly with subjective pain scores [7]. Consequently, objective and sensitive tools are needed, a situation that has led to a growing interest in molecular biomarkers.

A clinically valuable biomarkers must be easy to measure and correlate to pain ratings. An ideal biomarker should be measurable in samples that are easy and non-invasive to collect and handle. Saliva is an outstanding body fluid containing a complex mixture of proteins, peptides and other substance that may yield information about the pathophysiology behind TMD-myalgia and can be used to identify new biomarkers for the disorder [8]. Systemic analysis of proteins expressed in saliva (proteomics) [9], [10], [11] in patients with TMD-myalgia represents a new potential field of research, as the proteomic techniques constantly improves [12, 13]. In widespread myalgia, there are two studies that have investigated the salivary proteome. The authors applied gel-based proteomics to saliva samples from patients with fibromyalgia and reported altered protein expression between patients and controls [14, 15]. Other studies have suggested the involvement of serotonin, glutamate, NGF, SP, and BDNF in different chronic pain conditions [16], [17], [18], [19], [20], [21], [22].

However, like other body fluids, saliva is not a homogenous and a stable body fluid, as it is constantly changing. For example, the composition of saliva is affected by sampling methodology, circadian rhythms, environment, age, gender, oral hygiene, physical activity, medications, psychological status, and general health [23], [24], [25], [26], [27], [28], [29]. This characteristic of saliva limits its ability to be used as a diagnostic medium is the inter-and intra-individual variability, making the comparison between studies and patients challenging. For saliva-based diagnostics and techniques to be useful, there is a need for proper evaluation and standardization. Most studies using saliva as a diagnostic or prognostic medium do not provide proper description of participant preparation, time of sampling, sampling procedure, or the subsequent management of the sample [30, 31]. Since many studies also apply different collection approaches and sometimes do not describe their methods adequately, it is difficult to compare findings across studies.

This topical review aims to further explore saliva as a diagnostic fluid in TMD-myalgia by discussing the results of five clinical studies evaluating different saliva collection techniques and differences in salivary proteome and specific neuropeptides between TMD patients and healthy controls.

Methods

Participants

Sixty-nine healthy participants (47 women and 22 men), and thirty-nine patients (32 women and 7 men) with a diagnosis of myalgia or myofascial pain with or without referral according to the DC/TMD [32] were included in all studies.

Clinical examination and questionnaires

Participants in all studies underwent a general clinical dental examination and were evaluated by the Swedish version of the DC/TMD axis I and II [32]. The following instruments included in the DC/TMD axis II questionnaire were used to assess symptoms of depression, somatic symptoms, anxiety, psychological stress, jaw function, oral health, sleep disturbance, and pain catastrophizing: the Patient Health Questionnaire (PHQ-9 and PHQ-15), the Generalized Anxiety Disorder scale (GAD-7), the Perceived Stress Scale-10 (PSS-10), the Jaw Functional Limitation Scale (JFLS), the Oral Health Impact Profile (OHIP), the Insomnia Severity Index (ISI), and the Pain Catastrophizing scale (PCS).

Subjective and semi-objective pain measures

Pain rating

In all studies, the participants were asked to assess their current pain intensity in the orofacial region on a numeric rating scale (NRS). The scale ranges from 0 to 10, where 0 indicates ‘no pain’ and 10 indicates ‘worst possible pain’. Graded Chronic Pain Scale (GCPS) was used to assess pain intensity and pain-related disability. The characteristic pain intensity (CPI) was also assessed (NRS) with the first three question of the GCPS [32].

Pressure pain threshold

The PPT was recorded assessed by an electronic pressure algometer (Somedic Sales AB, Hörby, Sweden) at the most prominent point of the masseter muscle, and over a reference point on the tip of the index finger on the same side. The PPT was then recorded three times at each location. For analyses, the average threshold of the three recordings was used.

Sample collection

Saliva

Whole and/or glandular saliva was collected in all five studies as described by Jasim et al. 2016 [13]. Prior to saliva collection, participants were instructed to rinse their mouth with water to remove debris and moisturize the oral mucosa. In each study, samples were collected during the same circumstances and in the same order. A protease inhibitor cocktail (v/v 1:500 Sigma Aldrich, Saint Louis, MO, USA) was added to all saliva samples. Samples were then centrifuged to remove debris and the supernatant was fractionated into tubes and frozen at −70 °C until analyses.

Plasma

Venous blood samples were collected from all participants in connection with the saliva samples. The samples were collected from the cubital vein into 8.5-mL EDTA tubes. The samples were mixed gently and centrifuged within half an hour. The plasma was stored as aliquots at −70 °C until analysis.

Chemical analyses

Comparative proteomic analysis was performed with two-dimensional gel electrophoresis followed by identification with liquid chromatography tandem mass spectrometry.

BDNF, CGRP, and NGF in were first analysed with a capillary isoelectric focusing (IEF) immunoassay to detect isoforms. Then BDNF and NGF concentrations were analysed using multiplex electrochemiluminescence assay panel. Commercially-available enzyme kits were used to quantify the levels of SP and serotonin. Glutamate was analysed using a colorimetric assay, and 5-HT and SP were analysed by commercially available ELISA.

Statistics

The Shapiro-Wilks test was used to test for normality. For continuous variables with normal distribution, independent t-test was used to study differences between two independent groups or repeated measures analysis of variance (ANOVA) for repeated observations with Bonferroni as post-hoc test. Only substances that were detected in more than half of the samples were included in the statistical analysis. For categorical variables or variables that were non-normal distributed, the Mann-Whitney U-test was applied to study differences between two groups or Friedman’s ANOVA for repeated observations. When significant, post-hoc analysis with Wilcoxon matched pair-test was applied with Bonferroni correction.

The Pearson’s correlation test was used to test for significant correlations for normally distributed data. Otherwise, correlations between variables were tested for statistical significance using the Spearman correlation test adjusted for multiple comparisons according to Bonferroni.

Descriptive data are presented as mean and standard deviation (SD) or median and interquartile range (IQR). For all analyses, the significance level was set at p<0.05. Statistical analyses were performed using Statistica version 13 (StatSoft, Tulsa, OK, USA).

Principal component analysis (PCA) and Orthogonal Partial least squares discriminant analysis (OPLS-DA) were applied to identify multivariate correlations between the proteins and group membership, using SIMCA-P+ v.15.0 (UMETRICS, Umeå, Sweden) as described earlier [33] and in accordance with Wheelock and Wheelock [34]. To validate the model, obtained cross validated analysis of variance (CV-ANOVA) was used. The OPLS-DA model was considered of significant importance if the CV-ANOVA had a p-value<0.05.

Results

Proteomic profile of different saliva collection methods

The protein concentration, number of specific proteins, and protein pattern showed great differences between the sampling methods. The inter subject coefficient of variation (CV) of the protein concentration varied between 35 and 92% between the six collection methods in the study by Jasim et al. 2016. Least inter subject variability was observed for stimulated whole saliva (CV 35%), whereas unstimulated parotid saliva expressed the highest variability (CV 92%) followed by unstimulated whole saliva (CV 62%). The authors found no significant differences in total protein concentration between the sampling methods [13].

The authors detected between 94 and 464 protein spots in each gel. The smallest number of protein spots were detected in saliva originated from the parotid gland, while sublingual and stimulated whole saliva had the highest number of specific protein spots. Differences in the protein pattern were typically detected in the area for isoelectric point between 3 and 5 and molecular weight between 10 and 20 kDa. There were also differences in the typical salivary proteins such as salivary Alpha Amylase (SAA), Cystain N, Cystain S, and prolactin-inducible protein between collection methods. SAA expression was similar among all methods, while Cystain N, Cystain S, and prolactin-inducible protein varied considerably.

Statistical analysis of the comparative proteomic data in the study showed that extracellular proteins involved in response to stimulus could distinguished the different saliva sampling methods [13].

Pain biomarkers between different saliva collection methods

Jasim and co-authors found variations of NGF, CGRP, and BDNF in saliva. NGF and BDNF were detected in five different isoforms, while CGRP showed eight different isoforms in saliva [12]. The isoform pattern showed significant variations in expression between the different collection methods. In addition, when the authors analysed the total expression of NGF, BDNF, CGRP, SP, and glutamate, significant variations between the different saliva collection approaches could be observed as described in Figure 1.

Figure 1:

Salivary and plasma leves of

(A) nerve growth factor (NGF), (B) calcitonin gene related peptide (CGRP), (C) brain derived neurotrophic factor (BDNF), (D) glutamate, and (E) substance p (SP) expression in 20 healthy individuals by Jasim et al. 2018 [11]. The authors observed large discrepancies between different saliva collection methods. * indicates significant differences, p<0.05.

The study showed NGF levels in unstimulated whole saliva (1,313 ± 860) and sublingual saliva (966 ± 609) had lower expression compared to the other collection approaches. All stimulated samples showed significantly higher expression of NGF compared to the unstimulated samples. A similar tendency (i.e., elevated levels in stimulated samples compared to unstimulated samples) was also shown in the study for CGRP. However, post-hoc analysis was only significant for stimulated sublingual saliva. NGF could be detected in plasma and showed significantly higher expression compared to the saliva [12].

BDNF could only be detected adequately in unstimulated sublingual and stimulated parotid saliva in the study. The expression was significantly higher in stimulated parotid saliva compared to unstimulated sublingual saliva and plasma.

Similar to NGF, CGRP, and BDNF, the study showed large variation in glutamate and SP between the different saliva collection methods. Additional post-hoc analysis revealed significantly higher levels of glutamate in stimulated whole saliva compared to all the other saliva types. The glutamate level in stimulated whole saliva (34.2 ± 26.1 μg/L) were similar to the plasma level (39.4 ± 26.1 μg/L) and moderately correlated according to the authors (rs=0.56) [12].

The study showed SP was significantly more concentrated in sublingually derived saliva compared to saliva high in parotid content. Plasma SP was significantly higher compared to all evaluated saliva collection methods as illustrated in Figure 1 [12].

Daily variation of pain biomarkers

To study the variations of NGF, BDNF, glutamate, and SP across the day, unstimulated and stimulated whole saliva were by Jasim et al. repeatedly collected from early morning to late evening. Plasma samples were also collected simultaneously in connection with the first and last saliva sample [35].

NGF and BDNF expression in unstimulated and stimulated whole saliva showed in the study significant differences across the day. Further post-hoc analysis could confirm that NGF and BDNF were significantly higher in the morning sample and the expression decreased during the day (Figure 2A, B). Plasma NGF showed the opposite relation, with significantly lower expression in the morning sample compared to the evening sample. However, glutamate and SP concentration did not express any significant changes throughout the day [35] (Figure 2C, D).

Figure 2:

Salivary and plasma levels of

(A) Nerve growth factor (NGF), (B) brain derived neurotrophic factor (BDNF), (C) glutamate, and (D) substance p (SP) expression in ten healthy individuals throughout the day by Jasim et al. 2020 [35].

Proteomic profile of TMD myalgia

Statistical analysis of the comparative proteomics data by Jasim et al. revealed that 20 proteins (VIP>1.5) were at least two-fold higher or lower expressed in patients compared to controls [36]. Among these proteins, the authors identified twelve with significantly higher levels, whereas the remaining eight were significantly decreased in TMD myalgia compared to controls (Table 1). These identified proteins were involved in metabolic processes (n=11), immune response (n=6), and response to stress (n=7). The authors found no correlations between the significantly altered proteins (Figure 2) and any of the clinical parameters in TMD myalgia or subclasses.

Table 1:

Identified salivary proteins that were altered in patients with temporomandibular disorder myalgia compared to healthy controls according to Jasim et al. 2020 [36]. Proteins with a variable of importance (VIP) above 1.5 in the orthogonal partial least squares discriminant analysis model are shown. The p-Value is according to the Mann-Whitney data analysis. Arrows ↑ and ↓ indicate up and down regulated proteins in patients compared to controls.

Spot no	Protein	UniProt id	VIP	p-Value	Pat vs. Con
211	Immunoglobulin J chain	P01591	2.02608	0.005	↓
9,502	Phosphoglycerate kinase 1	P00558	1.95887	0.056	↑
7,202	Glyceraldehyde-3-phosphate dehydrogenase	P04406	1.94448	0.04	↑
2,102	Fatty acid-binding protein	Q01469	1.82302	0.042	↓
6,202	Immunoglobulin kappa light chain	P0DOX7	1.79123	0.04	↑
4,801	Alpha-amylase 1/Alpha-amylase 2B	P04745/P19961	1.78309	0.213	↑
2,501	Alpha-amylase 1/Alpha-amylase 2B	P04745/P19961	1.75617	0.007	↓
9,205	Cysteine-rich secretory protein 3	P54108	1.7448	0.053	↑
1,602	Zinc-alpha-2-glycoprotein	P25311	1.73792	0.026	↑
9,404	Chitinase-3-like protein 2	Q15782	1.71259	0.033	↑
5,401	Alpha-amylase 1/Alpha-amylase 2B	P04745/P19961	1.68841	0.06	↑
5,501	Alpha-amylase 1/Alpha-amylase 2B	P04745/P19961	1.67398	0.027	↑
209	Interleukin-1 receptor antagonist protein	P18510	1.6386	0.025	↑
3,601	Alpha-amylase 1/Alpha-amylase 2B	P04745/P19961	1.63012	0.168	↓
8,001	Protein S100-A8	P05109	1.62158	0.004	↓
1,202	Albumin (n terminal fragment)	P02768	1.57152	0.285	↑
212	Immunoglobulin J chain	P01591	1.5693	0.009	↓
5,603	Alpha-amylase 1/Alpha-amylase 2B	P04745/P19961	1.52998	0.172	↑
12	Thioredoxin	P10599	1.52274	0.176	↓
210	Immunoglobulin J chain	P01591	1.51922	0.028	↓

The alerted proteins were analysed in the study together with the clinical parameters to identify any differences between patients diagnosed with myalgia and patients diagnosed with myofascial pain. The authors found a significant OPLS-model where the enzyme phosphoglycerate kinase 1 was the most important protein (VIP>2) for separation between the two subclasses (Table 2) [36].

Table 2:

Differences between patients diagnosed with myalgia (n=10) and patients diagnosed with myofascial pain with/without referral (n=10) presented by Jasim et al. 2020 [36]. OPLS-model characteristics: R2=0.7, Q2=0.4, CV-ANOVA=0.01. Variables with variable of importance (VIP) above 1.0 in the orthogonal partial least squares discriminant analysis model are shown by the authors. The p-Value is according to the Mann-Whitney data analysis.

Variable	Myalgia	Myofascial pain	VIP	p-Value
Phosphoglycerate kinase 1	1,282 ± 519	323 ± 441	2.09004	0.001
PPT masseter muscle, kPa	227 ± 59	141 ± 31	1.94463	0.001
Level of physical activity^a	≥3 times/week	1-2 times/week	1.63919	0.039
PHQ-9 SCORE (0–36)	4 (6)	8 (8)	1.57094	0.121
Alpha-amylase 1/Alpha-amylase 2B	2,927 ± 1,885	1,102 ± 1,624	1.5689	0.017
Current pain intensity (NRS)	3.5 (3)	6 (1)	1.51112	0.023
CPI	53 (20)	73 (17)	1.47441	0.023
GCPS (Grade 0-IV)	2 (2)	2.5 (1)	1.43058	0.131
Alpha-amylase 1/Alpha-amylase 2B	1,785 ± 1,498	845 ± 827	1.38901	0.140
Chitinase-3-like protein 2	1,679 ± 1,177	675 ± 636	1.26495	0.026
Glyceraldehyde-3-phosphate dehydrogenase	5,862 ± 4,225	2,568 ± 4,822	1.25951	0.011
PHQ-15 score (0–30)	8 (11)	12 (5)	1.21037	0.273
PPT reference, kPa	419 ± 151	353 ± 103	1.21008	0.450
ISI score	9 (13)	12 (14)	1.18766	0.488
Headache duration (years)	3.0 ± 3.9	7.7 ± 4.4	1.11962	0.037
PSS score (0–40)	13 (11)	18.5 (7)	1.07296	0.121

PPT, pressure pain threshold; PHQ, the patient health questionnaire; NRS, numeric rating scale; CPI, characteristic pain intensity; GCPS, graded chronic pain scale; ISI, insomnia severity index; PSS, perceived stress scale. ^aMedian level of physical activity/week.

Pain biomarkers in TMD myalgia

Another study showed significantly different levels of salivary NGF, BDNF, and glutamate in patients with a diagnosis of TMD myalgia compared to pain-free healthy controls. Serotonin and SP did however not show any significant differences between patients and controls (Figure 3A–D) [37].

Figure 3:

Salivary and plasma levels in 39 patients diagnosed with temporomandibular disorder (TMD) myalgia according to the diagnostic criteria for TMD and 39 healthy pain-free controls (CTR) described by Jasim et al. 2020 [37].

(A) Nerve growth factor (NGF), (B) brain derived neurotrophic factor (BDNF), (C) substance p (SP), and (D) glutamate. According to the study patients expressed significantly lower levels of salivary NGF in comparison to controls (p=0.032). Plasma NGF was not statistically significant between groups (p=0.618). Salivary BDNF was lower in patients than in controls (p=0.028), while plasma BDNF was higher in patients compared controls (p=0.022). The authors found no significant differences for SP. Salivary (p=0.026) and plasma (p=0.043) levels of glutamate were significantly higher in the patients.

Patients in the study expressed significantly lower levels of salivary NGF and BDNF compared to pain-free controls. A similar pattern with lower levels of NGF in patients compared to controls was also found in plasma, but the difference was not statistically significant (Figure 3A). Plasma BDNF showed significant higher levels in patients compared to controls (Figure 3B).

The authors also showed no significant correlations between salivary NGF or BDNF and psychological variables in TMD myalgia. There was a reverse correlation between NGF and somatic symptoms (r_s=−0.462; n=78; p<0.001) in the study cohort. Among the healthy controls, a moderate correlation could be observed between salivary BDNF and perceived stress (r_s=−0.608; n=38; p<0.001), anxiety (r_s=−0.605; n=38; p<0.0005), and somatic symptoms (r_s=−0.593; n=38; p<0.001).

Salivary and plasma levels of glutamate in the study showed significant differences between patients and controls with the patient group expressing higher levels of glutamate both in saliva and in plasma compared to controls (Figure 3D). There were no signs of correlations between glutamate and psychological variables or pain measures in any group [37].

Discussion

In summary, Jasim and colleagues showed significant differences in the protein profile signature and the relative amount of specific pain-related molecules between different saliva collection approaches [12, 13]. These results support previous studies in saliva [38], [39], [40]; however, there are substantial differences. That is, up to six saliva collection methods were investigated and factors known to affect saliva composition were carefully considered by Jasim and colleagues. In addition, using new sensitive methods, for the first time several isoforms for NGF, BDNF, and CGRP were observed. Furthermore, plasma glutamate was shown to reflect the concentration in simulated whole saliva [12]. Another relevant observation that emerged from the review was the daily variation in salivary NGF and BDNF [35], a finding that resembled previous studies of plasma [41], [42], [43], [44]. These findings have led to the conclusion that independent of the method chosen several conditions should be standardized. The differences between saliva collection approaches for specific molecules as well as for the protein profile made it clear that the collection method is a key factor for successful detection of peptides and proteins.

What kind of saliva collection protocol is recommended in pain research? Whole saliva is a mixture of the saliva from the major and minor salivary glands as well as other fluids present in the oral cavity [24]. Consequently, gland-specific saliva should be used for gland-specific pathologies, and whole saliva should be used for local and systemic diseases. A drawback against the collection of whole saliva is the contamination of exogenous compounds such as microbiota, nasal secretions, blood contamination, and food debris. However, a majority of saliva research studies uses whole instead of glandular saliva [24]. The findings in this review suggest that the collection of gland-specific saliva is somehow invasive, requires special device, time-consuming, and results in small volumes. As in other studies, gland-specific saliva showed greater variability and less protein spots [39, 40, 45]. Based on findings by Jasim and co-authors and the literature, whole saliva shows compelling advantages. However, the study of whole saliva can be done using different collection procedures. Collection of unstimulated whole saliva by passive drooling seems to be the most frequently used and is regarded by researchers as a gold standard [14, 15, 24, 30, 43, 46], [47], [48], [49], [50], [51]. However, in clinical practice, this method is subject to some concerns. It is difficult to maintain the individual steady for several minutes without affecting and stimulating the saliva secretion, which most probably is the reason for the great inter-individual variability. However, unstimulated whole saliva is preferable for substances such as SP, whose concentration and therefore detection is considerably affected by the flow rate. Given the simplicity of the method, high volume, and low variability in stimulation, stimulated whole saliva were used to study proteins and peptides in chronic pain. To improve reproducibly, the sample should be collected in the morning two to 3 h after awaking to reduce the influence of the circadian rhythm, and the participants should rinse their mouth with water prior to collection.

This first study of the saliva proteome in TMD myalgia reveals that proteins related to metabolism, stress, and immunity were altered in patients [36] and supports some of the pathological aspects discussed in the literature [1, 4, 6, 7, 52]. The authors combination of 2DE analysis with LC-MS/MS allows for an explorative approach, not focusing on predetermined proteins, to understand the mechanisms and discover novel biomarkers. Using this technique, significant differences between myogenic TMD diagnoses in biological as well as clinical parameters were observed by the authors, suggesting distinctive pathological mechanisms. The results revealed that patients diagnosed with TMD myofascial pain expressed decreased levels in the glycolytic enzymes PGK-1 and GAPDH and the digestive enzyme SAA compared to patients with myalgia. These findings suggest that pathological mechanism associated to oxidative and psychological stress are more pronounced in TMD myalgia or may indicate a depletion in myofascial pain following prolonged or excessive secretion as described for cortisol under conditions of chronic stress [53]. Interestingly, patients with myofascial pain in the study reported higher pain intensity, depressive and somatic symptoms, perceived stress, and co-morbid headache [37]. In addition, these patients were described to have a significantly lower pressure pain threshold of the masseter muscle than patients with myalgia. This finding in combination with the known presence of pain spreading in myofascial pain correspond to central sensitization, whereas changes in the properties of neurons in the CNS leads to increased pain hypersensitivity [54]. Thus, patients with myofascial pain seem to have an altered nociceptive function that is unlike what patients with myalgia experience. The results from the review support that myofascial pain should be regarded as nociplastic pain condition and treated as such. The term nociplastic pain was recently introduced by the International Association for the Study of Pain Taxonomy to describe pain states that arises from altered nociception even though no clear evidence exists of tissue damage causing the activation of nociceptors or evidence for disease or lesion to the CNS triggering the chronic pain [55, 56].

When investigating specific pain-related proteins in saliva, patients surprisingly showed lower levels of NGF and BDNF, a finding that also correlated to psychological dysfunction [37]. These findings are supported by meta-analysis showing that circulatory NGF and BDNF are indicative biomarkers for depressive disorders [57, 58]. One may speculate that salivary NGF and BDNF mirror psychological maladjustment usually associated with TMD myalgia. Although patients with myofascial pain in the study reported higher pain intensity and scored higher in psychological malfunction, there were no statistically significant differences between subclasses [37].

Finally, the Jasim and colleagues showed increased glutamate in patients, and these findings were in line with other studies showing that glutamate levels positively correlate with perceived pain sensitivity [59], [60], [61], [62], [63]. Increased glutamate levels contribute to the nociceptive process by lowering neural threshold and/or increasing the pain response creating central sensitization [7, 59]. This could explain the increased salivary and plasma levels in TMD myalgia compared to pain-free controls. Elevated glutamate levels have also been reported in other pain conditions such as migraine, headache, and chronic widespread pain; consequently, glutamate is suggested as a potential biomarker for pain [47, 59, 60, 64, 65]. However, Jasim and colleagues are the first to support stimulated whole saliva as an alternative diagnostic medium to measure glutamate and to show that glutamate in saliva, as with interstitial fluid, is increased in TMD myalgia [7, 60, 63].

Conclusions

The greatest challenges for saliva diagnostics it to positively translating the identified biomarkers and results from the laboratory bench to clinical practice. Candidate biomarkers need to be verified and validated on a larger study group with appropriate clinical classification system. Proteome analysis provided significant insight, but 2-DE shows only a certain part of the proteome. Therefore, there is a need for complement analysis of proteins and peptides not detectable by this technique. Furthermore, there is a need to validate potential biomarkers and their clinical value in larger patient cohorts. We may be able to combine these potential biomarkers with other clinical features to better understand and diagnose TMD myalgia as well as subclasses and evaluate therapeutic outcomes.

Corresponding author: Hajer Jasim, Eastman Institutet, Folktandvården Stockholms Län AB, SE 113 24 Stockholm, Sweden; and Division of Oral Diagnostics & Rehabilitation, Department of Dental Medicine, Karolinska Institutet and Scandinavian Center for Orofacial Neuroscience (SCON), Section for Orofacial Pain and Jaw function, Huddinge, Sweden, BOX 4064, SE 141 04, Huddinge, Sweden, Phone: 468 524 880 42, E-mail: hajer.jasim@ki.se

Funding source: Stockholm County Council (SOF project) and the Swedish Rheumatism Association

Research funding: This research was funded by the Stockholm County Council (SOF project) and the Swedish Rheumatism Association.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.

References

1. Fernandez-de-las-Penas, C, Svensson, P. Myofascial temporomandibular disorder. Curr Rheumatol Rev 2016;12:40–54. https://doi.org/10.2174/1573397112666151231110947.Search in Google Scholar PubMed

2. Slade, GD, Ohrbach, R, Greenspan, JD, Fillingim, RB, Bair, E, Sanders, AE, et al.. Painful temporomandibular disorder: decade of discovery from OPPERA studies. J Dent Res 2016;95:1084–92. https://doi.org/10.1177/0022034516653743.Search in Google Scholar PubMed PubMed Central

3. Roda, RP, Fernandez, JMD, Bazan, SH, Soriano, YJ, Margaix, M, Sarrion, G. A review of temporomandibular joint disease (TMJD). Part II: clinical and radiological semiology. Morbidity processes. Med Oral Patol Oral Cirugía Bucal 2008;13:E102–9.Search in Google Scholar

4. List, T, Jensen, RH. Temporomandibular disorders: old ideas and new concepts. Cephalalgia 2017;37:692–704. https://doi.org/10.1177/0333102416686302.Search in Google Scholar PubMed

5. Cairns, BE. Pathophysiology of TMD pain-basic mechanisms and their implications for pharmacotherapy. J Oral Rehabil 2010;37:391–410. https://doi.org/10.1111/j.1365-2842.2010.02074.x.Search in Google Scholar PubMed

6. Gil-Martínez, A, Paris-Alemany, A, López-de-Uralde-Villanueva, I, Touche, RL. Management of pain in patients with temporomandibular disorder (TMD): challenges and solutions. J Pain Res 2018;11:571–87. https://doi.org/10.2147/jpr.s127950.Search in Google Scholar

7. Ernberg, M. Masticatory muscle pain biomarkers. In: Goulet, J, Velly, A, editors. Orofacial pain biomarkers. Berlin, Heidelberg: Springer; 2017:79–93 pp.10.1007/978-3-662-53994-1_6Search in Google Scholar

8. Loo, JA, Yan, W, Ramachandran, P, Wong, DT. Comparative human salivary and plasma proteomes. J Dent Res 2010;89:1016–23. https://doi.org/10.1177/0022034510380414.Search in Google Scholar PubMed PubMed Central

9. Yoshizawa, JM, Schafer, CA, Schafer, JJ, Farrell, JJ, Paster, BJ, Wong, DT. Salivary biomarkers: toward future clinical and diagnostic utilities. Clin Microbiol Rev 2013;26:781–91. https://doi.org/10.1128/cmr.00021-13.Search in Google Scholar

10. Chandramouli, K, Qian, PY. Proteomics: challenges, techniques and possibilities to overcome biological sample complexity. Hum Genom Proteonomics 2009;2009. https://doi.org/10.4061/2009/239204.Search in Google Scholar PubMed PubMed Central

11. Geyer, PE, Kulak, NA, Pichler, G, Holdt, LM, Teupser, D, Mann, M. Plasma proteome profiling to assess human health and disease. Cell Systems 2016;2:185–95. https://doi.org/10.1016/j.cels.2016.02.015.Search in Google Scholar PubMed

12. Jasim, H, Carlsson, A, Hedenberg-Magnusson, B, Ghafouri, B, Ernberg, M. Saliva as a medium to detect and measure biomarkers related to pain. Sci Rep 2018;8:3220. https://doi.org/10.1038/s41598-018-21131-4.Search in Google Scholar PubMed PubMed Central

13. Jasim, H, Olausson, P, Hedenberg-Magnusson, B, Ernberg, M, Ghafouri, B. The proteomic profile of whole and glandular saliva in healthy pain-free subjects. Sci Rep 2016;6:39073. https://doi.org/10.1038/srep39073.Search in Google Scholar PubMed PubMed Central

14. Bazzichi, L, Ciregia, F, Giusti, L, Baldini, C, Giannaccini, G, Giacomelli, C, et al.. Detection of potential markers of primary fibromyalgia syndrome in human saliva. Proteonomics Clin Appl 2009;3:1296–304. https://doi.org/10.1002/prca.200900076.Search in Google Scholar PubMed

15. Ciregia, F, Giacomelli, C, Giusti, L, Boldrini, C, Piga, I, Pepe, P, et al.. Putative salivary biomarkers useful to differentiate patients with fibromyalgia. J Proteonomics 2019;190:44–54. https://doi.org/10.1016/j.jprot.2018.04.012.Search in Google Scholar PubMed

16. Nijs, J, Meeus, M, Versijpt, J, Moens, M, Bos, I, Knaepen, K, et al.. Brain-derived neurotrophic factor as a driving force behind neuroplasticity in neuropathic and central sensitization pain: a new therapeutic target? Expert Opin Ther Targets 2015;19:565–76. https://doi.org/10.1517/14728222.2014.994506.Search in Google Scholar PubMed

17. Fischer, M, Wille, G, Klien, S, Shanib, H, Holle, D, Gaul, C, et al.. Brain-derived neurotrophic factor in primary headaches. J Headache Pain 2012;13:469–75. https://doi.org/10.1007/s10194-012-0454-5.Search in Google Scholar PubMed PubMed Central

18. Simao, AP, Mendonca, VA, de Oliveira Almeida, TM, Santos, SA, Gomes, WF, Coimbra, CC, et al.. Involvement of BDNF in knee osteoarthritis: the relationship with inflammation and clinical parameters. Rheumatol Int 2014;34:1153–7. https://doi.org/10.1007/s00296-013-2943-5.Search in Google Scholar PubMed

19. Komatsu, K, Hasegawa, H, Honda, T, Yabashi, A, Kawasaki, T. Nerve growth factor in saliva stimulated by mastication. Oral Sci Int 2008;5:78–84. https://doi.org/10.1016/s1348-8643(08)80011-0.Search in Google Scholar

20. Parris, WC, Kambam, JR, Naukam, RJ, Sastry, BVR. Immunoreactive substance P is decreased in saliva of patients with chronic back pain syndromes. Anesth Analg 1990;70:63–7. https://doi.org/10.1213/00000539-199001000-00010.Search in Google Scholar PubMed

21. Jablochkova, A, Backryd, E, Kosek, E, Mannerkorpi, K, Ernberg, M, Gerdle, B, 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. https://doi.org/10.2340/16501977-2593.Search in Google Scholar PubMed

22. Romariz, JAB, Nonnemacher, C, Abreu, M, Segabinazi, JD, Bandeira, JS, Beltran, G, et al.. The fear of pain questionnaire: psychometric properties of a Brazilian version for adolescents and its relationship with brain-derived neurotrophic factor (BDNF). J Pain Res 2019;12:2487–502. https://doi.org/10.2147/jpr.s199120.Search in Google Scholar PubMed PubMed Central

23. de Almeida Pdel, V, Gregio, AM, Machado, MA, de Lima, AA, Azevedo, LR. Saliva composition and functions: a comprehensive review. J Contemp Dent Pract 2008;9:72–80. https://doi.org/10.5005/jcdp-9-3-72.Search in Google Scholar

24. Capelo-Martínez, JL. Emerging sample treatments in proteomics. Switzerland: Springer; 2019:227 p.10.1007/978-3-030-12298-0Search in Google Scholar

25. Prodan, A, Brand, HS, Ligtenberg, AJ, Imangaliyev, S, Tsivtsivadze, E, van der Weijden, F, et al.. Interindividual variation, correlations, and sex-related differences in the salivary biochemistry of young healthy adults. Eur J Oral Sci 2015;123:149–57. https://doi.org/10.1111/eos.12182.Search in Google Scholar PubMed

26. Li-Hui, W, Chuan-Quan, L, Long, Y, Ru-Liu, L, Long-Hui, C, Wei-Wen, C. Gender differences in the saliva of young healthy subjects before and after citric acid stimulation. Clin Chim Acta 2016;460:142–5. https://doi.org/10.1016/j.cca.2016.06.040.Search in Google Scholar PubMed

27. Proctor, GB. The physiology of salivary secretion. Periodontology 2016;70:11–25. https://doi.org/10.1111/prd.12116.Search in Google Scholar PubMed

28. Izawa, S, Miki, K, Liu, X, Ogawa, N. The diurnal patterns of salivary interleukin-6 and C-reactive protein in healthy young adults. Brain Behav Immun 2013;27:38–41. https://doi.org/10.1016/j.bbi.2012.07.001.Search in Google Scholar PubMed

29. Murr, A, Pink, C, Hammer, E, Michalik, S, Dhople, VM, Holtfreter, B, et al.. Cross-Sectional association of salivary proteins with age, sex, body mass index, smoking, and education. J Proteome Res 2017;16:2273–81. https://doi.org/10.1021/acs.jproteome.7b00133.Search in Google Scholar PubMed

30. Kawas, SA, Rahim, ZH, Ferguson, DB. Potential uses of human salivary protein and peptide analysis in the diagnosis of disease. Arch Oral Biol 2012;57:1–9. https://doi.org/10.1016/j.archoralbio.2011.06.013.Search in Google Scholar PubMed

31. Fischer, HP, Eich, W, Russell, IJ. A possible role for saliva as a diagnostic fluid in patients with chronic pain. Semin Arthritis Rheum 1998;27:348–59. https://doi.org/10.1016/s0049-0172(98)80014-0.Search in Google Scholar PubMed

32. Schiffman, E, Ohrbach, R, Truelove, E, Look, J, Anderson, G, Goulet, JP, et al.. Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications: recommendations of the international RDC/TMD consortium network* and orofacial pain special interest groupdagger. J Oral Facial Pain Headache 2014;28:6–27. https://doi.org/10.11607/jop.1151.Search in Google Scholar PubMed PubMed Central

33. Olausson, P, Ghafouri, B, Backryd, E, Gerdle, B. 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. https://doi.org/10.2147/jpr.s125667.Search in Google Scholar

34. 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. https://doi.org/10.1039/c3mb70194h.Search in Google Scholar PubMed

35. Jasim, H, Ghafouri, B, Carlsson, A, Hedenberg-Magnusson, B, Ernberg, M. Daytime changes of salivary biomarkers involved in pain. J Oral Rehabil 2020;47:843–50. https://doi.org/10.1111/joor.12977.Search in Google Scholar PubMed

36. Jasim, H, Ernberg, M, Carlsson, A, Gerdle, B, Ghafouri, B. Protein signature in saliva of temporomandibular disorders myalgia. Int J Mol Sci 2020;21:2569. https://doi.org/10.3390/ijms21072569.Search in Google Scholar PubMed PubMed Central

37. Jasim, H, Ghafouri, B, Gerdle, B, Hedenberg-Magnusson, B, Ernberg, M. Altered levels of salivary and plasma pain related markers in temporomandibular disorders. J Headache Pain 2020;21:105. https://doi.org/10.1186/s10194-020-01160-z.Search in Google Scholar PubMed PubMed Central

38. Wu, S, Brown, JN, Tolic, N, Meng, D, Liu, X, Zhang, H, et al.. Quantitative analysis of human salivary gland-derived intact proteome using top-down mass spectrometry. Proteomics 2014;14:1211–22. https://doi.org/10.1002/pmic.201300378.Search in Google Scholar PubMed

39. Denny, P, Hagen, FK, Hardt, M, Liao, L, Yan, W, Arellanno, M, et al.. The proteomes of human parotid and submandibular/sublingual gland salivas collected as the ductal secretions. J Proteome Res 2008;7:1994–2006. https://doi.org/10.1021/pr700764j.Search in Google Scholar PubMed PubMed Central

40. Walz, A, Stuhler, K, Wattenberg, A, Hawranke, E, Meyer, HE, Schmalz, G, et al.. Proteome analysis of glandular parotid and submandibular-sublingual saliva in comparison to whole human saliva by two-dimensional gel electrophoresis. Proteomics 2006;6:1631–9. https://doi.org/10.1002/pmic.200500125.Search in Google Scholar PubMed

41. Begliuomini, S, Lenzi, E, Ninni, F, Casarosa, E, Merlini, S, Pluchino, N, et al.. Plasma brain-derived neurotrophic factor daily variations in men: correlation with cortisol circadian rhythm. J Endocrinol 2008;197:429–35. https://doi.org/10.1677/joe-07-0376.Search in Google Scholar

42. Choi, SW, Bhang, S, Ahn, JH. Diurnal variation and gender differences of plasma brain-derived neurotrophic factor in healthy human subjects. Psychiatr Res 2011;186:427–30. https://doi.org/10.1016/j.psychres.2010.07.028.Search in Google Scholar PubMed

43. Tirassa, P, Iannitelli, A, Sornelli, F, Cirulli, F, Mazza, M, Calza, A, et al.. Daily serum and salivary BDNF levels correlate with morning-evening personality type in women and are affected by light therapy. Riv Psichiatr 2012;47:527–34.Search in Google Scholar

44. Bersani, G, Iannitelli, A, Massoni, E, Garavini, A, Grilli, A, Giannantonio, MD, et al.. Ultradian variation of nerve growth factor plasma levels in healthy and schizophrenic subjects. Int J Immunopathol Pharmacol 2004;17:367–72. https://doi.org/10.1177/039463200401700316.Search in Google Scholar PubMed

45. Golatowski, C, Salazar, MG, Dhople, VM, Hammer, E, Kocher, T, Jehmlich, N, et al.. Comparative evaluation of saliva collection methods for proteome analysis. Clin Chim Acta 2013;419:42–6. https://doi.org/10.1016/j.cca.2013.01.013.Search in Google Scholar PubMed

46. Krief, G, Haviv, Y, Deutsch, O, Keshet, N, Almoznino, G, Zacks, B, et al.. Proteomic profiling of whole-saliva reveals correlation between burning mouth syndrome and the neurotrophin signaling pathway. Sci Rep 2019;9:4794. https://doi.org/10.1038/s41598-019-41297-9.Search in Google Scholar PubMed PubMed Central

47. Nam, JH, Lee, HS, Kim, J, Kim, J, Chu, MK. Salivary glutamate is elevated in individuals with chronic migraine. Cephalalgia 2017;38:1485–92. https://doi.org/10.1177/0333102417742366.Search in Google Scholar PubMed

48. Schulz, BL, Cooper-White, J, Punyadeera, CK. Saliva proteome research: current status and future outlook. Crit Rev Biotechnol 2013;33:246–59. https://doi.org/10.3109/07388551.2012.687361.Search in Google Scholar PubMed

49. Ahmadi-Motamayel, F, Shahriari, S, Goodarzi, MT, Moghimbeigi, A, Jazaeri, M, Babaei, P. The relationship between the level of salivary alpha amylase activity and pain severity in patients with symptomatic irreversible pulpitis. Restor Dent Endod 2013;38:141–5. https://doi.org/10.5395/rde.2013.38.3.141.Search in Google Scholar PubMed PubMed Central

50. Huang, CM. Comparative proteomic analysis of human whole saliva. Arch Oral Biol 2004;49:951–62. https://doi.org/10.1016/j.archoralbio.2004.06.003.Search in Google Scholar PubMed

51. Ghafouri, B, Tagesson, C, Lindahl, M. Mapping of proteins in human saliva using two-dimensional gel electrophoresis and peptide mass fingerprinting. Proteomics 2003;3:1003–15. https://doi.org/10.1002/pmic.200300426.Search in Google Scholar PubMed

52. Harper, DE, Schrepf, A, Clauw, DJ. Pain mechanisms and centralized pain in temporomandibular disorders. J Dent Res 2016;95:1102–8. https://doi.org/10.1177/0022034516657070.Search in Google Scholar PubMed PubMed Central

53. Hannibal, KE, Bishop, MD. Chronic stress, cortisol dysfunction, and pain: a psychoneuroendocrine rationale for stress management in pain rehabilitation. Phys Ther 2014;94:1816–25. https://doi.org/10.2522/ptj.20130597.Search in Google Scholar PubMed PubMed Central

54. Woolf, CJ. Central sensitization: implications for the diagnosis and treatment of pain. Pain 2011;152:S2–15. https://doi.org/10.1016/j.pain.2010.09.030.Search in Google Scholar PubMed PubMed Central

55. Kosek, E, Cohen, M, Baron, R, Gebhart, GF, Mico, JA, Rice, AS, et al.. Do we need a third mechanistic descriptor for chronic pain states? Pain. 2016;157:1382–6, https://doi.org/10.1097/j.pain.0000000000000507.Search in Google Scholar PubMed

56. Trouvin, AP, Perrot, S. New concepts of pain. Best Pract Res Clin Rheumatol 2019;33:101415. https://doi.org/10.1016/j.berh.2019.04.007.Search in Google Scholar PubMed

57. Kishi, T, Yoshimura, R, Ikuta, T, Iwata, N. Brain-derived neurotrophic factor and major depressive disorder: evidence from meta-analyses. Front Psychiatr 2017;8:308. https://doi.org/10.3389/fpsyt.2017.00308.Search in Google Scholar PubMed PubMed Central

58. Chen, YW, Lin, PY, Tu, KY, Cheng, YS, Wu, CK, Tseng, PT. Significantly lower nerve growth factor levels in patients with major depressive disorder than in healthy subjects: a meta-analysis and systematic review. Neuropsychiatric Dis Treat 2015;11:925–33. https://doi.org/10.2147/ndt.s81432.Search in Google Scholar PubMed PubMed Central

59. Schultz, J, Uddin, Z, Singh, G, Howlader, MMR. Glutamate sensing in biofluids: recent advances and research challenges of electrochemical sensors. Analyst 2020;145:321–47. https://doi.org/10.1039/c9an01609k.Search in Google Scholar PubMed

60. Gerdle, B, Ghafouri, B, Ernberg, M, Larsson, B. Chronic musculoskeletal pain: review of mechanisms and biochemical biomarkers as assessed by the microdialysis technique. J Pain Res 2014;7:313–26. https://doi.org/10.2147/jpr.s59144.Search in Google Scholar

61. Rosendal, L, Larsson, B, Kristiansen, J, Peolsson, M, Sogaard, K, Kjaer, M, et al.. Increase in muscle nociceptive substances and anaerobic metabolism in patients with trapezius myalgia: microdialysis in rest and during exercise. Pain 2004;112:324–34. https://doi.org/10.1016/j.pain.2004.09.017.Search in Google Scholar PubMed

62. Shimada, A, Castrillon, EE, Baad-Hansen, L, Ghafouri, B, Gerdle, B, Wahlen, K, et al.. Increased pain and muscle glutamate concentration after single ingestion of monosodium glutamate by myofascial temporomandibular disorders patients. Eur J Pain 2016;20:1502–12. https://doi.org/10.1002/ejp.874.Search in Google Scholar PubMed

63. Castrillon, EE, Ernberg, M, Cairns, BE, Wang, K, Sessle, BJ, Arendt-Nielsen, L, et al.. Interstitial glutamate concentration is elevated in the masseter muscle of myofascial temporomandibular disorder patients. J Orofac Pain 2010;24:350–60.Search in Google Scholar

64. Gerdle, B, Larsson, B, Forsberg, F, Ghafouri, N, Karlsson, L, Stensson, N, et al.. Chronic widespread pain: increased glutamate and lactate concentrations in the trapezius muscle and plasma. Clin J Pain 2014;30:409–20. https://doi.org/10.1097/ajp.0b013e31829e9d2a.Search in Google Scholar

65. Pyke, TL, Osmotherly, PG, Baines, S. Measuring glutamate levels in the brains of fibromyalgia patients and a potential role for glutamate in the pathophysiology of fibromyalgia symptoms: a systematic review. Clin J Pain 2017;33:944–54. https://doi.org/10.1097/ajp.0000000000000474.Search in Google Scholar

Received: 2022-08-08

Accepted: 2022-09-23

Published Online: 2022-10-14

Published in Print: 2023-01-27

Articles in the same Issue

https://doi.org/10.1515/sjpain-2022-0112

Keywords for this article

biomarkers; proteomics; saliva; TMD