Распространение нетрофической боли в многоразовом склерозе - системном обзоре и мета-анализе с использованием алгоритма NeuPSIG.

Introduction

Multiple sclerosis (MS) is a chronic, neurodegenerative disorder characterized by demyelination of the central nervous system (Reich et al. 2018 ). It can lead to profound physical, cognitive and social impairments. Although disease‐modifying therapies are effective at slowing down disease progression as well as relapse rates, there is currently no cure. Symptomatic treatment is thus essential to improve the quality of life in people with MS (pwMS). Pain is a common problem in pwMS, estimated to affect up to 86%, and it significantly reduces quality of life (Foley et al. 2013 ).

Pain can be classified as either nociceptive, nociplastic or neuropathic according to the International Association for the Study of Pain (IASP). Distinguishing between neuropathic and non‐neuropathic pain remains challenging, and with the change in definition, there has been an increased demand for greater scrutiny in the diagnosis (Hoegh and Hodges 2025 ). To address this, the Neuropathic Pain Special Interest Group (NeuPSIG) has developed a grading system for the identification of neuropathic pain (Finnerup et al. 2016 ; Schmid et al. 2023 ) (Figure 6 ). The NeuPSIG algorithm involves multiple criteria, divided across possible , probable and definite diagnostic certainty. To indicate possible neuropathic pain, patients must meet two criteria. First, the medical history should prompt a possible explanation for neuropathic pain, and patients should present with clinical symptoms. Second, the experienced pain distribution must be neuroanatomically plausible (i.e., the experienced pain is likely to correspond to the patient's neurological damage) (Finnerup et al. 2016 ; Schmid et al. 2023 ). To indicate probable neuropathic pain, pain must also be associated with sensory abnormalities (i.e., clinical signs such as allodynia and/or hyperalgesia) in the same neuroanatomical plausible distribution (Finnerup et al. 2016 ; Schmid et al. 2023 ). Lastly, to establish definite neuropathic pain, additional objective confirmation of a lesion or disease in the somatosensory nervous system that may explain the pain is required (Finnerup et al. 2016 ; Schmid et al. 2023 ).

The prevalence of neuropathic pain in pwMS has been estimated at around 27% (Rodrigues et al. 2023 ), significantly higher than in the general population, where estimates range from 7% to 10% (Baskozos et al. 2023 ; van Hecke et al. 2014 ). The neurodegenerative nature of MS likely drives the substantially elevated prevalence of neuropathic pain. However, pwMS can also present with non‐neuropathic pain, further complicating treatment, as neuropathic and non‐neuropathic pain may require different treatment approaches (Shkodina et al. 2024 ).

The NeuPSIG algorithm has not been consistently adopted in previous studies evaluating the prevalence of neuropathic pain in MS. Studies that rely largely on self‐reported questionnaires are at greater risk of both false‐positive and false‐negative findings (Vaegter et al. 2014 ). Consequently, diagnostic certainty levels may have profound implications for the estimated prevalence of neuropathic pain. To the best of our knowledge, this has not been considered in prior reviews in MS, potentially obscuring the true prevalence of neuropathic pain in this population.

To address this, the current systematic review and meta‐analysis aimed to separately estimate the prevalence of possible , probable and definite neuropathic pain, and to provide an overall estimate of neuropathic pain among pwMS. This is highly warranted to enhance transparency in prevalence estimates and to inform future epidemiological studies that aim to distinguish between neuropathic and non‐neuropathic pain in MS.

Methods

The current systematic review and meta‐analysis were based on a comprehensive literature search using PubMed, Embase and Scopus. The initial literature search was conducted on October 24th 2024, and subsequently updated on October 14th 2025. The review followed PRISMA guidelines and was preregistered at PROSPERO (CRD42024603211) before initiation of screening (Page et al. 2021 ). The review protocol was not published and thus cannot be accessed elsewhere. A set of specific search terms was developed using a framework that adopted Medical Subject Headings. The final search string was tailored accordingly to ensure comprehensive coverage of neuropathic pain and related terms in pwMS (see Tables S1 and S2 for further details). To be included in this review, studies had to (1) include pwMS, (2) at least fulfil the criteria for possible neuropathic pain according to the NeuPSIG algorithm and (3) be an original study published between 1990 and 2025. Studies were excluded if they investigated only specific neuropathic pain conditions or manifestations (e.g., trigeminal neuralgia, painful optic neuritis or Lhermitte's phenomenon) rather than neuropathic pain as an overall pain phenotype, as this was outside the scope of the review. The reason for this was the substantial heterogeneity in the reported prevalence of such subtypes (Foley et al. 2013 ).

Data Extraction

First, two independent reviewers (R.C.J. and V.K.B.) conducted a title‐and‐abstract screening using http://covidence.org/ (2025). Both were blinded to each other's decisions, and a collective agreement was ultimately reached. Second, initially included studies were assessed for eligibility. Finally, relevant data from included studies were extracted into a separate Excel spreadsheet covering the following information: study design, sample size, sex distribution, disease duration, Expanded Disability Status Scale (EDSS), MS phenotypes, MS‐diagnostics, diagnostic certainty of neuropathic pain (i.e., fulfilment of NeuPSIG criteria), estimated prevalence of neuropathic pain and current use of pharmacological analgesics (Table 3 ). If prevalence estimates were not explicitly reported, they were calculated as the number of cases divided by the total sample size, multiplied by 100. One reviewer (R.C.J.) extracted all data, with another (V.K.B.) verifying the findings. Requests for missing data were made to the corresponding authors of the studies.

Amendments After Pre‐Registration

Initially, the current review aimed exclusively to estimate the prevalence of definite neuropathic pain in MS. However, upon reviewing the literature, the authors decided to include possible and probable neuropathic pain to elucidate potential discrepancies in prevalence estimates arising from differences in diagnostic certainty levels to clarify the overall estimate of neuropathic pain.

Regarding risk of bias analysis, the Joanna Briggs Institute's critical appraisal tool for analytical cross‐sectional studies was added. This was done because of the tailored design for cross‐sectional studies, which was not otherwise covered by the Newcastle–Ottawa Scale (Ma et al. 2020 ).

Categorization of Diagnostic Certainty of Neuropathic Pain

After extraction, prevalence estimates from individual studies were subsequently stratified by diagnostic certainty level (i.e., possible , probable or definite ) according to the following criteria:

To achieve possible diagnostic certainty of neuropathic pain, studies had to fulfil Criteria 1 and 2.

Criterion 1: To meet Criterion 1, studies needed to review participants' medical histories, providing evidence of a possible explanation of neuropathic pain, while confirming clinical symptoms. In this review, we accepted Criterion 1 if clinical symptoms were assessed via questionnaire or neurological examination. Additionally, participants had to have a valid diagnosis of MS i.e., MS confirmed by a neurologist using the McDonald or Poser criteria (Montalban et al. 2025 ; Poser et al. 1983 ), as the diagnosis inherently serves as evidence of neurodegeneration.

Criterion 2: To meet Criterion 2, studies needed to assess whether the pain distribution was neuroanatomically plausible. We accepted Criterion 2 if a neurological examination focusing on this aspect was conducted. Because most established questionnaires for distinguishing pain phenotypes were developed before the current NeuPSIG guidelines, we followed their recommendations and automatically accepted a possible diagnosis of neuropathic pain when Criterion 1 was met (Truini et al. 2023 ).

To advance to probable diagnostic certainty of neuropathic pain, studies additionally had to fulfil Criterion 3.

Criterion 3: To meet Criterion 3, studies had to perform bedside sensory tests in a neuroanatomically plausible distribution, including perception of vibration, touch, pinprick and thermal stimuli. Criterion 3 was accepted only if explicitly reported.

Lastly, for definite diagnostic certainty of neuropathic pain, studies additionally had to fulfil Criterion 4.

Criterion 4: To meet Criterion 4, studies needed to objectively confirm a lesion or disease in the somatosensory nervous system, possibly explaining the pain. We accepted Criterion 4 if studies used Magnetic Resonance Imaging (MRI) or other imaging techniques to assess neuropathic pain.

Quality Assessment

The quality assessment was conducted by two independent reviewers (R.C.J. and V.K.B.) across all included studies. In the event of any conflicting assessments, a consensus was reached through discussion. Cross‐sectional studies were evaluated using the Joanna Briggs Institute's (JBI) critical appraisal tool for analytical cross‐sectional studies. JBI comprises eight questions, each worth 1 point, for a maximum score of 8, indicating the lowest risk of bias. No cut‐off values have been established; therefore, a pragmatic framework was adopted, categorizing risk of bias as low (7–8), moderate (5–6) or high (< 4) (Barker et al. 2025 ).

Case–control and cohort studies were assessed using the Newcastle–Ottawa Scale (NOS). Depending on the study design, assessments were categorized into three areas: selection, comparability and exposure/outcome, covering eight subcategories. For case–control studies, these subcategories included adequate case definition, representativeness of cases, selection and definition of controls, comparability of cases and controls, ascertainment of exposure based on the study design or analysis, consistent methods of ascertainment for cases and controls and non‐response rate. For cohort studies, the subcategories covered are representativeness of the exposed cohort, selection of the non‐exposed cohort, ascertainment of exposure, confirmation that the outcome was not present at baseline, comparability of cohorts based on study design or analysis, outcome assessment, sufficient follow‐up time for outcomes to occur and adequacy of follow‐up. The NOS scores ranged from 0 (highest risk of bias) to 9 (lowest risk of bias). No cut‐off values have been established; therefore, a pragmatic framework was adopted, categorizing risk of bias as low (7–9), moderate (4–6) or high (< 4) (Wells et al. 2000 ).

Data Synthesis and Statistical Analysis

Meta‐analysis was performed in RStudio (version 2025.05.1+513) to estimate the overall prevalence and the prevalence of possible and definite neuropathic pain in MS. The analysis adhered to the Meta‐analysis of Observational Studies in Epidemiology framework and was conducted using the Generic Inverse Variance method with a random‐effects model due to the substantial heterogeneity between the results of included studies (Stroup et al. 2000 ). Individual prevalence estimates (entered as proportions) with corresponding standard errors (calculated as the square root of the proportion multiplied by 1 minus the proportion divided by the sample size) were entered in RStudio. Subsequently, pooled proportions were computed using the metagen() function from the meta package in R. Heterogeneity was assessed using the Cochrane Q test and quantified with I 2 statistics. Values greater than 50% indicated substantial heterogeneity, while values less than 50% indicated low or limited heterogeneity. A sensitivity analysis was conducted to assess the robustness of the findings; substantial changes in the estimated prevalences after removing individual outliers were considered significant if they affected the overall conclusions. Individual outliers were identified through visual inspection of the forest plots as studies with prevalence estimates clearly separated from the distribution of the remaining studies, and were considered likely to exert a disproportionate influence on the pooled estimates. The same approach was applied to assess the impact of EDSS, disease duration and disease progression in a clinical sensitivity analysis. Lower clinical extremes were defined as low disability (EDSS below 1), short disease duration (below 2 years) and/or low disease progression (100% RRMS), and were removed from the analyses to establish if this affected the estimates. Extracted data as well as analytical code (Rscript) for all analyses are available in the Supporting Information .

Results

The PRISMA flow chart is shown in Figure 1 . N = 32 individual papers were identified and included in data extraction and subsequently incorporated into the meta‐analysis. N = 26 studies, including n = 5399 pwMS, met the criteria for possible diagnostic certainty of neuropathic pain. N = 1 study, including n = 374 pwMS, met the criteria for probable diagnostic certainty of neuropathic pain. Lastly, N = 5 studies, including n = 2064 pwMS, met the criteria for definite diagnostic certainty of neuropathic pain (Tables 1 and 2 ).

Risk of Bias

The majority of the included studies had low to moderate risk of bias, assessed using JBI (for cross‐sectional studies) or NOS (for case–control and cohort studies) (Tables 3 and 4 ).

Prevalence of Neuropathic Pain According to the NeuPSIG Algorithm

Overall pooled estimates of neuropathic pain in pwMS revealed a prevalence of 22.3%, 95% CI (17.3; 27.3), with a heterogeneity of 97.1% (Figure 2 ). Stratified by diagnostic certainty, the estimated prevalence of possible neuropathic pain in pwMS was 22.0%, 95% CI (16.0; 28.0), with a heterogeneity of 97.4% (Figure 3 ). Only one eligible study was found complying with the criteria for probable neuropathic pain. Consequently, a meta‐analysis could not be performed due to the lack of multiple independent data points. The prevalence reported in this study was 23.7% (95% CI: 19.4; 28.0) ( N = 1) (Figure 4 ). Lastly, the estimated prevalence of definite neuropathic pain was 23.4%, 95% CI (11.1; 35.8), with a heterogeneity of 92.1% (Figure 5 ). The sensitivity analysis did not affect the overall conclusions of estimated prevalences after removing individual outliers (Feketová et al. 2017 ; Jallouli, Ghroubi, Ben Dhia, et al. 2025 ; Moisset et al. 2013 ), for either overall (18.8% [95% CI: 15.0; 22.7]) or possible (17.6% [95% CI: 13.2; 22.0]) neuropathic pain, indicating robustness of the results. After removing lower clinical extremes (Aliş et al. 2025 ; Ismail et al. 2025 ; Jallouli, Ghroubi, Ben Dhia, et al. 2025 ), the overall sensitivity analysis for low disability, duration, and progression similarly did not affect the conclusions (20.5% [95% CI: 15.4; 25.6]), indicating no clear impact.

Discussion

Given the underlying pathophysiological mechanism of the disease, it seems reasonable to expect an elevated prevalence of neuropathic pain in pwMS. The main findings support this assumption by presenting an overall prevalence of neuropathic pain of 22.3% (95% CI: 17.3; 27.3) in pwMS, which is substantially higher than the 7%–10% observed in the general population (Baskozos et al. 2023 ; van Hecke et al. 2014 ). When stratifying by diagnostic certainty in accordance with the NeuPSIG algorithm, results revealed a prevalence of 22.0% (95% CI: 16.0; 28.0) for possible neuropathic pain , 23.7% (95% CI: 19.4; 28.0) for probable neuropathic pain and 23.4% (95% CI: 11.1; 35.8) for definite neuropathic pain in pwMS (Figure 6 ). Conceptually, possible diagnostic certainty should encompass cases of definite neuropathic pain. The higher prevalence estimates observed for definite and probable neuropathic pain thus appear counterintuitive. This likely reflects substantial methodological heterogeneity among studies (Table 2 ), rather than true variation across diagnostic certainty levels. Furthermore, the estimate of probable neuropathic pain is based on a single study, precluding direct comparisons across diagnostic certainty levels. As such, our findings should not be interpreted as direct comparisons between diagnostic certainty but rather as individual prevalence estimates.

Across all the included studies, the majority only met the criteria for possible neuropathic pain, while only a few studies complied with probable or definite neuropathic pain. This is likely because questionnaires such as DN4, PainDETECT, and LANSS are relatively easy to administer. In contrast, the economic and professional constraints of physical and sensory examinations, as well as objective confirmatory tests (e.g., MRI or neurophysiological techniques), present major barriers to the routine implementation of such diagnostics (Truini et al. 2023 ). A previous study indicates that using predominantly self‐reported questionnaires when screening for neuropathic pain may increase the risk of misdiagnosis (Vaegter et al. 2014 ). However, based on the findings of this review, these questionnaires appear appropriate for initial screening and epidemiological purposes, as pooled prevalences yielded estimates numerically similar to those at the probable and definite certainty levels. This supports their use, consistent with their widespread use and current recommendations (Truini et al. 2023 ). A similar overall prevalence, however, does not reveal whether the same individuals would have had the same outcome with a different level of diagnostic certainty (Vaegter et al. 2014 ). It is thus not possible to assess the extent of false positives and/or negatives, emphasizing the importance of not only relying on estimates but also adopting a holistic approach when diagnosing pain phenotypes to improve accuracy and ultimately treatment outcomes (Soliman et al. 2025 ). Importantly, a possible diagnosis cannot replace the probable and definite certainty provided by NeuPSIG in clinical practice (Finnerup et al. 2016 ).

Conflicting Findings Between the Current and Previous Reviews

A previous review aimed to estimate the overall prevalence of neuropathic pain in pwMS (Rodrigues et al. 2023 ). However, its result (26.8%) differs from that of the current review (22.3%). This discrepancy may be due to their inclusion of studies that exclusively investigated pwMS with pain, likely overinflating estimates (Ata et al. 2021 ; Foley et al. 2022 ; Khan and Pallant 2007 ; Kim et al. 2019 , 2020 ; Solaro et al. 2004 ; Srotova et al. 2021 ; Young et al. 2017 ). The current review excluded such studies, as these cannot accurately estimate the true prevalence of neuropathic pain (Truini et al. 2023 ).

A Mechanistic Approach to Analgesic Treatment

Importantly, the algorithms used across diagnostic certainty levels do not yield a definitive diagnosis of neuropathic pain (i.e., not to be confused with definite neuropathic pain). Instead, the grading system evaluate the probability that the experienced pain is caused by neurological damage, with increasing certainty as more criteria are met (Finnerup et al. 2016 ). Furthermore, a distinct challenge arises in differentiating between pain phenotypes in MS. Due to the neurodegenerative pathophysiology of the disease, complete exclusion of neuropathic pain seems unlikely. A recent study supports this argument by finding that the majority of pwMS experience co‐occurring pains, often encompassing multiple phenotypes at once (Dagnew et al. 2025 ). Therefore, most of the pain experienced by pwMS will likely present as either co‐occurring pains (most likely) or strictly as neuropathic pain (less likely).

Descriptors such as nociceptive, nociplastic and neuropathic pain are not mechanisms themselves; rather, they aim to bridge the gap between the underlying mechanisms of the experienced pain and chronic pain classifications by grouping pain into clusters that can presumably be attributed to similar mechanisms. However, this appears irrelevant unless coupled with a mechanism‐specific treatment. While further research is still needed, descriptor‐based approaches have been proposed as tools for optimal prescription of analgesic treatments, as treatments for neuropathic pain differ from those for other pain phenotypes (Hoegh and Hodges 2025 ). Accurately identifying pain descriptors in accordance with best practice is thus essential, as it increases confidence in the underlying mechanism and, in turn, the likelihood of a successful treatment outcome. Moreover, awareness of pain phenotype allows clinicians and patients to collaborate in making informed decisions tailored to individual preferences, ultimately promoting patient autonomy (Soliman et al. 2025 ).

In MS, the McDonald criteria are the standard criteria for disease diagnosis. According to these criteria, patients must present with multiple lesions in the central nervous system (CNS) that are disseminated in space (Montalban et al. 2025 ). To assess these criteria, confirmatory tests (i.e., MRI) are required. All pwMS will thus already have objective evidence of CNS lesions, possibly explaining their experienced pain. However, determining whether lesions are neuroanatomically plausible for explaining pain in MS is complex, as the correlation between lesion location and pain is at best moderate‐to‐good (Ismail et al. 2025 ; Ouyang et al. 2024 ; Seixas et al. 2014 ; Svendsen et al. 2011 ). In Parkinson's disease, it has recently been suggested that when neuropathic pain is clinically present, but a lesion to the somatosensory system cannot be confirmed by objective assessments, the diagnosis is to be considered probable (Tinazzi et al. 2025 ). Given these considerations and in line with existing guidelines, the establishment of a probable diagnosis of neuropathic pain may be adequate for prompting analgesic treatments in pwMS, provided that the patient has a valid diagnosis of MS (Finnerup et al. 2016 ; Treede et al. 2019 ).

Correlation Between Neuropathic Pain and Disability, Disease Duration and MS Phenotypes

A high heterogeneity was observed in the analysis of the overall I 2 = 97.1 % prevalence, as well as of possible I 2 = 97.4 % and definite I 2 = 92.1 % neuropathic pain. This may be explained by differences in the characteristics of the included sample across the individual studies, such as EDSS, disease duration and MS phenotypes. It has been shown that increased lesion load is correlated with disease duration, Expanded Disability Status Scale (EDSS) and MS phenotypes, and is therefore expectedly (positively) associated with increased prevalence of neuropathic pain (Ismail et al. 2025 ; Solaro et al. 2018 ; Srotova et al. 2021 ; Svendsen et al. 2011 ). This seems especially true for the spinothalamic tract (i.e., the neural pain pathway), which has been linked to neuropathic pain (Truini et al. 2013 ), consistent with its well‐established role in the somatosensory nervous system. However, the sensitivity analysis indicated that studies with lower mean/median EDSS and disease duration, as well as those with less progressive MS phenotypes, did not appear to systematically bias the pooled estimates.

Limitations

First, a key strength of the current study, which may further explain discrepancies from previous findings, is its strict methodological approach (i.e., clear discrimination among diagnostic certainty levels). However, this review does not allow for a direct comparison of diagnostic methods, as prevalence estimates are not derived from the same MS population. Furthermore, the substantial differences in the number of studies included across the three diagnostic certainty strata as well as the heterogeneous methodology across studies may have influenced the overall estimates, making the findings subject to cautious interpretation, especially in regard to probable neuropathic pain, as this prevalence is based solely on one study (Ferraro et al. 2018 ). Importantly, comparability of prevalences across diagnostic certainty levels cannot be assumed even when they are numerically similar, as this likely reflects substantial methodological heterogeneity within each diagnostic certainty level rather than indicating the impact thereof.

Moreover, when it comes to central neuropathic pain, it should further be acknowledged that objectively identified lesions affecting the somatosensory nervous system may not necessarily be causally related to pain. Hence, achieving definite diagnostic certainty does not constitute absolute proof of central neuropathic pain but should rather be interpreted as a strong indication within the context of best clinical practice (Finnerup et al. 2016 ).

Additionally, the current review focused on overall neuropathic pain, and therefore excluded studies reporting exclusively on specific neuropathic pain subtypes (e.g., trigeminal neuralgia or Lhermitte's phenomenon) or co‐occurring pain when presented. However, it is acknowledged that distinguishing between individual subtypes upon diagnosing the underlying pain phenotype remains highly important for prevalence estimates (Foley et al. 2013 ). Furthermore, pain in MS is rarely exclusively neuropathic and often presents as a co‐occurrence of nociceptive and neuropathic pain characteristics, further complicating diagnoses (Tinazzi et al. 2025 ).

Clinical Implications and Future Directions

In line with NeuPSIG guidelines, a possible diagnosis appears appropriate for epidemiologic purposes and for initial examinations, prompting clinicians to conduct further neurological examinations in pwMS (Truini et al. 2023 ). The advantage of a grading system for neuropathic pain is that it allows many factors, such as prior experience and patient preferences, to be incorporated into the final judgement. Diagnostics should primarily rely on objective findings to align with research and guideline recommendations. Thus, methods that achieve probable or definite diagnostic certainty should be incorporated to differentiate between neuropathic and non‐neuropathic pain in pwMS in future non‐epidemiological studies and clinical settings. Furthermore, future studies directly comparing methods for possible , probable and definite neuropathic pain in the same population are warranted to confirm the findings of the current review. Lastly, highlighting potential limitations in applying the NeuPSIG algorithm to central neurodegenerative disorders may inform future refinement and use.

Conclusion

The findings of the current review revealed an overall prevalence of 22.3% (95% CI: 17.3; 27.3) for neuropathic pain in pwMS. When stratifying by diagnostic certainty according to the NeupSIG algorithm, findings revealed individual prevalence estimates for possible (22.0%, 95% CI: 16.0; 28.0), probable (23.7%, 95% CI: 19.4; 28.0) and definite (23.4%, 95% CI: 11.1; 35.8) neuropathic pain. While these estimates are numerically similar, they do not provide evidence of comparability across diagnostic certainty levels; rather, they reflect substantial methodological heterogeneity among the included studies. As such, in line with international recommendations, possible diagnostic certainty appears appropriate for epidemiological purposes as well as for initial screening of pain phenotype in pwMS. However, probable and/or definite diagnoses should be adopted in clinical practice and in future studies investigating pain‐phenotype‐based effects in pwMS.

Author Contributions

The concept of the current review was developed in collaboration between M.H., U.D. and R.C.J. Screening of papers, data extraction, and risk‐of‐bias analysis were conducted by R.C.J. and V.K.B. All the aforementioned authors, as well as L.G.H., contributed substantially by participating in the discussion and interpretation of the results and by commenting on the manuscript drafted by the first author. All authors have approved the final version of the manuscript and take full responsibility for the content of this paper.

Funding

This research did not receive any grants from funding agencies in the public, commercial or not‐for‐profit sectors.

Disclosure

The manuscript was drafted by the authors. During the final linguistic editing of the manuscript, the authors used ChatGPT.com to further improve its readability and language. After using this tool, the authors reviewed all edits and further adapted the manuscript. The authors take full responsibility for the content of the published article.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: Extracted data_NP in MS.

Data S2: Rscript_NP in MS.

Table S1: Conceptual framework providing a comprehensive and scoping overview covering the epidemiology of neuropathic pain and related terms in pwMS.

Table S2: Search strings used to identify relevant literature in PubMed, Embase and Scopus.

Acknowledgements

We would like to acknowledge Bo M. Bibby for his valuable statistical advice during the synthesis of the extracted data for the meta‐analysis.

References

Abou, L. , Whibley D., Clauw D. J., and Kratz A. L.. 2024. “Widespread Pain With Nociplastic Features Is an Independent Predictor of Low Physical Activity in People With Multiple Sclerosis.” Journal of Pain 25, no. 2: 418–427. 10.1016/j.jpain.2023.09.005.

Aliş, C. , Kose N., Sen Kilic S., Genc G., and Bulut S.. 2025. “Predictors of Poor Sleep Quality in Multiple Sclerosis: The Independent Role of Anxiety and Restless Legs Syndrome.” Sleep Medicine 134: 106691. 10.1016/j.sleep.2025.106691.

Ata, E. , Özsoy‐Ünübol T., and Demir S.. 2021. “Evaluation of Neuropathic Extremity Pain in Patients With Multiple Sclerosis.” Noro Psikiyatri Arsivi 58, no. 3: 213–216. 10.29399/npa.24802.

Balshi, A. , Manning N., Dempsey J., Jun C., Baber U., and Sloane J. A.. 2024. “Clinical Course of Multiple Sclerosis With Comorbid Endometriosis: A Matched Cohort Study.” Multiple Sclerosis and Related Disorders 82: 105377. 10.1016/j.msard.2023.105377.

Barker, T. H. , Hasanoff S., Aromataris E., et al. 2025. “The Revised JBI Critical Appraisal Tool for the Assessment of Risk of Bias for Analytical Cross‐Sectional Studies.” JBI Evidence Synthesis 24: 401–408. 10.11124/jbies-24-00523.

Baskozos, G. , Hébert H. L., Pascal M. M., et al. 2023. “Epidemiology of Neuropathic Pain: An Analysis of Prevalence and Associated Factors in UK Biobank.” Pain Reports 8, no. 2: e1066. 10.1097/pr9.0000000000001066.

Boivie, J. 1999. “Central Pain.” In Textbook of Pain, edited by Wall P. D. and Melzack R., 4th ed., 879–914. Churchill Livingstone.

Boneschi, F. M. , Colombo B., Annovazzi P., et al. 2008. “Lifetime and Actual Prevalence of Pain and Headache in Multiple Sclerosis.” Multiple Sclerosis 14, no. 4: 514–521. 10.1177/1352458507085551.

Bozhenko, M. , Nehrych T., and Bozhenko N.. 2021. “Psycho‐Emotional Aspects in the Formation of Pain Syndrome Characteristics in Patients With Multiple Sclerosis.” In Proceedings of the Shevchenko Scientific Society. Medical Sciences , 65. 10.25040/ntsh2021.02.15.

Clemenzi, A. , Pompa A., Casillo P., et al. 2014. “Chronic Pain in Multiple Sclerosis: Is There Also Fibromyalgia? An Observational Study.” Medical Science Monitor 20: 758–766. 10.12659/MSM.890009.

Da Silva, J. V. M. , De Oliveira B. F. A., Do Nascimento O. J. M., et al. 2015. “Increased Multiple Sclerosis Relapses Related to Lower Prevalence of Pain.” Arquivos de Neuro‐Psiquiatria 73, no. 7: 593–600. 10.1590/0004-282X20150073.

Dagnew, B. , Campbell J. A., Laslett L. L., et al. 2025. “Understanding Pain Types and the Lived Experiences of Individuals With Multiple Sclerosis and Pain: A Mixed Methods Study.” Multiple Sclerosis and Related Disorders 104: 106778. 10.1016/j.msard.2025.106778.

Daly, D. , and Sweeney B.. 2022. “Patterns of Use and Effectiveness of Analgesics and Cannabinoids for Pain Relief Among Irish Patients With Multiple Sclerosis.” Journal of the Neurological Sciences 429: 118115. 10.1016/j.jns.2021.118115.

Feketová, S. , Waczulíková I., Mareš J., and Kukumberg P.. 2017. “Central Pain in Patients With Multiple Sclerosis.” Multiple Sclerosis 18, no. 4: 98. 10.1177/1352458512459019.

Ferraro, D. , Plantone D., Morselli F., et al. 2018. “Systematic Assessment and Characterization of Chronic Pain in Multiple Sclerosis Patients.” Neurological Sciences 39, no. 3: 445–453. 10.1007/s10072-017-3217-x.

Finnerup, N. B. , Haroutounian S., Kamerman P., et al. 2016. “Neuropathic Pain: An Updated Grading System for Research and Clinical Practice.” Pain 157, no. 8: 1599–1606. 10.1097/j.pain.0000000000000492.

Foley, P. , Kong Y., Dirvanskiene R., et al. 2022. “Coupling Cognitive and Brainstem Dysfunction in Multiple Sclerosis‐Related Chronic Neuropathic Limb Pain.” Brain Communications 4, no. 3: fcac124. 10.1093/braincomms/fcac124.

Foley, P. L. , Vesterinen H. M., Laird B. J., et al. 2013. “Prevalence and Natural History of Pain in Adults With Multiple Sclerosis: Systematic Review and Meta‐Analysis.” Pain 154, no. 5: 632–642. 10.1016/j.pain.2012.12.002.

Grasso, M. G. , Clemenzi A., Tonini A., et al. 2008. “Pain in Multiple Sclerosis: A Clinical and Instrumental Approach.” Multiple Sclerosis 14, no. 4: 506–513. 10.1177/1352458507085553.

Grau‐López, L. , Sierra S., Martínez‐Cáceres E., and Ramo‐Tello C.. 2011. “Análisis del Dolor en Pacientes Con Esclerosis múltiple.” Neurología 26, no. 4: 208–213. 10.1016/j.nrl.2010.07.014.

Heitmann, H. , Biberacher V., Buck D., et al. 2015. “Prevalence of Neuropathic Pain in Early Multiple Sclerosis.” Multiple Sclerosis 23, no. 11: 407. 10.1177/1352458515602642.

Heitmann, H. , Haller B., Tiemann L., et al. 2020. “Longitudinal Prevalence and Determinants of Pain in Multiple Sclerosis: Results From the German National Multiple Sclerosis Cohort Study.” Pain 161, no. 4: 787–796. 10.1097/j.pain.0000000000001767.

Hoegh, M. , and Hodges P.. 2025. “Pain Science in Practice (Part 8): Nociceptive, Neuropathic, and Nociplastic Pain.” Journal of Orthopaedic & Sports Physical Therapy 55, no. 9: 1–6. 10.2519/jospt.2025.13335.

Hyun, J. W. , Jang H., Yu J., et al. 2020. “Comparison of Neuropathic Pain in Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis.” Journal of Clinical Neurology (Korea) 16, no. 1: 124–130. 10.3988/jcn.2020.16.1.124.

Ismail, M. H. , ElMotayam A. S. E., Saadawy S. F., Elfwakhry R. M., Fawzy H. M., and Sarhan N. T.. 2025. “Neuropathic Pain in Multiple Sclerosis: Serum Biomarkers and MRI Lesion Localization.” Egyptian Journal of Neurology, Psychiatry and Neurosurgery 61, no. 1: 29. 10.1186/s41983-025-00955-8.

Jallouli, S. , Ghroubi S., Ben Dhia I., et al. 2025. “Static Postural Balance and Manual Dexterity Are Sex‐Dependent Among a Sample of Relapsing‐Remitting Multiple Sclerosis Patients.” Neurological Research 47, no. 6: 466–479. 10.1080/01616412.2025.2484426.

Jallouli, S. , Ghroubi S., Dammak M., et al. 2025. “12‐Week Melatonin Supplementation Improved Dynamic Postural Stability and Walking Performance in Persons Living With Multiple Sclerosis: A Randomized Controlled Trial.” Behavioural Brain Research 476: 115191. 10.1016/j.bbr.2024.115191.

Kahraman, T. , Özdoğar A. T., Ertekin Ö., and Özakbaş S.. 2019. “Frequency, Type, Distribution of Pain and Related Factors in Persons With Multiple Sclerosis.” Multiple Sclerosis and Related Disorders 28: 221–225. 10.1016/j.msard.2019.01.002.

Kalia, L. V. , and O'Connor P. W.. 2005. “Severity of Chronic Pain and Its Relationship to Quality of Life in Multiple Sclerosis.” Multiple Sclerosis 11, no. 3: 322–327. 10.1191/1352458505ms1168oa.

Kasap, Z. , and Uǧurlu H.. 2023. “Pain in Patients With Multiple Sclerosis.” Turkish Journal of Physical Medicine and Rehabilitation 69, no. 1: 31–39. 10.5606/tftrd.2022.10524.

Khan, A. , Kamran S., Ponirakis G., et al. 2018. “Peripheral Neuropathy in Patients With Multiple Sclerosis.” PLoS One 13, no. 3: e0193270. 10.1371/journal.pone.0193270.

Khan, F. , and Pallant J.. 2007. “Chronic Pain in Multiple Sclerosis: Prevalence, Characteristics, and Impact on Quality of Life in an Australian Community Cohort.” Journal of Pain 8, no. 8: 614–623. 10.1016/j.jpain.2007.03.005.

Kim, J. A. , Bosma R. L., Hemington K. S., et al. 2019. “Neuropathic Pain and Pain Interference Are Linked to Alpha‐Band Slowing and Reduced Beta‐Band Magnetoencephalography Activity Within the Dynamic Pain Connectome in Patients With Multiple Sclerosis.” Pain 160, no. 1: 187–197. 10.1097/j.pain.0000000000001391.

Kim, J. A. , Bosma R. L., Hemington K. S., et al. 2020. “Cross‐Network Coupling of Neural Oscillations in the Dynamic Pain Connectome Reflects Chronic Neuropathic Pain in Multiple Sclerosis.” Neuroimage Clin 26: 102230. 10.1016/j.nicl.2020.102230.

Ma, L. L. , Wang Y. Y., Yang Z. H., Huang D., Weng H., and Zeng X. T.. 2020. “Methodological Quality (Risk of Bias) Assessment Tools for Primary and Secondary Medical Studies: What Are They and Which Is Better?” Military Medical Research 7, no. 1: 7. 10.1186/s40779-020-00238-8.

Moisset, X. , Ouchchane L., Guy N., Bayle D. J., Dallel R., and Clavelou P.. 2013. “Migraine Headaches and Pain With Neuropathic Characteristics: Comorbid Conditions in Patients With Multiple Sclerosis.” Pain 154, no. 12: 2691–2699. 10.1016/j.pain.2013.07.050.

Montalban, X. , Lebrun‐Frénay C., Oh J., et al. 2025. “Diagnosis of Multiple Sclerosis: 2024 Revisions of the McDonald Criteria.” Lancet Neurology 24, no. 10: 850–865. 10.1016/s1474-4422(25)00270-4.

Osterberg, A. , Boivie J., and Thuomas K. A.. 2005. “Central Pain in Multiple Sclerosis—Prevalence and Clinical Characteristics.” European Journal of Pain 9, no. 5: 531–542. 10.1016/j.ejpain.2004.11.005.

Ouyang, H. , Li X., Xu H., et al. 2024. “Risk Factors of Neuropathic Pain in Multiple Sclerosis: A Retrospective Case‐Cohort Study.” Frontiers in Immunology 15: 1309583. 10.3389/fimmu.2024.1309583.

Page, M. J. , McKenzie J. E., Bossuyt P. M., et al. 2021. “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews.” BMJ 372: n71. 10.1136/bmj.n71.

Pompa, A. , Clemenzi A., Troisi E., et al. 2015. “Chronic Pain in Multiple Sclerosis Patients: Utility of Sensory Quantitative Testing in Patients With Fibromyalgia Comorbidity.” European Neurology 73, no. 5‐6: 257–263. 10.1159/000381211.

Poser, C. M. , Paty D. W., Scheinberg L., et al. 1983. “New Diagnostic Criteria for Multiple Sclerosis: Guidelines for Research Protocols.” Annals of Neurology 13, no. 3: 227–231. 10.1002/ana.410130302.

Reich, D. S. , Lucchinetti C. F., and Calabresi P. A.. 2018. “Multiple Sclerosis.” New England Journal of Medicine 378, no. 2: 169–180. 10.1056/NEJMra1401483.

Rodrigues, P. , da Silva B., and Trevisan G.. 2023. “A Systematic Review and Meta‐Analysis of Neuropathic Pain in Multiple Sclerosis: Prevalence, Clinical Types, Sex Dimorphism, and Increased Depression and Anxiety Symptoms.” Neuroscience and Biobehavioral Reviews 154: 105401. 10.1016/j.neubiorev.2023.105401.

Schmid, A. B. , Tampin B., Baron R., et al. 2023. “Recommendations for Terminology and the Identification of Neuropathic Pain in People With Spine‐Related Leg Pain. Outcomes From the NeuPSIG Working Group.” Pain 164, no. 8: 1693–1704. 10.1097/j.pain.0000000000002919.

Seixas, D. , Foley P., Palace J., Lima D., Ramos I., and Tracey I.. 2014. “Pain in Multiple Sclerosis: A Systematic Review of Neuroimaging Studies.” Neuroimage Clin 5: 322–331. 10.1016/j.nicl.2014.06.014.

Seixas, D. , Sá M. J., Galhardo V., Guimarães J., and Lima D.. 2011. “Pain in Portuguese Patients With Multiple Sclerosis.” Frontiers in Neurology 2: 20. 10.3389/fneur.2011.00020.

Shahrbanian, S. , Duquette P., and Mayo N. E.. 2019. “Does Type of Pain Predict Pain Severity Changes in Individuals With Multiple Sclerosis? A Longitudinal Analysis Using Generalized Estimating Equations.” Journal of Advances in Medical and Biomedical Research 27, no. 122: 9–15. 10.30699/jambs.27.122.9.

Shkodina, A. D. , Bardhan M., Chopra H., et al. 2024. “Pharmacological and Non‐Pharmacological Approaches for the Management of Neuropathic Pain in Multiple Sclerosis.” CNS Drugs 38, no. 3: 205–224. 10.1007/s40263-024-01072-5.

Solaro, C. , Brichetto G., Amato M. P., et al. 2004. “The Prevalence of Pain in Multiple Sclerosis: A Multicenter Cross‐Sectional Study.” Neurology 63, no. 5: 919–921. 10.1212/01.wnl.0000137047.85868.d6.

Solaro, C. , Cella M., Signori A., et al. 2018. “Identifying Neuropathic Pain in Patients With Multiple Sclerosis: A Cross‐Sectional Multicenter Study Using Highly Specific Criteria.” Journal of Neurology 265, no. 4: 828–835. 10.1007/s00415-018-8758-2.

Soliman, N. , Moisset X., Ferraro M. C., et al. 2025. “Pharmacotherapy and Non‐Invasive Neuromodulation for Neuropathic Pain: A Systematic Review and Meta‐Analysis.” Lancet Neurology 24, no. 5: 413–428. 10.1016/s1474-4422(25)00068-7.

Srotova, I. , Kocica J., Vollert J., et al. 2021. “Sensory and Pain Modulation Profiles of Ongoing Central Neuropathic Extremity Pain in Multiple Sclerosis.” European Journal of Pain 25, no. 3: 573–594. 10.1002/ejp.1695.

Stroup, D. F. , Berlin J. A., Morton S. C., et al. 2000. “Meta‐Analysis of Observational Studies in Epidemiology: A Proposal for Reporting. Meta‐Analysis of Observational Studies in Epidemiology (MOOSE) Group.” JAMA 283, no. 15: 2008–2012. 10.1001/jama.283.15.2008.

Svendsen, K. B. , Sørensen L., Jensen T. S., Hansen H. J., and Bach F. W.. 2011. “MRI of the Central Nervous System in MS Patients With and Without Pain.” European Journal of Pain 15, no. 4: 395–401. 10.1016/j.ejpain.2010.09.006.

Tinazzi, M. , Gandolfi M., Artusi C. A., et al. 2025. “Advances in Diagnosis, Classification, and Management of Pain in Parkinson's Disease.” Lancet Neurology 24, no. 4: 331–347. 10.1016/s1474-4422(25)00033-x.

Treede, R. D. , Rief W., Barke A., et al. 2019. “Chronic Pain as a Symptom or a Disease: The IASP Classification of Chronic Pain for the International Classification of Diseases (ICD‐11).” Pain 160, no. 1: 19–27. 10.1097/j.pain.0000000000001384.

Treede, R.‐D. , Jensen T. S., Campbell J. N., et al. 2008. “Neuropathic Pain.” Neurology 70, no. 18: 1630–1635. 10.1212/01.wnl.0000282763.29778.59.

Truini, A. , Aleksovska K., Anderson C. C., et al. 2023. “Joint European Academy of Neurology‐European Pain Federation‐Neuropathic Pain Special Interest Group of the International Association for the Study of Pain Guidelines on Neuropathic Pain Assessment.” European Journal of Neurology 30, no. 8: 2177–2196. 10.1111/ene.15831.

Truini, A. , Barbanti P., Pozzilli C., and Cruccu G.. 2013. “A Mechanism‐Based Classification of Pain in Multiple Sclerosis.” Journal of Neurology 260, no. 2: 351–367. 10.1007/s00415-012-6579-2.

Truini, A. , Galeotti F., La Cesa S., et al. 2012. “Mechanisms of Pain in Multiple Sclerosis: A Combined Clinical and Neurophysiological Study.” Pain 153, no. 10: 2048–2054. 10.1016/j.pain.2012.05.024.

Vaegter, H. B. , Andersen P. G., Madsen M. F., Handberg G., and Enggaard T. P.. 2014. “Prevalence of Neuropathic Pain According to the IASP Grading System in Patients With Chronic Non‐Malignant Pain.” Pain Medicine 15, no. 1: 120–127. 10.1111/pme.12273.

van Hecke, O. , Austin S. K., Khan R. A., Smith B. H., and Torrance N.. 2014. “Neuropathic Pain in the General Population: A Systematic Review of Epidemiological Studies.” Pain 155, no. 4: 654–662. 10.1016/j.pain.2013.11.013.

Veličkaitė, G. , Jucevičiūtė N., Balnytė R., Laucius O., and Vaitkus A.. 2020. “Pain Characteristics and Associations With Quality of Life in Patients With Multiple Sclerosis in Lithuania.” Medicina (Kaunas, Lithuania) 56, no. 11: 596. 10.3390/medicina56110596.

Vidović, V. , Rovazdi M., Slivar S. R., Kraml O., and Kes V. B.. 2014. “Pain Syndromes in Multiple Sclerosis Patients—Patient Experience at Lipik Special Hospital for Medical Rehabilitation.” Acta Clinica Croatica 53, no. 4: 405–410.

Wells, G. A. , Shea B. J., O'Connell D., et al. 2000. “The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta‐Analyses.” https://ohri.ca/en/who‐we‐are/core‐facilities‐and‐platforms/ottawa‐methods‐centre/newcastle‐ottawa‐scale .

Widerström‐Noga, E. , Loeser J. D., Jensen T. S., and Finnerup N. B.. 2017. “AAPT Diagnostic Criteria for Central Neuropathic Pain.” Journal of Pain 18, no. 12: 1417–1426. 10.1016/j.jpain.2017.06.003.

Young, J. , Amatya B., Galea M. P., and Khan F.. 2017. “Chronic Pain in Multiple Sclerosis: A 10‐Year Longitudinal Study.” Scandinavian Journal of Pain 16: 198–203. 10.1016/j.sjpain.2017.04.070.

Associated Data

Supplementary Materials

Data S1: Extracted data_NP in MS.

Data S2: Rscript_NP in MS.

Table S1: Conceptual framework providing a comprehensive and scoping overview covering the epidemiology of neuropathic pain and related terms in pwMS.

Table S2: Search strings used to identify relevant literature in PubMed, Embase and Scopus.