Studies on immune response in periodontal tissues and potential immunomodulative treatment became an important area of periodontal research. The first commercially available genetic test was developed almost 25 years ago. It based on the discovery that individuals bearing mutated allele encoding interleukin (IL)-1 gene cluster produced much more of this cytokine in response to a fixed bacterial load than persons with common allele. In the first published study, non-smoking individuals with rare allele had over 19-fold higher ratio to manifest severe periodontitis than persons with common allele (Kornman et al.
1997). Although further studies did not report such unanimous results, the link between gene polymorphisms, host response level and periodontal inflammation was proven (Huynh-Ba et al.
2007). The test itself was regarded as a predictor of possible future periodontitis (Rudick et al.
2019). There were numerous studies on other possible polymorphisms, such as Fcγ, interferon γ and IL-13, connected with periodontal symptoms (Chai et al.
2012; Shi et al.
2017; Wang et al.
2017; Zhang et al.
2018a). A search for potential markers of periodontitis onset or progression resulted in many potential candidates for such agent—among others matrix metalloproteinase (MMP)-8, IL-1, IL-6, tumor necrosis factor (TNF)-α, PGE
2 (Barros et al.
2016; Chen et al.
2019; He et al.
2018; Isola
2021). The goal was to be able to quickly and efficiently screen the population to reach individuals requiring more frequent and thorough prophylactic actions, on the other hand reducing clinical efforts put on the rest of the population, not needing so frequent meetings with periodontists or hygienists. The biomarker source would be crevicular fluid of gingival sulcus (gingival crevicular fluid, GCF), scarce serum transudate from the pocket (more abundant during inflammation) (Barros et al.
2016), or saliva, much easier to obtain in relatively large quantities (He et al.
2018). Studies have shown that biomarkers were present in GCF and saliva long before any clinically visible effects of periodontal tissue destruction. So, establishing a viable biomarker of periodontal disease would have a great impact on the disease prevention. An example of such chemical process is increased alkalization of a diseased site. A rise of pH in a pocket to the value of 8.5 is the result of elevated ammonium levels, being a metabolite of protein degradation by periopathogens. Such a rise promotes precipitation of calcium carbonate from saliva or GCF and calculus formation (Barros et al.
2016). Experimental modelling of periodontal disease was able to be processed in an environment able to control multiple risk factors due to an animal model. The protocol of ligation of periodontal tissues in rats has been modernized, described and evaluated (Lin et al.
2021). Further on, results obtained in studies on animals were verified clinically in research, and data were subjected to reviews and meta-analyses. According to the studies, over 90 possible biomarkers have been evaluated in GCF (Loos and Tjoa
2005). Among those are markers directly related to immune reactions. The most extensive research regarded endogenous MMPs, as proteolytic enzymes directly related to periodontal tissue destruction. The influence of MMP-8 on the collagen structure and healing processes was observed on the animal model (Gajendrareddy et al.
2013). Salivary levels of MMP-8 were evaluated in a meta-analysis assessing ten studies on 864 individuals overall. Eight of those studies showed significant elevation of MMP-8 in saliva of periodontal patients, two studies showed opposite results (Zhang et al.
2018b). A South Korean study assessed the influence of non-surgical periodontal treatment on MMP-8 and IL-1β levels in saliva (Kim and Kim
2020). They did not find a statistical difference for either of those two cytokines, although it must be noted that due to heterogeneity only two studies were qualified for the meta-analysis (Kim and Kim
2020). Ghassib et al. (
2019) did not include MMP-8 in the meta-analysis presented in their manuscript, although they stated that literature review shows MMP-8 as a valid prognostic marker of periodontal inflammation. A review of 61 articles regarding potential use of MMP-8 as a biomarker of periodontitis shows this enzyme as a strong candidate for an indicator of periodontal inflammation (Al-Majid et al.
2018). Another review, performed by Brazilian researchers, also confirmed the potential of MMP-8 as a prognostic marker for development of periodontitis (De Morais et al.
2018). A cross sectional evaluation of MMP-9 in saliva performed by South Korean researchers proved to be effective in periodontitis screening (Kim et al.
2020). The next group of markers would be inflammatory mediators, such as TNF-α, IL-6, or previously mentioned IL-1. Studies on animals have shown that the antagonists for both IL-1 and TNF significantly reduce recruitment of inflammatory cells in bone proximity (Assuma et al.
1998). Slovenian researchers observed the effect of subcutaneous administration of recombinant human TNF-α on experimental periodontitis in rats, and noticed statistically significant synergistic effect of ligature irritation of periodontium and TNF-α administration, while neither of those treatments alone resulted in a significant increase of periodontal breakdown (Gaspersic et al
2003). A meta-analysis of nine papers regarding patients with periodontitis and type 2 diabetes mellitus showed significantly higher levels of IL-1β in gingival crevicular fluid in those patients compared to the ones with periodontitis alone. No such phenomenon was observed for IL-6 and TNF-α (Atieh et al.
2014). Similar results were reported by Stadler et al. (
2016) in their meta-analysis, where elevated levels of IL-6 were also observed. Caldeira et al. (
2021) evaluated gene expression of IL-1 and IL-6 in gingival tissues and relevant protein levels in gingival crevicular fluid, and confirmed a rise of both markers in the course of inflammation. A cross-sectional study by Ebersole et al. (
2013) conducted on 80 individuals has shown the significant elevation of MMP-8 and decrease of IFN-α in periodontitis patients compared to healthy persons (up to 13-fold and 9-fold, respectively). The mediators mentioned above are related with the connective tissue degradation. A particle associated with bone resorption is prostaglandin E2. It was also evaluated in the cited study, showing a statistically significant, though not so high, increase in periodontitis patients (Ebersole et al.
2013). Another set of mediators related to bone metabolism seems to be even more promising. Ligand of receptor activator of NF-κB (RANKL) is on osteoclasts and its activation is associated with bone resorption. Osteoprotegerin (OPG) is a blocker of this receptor, being produced by—among others—osteoblasts. Studies conducted on animals proved that delivery of OPG inhibits resorption of the alveolar bone in an experimental ligature-induced model (Jin et al.
2007). According to studies on humans, the RANKL/OPG ratio turned out to be a reliable marker of the processes occurring in periodontium (Caldeira et al.
2021). Elevation of RANKL with simultaneous decrease of OPG is observed in periodontitis, whereas the opposite phenomenon occurs in healthy periodontium (Belibasakis and Bostanci
2012; Caldeira et al.
2021) or during the healing process (López Roldán et al.
2020). There are also biomarkers related to oxidative stress, whose role will be explained further. A recent meta-analysis of 32 articles, although suffering from high heterogeneity, showed elevated malondialdehyde both in saliva and GCF when comparing periodontitis with healthy periodontium (Chen et al.
2019). The previously mentioned markers are presented in Table
1.
Table 1
Chosen biomarkers for periodontal inflammation, their main function, measured clinical periodontal parameters and the referring articles mentioned in the text
MMP-8 | Matrix metalloproteinase-8 | Degradation of the type I, II and III collagen fibers in the connective tissue | SMD for MMP 1.195 (95% CI 0.72, 1.67) | Meta-analysis Zhang et al. ( 2018b) |
SMD for MMP 35.90 (95% CI − 31.52, 103.33) | Meta-analysis Kim and Kim ( 2020) |
N/A (61 studies reviewed) | Systematic review Al-Majid et al. ( 2018) |
N/A (6 studies reviewed) | Systematic review de Morais et al. ( 2018) |
MMP-9 | Matrix metalloproteinase-9 | Degradation of the type IV and V collagen fibers in the connective tissue | Salivary levels in ng/mL 283.5 vs 52.6 (periodontitis vs health), p < 0.0001 | Cross-sectional study Ebersole et al. ( 2013) |
IL-1 | Interleukin 1 | Stimulation of the non-specific component of the inflammatory response, induced by the Nfkappaβ | Salivary levels in ng/ml 370.7 vs 191.9 (periodontitis vs health), p = 0.001, after adjustment for risk factors of periodontitis 16.7 vs 12.5 (respectively), p = 0.016 | Cross-sectional study Kim et al. ( 2020) |
Mean difference (diabetics with periodontitis vs nondiabetics with periodontitis) 0.90 (95% CI 0.39, 1.41) | Meta-analysis Atieh et al. ( 2014) |
IL-1 raised in GCF of periodontal patients (SMD 1.43; 95% CI 0.93, 1.92) | Meta-analysis Stadler et al. ( 2016) |
IL-1β raised in peri-implant mucositis (SMD 1.94; 95% CI 0.87, 3.35) and peri-implantitis (SMD 2.21; 95% CI 1.32, 3.11) | Meta-analysis Ghassib et al. ( 2018) |
IL-1β mRNA raised in gingival tissue (mean difference 3.62; 95%CI: 3.43, 3.81), IL-1β raised in GCF (mean difference 95.94; 95% CI 81.96, 109.92) | Meta-analysis Caldeira et al. ( 2021) |
IL-6 | Interleukin 6 | Promotion of the osteoclasts’ formation | Salivary levels in ng/mL 90.9 vs 7.2 (periodontitis vs health), p < 0.0001 | Cross-sectional study Ebersole et al. ( 2013) |
No significant difference for diabetics with periodontitis vs nondiabetics with periodontitis: 0.70 (95% CI − 0.70, 1.41) | Meta-analysis Atieh et al. ( 2014) |
IL-6 raised in GCF of periodontal patients (SMD 1.64; 95% CI 0.66, 2.63) | Meta-analysis Stadler et al. ( 2016) |
IL-6 raised in peri-implant mucositis (SMD 1.17; 95% CI 0.16, 3.19) and peri-implantitis (SMD 1.72; 95% CI 0.56, 2.87) | Meta-analysis Ghassib et al. ( 2018) |
IL-6 mRNA raised in gingival tissue (mean difference 5.42; 95% CI 5.15, 5.68), IL-6 raised in GCF (mean difference 3.63; 95% CI 3.03, 4.23) | Meta-analysis Caldeira et al. ( 2021) |
TNF-α | Tumor necrosis factor alpha | Stimulation of the non-specific component of the inflammatory response, strong chemoattractant | Salivary levels in ng/mL 35.6 vs 3.3 (periodontitis vs health), p < 0.0001 | Cross-sectional study Ebersole et al. ( 2013) |
No significant difference for diabetics with periodontitis vs nondiabetics with periodontitis: 0.33 (95% CI − 0.19, 0.86) | Meta-analysis Atieh et al. ( 2014) |
TNF-α raised in peri-implant mucositis (SMD 3.91; 95% CI 1.13, 6.70) and peri-implantitis (SMD 3.78; 95% CI 1.67, 5.89) | Meta-analysis Ghassib et al. ( 2018) |
PGE2 | Prostaglandin E2 | Stimulation of osteoclast activity | Salivary levels in ng/mL 5.4 vs 1.9 (periodontitis vs health), p = 0.07 | Cross-sectional study Ebersole et al. ( 2013) |
RANKL | Receptor Activator of Nuclear factor Kappa-B Ligand | Stimulation of osteoclast activity | Salivary levels in ng/mL 226.1 vs 180 (periodontitis vs health), p = 0.91 | Cross-sectional study Ebersole et al. ( 2013) |
N/A (11 studies reviewed) | Systematic review Belibasakis and Bostanci ( 2012) |
Elevation of RANKL in GCF in periodontitis vs health (mean difference 0.32; 95% CI 0.20, 0.43) | Meta-analysis Caldeira et al. ( 2021) |
OPG | Osteoprotegerin | Blocker of RANK | Levels in pg/ml measured at periodontitis and healthy sites 95.5 vs 56.5, respectively, p < 0.001. After treatment 68.9 vs 60.6, difference statistically non-significant | Longitudinal study Lopez Roldan et al. ( 2020) |
N/A (11 studies reviewed) | Systematic review Belibasakis and Bostanci ( 2012) |
MDA | Malondialdehyde | Product of the peroxidation of polyunsaturated fatty acids | Levels in pg/ml measured at periodontitis and healthy sites 3.1 vs 7.0, respectively, p < 0.001. After treatment 6.6 vs 6.7, difference statistically non-significant | Longitudinal study Lopez Roldan et al. ( 2020) |
| | | | SMD for salivary MDA 1.74 (95% CI), SMD for MDA in GCF 2.86 (95% CI) | Meta-analysis Chen et al. ( 2019) |