Inflammatory Biomarkers in Irreversible Pulpitis and Pulp Necrosis: A Systematic Review and Meta-Analysis

Rahman Wahyudi; Panuroot Aguilar; Chidsanu Changsiripun; Attawood Lertpimonchai; Lakshman Samaranayake; Zar Chi Soe; Thanaphum Osathanon; Vincent Everts; Chalida Nakalekha Limjeerajarus; Nuttapol Limjeerajarus

doi:10.1016/j.identj.2026.109453

. 2026 Feb 26;76(2):109453. doi: 10.1016/j.identj.2026.109453

Inflammatory Biomarkers in Irreversible Pulpitis and Pulp Necrosis: A Systematic Review and Meta-Analysis

Rahman Wahyudi ^a,^b, Panuroot Aguilar ^c, Chidsanu Changsiripun ^d, Attawood Lertpimonchai ^e,^f, Lakshman Samaranayake ^f,^g,^h, Zar Chi Soe ^a, Thanaphum Osathanon ^i,^j, Vincent Everts ^k,^l, Chalida Nakalekha Limjeerajarus ^m,^n,^⁎, Nuttapol Limjeerajarus ^l

PMCID: PMC12955635 PMID: 41747365

Abstract

Statement of Problem

Accurate differentiation between irreversible pulpitis (IP) and pulp necrosis (PN) is crucial for clinical treatment planning, yet meta-analyses comparing their inflammatory biomarkers are lacking. Isolated findings limit the identification of consistent biomarker patterns, hindering diagnostic accuracy and stage-specific treatment development.

Objectives

This systematic review and meta-analysis aimed to compare expression levels of inflammatory biomarkers in permanent teeth diagnosed with IP or PN vs healthy pulp, to elucidate disease-specific molecular profiles.

Methods

Electronic searches were performed in PubMed, Scopus, and Cochrane databases for studies published from inception to 2024. Eligible studies included human permanent teeth with IP or PN and reported quantitative protein-based biomarker levels. Forty-three studies met the inclusion criteria, with 26 included in the meta-analysis. Data were pooled using random-effects models, and standardized mean differences were calculated.

Results

Symptomatic IP (SIP) showed significantly elevated TNF-α, IL-2, IL-6, IL-8, Substance P, CGRP, and catalase compared to healthy pulp. Asymptomatic IP (AIP) also exhibited significantly increased TNF-α despite the absence of clinical symptoms. No significant difference in TNF-α was observed between SIP and AIP. High heterogeneity was observed due to variation in sample types, analytical methods, and diagnostic criteria. Although a meta-analysis for PN was not feasible, descriptive analysis revealed consistently elevated TNF-α, IFN-γ, IL-10, and TGF-β levels in pulp necrosis.

Conclusion

Both SIP and AIP exhibit pro-inflammatory profiles, with TNF-α elevated regardless of symptoms. Molecular biomarkers may better reflect pulp status than clinical signs.

Significance

Elevated TNF-α levels observed in SIP and AIP indicate the presence of underlying inflammatory activity even in the absence of clinical symptoms. This finding highlights its potential value in helping to determine the appropriate window for vital pulp therapy.

Key words: Irreversible pulpitis, Pulp necrosis, Inflammatory biomarkers, Meta-analysis, Dental pulp inflammation

Graphical abstract

Introduction

Diagnosing pulpal diseases in everyday clinical practice remains challenging because current methods, such as thermal testing, electric pulp testing, percussion, and radiographic evaluation, primarily assess neural responses or structural changes rather than actual pulpal inflammation.¹^,² These tests often yield inconsistent or subjective results, and symptoms do not always correlate with the underlying biological status of the pulp. As a result, distinguishing the pulp conditions is difficult, leading to potential misdiagnosis and overtreatment.

Proteins and enzyme-based signalling cascades are fundamental to cellular function, regulating various physiological and pathological processes. These molecular mechanisms play a crucial role in maintaining homeostasis, mediating immune responses, and influencing tissue regeneration and repair. In disease states, alterations in protein expression and enzyme activity often reflect underlying pathological changes, making them valuable biomarkers for detecting, monitoring, and predicting disease progression.³ In the context of dentin-pulp diseases, the assessment of pulp vitality is a key determinant in clinical decision-making. Pulp vitality reflects the functional state of the dental pulp, indicating its ability to respond to injury, inflammation, or infection. An accurate evaluation of pulp status is essential for distinguishing between reversible and irreversible conditions, guiding appropriate treatment planning, and improving long-term therapeutic outcomes.⁴

Irreversible pulpitis and pulp necrosis are distinct pathological conditions within the spectrum of pulp diseases.⁵ Irreversible pulpitis is characterized by persistent inflammation of the dental pulp, often due to deep caries, trauma, or iatrogenic dental procedures. The pulp remains vascularized but exhibits heightened inflammatory responses, including increased expression of pro-inflammatory cytokines such as interleukin-6 (IL-6), interleukin-8 (IL-8), and tumour necrosis factor alpha (TNF-α). If untreated, irreversible pulpitis can progress to pulp necrosis, a condition where the pulp loses its vascular supply, leading to the tissue death. This transition is often accompanied by a shift in biomarker expression, with increased levels of necrosis-associated mediators and reduced anti-inflammatory or regenerative factors.⁶ Irreversible pulpitis, if diagnosed early, can sometimes be managed with vital pulp therapy (eg, pulp capping or pulpotomy) to preserve pulp function. In contrast, pulp necrosis typically necessitates endodontic treatment, such as root canal therapy, to prevent the spread of infection and restore tooth function.⁶^,⁷

Despite the clinical importance of distinguishing between irreversible pulpitis and pulp necrosis, there is a lack of comprehensive meta-analyses that systematically compare the expression of inflammatory biomarkers in these conditions. While existing studies report individual biomarkers in isolated settings, no large-scale comprehensive analyses,8, 9, 10 with integrated findings from both clinical and laboratory studies to establish definitive patterns of inflammatory mediator expression on different stages of pulp disease, are available in the literature, as yet. Hence, a meta-analysis could provide valuable insights into the molecular mechanisms underlying pulp inflammation and degeneration, ultimately enhancing diagnostic accuracy and guiding more effective treatment strategies.

This study aims to compare the level of inflammatory biomarker proteins in permanent teeth with pulp necrosis and irreversible pulpitis and those in healthy permanent teeth. By evaluating differences in biomarker expression, the analysis may help define characteristic inflammatory profiles that differentiate these conditions from healthy pulp tissue. Additionally, this review seeks to identify specific biomarker profiles associated with pulp necrosis and irreversible pulpitis. Understanding these molecular signatures could improve diagnostic precision, facilitate early intervention, and contribute to the development of targeted therapeutic approaches for pulp diseases.

Materials and methods

Protocol and registration

This systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study protocol was registered in PROSPERO (registration number: CRD42024512042).

Research questions

- i.
  Primary Research Question:
  
  What are the differences in the levels of inflammatory biomarker proteins between permanent teeth with pulp necrosis or irreversible pulpitis and healthy permanent teeth?
- ii
  Secondary Research Questions:
  - 1.
    Which specific inflammatory biomarkers are significantly elevated in permanent teeth with pulp necrosis or irreversible pulpitis compared to healthy controls?
  - 2.
    How do the levels of inflammatory biomarkers correlate with the severity of pulp necrosis or irreversible pulpitis?
  - 3.
    Are there specific biomarker profiles that can distinguish between pulp necrosis and irreversible pulpitis?

Eligibility criteria

The PECOS framework was applied to evaluate the eligibility of the documents.

•
Population (P): Permanent teeth.
•
Exposure (E): Irreversible pulpitis and pulp necrosis.
•
Comparator (C): Healthy pulp (control group).
•
Outcome (O): Biomarkers proteins.
•
Study design (S): Randomized Controlled Trials and Cross-Sectional.

Eligible studies must report on at least one protein biomarker associated with inflammation in dental pulp tissue. The focus was on biomarkers relevant to inflammatory pulp pathologies having comparative data on diseased and healthy pulpal tissues. Specifically, they should compare healthy pulp or normal tissue in permanent teeth, irreversible pulpitis or an equivalent condition (such as chronic pulpitis or severe pulp inflammation), and pulp necrosis or an equivalent condition (such as gangrenous pulp or necrotic pulp tissue). The American Association of Endodontists (AAE) diagnostic guide titled Endodontic Diagnosis (AAE Communique, Fall 2013) was used as a reference guideline to determine the appropriate diagnostic category in studies where the diagnosis was not explicitly stated.¹¹ This comparison allows for a better understanding of biomarker expression in different stages of pulp disease.

The inclusion criteria of the study were as follows: (1) Full demographic data as well as statistical analysis comparing biomarker levels between healthy and diseased states. (2) Data presentation with mean ± standard deviation or median and quartile values for accurate comparison. (3) Studies with clearly differentiated biomarker expression levels in normal/healthy pulp tissue vs diseased conditions such as irreversible pulpitis and pulp necrosis. (4) Furthermore, studies must include the biomarker analytes and the techniques used for biomarker quantification. The meta-analysis included only biomarkers that were investigated in three or more studies, had a clear diagnosis, and all analytes were required to be derived specifically from dental pulp tissue.

Studies were excluded due to the following criteria. First, review articles, including systematic reviews, literature reviews (comprehensive, narrative, or scoping), and meta-analyses, were excluded as they lacked primary data on biomarker expression and analysis. Second, case reports or case series were excluded due to their limited sample size. They lack control groups and standardized data reporting, making them unsuitable for quantitative synthesis in meta-analysis.

Similarly, animal studies were excluded to avoid differential findings between human and animal data, thereby maintaining clinical relevance. Lastly, articles were excluded with incomplete descriptors of pulp pathology. Studies that did not clearly specify the pulp condition or provided inadequate information for classification could not be reliably assessed and were therefore omitted.

Search strategy and study selection

This meta-analysis includes English language articles published from inception to 2024. Only articles indexed in PubMed, Cochrane, or Scopus were considered for inclusion to ensure the reliability and credibility of the sources. The records from all search engines were merged into a Microsoft Excel (Microsoft 365 Apps for enterprise) spreadsheet, and the duplicate records were deleted. Supplementary Table 1 presents the search strategy applied for selecting articles across various search engines. After retrieving the articles, two investigators (R.W. and C.N.L.) independently screened the titles and abstracts of each study based on the predefined eligibility criteria to identify publications relevant to the research topic. In cases where there were disagreements between the reviewers, a third investigator (P.A.) was consulted to facilitate a consensus, ensuring that all irrelevant studies were excluded. Finally, the full texts of the selected articles were thoroughly reviewed, and only those that met the inclusion criteria were included in the analysis.

Data collection

Data extraction was conducted manually by reviewing each article and selecting relevant information. The extracted data were compiled into a Microsoft Excel spreadsheet for organization and analysis. Two reviewers (R.W. and C.N.L.) independently assessed the extracted information to ensure accuracy and consistency. Any discrepancies between the reviewers were resolved through discussion with a third investigator (P.A.) until consensus was reached. The number of samples, age range, gender, diagnosis of the pulp, name of the biomarkers investigated, and measurement values were then collected from each article. The mean and standard deviation (SD) values of the biomarkers investigated in each article were collected. If the studies did not explicitly report the mean and standard deviation (SD), these values were estimated from the available data (sample size, median, interquartile range, or range) using the validated methods proposed by Wan et al (2014). Specifically, medians and interquartile ranges (IQRs) were converted to means and standard deviations (SDs) using the formulas: mean ≈ (Q1 + median + Q3) / 3 and SD ≈ IQR / 1.35, where IQR = Q3 − Q1, as recommended by Wan et al.¹² Seven studies required this conversion.13, 14, 15, 16, 17, 18, 19 This approach ensured consistency in data representation among all included studies.

Quality assessment and risk of bias assessment

The methodological quality and risk of bias of all included studies were evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Tools, selected according to the specific study design. Only two designs were present in the final dataset, Cross-Sectional (CS) studies and Randomized Controlled Trials (RCTs), and each was assessed using the corresponding JBI checklist. Each domain in the JBI tool was rated using one of three responses: 'Yes' (criterion clearly met), 'No' (criterion not met), or 'Unclear' (insufficient information to determine compliance). These ratings reflect the extent to which each study adhered to methodological standards related to sampling, measurement validity, confounding, and reporting quality. The overall quality and risk-of-bias profile of the included studies were summarized to support the interpretation of the meta-analysis findings and to contextualize potential sources of variability across studies.

Certainty of evidence assessment (GRADE)

The certainty of evidence for each biomarker was assessed using the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach.²⁰ Because the included studies comprised both Randomized Controlled Trials (RCTs) and observational cross-sectional studies, the initial certainty ratings followed standard GRADE guidance, in which evidence from RCTs begins at a 'High' level of certainty, while evidence from observational studies begins at a 'Low' level of certainty.

Certainty of evidence was subsequently downgraded or upgraded according to GRADE domains. Downgrading was applied for risk of bias (based on JBI assessments), inconsistency (substantial heterogeneity), indirectness (differences in population, assay method, or biomarker definition), imprecision (wide CIs or small sample sizes), and publication bias (evidence of funnel plot asymmetry or selective reporting). For observational studies, upgrading was considered when there was a large effect size, a dose-response relationship, or when all plausible confounding would likely reduce rather than increase the observed effect. Each biomarker was then assigned a final certainty rating (High, Moderate, Low, or Very Low), and all judgments were summarized in a GRADE Summary of Findings table.

Statistical and sensitivity analysis

Statistical analysis was run by RevMan 5 software (Version 5.4. The Cochrane Collaboration, 2020) for the construction of the meta-analysis. Due to the variation in scales and methods used to measure the same outcome across individual studies, the standardized mean difference (SMD) was applied to evaluate outcome measurement and facilitate data comparison. The SMD with 95% CI was estimated using a random or fixed effects model according to the statistical heterogeneity evaluated by the Chi-square test (P < .1) and I² statistic.

For the meta-analysis, included studies were categorized based on the diagnostic groups reported. Biomarker expression levels were compared between the following pairs: healthy pulp vs symptomatic irreversible pulpitis (SIP), healthy pulp vs asymptomatic irreversible pulpitis (AIP), and SIP vs AIP. Only studies that clearly reported mean, standard deviation (or those with enough data to calculate the mean and standard deviation), and sample size for each group were included in the pooled analysis. When multiple diagnostic groups were reported within a study, relevant data were extracted separately for each comparison. This grouping approach allowed for a clearer understanding of biomarker variations across distinct pulp inflammation stages.

A sensitivity analysis was performed to assess the robustness of the pooled estimates. Studies that reported medians and interquartile ranges (or ranges) and required conversion to mean and standard deviation were excluded, and the meta-analysis was repeated using only studies that reported complete parametric data. The purpose of this analysis was to determine whether the overall findings were influenced by the inclusion of converted data.

Subgroup and meta-regression analysis

Subgroup analyses were conducted to explore potential sources of heterogeneity across studies. For each biomarker with at least two studies per subgroup, analyses were stratified according to the assay method used for biomarker quantification (eg, ELISA, fluorometric assay, spectrophotometric assay, radioimmunoassay, flow cytometry, radioreceptor assay, EIA). Separate pooled effect estimates were calculated for each subgroup using a random-effects model. Between-subgroup differences were assessed using the χ² test for subgroup differences provided in RevMan. The aim of these analyses was to determine whether variability in assay methodology contributed to the heterogeneity observed in the overall pooled effects.

Meta-regression was performed using R (version X.X; R Foundation for Statistical Computing, Vienna, Austria) and RStudio (Posit PBC, Boston, MA, USA) to evaluate potential sources of between-study heterogeneity. Two moderators were examined: country of origin and biomarker category (inflammatory, oxidative, or neuropeptide). Random-effects meta-regression models were fitted using each moderator independently, with standardized mean differences (SMDs) as the effect size and corresponding standard errors as model weights. Categorical moderators were dummy-coded automatically by the software. The significance of each moderator was assessed using the omnibus test of moderators (QM), with P < .05 interpreted as evidence that the moderator explained a meaningful proportion of heterogeneity.

Results

Study selections

The PRISMA statement flowchart summarizing the selection process is shown in Figure 1. Initially, a total of 3263 records were identified through a comprehensive search of three major electronic databases, namely PubMed (n = 589), Cochrane (n = 36), and Scopus (n = 2638). Prior to the screening process, 210 duplicate records were removed. In addition, 23 records were excluded as they consisted of unfinished or registered abstract-only articles. This resulted in 3030 articles that proceeded to title and abstract screening. During the screening phase, 2043 articles were excluded due to irrelevance based on their titles and abstracts. Subsequently, 987 full-text articles were sought for retrieval, and all were successfully obtained and assessed for eligibility.

Fig 1 dummy alt text — The research framework. The PRISMA flowchart shows the process of selecting studies for the review. A total of 3263 records were found from three databases. After removing duplicates and unfinished articles, 3030 records were screened. From these, 987 full-text articles were further evaluated. In the end, 45 studies were included in the review, with 26 used for meta-analysis. Most exclusions were due to missing data, unrelated topics, or studies using animals, isolated cells, or primary teeth.

Following full-text assessment, a total of 944 articles were excluded for various reasons. Specifically, 22 articles were written in languages other than English, 73 were not related to the primary topic of the study, and 20 were review articles rather than original research. Additionally, 332 articles were excluded due to missing essential statistical data such as mean and standard deviation or median and quartile values. Another 201 articles were excluded during the eligibility assessment phase due to the absence of clear information regarding the specific biomarkers investigated, while 296 were conducted exclusively on animal models, cellular models, or primary teeth. These figures reflect the total number of excluded articles of 944 Ultimately, 43 articles fulfilled the inclusion criteria and were included in the qualitative review. Of these, 26 articles provided sufficient data to be included in the meta-analysis.

Characteristics of included studies

A total of 43 studies were included in the review (Table 1). The sample size across studies ranged from 8 to 104 participants. Age ranges were reported in 31 studies, most commonly falling between 18 and 50 years. Gender distribution was specified in 25 studies, with several reporting balanced groups of males and females; however, 18 studies did not provide gender information.

Table 1.

Characteristics of studies included in the systemic review and meta-analysis.

No	Study	Diagnosis	Number of samples	Age range	Gender	Country	Perio/endo lesion	Systemic disease	Biomarkers investigated	Sample type	Method of biomarker measurement
1	Wu et al¹⁸	Asymptomatic irreversible pulpitis	112	14-72	Not specified	China	No	Healthy individuals	IL-1β, IL-6, IL-8, TNF-α	Pulp exudate	Cytometric Bead Array
2	Anderson et al⁵⁹	Asymptomatic irreversible pulpitis, pulp necrosis	80	20-55	Not specified	USA	No	Not specified	IL-2	Pulp tissue	Cytokine Assay Kit
3	Lepinski et al⁶⁰	Irreversible pulpitis	22	Not specified	Not specified	USA	No	Not specified	Bradykinin	Pulp tissue	Microdialysis, Radioimmunoassay
4	Nakanishi et al⁶¹	Irreversible pulpitis	36	Not specified	Not specified	Japan	No	Not specified	IgG, IgA, IgM, elastase, PGE2, IL-1, IL-6, TNF-α	Pulp blood	ELISA
5	Spoto et al⁶²	Irreversible pulpitis	60	20-27	Not specified	Italy	No	Not specified	Aspartate Aminotransferase (AST)	Pulp tissue	Enzymatic Assay
6	Bowles et al⁶³	Irreversible pulpitis	24	13-53	Not specified	USA	No	Not specified	Substance P	Pulp tissue	Microdialysis, Radioimmunoassay
7	Abouelenien et al⁴³	Pulp necrosis	36	20-50	Not specified	Egypt	Apical periodontitis	Healthy (ASA I or II)	IL-8	Periapical tissue	ELISA
8	Barkhordar et al⁶⁴	Pulp necrosis	12	Not specified	Not specified	USA	Periapical lesions	Not specified	IL-6	Pulp and periapical tissue	ELISA
9	Proctor et al⁶⁵	Pulp necrosis	27	Not specified	Not specified	USA	No	Healthy individuals	C-Reactive Protein (CRP)	Pulp tissue, serum	ELISA
10	Toledo et al²³	Pulp necrosis	86	19-58	Not specified	Brazil	Periapical lesion present in some cases	Not specified	IL-10, IL-17, TGF-β, CCL4, CCL20	Periapical tissue	ELISA
11	Li et al²²	Pulp necrosis with Chronic apical periodontitis	20	18-65	Not specified	China	Periapical lesions	No systemic diseases	TGF-β	Periapical granulation tissue	ELISA, Immunohistochemistry, RT-PCR
12	Gresser et al²¹	Pulp necrosis, endodontic failure	53	Not specified	Not specified	Germany	Endodontic failure	Not specified	TNF-α, IL-10, IFN-γ	Serum, pulp tissue	Luminex, ELISA
13	Topçu et al⁶⁶	Reversible and irreversible pulpitis	20	15-25	12 females, 8 males	Turkey	No	Healthy individuals	Catalase	Pulp tissue	Spectrophotometry
14	Abd-Elmegui et al⁶⁷	Reversible pulpitis, irreversible pulpitis	35	Not specified	Not specified	Canada	No	Not specified	Osteocalcin	Pulp tissue	Multiplex Assay
15	Mente et al¹³	Reversible pulpitis, symptomatic irreversible pulpitis	44	18-74	19 males, 25 females	Germany	No	Not specified	MMP-9	Pulpal blood	Fluorometric Assay
16	Nawal et al¹⁶	Reversible pulpitis, symptomatic irreversible pulpitis	72	14-53	34 males, 38 females	India	No	Healthy individuals	IL-8, TNF-α	Pulpal blood	ELISA
17	Halkai et al⁶⁸	Symptomatic and asymptomatic irreversible pulpitis	45	Not specified	Not specified	India	No	No systemic diseases	Salivary alpha-amylase	Saliva	Spectrophotometry
18	Pezelj-Ribaric et al⁶⁹	Symptomatic and asymptomatic irreversible pulpitis	60	Not specified	Not specified	Croatia	No	Not specified	TNF-α	Pulp tissue	ELISA
19	Sattari et al³¹	Symptomatic and asymptomatic irreversible pulpitis	45	13-46	Not specified	Iran	No	Healthy individuals	CGRP, Substance P	Pulp tissue	Enzyme Immunoassay
20	Akbal Dincer et al¹⁷	Symptomatic irreversible pulpitis	40	16-50	Not specified	Turkey	No	Not specified	Neurokinin A, Substance P, IL-8, MMP-8	Pulp tissue, Gingival Crevicular Fluid	ELISA
21	Awawdeh et al⁷⁰	Symptomatic irreversible pulpitis	46	18-71	28 males, 18 females	Jordan	No	No systemic diseases	Substance P, Neurokinin A, CGRP	Pulp tissue	Radioimmunoassay
22	Awawdeh, Lundy et al⁷¹	Symptomatic irreversible pulpitis	54	18-71	34 males, 20 females	UK	No	No systemic diseases	Substance P, Neurokinin A, CGRP	Gingival Crevicular Fluid	Radioimmunoassay
23	Castillo-Silva et al⁷²	Symptomatic irreversible pulpitis	75	17-40	26 males, 49 females	Mexico	No	No systemic diseases	CGRP	Pulp tissue	ELISA, qPCR
24	Caviedes-Bucheli et al⁷³	Symptomatic irreversible pulpitis	15	20-40	Not specified	Colombia	No	Not specified	CGRP	Pulp tissue	Flow Cytometry
25	Caviedes-Bucheli et al⁷⁴	Symptomatic irreversible pulpitis	6	19-36	Not specified	Colombia	No	Not specified	CGRP receptor	Pulp tissue	Radioreceptor Assay
26	Caviedes-Bucheli et al⁷⁵	Symptomatic irreversible pulpitis	18	19-40	Not specified	Colombia	No	Not specified	CGRP, Substance P, NKA, NPY	Pulp tissue	Radioimmunoassay
27	Caviedes-Bucheli et al⁷⁶	Symptomatic irreversible pulpitis	13	18-49	Not specified	Colombia	No	Not specified	CGRP receptor, CD163	Pulp tissue	Flow Cytometry
28	Esposito et al⁷⁷	Symptomatic irreversible pulpitis	10	Not specified	Not specified	Italy	Not specified	Not specified	Catalase activity	Pulp tissue	Enzymatic Assay (Catalase Activity)
29	Golbasi et al⁷⁸	Symptomatic irreversible pulpitis	48	16-50	16 males, 32 females	Turkey	No	No systemic diseases	ADAMTS-1, ADAMTS-4, ADAMTS-9, TIMP-3	Pulp tissue	ELISA
30	Gulzar et al⁷⁹	Symptomatic irreversible pulpitis	10	Not specified	Not specified	India	No	Not specified	Nerve Growth Factor (NGF)	Saliva	ELISA
31	Huang et al⁸⁰	Symptomatic irreversible pulpitis	29	Not specified	Not specified	USA	No	Healthy individuals	IL-8	Pulp tissue	ELISA, Immunohistochemistry
32	Karapanou et al⁸¹	Symptomatic irreversible pulpitis	25	18-53	10 males, 8 females	USA	No	Healthy individuals	IL-8, TNF-α	Gingival crevicular fluid	ELISA
33	Kritikou et al³³	Symptomatic irreversible pulpitis	23	16-18	Not specified	Romania	No	No systemic diseases	IL-2, IL-17, TNF-α, MMP-7, MMP-9, SOD3, Catalase	Pulp tissue	ELISA
34	Rauschenberger et al⁸²	Symptomatic irreversible pulpitis	40	Not specified	Not specified	USA	No	Healthy individuals	IL-2	Pulp tissue	ELISA
35	Sabeti M.A. et al⁸³	Symptomatic irreversible pulpitis	36	Not specified	15 males, 19 females	USA	No	Not specified	IL-1a, IL-6, IL-8, MMP9, MMP1	Pulpal blood	Luminex Assay
36	Sharma et al¹⁴	Symptomatic irreversible pulpitis	50	15-35	25 males, 25 females	India	No	Healthy individuals	MMP-9	Pulp blood	Fluorometric Assay
37	Soliman A.A. et al⁸⁴	Symptomatic irreversible pulpitis	36	18-50	Not specified	Egypt	No	Healthy individuals	IL-8	Pulpal blood	ELISA
38	Varvara et al⁸⁵	Symptomatic irreversible pulpitis	25	13-35	14 females, 11 males	Italy	No	Healthy individuals	Superoxide Dismutase (SOD)	Pulp tissue	Spectrophotometry
39	Jain S et al⁸⁶	Symptomatic irreversible pulpitis, pulpal necrosis with periapical periodontitis	60	18-45	22 males, 38 females	India	Apical periodontitis	No systemic diseases	IL-1β	Periapical tissue fluid	ELISA
40	ElSalhy et al¹⁵	Symptomatic irreversible pulpitis, asymptomatic caries exposure	108	18-60	57 males, 51 females	Kuwait	No	Healthy individuals	IL-2, IL-6, IL-8, IL-10, TNF-α, IFN-γ	Pulp blood	ELISA
41	Ayoub et al¹⁹	Symptomatic irreversible pulpitis, asymptomatic irreversible pulpitis	69	18-64	32 males, 37 females	USA	No	No systemic diseases	IL-1β, TNF-α, hBD-2, hBD-3	Pulp tissue	Luminex, ELISA
42	Accorsi-Mendonça et al⁸⁷	Symptomatic pulpitis	20	Not specified	Not specified	Brazil	No	Not specified	MMP-2, MMP-9, TIMP-2, MPO	Pulp tissue	Zymography, ELISA, MPO Assay
43	Isett et al⁸⁸	Untreated irreversible pulpitis	40	Not specified	Not specified	USA	No	Healthy individuals	PGE2, IL-8	Pulp tissue	Enzyme Immunoassay

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Summarizes the basic information from the 43 included studies. It shows the number of samples, age range, gender, diagnosis type, country, presence of other diseases, and which biomarkers were measured. It also notes what type of sample was collected (like pulp tissue or blood) and which lab method was used to measure the biomarkers. The studies came from many countries and used different approaches, which explains some of the differences seen in the results.

Regarding geographic distribution, the studies originated from 18 countries. The most represented countries were the USA (11 studies), followed by India (5 studies), and Colombia (4 studies). Other contributing countries included Brazil, Canada, China, Croatia, Iran, Jordan, Japan, Kuwait, Romania, and the UK.

In terms of diagnosis, symptomatic irreversible pulpitis was the most frequently studied condition, reported in 28 studies, either alone or alongside other conditions such as asymptomatic irreversible pulpitis, pulp necrosis, or reversible pulpitis. Five studies examined pulp necrosis specifically, while 8 studies included mixed diagnoses. Most studies (n = 37) were conducted on systemically healthy individuals, while 6 studies either did not specify systemic health status or included cases with systemic conditions.

Biomarkers assessed varied considerably. Commonly studied inflammatory mediators included TNF-α (in 9 studies), IL-6 (7 studies), IL-8 (11 studies), MMP-9 (5 studies), and IL-1β (3 studies). Several studies also investigated other targets such as CGRP, Substance P, PGE2, catalase, SOD, and bradykinin, reflecting the diversity in molecular markers used to characterize pulpal inflammation. Sample types used for biomarker analysis included pulp tissue (in 31 studies), pulpal blood (5 studies), followed by periapical tissue, and gingival crevicular fluid. The most common method of biomarker detection was ELISA (used in 25 studies), followed by spectrophotometry, immunoassays, multiplex assays, qPCR, flow cytometry, and radioimmunoassay.

In the descriptive analysis of pulp necrosis, several biomarkers were consistently reported in at least two studies. TNF-α was identified in the studies by Ayoub et al¹⁹ and Gresser et al.²¹ IL-8, IL-10, and IFN-γ were all reported in studies by ElSalhy et al¹⁵ and Gresser et al.²¹ TGF-β was detected in necrotic cases in the studies by Li et al²² and Toledo et al.²³ The biomarkers were derived from pulp tissue, pulpal blood, or associated periapical tissues.

In summary, the included studies demonstrated substantial heterogeneity in design, biomarker targets, and methodology, with a predominant focus on inflammatory conditions of the pulp in systemically healthy patients.

Assessment of the quality of included studies and GRADE certainty of evidence

Table 2 presents reports of the characteristics of assessment of quality of included studies. The methodological quality of included studies was assessed using the Joanna Briggs Institute (JBI) appraisal tools for randomized controlled trials (RCTs) and cross-sectional studies.²⁴ For RCTs, randomization was used and appropriately concealed in all studies, and baseline similarities between groups were confirmed. In all RCT studies, participants were blinded to their treatment groups, which helped reduce bias and improve reliability of the outcome measurements. Appropriate statistical analyses were consistently applied, leading to an overall high-quality appraisal.

Table 2.

The quality assessment results.

A.
No	Study	Were the criteria for inclusion in the sample clearly defined?	Were the study subjects and the setting described in detail?	Was the exposure measured in a valid and reliable way?	Were objective, standard criteria used for measurement of the condition?	Were confounding factors identified?	Were strategies to deal with confounding factors stated?	Were the outcomes measured in a valid and reliable way?	Was appropriate statistical analysis used?	Overall appraisal of risk of bias
1	Abd-Elmegui et al⁶⁷	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
2	Accorsi-Mendonça et al⁸⁷	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
3	Akbal Dincer et al¹⁷	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
4	Anderson et al⁵⁹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
5	Awawdeh and Lundy et al⁷¹	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	Moderate
6	Awawdeh et al⁷⁰	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
7	Ayoub et al¹⁹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
8	Barkhordar et al⁶⁴	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
9	Bowles et al⁶³	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
10	Castillo-Silva et al⁷²	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
11	Caviedes-Bucheli et al⁷³	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
12	Caviedes-Bucheli et al⁷⁴	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
13	Caviedes-Bucheli et al⁷⁵	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
14	Caviedes-Bucheli et al⁷⁶	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
15	ElSalhy et al¹⁵	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
16	Esposito et al³²	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
17	Globasi et al⁷⁸	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
18	Gresser et al²¹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
19	Gulzar et al⁵⁷	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
20	Halkai et al⁶⁸	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
21	Huang et al⁸⁰	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
22	Jain et al⁸⁶	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
23	Karapanou et al⁸¹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
24	Kritikou et al³³	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
25	Lepinski et al⁶⁰	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
26	Li et al²²	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
27	Nakanishi et al⁶¹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
28	Nawal et al¹⁶	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
29	Pezelj-Ribaric et al⁶⁹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
30	Proctor et al⁶⁵	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
31	Rauschenberger et al⁸²	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
32	Sabeti et al⁸³	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
33	Sattari et al³¹	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
34	Spoto et al⁶²	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
35	Toledo et al²³	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
36	Topçu et al⁶⁶	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
37	Varvara et al⁸⁵	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate
38	Wu et al¹⁸	Yes	Yes	Yes	Yes	No	No	Yes	Yes	Moderate

B.
No	Study	Randomization used?	Allocation concealed?	Baseline similarity?	Blinding applied?	Outcome measures reliable?	Appropriate statistical analysis?	Overall appraisal of risk of bias
1	Abouelenien et al⁴³	Yes	Yes	Yes	Yes	Yes	Yes	Moderate
2	Isett et al⁸⁸	Yes	Yes	Yes	Yes	Yes	Yes	Moderate
3	Mente et al¹³	Yes	Yes	Yes	Yes	Yes	Yes	Moderate
4	Sharma et al¹⁴	Yes	Yes	Yes	Yes	Yes	Yes	Moderate
5	Soliman et al⁸⁴	Yes	Yes	Yes	Yes	Yes	Yes	Moderate

Open in a new tab

The tables present the quality assessment of the included studies. For cross-sectional studies, it looked at whether the study design was clear, if the exposure and outcomes were measured properly, and whether the data analysis was appropriate. Most studies did not explain how they handled confounding factors, but overall they were still considered acceptable (A). For randomized controlled trials, the table shows that all of them used proper randomization, blinding, and reliable outcome measurements (B).

In the cross-sectional studies assessed with the JBI tool, all studies had clearly defined inclusion criteria and adequately described study subjects and settings. However, most of these studies did not clearly address potential confounding factors. Despite this limitation, outcome measures were reliably obtained, and suitable statistical analyses were consistently used. Consequently, these cross-sectional studies were considered to have an overall acceptable methodological quality suitable for inclusion in the analysis.

The certainty of evidence for all biomarkers evaluated in the SIP vs healthy pulp comparison was rated as Low according to the GRADE framework (Supplementary Table 2). Downgrading was primarily due to moderate risk of bias, substantial to very high heterogeneity, and imprecision across studies. Some biomarkers were further downgraded for possible publication bias, reflecting concerns about selective reporting and funnel plot asymmetry. For AIP vs healthy pulp, only TNF-α was eligible for GRADE evaluation. The certainty of evidence was also rated Low, downgraded for moderate risk of bias, very high heterogeneity, and imprecision. Overall, the GRADE assessment indicates that, despite consistent patterns across studies, the strength of evidence supporting biomarker differences in SIP and AIP remains limited, and conclusions should be interpreted with caution.

Results of meta-analysis

Of the 26 studies included in the meta-analysis, only those involving symptomatic irreversible pulpitis and asymptomatic irreversible pulpitis were identified. The details are as follows:

Symptomatic irreversible pulpitis vs healthy pulp

Figure 2 shows the meta-analysis results of the symptomatic irreversible pulpitis (SIP) vs healthy pulp. The provided forest plots illustrate meta-analyses comparing the expression levels of TNF-α, CGRP, IL-2, IL-8, Substance P, IL-6, MMP-9, and Catalase in healthy pulp vs SIP. Figure 2A shows a meta-analysis of three studies comparing MMP-9 levels between SIP and healthy pulp. The pooled results indicated a non-significant overall decrease in MMP-9 levels in the SIP group (SMD = −1.27, 95% CI: −2.85 to 0.31, P = .12). The analysis showed high heterogeneity (I² = 92%), suggesting considerable variability among the included studies. Meta-analysis of three studies comparing catalase levels between SIP and healthy pulp is presented in Figure 2B. The pooled results showed a significant increase in catalase levels in SIP compared to healthy pulp (SMD= −1.41, 95% CI: −2.35 to −0.46, P = .004). The negative SMD and leftward direction of the forest plot indicate higher catalase expression in the SIP group. Moderate to high heterogeneity was observed among the studies (I² = 77%, P = .01), suggesting variability in effect sizes. This increase in catalase may reflect an upregulated antioxidant response to elevated oxidative stress in inflamed pulp tissue. The meta-analysis of four studies assessing Substance P levels is shown in Figure 2C. It showed significant increase in the SIP group compared to healthy pulp, with a SMD of −3.16 (95% CI: −5.54 to −0.78, P = .009). High heterogeneity was present (I² = 92%), indicating substantial variability among the included studies. Figure 2D is the pooled analysis of four studies evaluating IL-2 levels. It demonstrated a significant elevation in the SIP group (SMD = −1.61, 95% CI: −3.14 to −0.08, P = .04), with very high heterogeneity (I² = 94%). This suggests consistent differences between groups, though the effect sizes varied across studies. The meta-analysis of five studies comparing TNF-α levels revealed in Figure 2E. It showed a significantly higher concentration in SIP compared to healthy pulp (SMD = −2.08, 95% CI: −3.46 to −0.70, P = .003). High heterogeneity was also observed (I² = 92%), reflecting considerable variability among the studies.

Fig 2 dummy alt text — SIP vs healthy pulp. Forest plots comparing biomarker expression between symptomatic irreversible pulpitis (SIP) and healthy pulp. The meta-analysis illustrates standardized mean differences (SMDs) for individual biomarkers: (A) TNF-α, (B) IL-2, (C) IL-6, (D) IL-8, (E) Substance P, (F) CGRP, (G) Catalase, and (H) MMP-9. It shows significantly higher expression of several inflammatory and neurogenic biomarkers in SIP, including TNF-α, IL-2, IL-6, IL-8, Substance P, and CGRP. Catalase was also significantly elevated, suggesting increased oxidative stress. In contrast, MMP-9 levels showed a non-significant trend toward reduction. These findings highlight the upregulation of inflammatory pathways in SIP compared to healthy pulp tissue.

Understanding the Figure:

• Each green square represents an individual study's effect size (SMD), with its size proportional to study weight.

• Horizontal lines show the 95% confidence interval (CI).

• The vertical line (SMD = 0) represents no difference between SIP and healthy pulp.

• Diamonds indicate the pooled effect for each biomarker; their width reflects the pooled CI.

• High I² values reflect substantial heterogeneity across studies.

• Values to the left of zero = biomarker levels higher in SIP.

The meta-analysis of six studies comparing IL-8 levels is shown in Figure 2F. It revealed a significantly higher concentration of IL-8 in SIP compared to healthy pulp (SMD = −1.86, 95% CI: −2.42 to −1.30, P < .00001). Moderate heterogeneity was observed (I² = 68%), indicating some variability among the included studies.

Figure 2G presents the meta-analysis of three studies assessing IL-6 levels. The pooled results showed a significantly higher IL-6 concentration in SIP than in healthy pulp (SMD = −2.75, 95% CI: −4.82 to −0.68, P = .009). High heterogeneity was present (I² = 91%), reflecting substantial inter-study variation. The meta-analysis of seven studies comparing CGRP levels is shown in Figure 2H. It demonstrated a significantly elevated CGRP concentration in SIP relative to healthy pulp (SMD = −2.40, 95% CI: −3.41 to −1.38, P < .00001). Considerable heterogeneity was also observed (I² = 84%), suggesting notable differences among the studies included.

Asymptomatic irreversible pulpitis vs healthy pulp

The meta-analysis of three studies comparing TNF-α levels is shown in Figure 3. It revealed a significantly higher concentration in asymptomatic irreversible pulpitis (AIP) compared to healthy pulp (SMD = −1.41, 95% CI: −2.82 to −0.00, P = .05), suggesting an upregulation of TNF-α even in the absence of clinical symptoms. High heterogeneity was observed (I² = 91%, P < .0001), reflecting substantial variability in effect sizes, which may be attributed to differences in methodology, sample sources, or patient characteristics.

Fig 3 dummy alt text — AIP vs Healthy pulp and AIP vs SIP. The figure shows the comparison of TNF-α levels in asymptomatic irreversible pulpitis (AIP) vs healthy pulp, and AIP vs symptomatic irreversible pulpitis (SIP). The results showed that TNF-α was significantly higher in AIP than in healthy pulp, even though AIP has no symptoms. This suggests that inflammation can occur even without pain. When comparing AIP to SIP, there was no significant difference in TNF-α levels, meaning that the presence of symptoms may not reflect the true level of inflammation. However, there was high variability among the studies included.

Asymptomatic irreversible pulpitis vs symptomatic irreversible pulpitis

The meta-analysis of three studies comparing TNF-α levels between asymptomatic irreversible pulpitis and symptomatic irreversible pulpitis is shown in Figure 3. The pooled results revealed no statistically significant difference in TNF-α concentrations between the two conditions (SMD = −0.19, 95% CI: −1.45 to 1.06, P = .76). This finding suggests that TNF-α expression may not be directly correlated with the presence of clinical symptoms such as pain and may instead reflect an underlying inflammatory state common to both AIP and SIP. However, substantial heterogeneity was observed among the studies (I² = 93%, P < .00001), indicating notable variability in the reported effect sizes.

Sensitivity analysis results

Excluding studies for which medians and interquartile ranges were converted to means and standard deviations did not substantially alter the pooled effect estimates for any biomarker (Supplementary Figure 1). The direction and magnitude of effects remained consistent, indicating that the primary findings were robust to the exclusion of converted data.

Subgroup and meta-regression analysis results

Subgroup analyses based on assay method showed that methodological differences contributed to heterogeneity for several biomarkers. Significant subgroup differences were observed for MMP-9 (χ² = 6.86, P = .009), Catalase (χ² = 8.54, P = .003), and Substance P (χ² = 6.02, P = .047), indicating that variability in measurement technique influenced the pooled effect estimates for these markers. In contrast, no significant subgroup differences were found for IL-2, IL-8, or IL-6, suggesting that assay method did not explain heterogeneity for these biomarkers. Overall, these findings indicate that assay-related methodological variation accounted for heterogeneity in some, but not all, biomarker outcomes (Supplementary Figures 2).

Meta-regression was performed to explore whether study-level factors contributed to the substantial heterogeneity observed across studies. Using biomarker category (inflammatory, oxidative, neuropeptide) as a moderator, the model did not show a statistically significant effect (QM(df = 2) = 3.47, P = .18), indicating that differences in biomarker type did not account for between-study variability. Similarly, meta-regression using country of origin as a moderator also showed no significant association with effect sizes (QM(df = 12) = 8.73, P = .73). These findings indicate that neither biomarker category nor country of origin contributed meaningfully to explaining the heterogeneity in the pooled estimates (Supplementary Figures 3).

Discussion

Inflammatory and neurogenic biomarkers in pulpal disease progression

Dentin-pulp inflammation is a progressive condition involving a cascade of molecular changes that distinguish healthy pulp, reversible pulpitis, irreversible pulpitis (SIP and AIP), and pulp necrosis (Figure 4). In healthy pulp, anti-inflammatory cytokines such as TGF-β and antioxidant enzymes like catalase help to maintain immune balance and tissue integrity.²⁵^,²⁶ Upon microbial invasion or injury, pro-inflammatory cytokines-including IL-1β, IL-6, and TNF-α-are released, triggering immune activation and cellular recruitment. Neuropeptides such as Substance P (SP) and CGRP also increase, contributing to neurogenic inflammation and pain sensitivity. At this early stage, antioxidant defences remain functional and MMP-9 levels are low, allowing the potential for tissue recovery.27, 28, 29 As inflammation progresses into irreversible pulpitis, our meta-analysis demonstrated significantly elevated levels of TNF-α, IL-2, IL-6, IL-8, SP, and CGRP in SIP compared to healthy pulp, reflecting a shift toward chronic inflammation and heightened neurogenic response.³⁰^,³¹ Catalase was increased, indicating elevated oxidative stress, while MMP-9 showed a variable trend toward elevation, suggesting progressive matrix degradation.³²^,³³

Fig 4 dummy alt text — Progression of pulpal disease and associated biomarker signatures from health to necrosis. This figure illustrates the progression of pulpal disease based on biomarker expression and clinical symptoms. Healthy pulp shows low levels of inflammatory biomarkers and no symptoms. In SIP, there is pain along with increased levels of pro-inflammatory cytokines (TNF-α, IL-2, IL-6, IL-8), neurogenic markers (CGRP, SP), and an antioxidant response (catalase). The AIP shows elevated TNF-α despite the absence of pain, indicating subclinical inflammation. In pulp necrosis, pain is absent, but lingering inflammation (TNF-α, IFN-γ) and regenerative-related markers (TGF-β, IL-10) are present. (Illustration was created in BioRender. Osathanon, T. (2026) https://BioRender.com/a2vfky3).

Interestingly, TNF-α levels were also significantly elevated in AIP, despite the absence of clinical symptoms, supporting the presence of subclinical inflammation. These findings highlight a critical disconnect between molecular pathology and patient-reported symptoms. SP and CGRP, for example, were notably increased in SIP and are known to contribute to vasodilation, vascular permeability, and nociceptor sensitization-mechanisms consistent with symptomatic pain.³¹^,³⁴ Meanwhile, the elevation of TNF-α in AIP reinforces its role in pulp degeneration even without symptoms. Previous studies have linked TNF-α to both early and late-stage pulpitis, with continued expression correlating with pulp necrosis and periapical pathology.³⁵ Animal models further support that TNF-α overexpression alone can induce pulpitis-like inflammation. Together, these results underscore the potential of specific biomarkers to reflect disease severity more accurately than clinical symptoms alone.⁷^,36, 37, 38, 39 Clinically, this opens the possibility for a marker-based treatment approach, emphasizing the use of adjunctive medications in vital pulp therapy (VPT) that can reduce the expression of inflammatory cytokines and neuropeptides that were proven to be increased, as shown in this meta-analysis.

Since there were fewer than three studies investigating biomarkers in pulp necrosis, a meta-analysis could not be performed. However, based on the collected data, several biomarkers were identified, including TGF-β, IL-10, IFN-γ, and TNF-α. TGF-β is a key regulator of immune responses and tissue remodelling, with a paradoxical function in pulp pathology.⁴⁰ In the early stages of pulp inflammation, TGF-β promotes dentin repair, stimulates odontoblast differentiation, and regulates immune homeostasis. However, when inflammation becomes chronic, persistent TGF-β signalling shifts its function from tissue healing to fibrosis and apoptosis.⁴¹^,⁴² In pulp necrosis, TNF-α promotes inflammation, immune cell recruitment, and tissue breakdown, indicating ongoing immune activation. IFN-γ supports a chronic immune response by activating macrophages and enhancing microbial defence. In contrast, IL-10 acts as an anti-inflammatory cytokine, helping to suppress excessive immune activity and limit tissue damage. Together, these markers reflect the dynamic immune environment in pulp necrosis.¹⁰^,²¹^,⁴³

Clinical implications and future diagnostic approaches

These biomarker patterns offer valuable insights for improving diagnostic accuracy and treatment planning in endodontics. The elevated levels of cytokines such as TNF-α, IL-6, and IL-8, along with neuropeptides like SP and CGRP, in SIP support their use as potential diagnostic markers of symptomatic inflammation.⁴⁴^,⁴⁵ Although AIP is absent of symptoms, our finding of elevated TNF-α supports the presence of subclinical inflammation and underscores the limitations of relying solely on patient-reported pain. This highlights a critical gap in symptom-based diagnostic models and underscores the need for objective, biology-based tools to guide intervention.

Our meta-analysis showed that no single biomarker clearly differentiates SIP, AIP, and pulp necrosis. However, the relative levels of these markers vary across the three diagnoses, suggesting potential value for clinical decision-making. Traditionally, root canal therapy has been the treatment of choice for SIP, AIP, and pulp necrosis.⁴⁶^,⁴⁷ However, recent trends have shifted, with clinicians increasingly adopting less invasive approaches for managing irreversible pulpitis.48, 49, 50, 51 A recent clinical trial supports this minimally invasive concept, showing that teeth with clinical signs of irreversible pulpitis still achieved high VPT success rates when intervention occurred early.⁴⁸ Despite this shift, the decision to perform RCT or VPT in cases of irreversible pulpitis remains highly subjective.⁴⁶ In this context, the consistent elevation of TNF-α across AIP, SIP, and pulp necrosis adds diagnostic value and may help objectively define the appropriate window for VPT.

A cross-sectional pilot study by Elfezary et al⁵² demonstrated that a rapid CRP point-of-care test using pulpal blood from deep carious lesions had high diagnostic accuracy. While the test focused on CRP, the same platform could be adapted for markers like IL-6, TNF-α, and MMPs, which were elevated as shown in our meta-analysis. Such chairside diagnostics may enhance the accuracy of pulp status assessment, especially in uncertain cases. Minimally invasive sampling methods from dentinal fluid, pulpal blood, or gingival crevicular fluid have been proposed for biomarker detection.⁵³ Additionally, emerging technologies such as lateral flow immunoassays and microfluidic lab-on-a-chip platforms may soon allow rapid, multiplexed biomarker analysis at the point of care.⁵⁴^,⁵⁵ Incorporating these tools into routine diagnostics could enhance early detection, support timely interventions, and reduce unnecessary root canal treatments in cases where the pulp remains vital.

Future biomarker-based diagnostics may enable rapid chairside tools that distinguish SIP, AIP, and pulp necrosis with greater precision than current clinical methods. These assays, particularly those targeting inflammatory mediators such as TNF-α, could play a crucial role in clinical situations where decision-making is most challenging. For example, during extended pulpotomy procedures, clinicians often rely on subjective indicators such as the colour and controllability of pulpal bleeding to judge whether the remaining pulp is sufficiently healthy for vital pulp therapy.⁴⁷^,⁵⁶^,⁵⁷ These indicators, however, can be difficult to interpret and may lead to inconsistent clinical decisions. Once validated and implemented, chairside biomarker tests could provide immediate, objective assessment of the inflammatory status of the remaining pulp tissue. This would assist clinicians in determining whether the tooth remains a viable candidate for VPT or whether root canal therapy is required, thereby improving treatment predictability and avoiding high-cost RCT. The PoC tests are already used chairside in peri-implant diagnostics,⁵⁸ and the elevated biomarkers identified in this meta-analysis could support the development of similar tools for pulpitis.

Limitations

While this meta-analysis provides valuable insights, certain limitations should be acknowledged. One notable limitation is the high heterogeneity observed across studies, with I² values frequently exceeding 90%. This reflects differences in methodologies, sample populations, and measurement techniques, which may introduce variability in the results. Factors such as sample collection methods, processing techniques, and biomarker quantification approaches (eg, ELISA, qPCR, or immunohistochemistry) likely contribute to this heterogeneity. Some analyses, particularly those comparing symptomatic and asymptomatic pulpitis, have relatively small sample sizes. This may impact statistical power and the generalizability of the findings. Another consideration is the inconsistency in biomarker selection across studies. Not all biomarkers were measured in each study, leading to variations in data availability. Some biomarkers also exhibited conflicting trends, which may be due to differences in study designs, patient populations, or disease progression stages. Given the extremely high heterogeneity across studies (I² > 90%), the pooled results may not fully represent the true effect and must be interpreted cautiously. Such variability suggests that differences in study design, sampling methods, or diagnostic criteria strongly influence biomarker levels, making direct application to clinical practice still inappropriate.

Variability in diagnostic criteria for irreversible pulpitis is another potential limitation. Differences in how studies classify pulpitis may introduce inconsistencies in sample selection. Additionally, some studies may include pulp samples from different stages of the disease, further contributing to variations in biomarker expression. Most studies included in this meta-analysis are cross-sectional, meaning that they provide a 'snapshot' rather than a progression of biomarker changes over time. Longitudinal studies tracking biomarker expression throughout pulpitis progression could offer a more comprehensive understanding of their roles.

Another limitation is that only studies published in English were included, which may introduce language bias and limit the comprehensiveness of the review by potentially excluding relevant data published in other languages.

Conclusion

Within the limitations of this systematic review and meta-analysis, distinct biomarker profiles were identified in irreversible pulpitis (IP) and pulp necrosis (PN). Elevated TNF-α in both symptomatic and asymptomatic IP suggests subclinical inflammation, while PN shows markers of chronic immune activity. These findings highlight the potential of molecular biomarkers, particularly TNF-α-as objective indicators that surpass symptom-based diagnosis. Incorporating biomarker testing (eg, pulpal fluid analysis) could improve diagnostic accuracy, guide treatment decisions (vital pulp therapy vs. root canal), and reduce overtreatment. Future point-of-care cytokine assays may enable more personalized, stage-specific endodontic interventions.

Funding

This research is funded by the Dental Faculty Research Grant (DRF69_006 to CNL), Faculty of Dentistry, Chulalongkorn University, and the Second Century Fund (C2F), Chulalongkorn University (to RW and CNL). LS was supported by the Chulalongkorn University, Second century (C2F) high potential professoriate fund, Faculty of Dentistry.

Data availability

Data is provided within the manuscript or supplementary information files.

Author contributions

Conceptualization: Wahyudi, Aguilar, Changsiripun, C.N. Limjeerajarus, N. Limjeerajarus.

Methodology: Wahyudi, Aguilar, Changsiripun, Samaranayake.

Software: Wahyudi, Changsiripun.

Validation: Wahyudi, Aguilar, Samaranayake, C.N. Limjeerajarus.

Formal analysis: Wahyudi, Changsiripun, C.N. Limjeerajarus, N. Limjeerajarus.

Investigation: Wahyudi, Aguilar, C.N. Limjeerajarus.

Resources: Changsiripun, Lertpimonchai, Osathanon.

Data Curation: Wahyudi, C.N. Limjeerajarus.

Writing - Original Draft: Wahyudi, C.N. Limjeerajarus.

Writing - Review & Editing: Samaranayake, Soe, Osathanon, Everts, N. Limjeerajarus.

Visualization: Wahyudi, Soe.

Supervision: Lertpimonchai, Samaranayake, Soe, Osathanon, Everts.

Project administration: C.N. Limjeerajarus, N. Limjeerajarus.

Funding acquisition: C.N. Limjeerajarus, N. Limjeerajarus.

Conflict of interest

None disclosed.

Acknowledgements

The authors thank Assist. Prof. Soranun Chantarangsu from the Department of Oral Pathology, Faculty of Dentistry, Chulalongkorn University, for her guidance and assistance with the meta-analysis.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.identj.2026.109453.

Appendix. Supplementary materials

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx^{(306.7KB, docx)}

mmc2.jpg^{(1MB, jpg)}

mmc3.jpg^{(446.3KB, jpg)}

mmc4.jpg^{(1.3MB, jpg)}

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mmc7.jpg^{(525.3KB, jpg)}

mmc8.docx^{(15.4KB, docx)}

mmc9.docx^{(17KB, docx)}

Data Availability Statement

Data is provided within the manuscript or supplementary information files.

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PERMALINK

Inflammatory Biomarkers in Irreversible Pulpitis and Pulp Necrosis: A Systematic Review and Meta-Analysis

Rahman Wahyudi

Panuroot Aguilar

Chidsanu Changsiripun

Attawood Lertpimonchai

Lakshman Samaranayake

Zar Chi Soe

Thanaphum Osathanon

Vincent Everts

Chalida Nakalekha Limjeerajarus

Nuttapol Limjeerajarus

Abstract

Statement of Problem

Objectives

Methods

Results

Conclusion

Significance

Graphical abstract

Introduction

Materials and methods

Protocol and registration

Research questions

Eligibility criteria

Search strategy and study selection

Data collection

Quality assessment and risk of bias assessment

Certainty of evidence assessment (GRADE)

Statistical and sensitivity analysis

Subgroup and meta-regression analysis

Results

Study selections

Fig. 1.

Characteristics of included studies

Table 1.

Assessment of the quality of included studies and GRADE certainty of evidence

Table 2.

Results of meta-analysis

Symptomatic irreversible pulpitis vs healthy pulp

Fig. 2.

Asymptomatic irreversible pulpitis vs healthy pulp

Fig. 3.

Asymptomatic irreversible pulpitis vs symptomatic irreversible pulpitis

Sensitivity analysis results

Subgroup and meta-regression analysis results

Discussion

Inflammatory and neurogenic biomarkers in pulpal disease progression

Fig. 4.

Clinical implications and future diagnostic approaches

Limitations

Conclusion

Funding

Data availability

Author contributions

Conflict of interest

Acknowledgements

Footnotes

Appendix. Supplementary materials

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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