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Peri-implantitis is diagnosed from clinical inflammation and progressive supporting bone loss. Biomarkers may add information about current biological activity, tissue degradation or systemic inflammatory context, but their meaning depends on the sampling matrix, assay method and intended clinical decision.
Different biomarker roles require different evidence
| Role | Question | Required evidence |
|---|---|---|
| Diagnostic | Is disease present now? | Accuracy against a defined reference standard in the intended population. |
| Prognostic | Will the site progress or recur? | Longitudinal sampling before a prespecified future outcome. |
| Predictive | Who is likely to benefit from a specific intervention? | Demonstration of treatment-by-marker interaction. |
| Monitoring | Has biological activity changed? | Repeatability, biological variation and responsiveness to meaningful change. |
| Mechanistic | Which pathway is active? | Biological plausibility supported by tissue, fluid or experimental evidence. |
A biomarker that distinguishes established peri-implantitis from health in a cross-sectional study may still fail to predict future bone loss. Diagnostic, prognostic, predictive and monitoring claims are not interchangeable.
Sampling matrix defines the biological window
| Matrix | Biological scope | Main limitation |
|---|---|---|
| Peri-implant crevicular fluid | Site-specific fluid containing cytokines, enzymes and tissue-breakdown products. | Small volume and sensitivity to collection technique, contamination and fluid flow. |
| Saliva | Whole-mouth mixture from glands, mucosa, teeth, implants and microbial communities. | Dilution and limited localization to an individual implant. |
| Serum or plasma | Systemic inflammatory and metabolic environment. | Cannot identify the anatomical source of inflammation. |
| Complete blood count | Circulating immune-cell and platelet populations. | Highly nonspecific and sensitive to infection, medication and systemic disease. |
| Peri-implant tissue | Cellular and spatial lesion biology. | Invasive and unsuitable for routine repeated monitoring. |
Biomarker domains
- Inflammatory cytokines: IL-1 beta, IL-6, TNF-alpha and IL-17.
- Proteolytic enzymes: active MMP-8, other MMPs and cathepsin K.
- Bone-turnover mediators: RANKL, OPG and the RANKL/OPG relationship.
- Vascular and stress-related markers: VEGF and cortisol.
- Microbial features: community composition, virulence genes and functional pathways.
- Systemic measures: CRP, IL-6, WBC and CBC-derived inflammatory indices.
- Omics signatures: transcriptomic, proteomic and metagenomic panels.
A statistically significant difference in mean concentration does not demonstrate that individual patients can be classified accurately. Biological relevance and diagnostic usefulness must be evaluated separately.
Collection and analytical control
PICF results depend on the sampled surface, isolation method, strip position, collection time, visible contamination, fluid volume, storage conditions and assay platform. Different surfaces of one implant may show different activity, while pooling several sites can dilute a focal signal.
Concentration and total amount may lead to different conclusions because inflamed sites frequently produce more crevicular fluid. A clinically interpretable change must also exceed expected analytical error and short-term biological fluctuation.
Active MMP-8 as a point-of-care candidate
Active matrix metalloproteinase-8 reflects collagenolytic activity and can be measured with chairside lateral-flow or quantitative point-of-care formats. Recent studies and systematic reviews report promising discrimination of peri-implantitis. Performance nevertheless depends on the tested population, reference definition, fluid matrix, assay and selected threshold.
A rapid chairside result remains an adjunct. Point-of-care availability does not remove the need for analytical validation, threshold confirmation, reproducibility and evidence that using the result improves clinical decisions.
RANKL, OPG and bone remodelling
RANKL promotes osteoclast differentiation, whereas osteoprotegerin acts as a decoy receptor that limits RANKL signalling. The RANKL/OPG relationship is biologically close to bone resorption and has shown differences between healthy and diseased peri-implant sites. Evidence remains limited by heterogeneous sampling and analytical protocols.
Single markers and multimarker panels
Peri-implantitis combines microbial challenge, immune signalling, matrix degradation and bone resorption. A single analyte rarely captures all phases. Combining inflammatory, regulatory, proteolytic and bone-related markers can improve discrimination within a development dataset, but each additional variable increases the risk of overfitting.
Multimarker models require calibration and testing in an independent population. Their relevant comparator is not the absence of testing, but the combination of standard clinical and radiographic assessment without the biomarker panel.
Systemic biomarkers and blood-derived indices
CRP, IL-6, leukocyte counts and indices such as NLR, PLR, MLR, SII and AISI/PIV describe systemic inflammatory context. They do not localize the source of inflammation. No universally accepted peri-implantitis-specific threshold has been validated for these indices.
Acute infection, medication, autoimmune disease, cancer, obesity, diabetes, smoking and haematological conditions can dominate the blood signal. Systemic markers therefore require medical context and adjustment for shared determinants.
Reading diagnostic-accuracy measures
| Measure | Meaning | Important qualification |
|---|---|---|
| Sensitivity | Proportion of diseased cases correctly classified as positive. | High sensitivity does not guarantee high specificity. |
| Specificity | Proportion of non-diseased cases correctly classified as negative. | Depends on the spectrum and definition of controls. |
| Positive predictive value | Probability of disease after a positive result. | Changes with disease prevalence. |
| Negative predictive value | Probability of no disease after a negative result. | Changes with disease prevalence. |
| ROC AUC | Overall discrimination across possible thresholds. | Does not demonstrate calibration or clinical utility. |
A high AUC does not guarantee a clinically useful threshold or benefit to patients. Confidence intervals, threshold selection, missing data, disease spectrum and the independence of the validation sample all affect interpretation.
From discovery to clinical translation
- Analytical validation: establish assay accuracy, precision, detection limits and stability.
- Clinical validation: test the marker against a defined reference standard in intended patients.
- External validation: confirm performance in another centre, population and operator setting.
- Incremental value: demonstrate improvement beyond probing, bleeding and radiography.
- Clinical utility: show that marker-guided decisions improve care or outcomes.
Prediction, treatment response and precision diagnostics
Prediction requires that biomarker sampling precedes future progression. Treatment-response research requires reproducible change after effective inflammation control and a relationship with clinically meaningful outcomes.
Transcriptomic, metagenomic and multi-omic signatures can identify candidate pathways and patient clusters. Machine-learning performance can be inflated by small datasets, feature selection outside the validation loop and lack of external testing. Prospective clinical validation remains necessary.
Clinical and biological synthesis
- Clinical inflammation and progressive bone loss remain the diagnostic foundation.
- The sampling matrix should match the local or systemic biological question.
- aMMP-8, bone-related mediators and multimarker panels remain adjunctive candidates.
- Group differences do not automatically establish diagnostic accuracy.
- External validation and incremental value are required before clinical adoption.
- Cross-sectional separation does not establish prognosis, treatment response or systemic causality.
Download the full educational material
The extended PDF presents sampling matrices, biomarker domains, point-of-care candidates, diagnostic-accuracy measures and the full pathway from analytical validation to clinical utility.
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Selected references
- Berglundh T, Armitage G, Araujo MG, et al. Peri-implant diseases and conditions: consensus report of workgroup 4 of the 2017 World Workshop. Journal of Clinical Periodontology. 2018;45(Suppl 20):S286-S291.
- La Monaca G, et al. Biomarkers in peri-implant crevicular fluid of healthy implants and those with peri-implant diseases: a systematic review and meta-analysis. 2025. PMID: 40101934.
- Lumbikananda S, et al. Oral fluid biomarkers for peri-implantitis: a scoping review. 2024. PMID: 38065782.
- Comparison of RANKL and OPG levels in peri-implant crevicular fluid between healthy and diseased peri-implant tissues: a systematic review and meta-analysis. 2021. PMID: 34264378.
- Diagnostic potential of active matrix metalloproteinase-8 for peri-implantitis: a systematic review. 2026. PMID: 41790996.
- Diagnostic value of MMP-8, IL-1 beta and IL-6 in peri-implantitis: a systematic review and meta-analysis. 2026. PMID: 42671640.
- A scoping review of diagnostic techniques used for detection of peri-implantitis around endosseous dental implants. 2025. PMID: 41584075.
- Transcriptomic and metagenomic biomarkers in peri-implantitis: a systematic review, diagnostic meta-analysis and functional meta-synthesis. 2025. PMID: 40981185.
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This educational material does not replace individual clinical assessment, laboratory interpretation or medical consultation.
This work was co-funded by the European Union under grant agreement No. 2024-1-PL01-KA220-HED-000251208. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the Foundation for the Development of the Education System (FRSE). Neither the European Union nor FRSE can be held responsible for them.