1. Introduction
Chronic pain as a non-communicable disease is a global concern; it poses a major burden on the health of the people and the health care system. According to a meta-analysis that collected data on the worldwide burden of diseases in Europe, Asia, and the United States and included 25 studies, the prevalence of chronic pain is around 20% [ 1 ]. Chronic pain has multiple origins that are biological, psychological, and socio-demographic. It is caused by injury or disease and may involve musculoskeletal conditions such as arthritis, back pain, or fibromyalgia, and neuropathic conditions such as diabetic neuropathy or post-herpetic neuralgia [ 2 , 3 ]. The prevalence of LBP increases with age, and women have been found to have more severe as well as widespread pain than men [ 4 , 5 ]. Several patient or disease factors cause the burden to be higher, including lower income and education, and a history of abuse or trauma. Smoking and lack of physical activity also play a part in chronic pain as a lifestyle factor [ 6 ].
The burden of chronic pain extends beyond physical suffering. Pain’s economic consequence is more extensive than that of most other illnesses, mainly because of its cost-effect on productivity, which involves high absence from work rates, low activity levels, and early retirement [ 7 , 8 ]. For example, anxiety caused by pain, especially during flare-ups, was depicted in the US study, where arthritis would cost $7.1 billion in lost productive work time, with 65.7% of this cost attributed to the 38% of workers experiencing pain exacerbations [ 9 ].
There is a pressing need for better tools to assess, monitor, and manage chronic pain. Biomarkers have been suggested as one such tool; however, several challenges remain in their clinical application. The heterogeneity of pain conditions complicates the identification of universally applicable biomarkers. Furthermore, the transition from laboratory research to clinical practice is often hindered by issues related to specificity, accessibility, and the need for standardized protocols [ 10 ]. As a result, while some biomarkers have shown promise, many are still in the exploratory phase, requiring further validation through large-scale studies that account for confounding factors and variability in pain presentations [ 11 , 12 ].
New directions in pain management focus on the application of the principles of pharmacogenomics, epigenomics, and proteomics as a roadmap to creating individualized treatment strategies for patients with chronic pain. Neuroimaging has also enhanced knowledge of the perception and sustained nature of chronic pain and its neural basis. Pharmacological, psychological, and physical methods are also increasingly regarded as important intervention techniques. Nevertheless, there are some issues concerning the application of these findings into routine clinical practice, which supports the necessity of further interdisciplinary cooperation and a patient-centered approach to this complex and widespread issue.
A clinically useful biomarker would require a consistent association with pain-related outcomes, analytical reliability, and external validation. Recent years have seen rapid growth in biomarker research for chronic pain, accompanied by an increasing number of systematic reviews across diverse biomarker classes and pain conditions. Nevertheless, this evidence remains fragmented, with substantial variation in scope, methodology, outcome definitions, and conclusions. This limits comparability and obscures consistent patterns. Many reviews focus on single biomarker domains or specific conditions, further restricting broader interpretation. Additionally, the methodological quality of published reviews varies widely, and conclusions often do not adequately account for review-level limitations. Therefore, the clinical relevance of largely exploratory biomarkers may be overstated. This umbrella review synthesizes evidence from existing systematic reviews on pain biomarkers. It aims to summarize the current findings, distinguish mechanistic insight from clinical applicability, and identify areas in the literature that require further research.
2. Materials and Methods
2.1. Search Strategy
We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [ 13 ] guidelines during the execution of this review, supplemented by recommendations for conducting systematic reviews of systematic reviews. The PRISMA checklist is provided in the Supplementary Materials (Table S1: PRISMA checklist) . The protocol was registered in the Open Science Framework (DOI 10.17605/OSF.IO/NDRPV). A predesigned search strategy was used to conduct a systematic search in the PubMed, Scopus, and Cochrane Library databases from the inception until 2 June 2025.
The search terms used were:
Cochrane library:
(“systematic review”:ti,ab,kw OR “meta-analysis”:ti,ab,kw)
AND (“pain”:ti,ab,kw OR “chronic pain”:ti,ab,kw)
AND (“biomarker”:ti,ab,kw OR “biological marker”:ti,ab,kw)
PubMed
((“systematic review”[Publication Type] OR “meta-analysis”[Publication Type]
OR “systematic review”[Title/Abstract] OR “meta-analysis”[Title/Abstract])
AND (“pain”[Title/Abstract] OR “chronic pain”[Title/Abstract])
AND (“biomarker”[Title/Abstract] OR “biological marker”[Title/Abstract] OR “Biological Markers”[MeSH]))
Scopus
(TITLE-ABS-KEY(“systematic review” OR “meta-analysis”)
AND TITLE-ABS-KEY(pain OR “chronic pain”)
AND TITLE-ABS-KEY(“biomarker” OR “biological marker”))
2.2. Inclusion and Exclusion Criteria
Inclusion Criteria were:
Study type: Systematic reviews (with or without meta-analysis).
Population/Condition: Patients with pain, including chronic pain, across any disease or condition.
Intervention/Exposure: Biomarkers were defined as objectively measured biological, imaging, or neurophysiological indicators associated with pain. Biomarkers may be clinical, biochemical, or radiological/imaging. Clinical validation was not required for inclusion, and biomarkers were considered irrespective of their stage of translational development.
Comparators: Any comparator or none (different biomarkers, usual clinical assessment, placebo, or no comparator).
Outcomes: Reported role of biomarkers in the detection, assessment, or monitoring of pain.
Language: English. The translation of non-English reviews was not feasible within the resources of the project. This may introduce language bias.
Exclusion Criteria were:
Primary studies (RCTs, observational studies).
Articles where biomarkers are not studied in relation to pain.
2.3. Literature Screening and Data Extraction
Two reviewers underwent calibration training to ensure consistent interpretation of the eligibility criteria and biomarker definitions. Data extraction was conducted independently by two reviewers using a predefined and pilot-tested data extraction form. They first reviewed titles and abstracts, then assessed the full texts. The results of the literature screening were compared, and disagreements at any stage of screening or data extraction were resolved through discussion and consensus between the two reviewers. If consensus could not be reached, a third reviewer was consulted to adjudicate the decision. The process was conducted using standard reference management and spreadsheet software (Word and Excel). The data extraction process encompassed various parameters including author, country, year, citations, study design (SR&MA of RCTs or observational studies), goals of the SR, diseases and conditions (in which biomarkers were studied), brief characteristics of patients, biomarkers (clinical, biochemical, radiological), number of patients included in the SR, total number of studies included in the SR, effect of biomarkers on decision-making and outcomes (were they useful in diagnosis, prescription of treatment and improvement of outcomes?), benefits of studied biomarkers, limitation of studied biomarkers, future directions and opportunities, and study conclusions and comments.
Given the heterogeneity of population, interventions, and biomarkers assessed, a narrative synthesis approach was used to summarize and compare findings across reviews.
2.4. Methodological Quality Evaluation of Included Studies
Two authors independently assessed the included studies using the 16 items of the AMSTAR-2 tool [ 14 ]. Overall confidence ratings were assigned according to the AMSTAR-2 rule of thumb for critical domains: items 2, 4, 7, 9, 11, 13, and 15. Reviews with no or only one non-critical weakness and no critical flaws were rated as high confidence; those with more than one non-critical weakness but no critical flaws were rated as moderate confidence. Reviews with one critical flaw, even if all other domains were adequate, were rated as low confidence, while those with two or more critical flaws were rated as critically low confidence. If a meta-analysis was not conducted, items 11–15 were coded as “not applicable” and not penalized. In cases where supplementary data could not be accessed, the corresponding item was considered unmet.
3. Results
We originally found 916 publications. Of them, 426 duplicates were removed, 490 publications were screened, and 441 publications did not match the criteria. We finally included 49 systematic reviews and meta-analyses [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] that together covered the results of 1369 studies. Of these, 1279 studies were human-only studies, whereas 105 studies involved both human and animal models. The total number of patients reported in these studies was 269,768 (10 SRs did not report the number of patients or involved animal models). The process of database search and consequent exclusion and inclusion of studies is depicted in Figure 1 .
Below, we present the key findings categorized by patient characteristics, diseases/conditions, and the various types of biomarkers studied.
3.1. Patients’ Characteristics
Table 1 provides an overview of the following characteristics—study design, goals, diseases/conditions, brief characteristics of patients, biomarkers’ name, effect, benefits, and limitations, number of patients, number of the original studies included, future directions, and conclusions—of the included systematic reviews.
3.2. Diseases/Conditions
We examined systematic reviews and meta-analyses (SRs and MAs) that reported on the use of biomarkers across a wide range of pain-related diseases and conditions provided in Table 1 . These included low back pain (LBP) (16 studies), fibromyalgia (10 studies), osteoarthritis (OA) (8 studies), migraine (5 studies), peripheral neuropathies (4 studies), endometriosis (2 studies), ankylosing spondylitis (AS) (3 studies), cancer-induced pain (3 times), multiple sclerosis (MS) (2 time), trigeminal pain (2 studies), rheumatoid arthritis (RA) (2 studies), bladder pain syndrome (2 studies), cluster headache (2 studies), shoulder disorders (2 studies), irritable bowel syndrome (2 studies), temporomandibular disorders (2 studies), and pancreatitis (2 studies). The following conditions were reported only once: spinal cord injury, somatoform pain disorder, type 1 diabetes–related neuropathy, phantom limb pain, musculoskeletal pain, interstitial cystitis, myofascial pain syndrome, delayed onset muscle soreness, sciatica, whiplash, lateral epicondylalgia, lumbar radicular pain, adhesive capsulitis, trapezius myalgia, central fatigue syndrome, chikungunya virus infection, pediatric musculoskeletal pain, neonatal procedural pain, and central post-stroke pain.
3.3. Geographical Coverage
The systematic reviews and meta-analyses included in this study reported the prevalence of pain among patients from a wide array of geographical regions and countries, as detailed in Table 1 . Specific countries included Portugal (2 studies), the USA (6 studies), Germany (4 studies), Spain (5 studies), France (1 study), Switzerland (2 studies), Brazil (3 studies), the Netherlands (3 studies), the UK (2 studies), Australia (4 studies), China (4 studies), Canada (2 studies), Italy (1 study), Norway (1 study), Sweden (1 study), Scotland (1 study), Poland (1 study), Colombia (1 study), Greece (1 study), Denmark (1 study), and Hong Kong (1 study).
3.4. Methodological Quality Assessment
Based on the AMSTAR-2 analysis in Table 2 , 1 study was found to be high-quality [ 62 ], 1 study was moderate-quality [ 58 ], 5 studies were low-quality [ 16 , 20 , 39 , 47 , 59 ], and 42 studies were critically low-quality [ 15 , 17 , 18 , 19 , 21 , 22 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 43 , 44 , 45 , 46 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 60 , 61 , 63 ]. One study [ 23 ] was not assessed for methodological quality as AMSTAR-2 is not applicable to narrative reviews. The AMSTAR assessment of the included studies reveals variability in methodological quality. Common strengths include clearly articulated research questions and literature search strategies. However, many studies lacked justification for the selection of study designs and did not report excluded studies. Techniques for assessing the risk of bias were satisfactory in some studies but inconsistent overall. Meta-analyses generally adhered to appropriate statistical methods, but assessments of the potential impact of bias were less frequently reported. Improvements are needed in the investigation and explanation of heterogeneity and publication bias. While many studies declared conflicts of interest, this was not universally observed.
3.5. Biomarkers of Pain
The results are organized according to biomarker type. The synthesis of findings is presented hierarchically, prioritizing evidence derived from human studies over non-human studies. In this umbrella review, the interpretation of biomarker evidence is based on the conclusions reported by the included systematic reviews, as summarized in Table 1 , reflecting how those reviews interpreted their findings according to their respective analyses. No independent grading of biomarker evidence strength or clinical validity was performed. The aim was to highlight the key biomarkers identified and discuss their potential clinical implications.
3.6. Immune System Biomarkers
3.6.1. Evidence Reported as Consistent in the Included Systematic Reviews
There is consistent evidence of an association between chronic pain conditions and inflammation. It was found that specific pro-inflammatory markers are consistently elevated in chronic pain. For example, studies on non-specific low back pain (NsLBP) and chronic low back pain revealed that elevated levels of C-reactive protein (CRP), Interleukin-6 (IL-6), and Tumor Necrosis Factor-alpha (TNF-α) may be associated with the presence and pain severity [ 25 , 51 , 52 , 56 ]. Furthermore, CRP, IL-6, and TNF-α were found to be reduced by resistance and neuromuscular training in osteoarthritis (OA) patients with pain caused by physical dysfunction [ 47 ]. In addition to NsLBP and OA, inflammatory markers may have shown prognostic value in the context of Chikungunya virus infection. IL-6, IL-4, and CRP may work as risk indicators of developing joint pain and chronic arthritis after the infection’s acute phase [ 48 ]. Finally, one review found that the difference in levels of IL-6, IL-8, TNF-α, and brain-derived neurotrophic factor (BDNF) may have the potential to be used for stratification of patients with fibromyalgia into subgroups for future targeted therapies [ 21 ].
3.6.2. Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
Even though the association between inflammatory biomarkers and pain is promising, there is limited clinical utility. There are several reviews with very low to low-level evidence of a clear association between pain intensity and inflammatory biomarkers [ 16 , 27 , 36 , 37 , 43 , 57 ]. These reviews also lack longitudinal data, which makes it difficult to identify whether the inflammation is a cause or a consequence of chronic pain. One meta-analysis on diabetic peripheral neuropathy (DPN) showed that TNF-α can differentiate painful from non-painful DPN. However, the results were limited by heterogeneity in study design [ 22 ]. In other reviews, inflammatory biomarkers provided insights into the pathological processes of chronic regional pain syndrome (CRPS) Type I [ 20 ], idiopathic frozen shoulder [ 31 ], OA [ 45 ], and sciatica [ 33 ]. Nevertheless, these reviews reported a small sample size, confounding by other diseases, and a lack of standardized diagnostic criteria.
3.7. Proteomic Biomarkers
3.7.1. Evidence Reported as Consistent in the Included Systematic Reviews
Besides inflammatory biomarkers, biomarker research is shifting towards the analysis of multi-protein panels. One systematic review demonstrated promising diagnostic accuracy in distinguishing healthy people from patients with fibromyalgia. Such panels of proteins as transferrin, α2-macroglobulin, and specific immunoglobulin fractions, when used in conjunction with decision tree models, may be more accurate than a single marker [ 50 ].
3.7.2. Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
Other studies that investigated proteomic biomarkers yielded unvalidated findings. One scoping review found an increased level of fecal and urinary markers (macrophage migration inhibitory factor (MIF) and fecal glyceraldehyde) in patients with bladder pain syndrome (BPS). It was noted that this correlation lacked validation [ 29 ]. Another review compared myofascial pain syndrome (MPS) and delayed onset muscle soreness (DOMS) and identified several useful biomarkers for diagnosis. Nevertheless, most of the studies were heterogeneous [ 26 ]. A systematic review on cancer-induced bone pain did not conclude any genetic clinical biomarker [ 34 ].
3.7.3. Animal Models
In this umbrella review, one systematic review of animal studies showed how physiotherapy may modulate neurotrophins, neurotransmitters, and cytokines [ 18 ]. One systematic review that involved human and animal studies helped understand the pathogenesis and mechanisms of endometriosis pain, but lacked RCTs with a large sample size [ 32 ]. Non-human studies may provide a foundation for understanding the mechanism of action in order to inform future human trials.
3.8. Structural and Functional Neuroimaging Biomarkers
3.8.1. Evidence Reported as Consistent in the Included Systematic Reviews
Since chronic pain is a condition associated with the central nervous system, there might be evidence in neuroimaging studies. Trigeminal neuralgia was investigated in one review. The study found consistent brain changes in the thalamus, cingulate, and insula [ 28 ]. A meta-analysis on chronic low back pain (CLBP) found significant gray matter volume changes. Specifically, the volume was decreased in the left superior frontal gyrus, left insula, and right striatum and increased in the left striatum and left post-central gyrus [ 54 ]. These regions are involved in the reward mechanism, emotions, but most importantly, in pain processing. Thus, such neuroimaging signatures may be proposed as potential biomarkers for chronic pain development.
3.8.2. Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
The clinical utility of neuroimaging is not yet fully realized due to limitations in several studies. One systematic review found only limited evidence that reduced neck muscle size can be used as a quantitative imaging biomarker for neck and shoulder pain [ 40 ]. Another review proposed an association between high-intensity zones (HIZs) and low back pain. The study was limited by a small sample size and the absence of standardized protocols [ 35 ]. Next, two systematic reviews provided data on lesion locations and underlying mechanisms confirmed by neuroimaging, but the development of biomarkers for prognosis has not been proposed yet [ 46 , 63 ]. Finally, an inconsistent correlation between magnetic resonance imaging (MRI) and OA patients’ reported pain was found in another review [ 42 ].
3.9. Neurophysiological Testing
3.9.1. Evidence Reported as Consistent in the Included Systematic Reviews
EEG is a non-invasive procedure that may identify electrical patterns with high temporal resolution. Several systematic reviews reported consistent resting-state EEG pattern changes in chronic neuropathic pain. EEG showed patterns of increase in theta power and a decrease in alpha and beta power [ 15 , 19 ]. Moreover, meta-analysis found that gamma-band oscillations (GBOs) may be positively correlated with subjective pain [ 59 ]. Specifically, the frequency and spatial location of GBOs varied with the pain type. Phasic (acute) pain-induced GBOs were higher in frequency (~66 Hz) and localized to the sensorimotor cortex. Chronic pain-associated GBOs were lower in frequency (~55 Hz) and predominantly localized over the prefrontal cortex [ 59 ].
3.9.2. Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
Other EEG measures were limited by study methodology to clearly identify reliable biomarkers [ 17 , 60 ]. One review showed that patients with chronic pain may decrease the threshold for activation of defensive reflexes in comparison to healthy patients [ 38 ]. This suggests an increased need for body protection. Nonetheless, the review reported high heterogeneity and issues with missing data.
3.10. Neurochemical Biomarkers
3.10.1. Evidence Reported as Consistent in the Included Systematic Reviews
Such a neurochemical biomarker as calcitonin gene-related peptide (CGRP) was shown to be consistently elevated in patients with headache attacks in one systematic review [ 55 ]. This may make CGRP a promising marker. Likewise, CGRP may be targeted by therapies for migraine and cluster headache management [ 55 , 61 ].
3.10.2. Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
Other neurochemicals only provided limited and conflicting evidence. A scoping review stated that beta-endorphin as a biomarker for chronic pain has limited evidence for clinical use due to small sample sizes, limited follow-up times, and lack of control groups [ 23 ].
3.10.3. Animal Models
In the context of neurochemical biomarkers, one systematic review with preclinical animal models has provided insight into the intervention mechanisms. It reported that physiotherapy could modulate the opioid system (endorphins, receptors) and neurotransmitter substance P [ 18 ]. This may provide a biological rationale for how physiotherapy alleviates pain on a cellular level.
3.11. Genetic Biomarkers
Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
One systematic review found that some genetic variants, like OPRM1 and COMT genes, may be linked to poor recovery, but the evidence was limited by heterogeneity, small cohort studies, and the absence of combined analysis of genetic biomarkers and clinical data [ 39 ].
3.12. Hormonal and Metabolic Biomarkers
3.12.1. Evidence Reported as Consistent in the Included Systematic Reviews
The studies focused on hormonal and metabolic biomarkers reflect the multisystemic nature of chronic pain. One study reviewed burning mouth syndrome (BMS) and found a significant increase in salivary cortisol when compared to controls [ 53 ]. Salivary cortisol was also recommended as a promising biomarker for anxiety [ 53 ]. Salivary biomarkers were also proposed for monitoring and predicting orthodontic treatment stages [ 49 ]. In the same way, changes in the level of metabolites like amino acids, lipids, and carbohydrates have been reported to have an association with chronic pain. However, these metabolites could not be validated for clinical use due to study heterogeneity and a lack of large-scale validation studies [ 24 ].
3.12.2. Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
Evidence on other hormonal and metabolic biomarkers is inconclusive. Particularly, a systematic review on hypothalamic–pituitary–adrenal (HPA) axis biomarkers in fibromyalgia reported high heterogeneity and a lack of consistent patterns, making it unreliable for diagnosis as of now [ 58 ]. Another study investigated neonatal pain and found that cortisol level changes with pain and analgesia, but its variability was too great to conclude it a reliable biomarker for clinical practice [ 62 ]. Thus, for now, behavioral scales remain the primary tool.
3.13. Tissue-Specific Biomarkers
Evidence Reported as Limited or Inconclusive in the Included Systematic Reviews
Several included studies investigated biomarkers for a specific tissue part. This involved joint (OA), skin (CRPS, frozen shoulder), and endogenous pain inhibitory pathways. All of the studies lack full clinical validation.
One review on hip OA identified urinary CTX-II and serum CRP and COMP, but these molecular biomarkers were not adequately validated [ 44 ]. Next, a systematic review on MRI biomarkers for patients with OA found only inconsistent results for the quantitative cartilage morphometry technique as a total knee replacement outcome prognostic tool [ 46 ]. Another review that investigated knee OA concluded six potential phenotypes but reported a lack of standard phenotype definitions [ 41 ].
Regarding CRPS and frozen shoulder, it was found that nerve fiber density could provide insights into the pathophysiology of the diseases. The main limitation was the lack of standardized diagnostic criteria and the confounding caused by prior interventions or surgeries [ 20 ].
Conditioned pain modulation (CPM), a measure of endogenous pain inhibitory pathways, was also studied as a potential biomarker for chronic pain diagnosis [ 30 ]. The results did not show a promising correlation.
4. Discussion
This review aimed to explore various biomarkers associated with pain disorders to understand their application and challenges in clinical settings. To our knowledge, no prior umbrella review has comprehensively addressed this topic.
It was found that the recurrent association of IL-6, TNF-α, and CRP with pain severity across musculoskeletal conditions like OA and LBP [ 25 , 47 , 51 , 52 , 56 ] suggests that they may represent a common inflammatory pain phenotype. It may mean that pain functions as a driver for the inflammatory response and that low-grade inflammation could be a major mechanism responsible for chronic pain development, regardless of the initial cause. Additionally, inflammatory biomarkers may have utility for early diagnosis in various contexts like fibromyalgia and even Chikungunya virus infection [ 21 , 48 ]. Nevertheless, the clinical utility is often limited by small sample sizes, methodological heterogeneity, and a lack of standardized diagnostic criteria in cases of DPN, CRPS, idiopathic frozen shoulder, OA, and sciatica [ 20 , 22 , 31 , 33 , 45 ]. Taken together, the AMSTAR-2 assessment revealed that the overall methodological quality of systematic reviews investigating immune biomarkers was limited, with most reviews rated as low or critically low confidence.
Besides inflammatory biomarkers, research on proteomic biomarkers might also be significant for chronic pain. Chronic pain requires a multi-dimensional biological signature. Inflammatory biomarkers and a panel of proteins, including transferrin, α2-macroglobulin, may be used in combination with machine learning [ 50 ], thereby providing a more accurate diagnosis. However, exploration of broader immune and proteomic markers (e.g., macrophage migration inhibitory factor (MIF) and fecal glyceraldehyde) is not validated yet [ 26 , 29 ]. The AMSTAR-2 assessment also revealed that these systematic reviews were of low quality.
The next identified biomarker was neuroimaging. The recent evidence may prove that reduction in gray matter in the insula [ 28 ], which is responsible for interception and emotion, suggests that chronic pain may “rewire” the brain and create a self-perpetuating cycle that integrates the physical sensation with psychological distress. This could reflect fundamental neuroplastic change triggered in patients with prolonged pain. This may also be proved by EEG, where anatomical shift in GBOs suggests that chronic pain is fundamentally different from acute pain [ 59 ], most likely due to the role of cognitive and emotional factors in the maintenance of chronic pain. On the other hand, such neuroimaging as MRI fails to provide any data on the patient’s reported symptoms (e.g., OA) [ 42 ]. This probably shows that chronic pain is a complex phenomenon that is not only affected by central sensitization but also by a range of factors. AMSTAR-2 indicated that the evidence base for neuroimaging biomarkers was methodologically limited.
Another biomarker, CGRP, has shown promise in several pain conditions: headache, cluster headache, and headache attacks [ 55 , 61 ]. CGRP might be a target biomarker for therapies, although other genetic and neurochemical biomarkers (beta-endorphin, OPRM1, and COMT genes) show conflicting evidence [ 39 ]. Such biomarkers as opioid system receptors and substance P (proven working on animals) may guide the design for future human trials [ 18 ]. Nevertheless, AMSTAR-2 showed that the systematic review was evaluated as of low quality.
Hormonal biomarkers, although only emerging, reflect the complexity of chronic pain. The use of non-invasive fluid, like saliva (salivary cortisol biomarker), may help to avoid the confounding stress of invasive procedures and encourage the creation of multi-systemic models of pain prognosis [ 49 , 53 ]. Since the studies about hormonal biomarkers are recent, other hormonal markers like HPA, metabolites, or cortisol in neonates have less conclusive results [ 24 ].
Regarding joint (CTX-II and serum CRP, and COMP), skin (nerve fiber density), and other tissue-specific biomarkers (CPM), current evidence may not recommend them as reliable biomarkers [ 20 , 30 , 41 , 44 , 46 ].
4.1. Gaps and Future Directions
One of the major gaps of the included studies was the lack of high-quality data. Most of the studies were deemed to be of low quality due to the scarcity of longitudinal studies. The included studies in systematic reviews were cross-sectional, and the identification of a causal relationship between biomarkers and pain intensity could not be performed. Next, there has been methodological heterogeneity. Studies on the same pain condition or disease used different assays, collection time points, and pain assessment scales. Thus, the reliability of meta-analyses was hindered by a lack of standardization protocols. Furthermore, many studies were limited by small sample sizes, making it difficult to generalize the findings. The accumulation of consistent evidence-based findings may be achieved if future studies focus on longitudinal studies, protocol standardization, and inclusion of broad and diverse cohorts.
The findings of this umbrella review pose an important distinction between the research value and the clinical value of pain-related biomarkers. From a research perspective, the identified biomarkers provide meaningful insights into the pathophysiology of chronic pain, supporting contemporary models that emphasize neuroplasticity, immune activation, and central sensitization. Biomarkers serve primarily as tools for hypothesis generation, mechanistic exploration, and patient phenotyping in experimental settings. The clinical value of these biomarkers remains limited. Current evidence does not support the routine clinical implementation of pain biomarkers, either as diagnostic tools or as guides for personalized therapy. The translation of biomarker utility into a clinical reality requires a large-scale, valid, and mechanism-based approach. If current methodological limitations are addressed, next step is prioritizing multi-biomarker panels. Combining different biomarker types to develop a multidimensional panel may be used for predicting pain comprehensively (involving various pain phenotypes). Additionally, focusing on non-invasive biomarkers, such as saliva, urine, or EEG, may facilitate their adoption in routine clinical settings.
4.2. Limitations of the Umbrella Review
One of the limitations of this umbrella review is that it included only articles published in English because resources for the translation of articles in other languages were not available. This may have introduced language bias. The next limitation is that the AMSTAR-2 assessment revealed that many of the systematic reviews were of low or critically low quality, with methodological flaws such as insufficient justification for study design, incomplete reporting of excluded studies, and inconsistent risk of bias assessment. Since the strength of evidence is constrained by the quality of the systematic reviews, the findings must be interpreted with due caution. In addition, the scope of several reviews was narrow, frequently focusing on specific biomarkers or single pain conditions, which further limits the generalizability of the synthesized findings. Moreover, categorization of evidence strength was based on the conclusions drawn by the included studies, as summarized in Table 1 , rather than on an independent certainty assessment, and should be interpreted as indicative rather than definitive. Finally, no formal assessment of overlap was conducted. This may have led to partial duplication and should be considered when interpreting the results.
5. Conclusions
The synthesis of evidence from systematic reviews and meta-analyses concludes that no single biomarker for chronic pain has achieved sufficient prospective validation for routine clinical use. Nevertheless, the evidence highlights several promising biomarker domains: inflammatory biomarkers (CRP, IL-6, TNF-α) that are responsible for inflammatory pain phenotype, neuroimaging and neurophysiological biomarkers that indicate central neuroplastic changes, and multi-protein panels that may exhibit high diagnostic accuracy.
In summary, the current evidence advances understanding of the biological and neurophysiological mechanisms underlying chronic pain, rather than providing tools ready for clinical decision-making. The translation of biomarker research into clinical practice remains limited by methodological heterogeneity, cross-sectional designs, and the lack of longitudinal and outcome-driven validation. Next steps involve addressing the pervasive methodological limitations via large, standardized, and longitudinal studies to reduce the gap between research area and clinical practice.
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Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm15020550/s1 , Table S1: PRISMA checklist.
Author Contributions
Conceptualization, D.V.; methodology, D.V. funding acquisition, D.V. and S.K.; writing—original draft preparation, D.V.; writing—review and editing, D.V., S.K., D.M. and B.N.; Data extraction: D.M. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
No new data were created or analyzed in this study.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript: PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses SR Systematic Review MA Meta-Analysis RCT Randomized Controlled Trial AMSTAR-2 A Measurement Tool to Assess Systematic Reviews, version 2 MRI Magnetic Resonance Imaging PET Positron Emission Tomography LBP Low Back Pain NsLBP Non-specific Low Back Pain CLBP Chronic Low Back Pain OA Osteoarthritis AS Ankylosing Spondylitis RA Rheumatoid Arthritis MS Multiple Sclerosis BMS Burning Mouth Syndrome BPS Bladder Pain Syndrome CRPS Complex Regional Pain Syndrome MPS Myofascial Pain Syndrome DOMS Delayed Onset Muscle Soreness DPN Diabetic Peripheral Neuropathy HIZ High-Intensity Zone CRP C-reactive Protein IL-6 Interleukin-6 IL-8 Interleukin-8 IL-4 Interleukin-4 TNF-α Tumor Necrosis Factor-alpha BDNF Brain-Derived Neurotrophic Factor MIF Macrophage Migration Inhibitory Factor CGRP Calcitonin Gene-Related Peptide HPA axis Hypothalamic–Pituitary–Adrenal axis CTX-II C-terminal Cross-linked Telopeptide of Type II Collagen COMP Cartilage Oligomeric Matrix Protein CPM Conditioned Pain Modulation EEG Electroencephalography MEG Magnetoencephalography GBO/GBOs Gamma-Band Oscillation(s) OPRM1 Opioid Receptor Mu 1 gene COMT Catechol-O-Methyltransferase gene
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