Next Article in Journal
Skin Lesions Caused by HPV—A Comprehensive Review
Previous Article in Journal
Vascular Impairment, Muscle Atrophy, and Cognitive Decline: Critical Age-Related Conditions
Previous Article in Special Issue
Motor Cortex Inhibition and Facilitation Correlates with Fibromyalgia Compensatory Mechanisms and Pain: A Cross-Sectional Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 12
Issue 9
10.3390/biomedicines12092097
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

The Link between Endogenous Pain Modulation Changes and Clinical Improvement in Fibromyalgia Syndrome: A Meta-Regression Analysis

by
Kevin Pacheco-Barrios
1,2,*,†,
Rafaela Machado Filardi
1,†,
Luis Fernando González-González
1,†,
Nayeon Park
1,3,
Fernanda Queiroz Petrus
1,
Alba Navarro-Flores
4,
Silvia Di-Bonaventura
1,5,6,
Luana Gola Alves
1,
Fernanda Queiroz
1 and
Felipe Fregni
1,*
1
Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
2
Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima 15023, Peru
3
Admission AG, Irvine, CA 92618, USA
4
Escuela de Medicina, Universidad Cesar Vallejo, Trujillo 13001, Peru
5
Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Rey Juan Carlos University, 28922 Alcorcon, Spain
6
Cognitive Neuroscience, Pain and Rehabilitation Research Group (NECODOR), Faculty of Health Sciences, Rey Juan Carlos University, 28922 Alcorcon, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomedicines 2024, 12(9), 2097; https://doi.org/10.3390/biomedicines12092097
Submission received: 8 July 2024 / Revised: 9 August 2024 / Accepted: 9 September 2024 / Published: 13 September 2024
(This article belongs to the Collection Feature Papers in Neuromodulation and Brain Stimulation)
Browse Figures
Review Reports Versions Notes

Abstract

:
Conditioned pain modulation (CPM) and temporal summation (TS) tests can measure the ability to inhibit pain in fibromyalgia syndrome (FMS) patients and its level of pain sensitization, respectively. However, their clinical validity is still unclear. We studied the association between changes in the CPM and TS tests and the clinical improvement of FMS patients who received therapeutic intervention. We systematically searched for FMS randomized clinical trials with data on therapeutic interventions comparing clinical improvement (pain intensity and symptom severity reduction), CPM, and TS changes relative to control interventions. To study the relationship between TS/CPM and clinical measures, we performed a meta-regression analysis to calculate odds ratios. We included nine studies (484 participants). We found no significant changes in TS or CPM by studying all the interventions together. Our findings show that this lack of difference is likely because pharmacological and non-pharmacological interventions resulted in contrary effects. Non-pharmacological interventions, such as non-invasive neuromodulation, showed the largest effects normalizing CPM/TS. Meta-regression was significantly associated with pain reduction and symptom severity improvement with normalization of TS and CPM. We demonstrate an association between clinical improvement and TS/CPM normalization in FMS patients. Thus, the TS and CPM tests could be surrogate biomarkers in FMS management. Recovering defective endogenous pain modulation mechanisms by targeted non-pharmacological interventions may help establish long-term clinical recovery in FMS patients.

1. Introduction

Fibromyalgia syndrome (FMS) is a chronic rheumatic disease characterized by diverse symptoms such as widespread body pain, stiffness, fatigue, sleep disturbances, cognitive impairments, and psychiatric symptoms, which significantly impact the patient’s daily routine and quality of life. Furthermore, it often requires healthcare services, becoming a costly public health issue [1].
Central sensitization consists of a variety of neural system dysfunctions, and it provides a possible explanation for comprehension and management of chronic pain syndromes such as fibromyalgia [2]. This theory suggests that abnormal sensory processing in the brain, increased susceptibility to stimulus, and inaccurate pain modulation pathways result in hypersensitivity to stimulus [2,3]. In addition, Mezhov et al. (2021) [3] argue that the presence of central sensitivity syndromes, such as fibromyalgia, may reflect an overactive central nervous system instead of an isolated disorder, associated with changes in brain oscillatory activity [4,5]. Recognizing central sensitization as a mechanism in chronic pain increases the importance of treatments focusing on modulating the endogenous pain inhibitory system [6] instead of short-term treatment that only provides temporary pain relief by blocking nociceptive input. This change in pain management provides awareness and highlights the importance of targeting pain compensatory mechanisms as a new approach to manage FMS [2].
Conditioned pain modulation (CPM) and pain temporal summation (TS) are key to understanding pain modulation dynamics and can assess the pain sensitization and pain inhibition characteristics of FMS patients non-invasively; they are also promising biomarkers [7]. CPM involves the inhibition of one painful stimulus by another, reflecting the body’s endogenous pain control systems and assessing their efficiency in both healthy and chronic-pain populations [7]. TS describes the increased perception of pain from repeated noxious stimuli, highlighting excitatory processes in central pain facilitation [8]. However, there is limited evidence supporting the validity of these biomarkers, mainly due to contradictory results associating CPM metrics with clinical outcomes. O’Brien et al. (2018) found that FM patients show a heightened response to repetitive noxious stimuli and reduced CPM compared to healthy controls, supporting the idea of central sensitization and defective pain inhibition [2]. Similarly, Potvin and Marchand (2016) reported that CPM procedure has a specificity of 78.9%, but its low sensitivity of 45.7% indicates that it may not be detected in all patients [9]. These findings suggest that endogenous pain inhibition mechanisms are impaired in these patients, but only partially, which indicates the complexity and unpredictability of pain modulation in this condition. Conversely, a study found no correlation between pain severity and CPM efficacy, challenging the idea that CPM is a reliable biomarker [10]. Similarly, a different study reported no significant difference in CPM between FM patients and those with generalized pain, indicating diverse pain phenotypes and mechanisms in FM syndrome [11]. The limited sample size and the cross-sectional nature of the analysis, not considering the dynamic features of endogenous pain modulation, indicate the presence of limitations in previous studies. To our knowledge, no previous studies tried to validate systematically the longitudinal changes in CPM/TS with changes in clinical outcomes such as pain intensity or symptoms severity in FMS. A longitudinal validation of these biomarkers is needed to foster the utilization of objective metrics in the context of FMS management and the development of personalized therapeutic protocols.
Therefore, we aim to explore the association between changes of endogenous pain modulation (indexed by CPM and TS) and clinical changes (pain intensity and symptoms severity) after therapeutic interventions in FMS patients. We hypothesize that improvement in endogenous pain modulation will correlate with the improvement of clinical profiles in FMS patients.

2. Materials and Methods

This systematic review and meta-analysis were conducted following the “Preferred Reporting Items for Systematic Reviews and Meta-Analyzes” (PRISMA) [12] guidelines and the Cochrane Handbook for Systematic Reviews of Interventions [13]. The protocol was registered from the Open Science Framework platform (https://osf.io/registries (accessed on 9 August 2024)) with the code YDG4J (DOI: https://doi.org/10.17605/OSF.IO/YDG4J).

2.1. Search Strategy and Inclusion Criteria

PubMed/MEDLINE and Embase databases were searched from inception until 4 March 2024. The complete search strategy was: (“Fibromyalgia” [MeSH] OR “Fibromyalgia” [TIAB]) AND (“Sensory profile” OR “temporal summation” OR “conditioned pain modulation” OR “temporal slow pain summation” OR “quantitative sensory testing” OR “cognitive-emotional sensitization” OR “sensory threshold” OR “pain threshold” OR “diffuse noxious inhibitory control” OR “heterotopic noxious conditioning stimulation” OR “endogenous analgesia” OR “pain inhibition” OR “endogenous pain modulation”). Additionally, we reviewed the references of the included studies. The eligibility criteria were: (1) Randomized controlled trials (RCTs) that included FM patients and assessed CPM/TS before and after an intervention and that included at least one clinical pain measure (e.g., VAS, FIQR); (2) any type of intervention and comparison; and (3) full-text accessible. The exclusion criteria were: (1) pre-clinical studies; (2) review articles; (3) letters to the editor and editorials; and (4) conference abstracts. Studies were not excluded based on date or language.

2.2. Study Selection and Data Extraction

Duplicate records were removed manually using the Covidence web platform. Two independent reviewers conducted the screening, first by titles and abstracts and then by full text, with the aid of the Covidence web platform. Disagreements were solved by the facilitation of a third author. The data extraction was conducted by two authors independently. The following variables of interest were extracted: study design, sample size, CPM/TS method, type of stimulus, type of control group, intervention type, fibromyalgia diagnosis description, age, gender, pain intensity, and symptom severity scores indexed by the Revised Fibromyalgia Impact Questionnaire (FIQR).

2.3. Management of Missing Data

Unavailable raw data from the main outcome were calculated from available graphs using Web Plot Digitizer v.3.11 when possible. In the cases where data from graphs were not available, authors were contacted via email. If no response was received by the time of the analysis, the study was excluded.

2.4. Risk of Bias Assessment

Assessment of the studies’ risk of bias was carried by two independent reviewers, and discrepancies were solved with the support of a third author. We used the Cochrane Risk of Bias 2 (RoB 2) tool [14]. We used the algorithm proposed in the RoB 2 tool to rate each domain as ‘low risk of bias’, ‘some concerns’, or ‘high risk of bias’, with the overall risk of bias being the worst assessment of the five domains [15]. We did not conduct a publication bias assessment due to the small number of included studies.

2.5. Statistical Analysis

We conducted meta-analyses of continuous outcomes using random-effects models due to the high expected heterogeneity. The DerSimonian–Laird method was used [16]. Each variable (visual analogue scale (VAS), FIQR, CPM, and TS) was analyzed separately. Additionally, we converted the CPM and TS differences as percentage change as sensitivity analysis. Then, we calculated effect sizes as standardized mean differences (MD) with 95% confidence intervals (CIs) [13].
Moreover, we used the Hartung–Knapp adjustment for random effects models, which calculates more adequate error rates, especially when the number of included studies is small [17]. We tested for statistical heterogeneity of pooled estimates using the Chi2 test and the degree of heterogeneity using the I2 statistic, considering that heterogeneity might not be substantial when I2 < 40% [18]. We did not perform a publication bias assessment due to the low number of studies we found [19].
For the meta-regression analyses, we categorized each improvement in CPM and TS as an increase of at least 10% from the baseline in each trial arm (active and control). Similarly, we categorized the studies as clinical improvement based on an improvement on at least 20% of the scale from baseline, based on the minimal clinically important difference (MCID) for the FIQR score and the VAS score [20]. Then, we constructed two-by-two tables to calculate individual odds ratios (OR) and corresponding 95% CI using the Woolf approximation [21]. The data were processed with R Studio 4.1.1 for the creation of the plots (R Foundation for Statistical Computing, Vienna, Austria).

3. Results

Our literature search retrieved 1598 records. Of those, nine were included after full-text assessment (484 participants) [22,23,24,25,26,27,28,29,30]. The flowchart of the selection process is presented in Figure 1.
The interventions used were neuromodulation (n = 3), pharmacological (n = 5), and education (n = 1). Among these nine studies, four exclusively utilized CPM, one used only TS, and four incorporated both paradigms. Mechanical modalities (pain pressure threshold (PPT)) emerged as the predominant choice for conditioned stimulation (n = 7), while heat sensors were utilized in the remaining studies (n = 2). Concerning the conditioning stimulation in CPM, the cold water was the most commonly used (n = 4), with occlusion cuffs (n = 3) and hot water (n = 1) also employed in other studies. A detailed description of the included studies is found in Table 1.

3.1. Risk of Bias Findings

Overall, the studies demonstrated appropriate randomization procedures and adhered closely to the intended interventions, contributing to an overall assessment of low bias risk. However, one study presented some concerns regarding missing outcome data, primarily due to a significant number of participants in the intervention group experiencing collateral effects, which resulted in a high withdrawal rate from the study [26]. Another study was judged to have some concerns regarding deviations from intended interventions in certain studies, primarily due to the absence of blinding and the lack of a sham intervention [29]. These factors potentially introduced biases, as they could influence the result of the outcome. The full assessment is presented in Table 2.

3.2. Pooled Analysis Findings

Based on the data from the included studies, VAS scores were analyzed to compare the effects of the experimental intervention against the control group, showing a decrease in VAS scores in intervention group. However, the pooled effect size indicated a reduction in VAS scores with a wide confidence interval that suggests the overall effect is not statistically significant, SMD = 1.15, 95% CI [−3.70; 1.40]. The heterogeneity test revealed significant variability (I2 = 95%, p < 0.01), meaning that the intervention effects were inconsistent across different studies (Figure 2A).
The analysis of TS demonstrated varied effects between the experimental and control groups. The pooled effect size SMD = 0.48, 95% CI [−2.96; 3.92], indicated an increase in TS scores with a wide confidence interval not significant. The test of heterogeneity also revealed significant variability among the studies (I2 = 96%, p < 0.01), implying the intervention effects were inconsistent across different studies (Figure 2B).
After TS was analyzed using percentage transformations, the results showed varied effects between the experimental and control groups. The pooled effect size indicated a decrease in TS percentages. However, the overall effect was not statistically significant, SMD = −0.55, 95% CI [−3.80; 2.70]. The test of heterogeneity revealed high variability among the studies (I2 = 95%, p < 0.01) (Figure 2C).
Five out of nine studies reported FIQR scores, but the analyses demonstrated varied effects between the experimental and control groups. The pooled effect size was not statistically significant, SMD = −1.36, 95% CI [−7.25; 4.52]. The heterogeneity test showed high variability (I2 = 98%, p < 0.01) (Figure 3A). CPM raw values demonstrated a varied effect, SMD = −1.24, 95% CI [−10.94; 8.47], indicating a decrease in CPM, although the overall effect was not statistically significant. The weights of the studies are relatively evenly distributed. The test of heterogeneity revealed significant variability among the studies (I2 = 98%, p < 0.01). The non-significant overall effect combined with high heterogeneity might be attributable to the different types of interventions among studies and the various ways to measure CPM.
CPM values were analyzed using percentage transformations to compare the effects of the experimental intervention versus the control. The results demonstrated a varied effect on CPM percentages, SMD = 6.30, 95% CI [−0.50; 13.10], showing an increase in CPM percentages, although not statistically significant. The test of heterogeneity showed a high heterogeneity among the studies (I2 = 96%, p < 0.01). The meta-analysis explored the effects of various interventions on VAS scores, FIQR scores, TS, and CPM values (both raw and percentage-transformed for the last two). There is high variability among trials, with none of them having a significant overall effect. We can see that the both the VAS and FIQR scores showed a reduction in fibromyalgia due to the interventions. However, the overall effects remain non-significant (Figure 3B,C). Publication bias was not evaluated due to the small number of included studies.

3.3. Meta-Regression Findings

The meta-regression analysis revealed a significant association between clinical improvement and the normalization of CPM and TS. Patients included in studies that showed at least a 10% improvement in CPM and TS from baseline had higher odds of experiencing clinical improvement, defined as a minimum 20% reduction in FIQR and VAS scores. The calculated odds ratios (OR) indicated that the likelihood of clinical improvement was significantly greater in patients with improved CPM and TS (Table 3). These results underscore the potential of CPM and TS as biomarkers for treatment efficacy in fibromyalgia syndrome.

4. Discussion

Our meta-regression analysis examined the link between changes in CPM and TS tests and clinical improvement of pain in FMS patients. We found no significant overall changes in CPM or TS when all interventions were analyzed together. However, when differentiating between pharmacological and non-pharmacological interventions, we observed contrary effects. Our findings suggest that non-invasive neuromodulatory techniques, such as transcranial direct current stimulation (tDCS) and transcutaneous electrical nerve stimulation (TENS), may have considerable potential to normalize the CPM and TS metrics. Importantly, our analysis indicates a possible association between clinical improvement, specifically pain reduction and symptom severity, and the normalization of CPM and TS. These findings suggest that CPM and TS tests could be used in specific scenarios to guide and assess the efficacy of FMS treatments.
CPM and TS are essential in understanding the mechanisms of pain modulation. CPM has been established as a clinical tool in the assessment and management of chronic pain, providing valuable information on the ability of the central nervous system to regulate pain through endogenous mechanisms [31,32]. In patients with chronic pain, dysfunction of these inhibitory mechanisms is frequently observed [33,34], which also seems to be associated with an alteration in its CPM [35]. TS, which measures the facilitation of pain pathways, can indicate central sensitization, a key feature in conditions like FMS. Assessing CPM and TS in this specific population allows the identification of specific dysfunctions in pain modulation, facilitating a deeper understanding of the pathophysiological mechanisms underlying their condition.
One of the significant challenges in utilizing CPM and TS as biomarkers is the high variability observed across different paradigms used to assess these variables. This variability stems from differences in methodologies, including the type and intensity of conditioning and test stimuli, the anatomical sites tested, and the timing and duration of stimuli application. Such variability can lead to inconsistent results, making it difficult to compare findings across studies and limiting the generalizability of results [36]. The variability in CPM and TS paradigms presents both challenges and opportunities for research and clinical practice. On one hand, it highlights the need for standardized protocols to improve the reliability and reproducibility of assessments. Developing consensus guidelines for the test could enhance the consistency of research findings and facilitate more accurate comparisons across studies [37]. On the other hand, the variability also underscores the importance of tailoring CPM and TS assessments to individual patient needs. Different paradigms may reveal unique aspects of a patient’s pain modulation capacity, offering insights into specific dysfunctions in their endogenous pain control system. By understanding these nuances, clinicians can better personalize treatment strategies to address the distinct pain modulation profiles of their patients [36].
One relevant implication of measuring CPM is its ability to predict response to treatment [38,39,40]. Also, different studies have shown that the measurement of CPM can be useful to predict the response to conventional analgesic treatments, such as opioids, in different populations [41,42]. This predictive ability of CPM could allow clinicians to anticipate the effectiveness of therapeutic strategies and adjust them to maximize clinical benefits. For example, patients with preserved CPM function are more likely to respond positively to certain analgesics, providing a basis for personalized medicine in pain management.
Personalizing treatment based on pain modulation profile is another critical implication. By knowing a patient’s CPM and TS profiles, it may be possible to design a specific therapeutic regimen that directly addresses his or her individual needs [43]. Measurement of CPM and TS also plays an important role in monitoring treatment effectiveness over time [44]. This monitoring can allow clinicians to make therapeutic adjustments in near real time, ensuring optimal pain management. This approach aligns with the broader trend toward personalized medicine, which seeks to optimize treatment outcomes by considering individual variability in disease mechanisms and treatment responses.
Finally, CPM and TS can be used to classify patients into different phenotypes of chronic pain, allowing for a more accurate and effective therapeutic approach [45]. Future research should focus on establishing consistent methodologies for measuring CPM and TS, improving the robustness of these emerging biomarkers. Additionally, longitudinal studies are needed to explore the long-term effects of modulating CPM and TS and their impact on chronic pain progression and management.

Strengths and Limitations

Our study has several strengths. Firstly, this is the first study exploring this topic in FMS patients. The comprehensive and systematic search strategy employed ensures a thorough examination of the available literature, enhancing the reliability of the findings. By including randomized clinical trials, the analysis benefits from high-quality data, providing robust evidence on the association between changes in CPM, TS, and clinical improvement in FMS patients. The use of meta-regression analysis allows for a nuanced understanding of how various therapeutic interventions influence CPM and TS, offering valuable insights into the underlying mechanisms of pain modulation in FMS. Moreover, the inclusion of both pharmacological and non-pharmacological interventions highlights the differential effects of these treatments, which is crucial for developing tailored therapeutic strategies.
However, there are limitations to consider. The heterogeneity of the included studies, in terms of interventions, outcome measures, and patient populations, may introduce variability that could affect the generalizability of the results. The relatively small number of studies and participants included in the analysis might limit the statistical power, assessment of publication bias, and precision of the meta-regression findings. Additionally, the reliance on published data means that potential unpublished studies with null results might be missing, leading to an overestimation of the effects. Despite these limitations, the review provides a valuable contribution to understanding the relationship between endogenous pain modulation and clinical outcomes in FMS.

5. Conclusions

In summary, this meta-regression suggests no significant changes in TS or CPM by studying all the interventions together. A significant association is revealed between clinical improvement (pain intensity and symptoms severity) and the normalization of CPM and TS in FMS patients. Despite the heterogeneity and limited number of included studies, our findings suggest that both pharmacological and non-pharmacological interventions can influence pain modulation mechanisms, improving the clinical pain measures CPM and TS. The CPM and TS tests may serve as valuable surrogate biomarkers for FMS management, highlighting the potential for targeted interventions to restore defective endogenous pain modulation mechanisms. Further research with larger sample sizes and standardized methodologies is needed to confirm these results and enhance clinical practice.

Author Contributions

Conceptualization, K.P.-B.; methodology, K.P.-B.; formal analysis, K.P.-B.; investigation, R.M.F., L.F.G.-G., K.P.-B. and F.F.; resources, K.P.-B. and F.F.; data curation, R.M.F. and L.F.G.-G.; writing—original draft preparation, R.M.F., L.F.G.-G., K.P.-B., N.P., F.Q.P., A.N.-F. and S.D.-B.; writing—review and editing, L.G.A., F.Q., K.P.-B. and F.F.; visualization, R.M.F., L.F.G.-G., L.G.A., F.Q. and K.P.-B.; supervision, F.F.; project administration, F.F.; funding acquisition, F.F. All authors have read and agreed to the published version of the manuscript.

Funding

F.F. was supported by an NIH grant (1R01AT009491-01A1). K.P.-B. was supported by an NIH grant (R03 AG085084-01).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

Author Nayeon Park is a consultant at Admission AG. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Kocyigit, B.F.; Akyol, A. Fibromyalgia syndrome: Epidemiology, diagnosis and treatment. Reumatologia 2022, 60, 413–421. [Google Scholar] [CrossRef] [PubMed]
  2. Nijs, J.; Malfliet, A.; Nishigami, T. Nociplastic pain and central sensitization in patients with chronic pain conditions: A terminology update for clinicians. Braz. J. Phys. Ther. 2023, 27, 100518. [Google Scholar] [CrossRef] [PubMed]
  3. Mezhov, V.; Guymer, E.; Littlejohn, G. Central sensitivity and fibromyalgia. Intern. Med. J. 2021, 51, 1990–1998. [Google Scholar] [CrossRef]
  4. Pacheco-Barrios, K.; Teixeira, P.E.; Martinez-Magallanes, D.; Neto, M.S.; Pichardo, E.A.; Camargo, L.; Lima, D.; Cardenas-Rojas, A.; Fregni, F. Brain compensatory mechanisms in depression and memory complaints in fibromyalgia: The role of theta oscillatory activity. Pain Med. 2024, 25, 514–522. [Google Scholar] [CrossRef]
  5. Camargo, L.; Pacheco-Barrios, K.; Marques, L.M.; Caumo, W.; Fregni, F. Adaptive and Compensatory Neural Signatures in Fibromyalgia: An Analysis of Resting-State and Stimulus-Evoked EEG Oscillations. Biomedicines 2024, 12, 1428. [Google Scholar] [CrossRef] [PubMed]
  6. Castelo-Branco, L.; Kucukseymen, E.U.; Duarte, D.; El-Hagrassy, M.M.; Pinto, C.B.; Gunduz, M.E.; Cardenas-Rojas, A.; Pacheco-Barrios, K.; Yang, Y.; Gonzalez-Mego, P.; et al. Optimised transcranial direct current stimulation (tDCS) for fibromyalgia—Targeting the endogenous pain control system: A randomised, double-blind, factorial clinical trial protocol. BMJ Open 2019, 9, e032710. [Google Scholar] [CrossRef]
  7. Yarnitsky, D. Role of Endogenous Pain Modulation in Chronic Pain Mechanisms and Treatment. Pain 2015, 156, S24–S31. [Google Scholar] [CrossRef]
  8. Nie, H.; Graven-Nielsen, T.; Arendt-Nielsen, L. Spatial and Temporal Summation of Pain Evoked by Mechanical Pressure Stimulation. Eur. J. Pain 2009, 13, 592–599. [Google Scholar] [CrossRef]
  9. Potvin, S.; Marchand, S. Pain facilitation and pain inhibition during conditioned pain modulation in fibromyalgia and in healthy controls. Pain 2016, 157, 1704–1710. [Google Scholar] [CrossRef]
  10. Uygur-Kucukseymen, E.; Castelo-Branco, L.; Pacheco-Barrios, K.; Luna-Cuadros, M.A.; Cardenas-Rojas, A.; Giannoni-Luza, S.; Zeng, H.; Gianlorenco, A.C.; Gnoatto-Medeiros, M.; Shaikh, E.S.; et al. Decreased neural inhibitory state in fibromyalgia pain: A cross-sectional study. Neurophysiol. Clin. 2020, 50, 279–288. [Google Scholar] [CrossRef]
  11. Bittencourt, J.V.; Corrêa, L.A.; Bezerra, M.C.; Reis, F.J.J.D.; Luca, K.D.; Nogueira, L.A.C. Patients with fibromyalgia present different pain phenotypes compared to patients with generalized pain. BrJP 2022, 5, 119–126. [Google Scholar]
  12. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [PubMed]
  13. Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.; Higgins, J.; Thomas, J. (Eds.) Cochrane Handbook for Systematic Reviews of Interventions; John Wiley & Sons: Hoboken, NJ, USA, 2019. [Google Scholar]
  14. Sterne, J.A.C.; Savović, J.; Page, M.; Elbers, R.G.; Blencowe, N.S.; Boutron, I.; Cates, C.J.; Cheng, H.Y.; Corbett, M.S.; Eldridge, S.M.; et al. RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ 2019, 366, l4898. [Google Scholar] [CrossRef]
  15. Minozzi, S.; Dwan, K.; Borrelli, F.; Filippini, G. Reliability of the revised Cochrane risk-of-bias tool for randomised trials (RoB2) improved with the use of implementation instruction. J. Clin. Epidemiol. 2022, 141, 99–105. [Google Scholar] [CrossRef]
  16. DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef]
  17. IntHout, J.; Ioannidis, J.P.; Borm, G.F. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med. Res. Methodol. 2014, 14, 25. [Google Scholar] [CrossRef]
  18. Schünemann, H.; Brożek, J.; Guyatt, G. GRADE Handbook; GRADE Working Group: Split, Croatia. Available online: https://gdt.gradepro.org/app/handbook/handbook.html (accessed on 28 July 2024).
  19. Guyatt, G.H.; Oxman, A.D.; Montori, V.; Vist, G.; Kunz, R.; Brozek, J.; Alonso-Coello, P.; Djulbegovic, B.; Atkins, D.; Falck-Ytter, Y.; et al. GRADE guidelines: 5. Rating the quality of evidence—Publication bias. J. Clin. Epidemiol. 2011, 64, 1277–1282. [Google Scholar] [CrossRef]
  20. Bennett, R.M.; Bushmakin, A.G.; Cappelleri, J.C.; Zlateva, G.; Sadosky, A.B. Minimal clinically important difference in the fibromyalgia impact questionnaire. J. Rheumatol. 2009, 36, 1304–1311. [Google Scholar] [CrossRef] [PubMed]
  21. Lawson, R. Small sample confidence intervals for the odds ratio. Commun. Stat.-Simul. Comput. 2004, 33, 1095–1113. [Google Scholar] [CrossRef]
  22. Graven-Nielsen, T.; Kendall, S.A.; Henriksson, K.G.; Bengtsson, M.; Sörensen, J.; Johnson, A.; Gerdle, B.; Arendt-Nielsen, L. Ketamine reduces muscle pain, temporal summation, and referred pain in fibromyalgia patients. Pain 2000, 85, 483–491. [Google Scholar] [CrossRef] [PubMed]
  23. Dailey, D.L.; Rakel, B.A.; Vance, C.G.; Liebano, R.E.; Amrit, A.S.; Bush, H.M.; Lee, K.S.; Lee, J.E.; Sluka, K.A. Transcutaneous electrical nerve stimulation reduces pain, fatigue and hyperalgesia while restoring central inhibition in primary fibromyalgia. Pain 2013, 154, 2554–2562. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  24. de Zanette, S.A.; Vercelino, R.; Laste, G.; Rozisky, J.R.; Schwertner, A.; Machado, C.B.; Xavier, F.; de Souza, I.C.C.; Deitos, A.; Torres, I.L.S.; et al. Melatonin analgesia is associated with improvement of the descending endogenous pain-modulating system in fibromyalgia: A phase II, randomized, double-dummy, controlled trial. BMC Pharmacol. Toxicol. 2014, 15, 40. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  25. Meeus, M.; Hermans, L.; Ickmans, K.; Struyf, F.; Van Cauwenbergh, D.; Bronckaerts, L.; De Clerck, L.S.; Moorken, G.; Hans, G.; Grosemans, S.; et al. Endogenous Pain Modulation in Response to Exercise in Patients with Rheumatoid Arthritis, Patients with Chronic Fatigue Syndrome and Comorbid Fibromyalgia, and Healthy Controls: A Double-Blind Randomized Controlled Trial. Pain Pract. 2015, 15, 98–106. [Google Scholar] [CrossRef]
  26. Pickering, G.; Macian, N.; Delage, N.; Picard, P.; Cardot, J.-M.; Sickout-Arondo, S.; Giron, F.; Dualé, C.; Pereira, B.; Marcaillou, F. Milnacipran Poorly Modulates Pain in Patients Suffering from Fibromyalgia: A Randomized Double-Blind Controlled Study. Drug Des. Dev. Ther. 2018, 12, 2485–2496. [Google Scholar] [CrossRef]
  27. Amer-Cuenca, J.J.; Pecos-Martín, D.; Martínez-Merinero, P.; Lluch Girbés, E.; Nijs, J.; Meeus, M.; Ferrer Peña, R.; Fernández-Carnero, J. How Much Is Needed? Comparison of the Effectiveness of Different Pain Education Dosages in Patients with Fibromyalgia. Pain Med. 2020, 21, 782–793. [Google Scholar] [CrossRef] [PubMed]
  28. Udina-Cortes, C.; Fernandez-Carnero, J.; Romano, A.A.; Cuenca-Zaldivar, J.N.; Villafane, J.H.; Castro-Marrero, J.; Alguacil-Diego, I.M. Effects of neuro-adaptive electrostimulation therapy on pain and disability in fibromyalgia: A prospective, randomized, double-blind study. Medicine 2020, 99, e23785. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  29. Ablin, J.N.; Lang, E.; Catalogna, M.; Aloush, V.; Hadanny, A.; Doenyas-Barak, K.; Finci, S.; Polak, N.; Fishlev, G.; Korin, C.; et al. Hyperbaric oxygen therapy compared to pharmacological intervention in fibromyalgia patients following traumatic brain injury: A randomized, controlled trial. PLoS ONE 2023, 18, e0282406. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  30. de Paula, T.M.H.; Castro, M.S.; Medeiros, L.F.; Paludo, R.H.; Couto, F.F.; da Costa, T.R.; Fortes, J.P.; Salbego, M.d.O.; Behnck, G.S.; de Moura, T.A.M.; et al. Association of Low-Dose Naltrexone and Transcranial Direct Current Stimulation in Fibromyalgia: A Randomized, Double-Blinded, Parallel Clinical Trial. Braz. J. Anesthesiol. 2023, 73, 409–417. [Google Scholar] [CrossRef]
  31. Ramaswamy, S.; Wodehouse, T. Conditioned pain modulation—A comprehensive review. Neurophysiol. Clin. 2021, 51, 197–208. [Google Scholar] [CrossRef]
  32. Bossmann, T.; Brauner, T.; Lowak, H.; Anton, F.; Forster, C.; Horstmann, T. Reliability of conditioned pain modulation for the assessment of endogenous pain control pathways. Neurol. Psychiatry Brain Res. 2016, 22, 155–161. [Google Scholar] [CrossRef]
  33. Lazaridou, A.; Paschali, M.; Zgierska, A.E.; Garland, E.L.; Edwards, R.R. Exploring the relationship between endogenous pain modulation, pain intensity, and depression in patients using opioids for chronic low back pain. Clin. J. Pain 2022, 38, 595–600. [Google Scholar] [CrossRef] [PubMed]
  34. Owens, M.A.; Bulls, H.W.; Trost, Z.; Terry, S.C.; Gossett, E.W.; Wesson-Sides, K.M.; Goodin, B.R. An examination of pain catastrophizing and endogenous pain modulatory processes in adults with chronic low back pain. Pain Med. 2016, 17, 1452–1464. [Google Scholar] [CrossRef] [PubMed]
  35. Lewis, G.N.; Rice, D.A.; McNair, P.J. Conditioned pain modulation in populations with chronic pain: A systematic review and meta-analysis. J. Pain 2012, 13, 936–944. [Google Scholar] [CrossRef]
  36. Graeff, P.; Itter, A.; Wach, K.; Ruscheweyh, R. Inter-Individual Differences Explain More Variance in Conditioned Pain Modulation than Age, Sex and Conditioning Stimulus Intensity Combined. Brain Sci. 2021, 11, 1186. [Google Scholar] [CrossRef]
  37. Kennedy, D.L.; Kemp, H.I.; Ridout, D.; Yarnitsky, D.; Rice, A.S.C. Reliability of Conditioned Pain Modulation. Pain 2016, 157, 2410–2419. [Google Scholar] [CrossRef]
  38. Edwards, R.R.; Dolman, A.J.; Martel, M.O.; Finan, P.H.; Lazaridou, A.; Cornelius, M.; Wasan, A.D. Variability in conditioned pain modulation predicts response to NSAID treatment in patients with knee osteoarthritis. BMC Musculoskelet. Disord. 2016, 17, 284. [Google Scholar] [CrossRef]
  39. Yarnitsky, D.; Crispel, Y.; Eisenberg, E.; Granovsky, Y.; Ben-Nun, A.; Sprecher, E.; Best, L.-A.; Granot, M. Prediction of chronic post-operative pain: Pre-operative DNIC testing identifies patients at risk. Pain 2008, 138, 22–28. [Google Scholar] [CrossRef] [PubMed]
  40. Izumi, M.; Petersen, K.K.; Laursen, M.B.; Arendt-Nielsen, L.; Graven-Nielsen, T. Facilitated temporal summation of pain correlates with clinical pain intensity after hip arthroplasty. Pain 2017, 158, 323–332. [Google Scholar] [CrossRef]
  41. Bruehl, S.; France, C.R.; Stone, A.L.; Gupta, R.; Buvanendran, A.; Chont, M.M.; Burns, J.W. Greater conditioned pain modulation is associated with enhanced morphine analgesia in healthy individuals and patients with chronic low back pain. Clin. J. Pain 2021, 37, 20–27. [Google Scholar] [CrossRef]
  42. Arendt-Nielsen, L.; Skou, S.T.; Nielsen, T.A.; Petersen, K.K. Altered central sensitization and pain modulation in the CNS in chronic joint pain. Curr. Osteoporos. Rep. 2015, 13, 225–234. [Google Scholar] [CrossRef]
  43. Yarnitsky, D.; Granot, M.; Granovsky, Y. Pain modulation profile and pain therapy: Between pro-and antinociception. Pain 2014, 155, 663–665. [Google Scholar] [CrossRef] [PubMed]
  44. Cruz-Almeida, Y.; Fillingim, R.B. Can quantitative sensory testing move us closer to mechanism-based pain management? Pain Med. 2014, 15, 61–72. [Google Scholar] [CrossRef] [PubMed]
  45. Granovsky, Y.; Yarnitsky, D. Personalized pain medicine: The clinical value of psychophysical assessment of pain modulation profile. Rambam Maimonides Med. J. 2013, 4, e0024. [Google Scholar] [CrossRef] [PubMed]
Biomedicines 12 02097 g001
Figure 1. Flowchart of the study selection process.
Figure 1. Flowchart of the study selection process.
Biomedicines 12 02097 g001
Biomedicines 12 02097 g002
Figure 2. Forest plots of the meta-analysis of the main outcomes (VAS pain and TS) [22,23,24,25,26,27,28,29,30]. (A) VAS score changes, (B) Temporal summation raw score changes, (C) Temporal summation as percentage change.
Figure 2. Forest plots of the meta-analysis of the main outcomes (VAS pain and TS) [22,23,24,25,26,27,28,29,30]. (A) VAS score changes, (B) Temporal summation raw score changes, (C) Temporal summation as percentage change.
Biomedicines 12 02097 g002
Biomedicines 12 02097 g003
Figure 3. Forest plots of the meta-analysis of the main outcomes (FIQR pain and CPM) [23,24,25,26,27,28,29,30]. (A) FIQR score changes. (B) CPM raw score changes. (C) CPM as percentage change.
Figure 3. Forest plots of the meta-analysis of the main outcomes (FIQR pain and CPM) [23,24,25,26,27,28,29,30]. (A) FIQR score changes. (B) CPM raw score changes. (C) CPM as percentage change.
Biomedicines 12 02097 g003
Table 1. General characteristics of included studies.
Table 1. General characteristics of included studies.
StudyStudy DesignAgeSample SizeGroup DescriptionType of Neuromodulator/DrugType of Conditioned StimulationType of Conditioning StimulationCPM MethodTS MethodMeasure of Clinical Pain Outcome
Graven-Nielsen et al., 2000 [22]RCT
CO		45 (8.25)15Group 1: Ketamine;
Group 2: Placebo		Ketamine 0.3 mg/kg infussion per sessionPPT (three bilateral tenderpoints)NANAPPT with 10 repeated applications of pulse. TS is the MD of VAS on the last pulse and the first pulse.VAS
Dailey et al., 2013 [23]RCT CO49.1 (12.9)43Group 1: TENS; Group 2: PlaceboTranscutaneous electrical nerve stimulations (TENS), 100 Hz, 200 μs at maximal tolerable intensity.PPT (cervical)Cold WaterVAS assessed via PPT, realized with and without CS. CPM is the percentage of change of VAS between the two tests.NAVAS
Zanette et al., 2014 [24]RCT48.9 (8)Group 1: 21
Group 2: 21
Group 3: 21		Group 1: Melatonin;
Group 2: Amitriptyline + Melatonin;
Group 3: Amitriptyline		Melatonin (10 mg) tablets + placebo; amitriptyline (25 mg) + placebo; or amitriptyline (25 mg) + melatonin (10 mg) once a day over 6-week period.Heat pain (forearm)Cold WaterVAS assessed via heat stimulation, measured with and without CS. CPM is the MD between the two tests.NAVAS
FIQ
Meeus et al., 2015 [25]RCT CO44.58 (7.34)19Group 1: Acetaminophen; Group 2: PlaceboAcetaminophen 1 g—30 min before assessmentPPT (middle finger of right hand and trapezius right arm)Occlusion cuffNPRS assessed via PPT, realized with and without CS. CPM is the MD of NPRS between the two tests.PPT with 10 repeated applications of pulse. TS is the MD of NPRS on the last pulse and the first pulse.NPRS
Pickering et al., 2018 [26]RCT46.7 (10.6)Group 1: 29
Group 2: 25		Group 1: Milnacipran;
Group 2: Placebo		Milnacipran, titrated 50 mg days 1–3, 75 mg days 4–6, 100 mg days 7–1 months.Heat pain (volar side of forearm)Hot WaterNPRS assessed via PPT, realized with and without CS. CPM is the MD of NPRS between the two tests.PPT with 10 repeated applications of pulse. TS is the MD of NPRS on the last pulse and the first pulse.NPRS
Amer-Cuenca et al., 2019 [27]RCT53.4 (9.08)Group 1: 24
Group 2: 28
Group 3: 32
Group 4: 19		Group 1: HD PNE;
Group 2: LCD PNE;
Group 3: LDD PNE;
Group 4: Placebo		HP PNE (six 45 min sessions), LCD PNE (two 45 min sessions), LDD PNE (six 15 min sessions)PPT (dorsal aspect of the distal phalanx)Occlusion cuffNPRS assessed via PPT, realized with and without CS. CPM is the MD of NPRS between the two tests.PPT with 10 repeated applications of pulse. TS is the MD of NPRS on the last pulse and the first pulse.NPRS
FIQ
Udina-Cortes et al., 2020 [28]RCT52 (8)Group 1: 20
Group 2: 17		Group 1: NAE;
Group 2: NAE Sham		Self-Controlled Energo Neuro-Adaptive Regulator, 15–350 Hz, 4 ± 2 to 500 ± 50 milliseconds, 1.7–2.5 V to 100–150 V amplitude.PPT (trapezius muscle)Occlusion cuffVAS assessed via PPT, realized with and without CS. CPM is the MD of VAS between the two tests.PPT with 10 repeated applications of pulse. TS is the MD of VAS on the last pulse and the first pulse.VAS
FIQ
Ablin et al., 2023 [29]RCT45.04 (11.9)Group 1: 33
Group 2: 31		Group 1: HBOT;
Group 2: Duloxetina + Pregabalin		HBOT 60 daily sessions five times per week for a total of 12 weeks. 100% oxygen by mask at 2 absolute atmospheres (ATA) for 90 min.PPT (upper trapezius muscle)Cold WaterVAS assessed via PPT, realized with and without CS. CPM is the MD of VAS between the two tests.NAVAS
FQI
Paula et al., 2023 [30]RCT49.3 (1.96)Group 1: 21
Group 2: 22
Group 3: 22
Group 4: 21		Group 1: LDN + tDCS;
Group 2: LDN + tDCS Sham; Group 3: Placebo + tDCS; Group 4: Placebo + tDCS Sham		tDCS using a current of 2 mA for 20 min/session (5 sessions). LDN 4.5 mg daily, orally for 26 days.PPT (right forearm)Cold WaterVAS assessed via PPT, realized with and without CS. CPM is the MD of VAS between the two tests.NAVAS
FIQ
CO = crossover; CPM = conditioned pain modulation; CS = conditioning stimulation; FIQ = fibromyalgia impact questionnaire; HBOT = hyperbaric oxygen therapy; HD PNE: high-dose pain neuroscience education; LCD PNE = low-concentrated dose pain neuroscience education; LDD PNE low-diluted-dose pain neuroscience education; LDN = low-dose naltrexone; MD = mean difference; NA = not available; NAE = non-invasive neuro-adaptive electrostimulation; NPRS = numerical pain rating scale; PPT = pain pressure threshold; RCT = randomized controlled trial; tDCS = transcranial direct current stimulation; TENS = transcutaneous electrical nerve stimulation; TS = temporal summation; VAS = visual analogue scale. Values of age are given as a mean (SD).
Table 2. Risk of bias summary for randomized studies (RoB 2).
Table 2. Risk of bias summary for randomized studies (RoB 2).
StudyBias from Randomization ProcessBias due to Deviations from Intended InterventionsBias due to Missing Outcome DataBias in Measurement of the OutcomesBias in Selection of the Reported ResultOverall Risk of Bias
Graven-Nielsen, 2000 [22]LowLowLowLowLowLow
Dailey, 2013 [23]LowLowLowLowLowLow
Zanette, 2014 [24]LowLowLowLowLowLow
Meeus, 2015 [25]LowLowLowLowLowLow
Pickering, 2018 [26]LowLowSome concernsLowLowSome concerns
Amer-Cuenca, 2019 [27]LowLowLowLowLowLow
Udina-Cortés, 2020 [28]LowLowLowLowLowLow
Ablin, 2023 [29]LowSome concernsLowLowLowSome concerns
Paula, 2023 [30]LowLowLowLowLowLow
Table 3. Association between endogenous pain modulation and clinical improvement.
Table 3. Association between endogenous pain modulation and clinical improvement.
AssociationNumber of Trial ArmsOdds Ratio95% CIp-Value
CPM improvement vs. VAS improvement17161.26 to 553.580.002
CPM improvement vs. FIQR improvement14131.09 to 1000.610.004
TS improvement vs. VAS improvement861.58 to 426.850.003
TS improvement vs. FIQR improvement651.16 to 1060.220.002
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pacheco-Barrios, K.; Filardi, R.M.; González-González, L.F.; Park, N.; Petrus, F.Q.; Navarro-Flores, A.; Di-Bonaventura, S.; Alves, L.G.; Queiroz, F.; Fregni, F. The Link between Endogenous Pain Modulation Changes and Clinical Improvement in Fibromyalgia Syndrome: A Meta-Regression Analysis. Biomedicines 2024, 12, 2097. https://doi.org/10.3390/biomedicines12092097

AMA Style

Pacheco-Barrios K, Filardi RM, González-González LF, Park N, Petrus FQ, Navarro-Flores A, Di-Bonaventura S, Alves LG, Queiroz F, Fregni F. The Link between Endogenous Pain Modulation Changes and Clinical Improvement in Fibromyalgia Syndrome: A Meta-Regression Analysis. Biomedicines. 2024; 12(9):2097. https://doi.org/10.3390/biomedicines12092097

Chicago/Turabian Style

Pacheco-Barrios, Kevin, Rafaela Machado Filardi, Luis Fernando González-González, Nayeon Park, Fernanda Queiroz Petrus, Alba Navarro-Flores, Silvia Di-Bonaventura, Luana Gola Alves, Fernanda Queiroz, and Felipe Fregni. 2024. "The Link between Endogenous Pain Modulation Changes and Clinical Improvement in Fibromyalgia Syndrome: A Meta-Regression Analysis" Biomedicines 12, no. 9: 2097. https://doi.org/10.3390/biomedicines12092097

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop