Immunodiagnostic potential of the RANK/RANKL/OPG ratio in gingival crevicular fluid for periodontitis

Thuraya K. Alwandawi

doi:10.3897/folmed.67.e144949

Abstract

Introduction: Periodontitis and apical periodontitis are multifactorial inflammatory diseases involving microbial activity and host responses that lead to tissue destruction. Biomarkers such as receptor activator of nuclear factor-kappa B (RANK), its ligand (RANKL), and the osteoprotegerin (OPG) system in gingival crevicular fluid (GCF) have been investigated for their diagnostic and prognostic roles in these conditions.

Aim: This study evaluates these biomarkers’ levels to differentiate between stage I and stage III periodontitis, and healthy controls.

Materials and methods: This cross-sectional study included 90 participants divided into three groups: stage I periodontitis, stage III periodontitis, and healthy control. GCF samples were collected from all participants in this study to measure the immunomarkers using ELISA.

Results: The result revealed significantly higher levels of RANKL and OPG in stage I periodontitis compared to stage III periodontitis and controls. Additionally, we observed a significant difference between the study groups in the RANK/OPG and RANKL/OPG ratios, with stage I periodontitis showing the highest ratios.

Conclusion: This study demonstrated that the levels of receptor activator of nuclear factor-kappa B (RANK), its ligand (RANKL), and osteoprotegerin (OPG) in the gingival crevicular fluid (GCF) can serve as valuable biomarkers for distinguishing between stage I and stage III periodontitis. The results showed a significant increase in RANKL and OPG levels in stage I periodontitis compared to stage III and control groups. Moreover, the RANK/OPG and RANKL/OPG ratios were significantly higher in stage I periodontitis, indicating their potential as diagnostic indicators.

Keywords

biomarkers, gingival crevicular fluid, OPG, periodontitis, RANK, RANKL, stage I, stage III

Introduction

Periodontitis is a chronic, multifactorial inflammatory disease marked by microbially associated and host-mediated inflammation, resulting in the loss of periodontal attachment.^[1] The pathophysiology of the illness encompasses critical molecular pathways that stimulate host-derived mediators, resulting in the degradation of marginal periodontal ligament fibers, apical displacement of the junctional epithelium, and apical proliferation of bacterial biofilm along the root surface.^[2] Severe chronic periodontitis (CP) ranks as the sixth most widespread illness worldwide, resulting in impairment, diminished quality of life, and social inequity.^[3] It results from a complex interplay of microbial, host, genetic, and environmental factors that influence the disease’s progression and outcomes.^[4]

In order to formulate an effective treatment strategy, it is imperative to understand the epidemiology of periodontitis, as this provides insight into both the likelihood of the disease’s presence and the diagnostic test’s utility. Current data suggests a tiered prevalence: approximately 10% of adults grapple with “severe periodontitis” (stages III or IV), another 10% maintain periodontal health, and the remaining 80% exhibit symptoms of either gingivitis or mild to moderate periodontitis (stages I or II). Notably, while the diagnosis of stages III and IV periodontitis can be straightforward, differentiating between gingivitis and milder forms of periodontitis is more nuanced, necessitating an evaluation of interdental clinical attachment loss (CAL).^[5] Such differentiation is paramount, as it aids in distinguishing gingivitis from periodontitis. Thus, the cornerstone of any periodontal health assessment should be the early detection of attachment or bone loss.^[6]

The innate immune system provides the primary defense against pathogens through nonspecific mechanisms such as phagocytosis, inflammatory mediator release, complement activation, and initiation of adaptive immunity. Innate immune cells involved in periapical inflammation include antigen-presenting cells, neutrophils, natural killer cells, and mast cells.^[7,8] The adaptive immune system offers targeted, enduring protection through cellular and humoral immunity.^[9] The receptor activator of nuclear factor-kappa B (RANK), its ligand (RANKL), and the osteoprotegerin (OPG) system serve as a principal regulator of bone metabolism in periodontal disease. RANKL, a cytokine analogous to TNF-α, activates RANK, facilitating osteoclast maturation through NF-kB and protein c-Fos, which results in bone resorption.

Osteoprotegerin (OPG), a soluble receptor, inhibits RANKL, reducing osteoclast activity and bone resorption.‌^[10,11] Imbalances in this system are associated with diseases like rheumatoid arthritis and osteoporosis, where elevated RANKL levels enhance osteoclastic activity, while higher OPG levels inhibit bone resorption.^[12,13]

Bone metabolism is tightly regulated by the RANK/RANKL/OPG system, which plays a crucial role in both physiological bone remodeling and pathological bone resorption.^[12]

RANK is a transmembrane receptor located on the surface of osteoclast precursors. It is stimulated by RANKL (receptor activator of nuclear factor-kappa B ligand), a cytokine of the tumor necrosis factor (TNF) superfamily. RANKL interacts with RANK, initiating osteoclast development, maturation, and activation, which results in bone resorption. This process is essential for bone remodeling but is also implicated in pathological conditions such as osteoporosis, rheumatoid arthritis, and periodontitis.^[12,13]

Conversely, osteoprotegerin is a soluble decoy receptor that obstructs bone resorption by binding to RANKL and inhibiting its interaction with RANK. This inhibitory process has a protective function in preserving bone density and periodontal stability. The equilibrium between RANKL and OPG dictates the degree of osteoclast activity and subsequent bone resorption. A raised RANKL/OPG ratio correlates with heightened bone resorption, while an increased OPG level inhibits the osteoclastogenesis and mitigates tissue degradation.^[13]

Studies suggest that alterations in RANK, RANKL, and OPG levels contribute to the progression of periodontal disease and periapical lesions. In advanced periodontitis, the upregulation of RANKL and suppression of OPG enhance osteoclast differentiation, leading to alveolar bone loss.^[14] Similarly, in odontogenic cysts and granulomas, higher RANKL expression correlates with lesion expansion and jaw resorption.^[15] Understanding the regulatory mechanisms of RANK/RANKL/OPG in periodontal disease and bone pathology may provide insights into diagnostic biomarkers and potential therapeutic targets.^[14,16]

Gingival crevicular fluid (GCF), a biomarker-rich fluid, reflects the immune-inflammatory response and bone remodeling activity in periodontal tissues.^[17] Its profile is influenced by the subgingival environment, while saliva provides insights into systemic inflammatory and medical conditions.^[18] Initially, GCF is a transudate derived from the osmotic gradients in the crevice, transitioning to an inflammatory exudate upon stimulation. This fluid aids in sulcus cleansing and epithelial adhesion to the tooth surface.^[19] The flow rate of GCF correlates with the periodontal tissues’ inflammatory state, providing valuable diagnostic insights.

Aim

To investigate the ratio abilities of GCF biomarker levels of RANK, RANKL, and OPG to progress stage I and stage III periodontitis.

Materials and methods

Study design

This cross-sectional research included 90 participants from the teaching hospital of the College of Dentistry at Al-Iraqia University in Baghdad, Iraq, conducted between May 2024 and October 2024. The individuals were categorized into three groups: stage I periodontitis, stage III periodontitis, and a healthy control group, with each group including 30 patients.

Ethical approval

The study adhered to ethical principles as outlined by the Declaration of Helsinki. All participants provided informed consent after a thorough explanation of the study’s objectives and procedures.

Inclusion and exclusion criteria

Inclusion criteria

1. Participants aged 18-50 years.
2. Clinical diagnosis: patients with mild or severe periodontitis made by a dental surgeon.
3. Absence of autoimmune diseases or any systemic diseases.

For the control group, absence of clinical signs of periodontal disease confirmed by a dental surgeon.

Exclusion criteria

1. Individuals with systemic diseases that can affect periodontal status (e.g., diabetes, rheumatoid arthritis, cardiovascular diseases).
2. Participants with a history of taking antibiotics or anti-inflammatory drugs within the past 3 months.
3. Pregnant or lactating women.
4. Individuals with a history of periodontal treatment or surgery within the past 6 months.
5. Smokers or individuals using tobacco products.
6. Individuals with oral conditions other than periodontitis (e.g., oral ulcers, tumors).
7. Individuals undergoing orthodontic treatment.

Periodontal health monitoring

Periodontitis is categorized based on Ababneh et al.^[20] into two stages: stage I periodontitis, which typically presents with 1-2 mm of clinical attachment loss and probing depths of between 4-5 mm, and stage III periodontitis, which involves more than 5 mm of CAL with probing depths greater than 6 mm. During oral examinations, several clinical parameters are utilized to assess the presence, severity, and progression of periodontitis. Bleeding on probing (BOP) occurs when a gentle insertion of a periodontal probe into the sulcus or pocket around a tooth leads to bleeding in inflamed tissues, indicating inflammation and active disease.^[21] Probing depth (PD) is measured using a calibrated periodontal probe to determine the depth of the sulcus or pocket from the gingival margin to the base of the pocket, with depths greater than 3 mm considered pathological and indicative of advancing periodontitis.^[22] Clinical attachment loss (CAL) is measured from a fixed point on the tooth, typically the cementoenamel junction (CEJ), to the base of the pocket, incorporating both gingival recession and probing depth to provide a comprehensive assessment of tissue loss.^[23]

GCF collection and volume determination

Patients were prepared ninety minutes prior to the sample collection, which took place between 9:00 and 11:00 a.m. They were instructed to refrain from eating and tooth brushing before the procedure. The targeted sites underwent a cleansing process involving rinsing with water, isolation with cotton rolls, and a gentle air spray.^[19] PerioPaper strips (PerioPaper®, Oraflow Inc., New York, USA) were used to collect fluid samples from the test groups. Supragingival plaque was removed using dry gauze before placing standard paper strips in the sulcus, where they remained for 30 seconds. Any strips that showed signs of blood staining were excluded from the sample set. To preserve the integrity of the samples, the paper strips were promptly transferred into sterile Eppendorf tubes containing 0.5 ml of preservative phosphate-buffered saline. The tubes were then centrifuged at 3000 rpm for 10 minutes and preserved at −80°C until prepared for laboratory examination.^[24] The identification of human receptor activator of nuclear factor κB (RANK), its ligand RANKL, and human osteoprotegerin (OPG) biomarkers was performed utilizing human enzyme-linked immunosorbent assay (ELISA) quantitative immunoassay kits (Lot Nos: E23XMB286, E23SMk751, and E23UMS852, Feiyuo company, respectively).

Statistical analysis

Data were analyzed using SPSS version 26. Normality was tested using the Shapiro-Wilk test. Comparisons between groups and genders were conducted using ANOVA test, chi-square and ratio statistics, with significance set at p<0.05.

Results

This study is a cross-sectional study aimed at identifying differences in specific GCF inflammatory biomarkers in stage I and stage III periodontitis compared with controls. An equal number of cases (n=30) were recruited for each group. All relevant clinical records were screened, and participants were selected based on the inclusion criteria.

The mean age groups of the included patients were 18-50 years (35.98±7.04), with a sex distribution of 50% male and 50% female. The statistical analysis revealed a non-significant difference in age or sex between groups (p>0.05) (Tables 1, 2).

Table 1.

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Age distribution across groups

Age groups
Group	Mean±SD	p-value
Stage I periodontitis	36.67±6.76	0.757
Stage III periodontitis	35.30±7.42
Control	35.98±7.07
Total	35.98±7.04

Table 2.

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Sex distribution between groups

		Group			Total	Chi-square p-value
		Stage I periodontitis N (%)	Stage III periodontitis N (%)	Control N (%)	Total	Chi-square p-value
Sex	Female	15 (33.3%)	15 (33.3%)	15 (33.3%)	45 (50.0%)	1.000
Sex	Male	15 (33.3%)	15 (33.3%)	15 (50.0%)	45 (50.0%)	1.000
Total		30 (33.3%)	30 (33.3%)	30 (33.3%)	90 (100.0%)

The results in Table 3 illustrated the levels of RANK, RANKL, and OPG in gingival crevicular fluid between study group (stage I periodontitis, stage III periodontitis, and healthy controls). RANK levels showed non-significant differences between the three groups (p=0.092). Conversely, RANKL levels were significantly elevated in the stage I periodontitis group (1.488±0.451 ng/mL) compared to both the stage III periodontitis (1.242±0.380 ng/mL) and control groups (1.233±0.374 ng/mL), with a statistically significant difference (p=0.012). OPG levels were notably higher in the stage I periodontitis group (0.084±0.025 ng/mL) compared to the stage III periodontitis (0.076±0.017 ng/mL) and the control group (0.056±0.042 ng/mL), with a highly significant difference (p<0.05).

Table 3.

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Biomarker levels in stage I periodontitis and stage III periodontitis and control groups

	Stage I periodontitis	Stage III periodontitis	Control	F	p-value
	Mean±SD	Mean±SD	Mean±SD	F	p-value
RANK	0.332±0.079	0.302±0.071	0.292±0.087	2.433	0.092
RANKL	1.488±0.451	1.242±0.380	1.233±0.374	4.575	0.012
OPG	0.084±0.025	0.076±0.017	0.056±0.042	8.342	0.000

The results in Table 4 illustrated the RANK/OPG ratios between the stage I periodontitis and stage III periodontitis, and control groups, revealing statistically significant differences (p=0.000).

Table 4.

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Ratio statistics for RANK/OPG in groups

Group	Mean %	±SD	Sig.	Coefficient of variation
Group	Mean %	±SD	Sig.	Mean centered
Stage I periodontitis	4.088	0.691	0.000	16.9%
Stage III periodontitis	4.116	1.142		27.7%
Control	8.132	5.195		63.9%

The mean ± SD of RANK/OPG ratio was notably higher in the control group (8.132±5.195) compared to the stage I group (4.088±0.691) and stage III group (4.116±1.142). The stage I and stage III periodontitis groups displayed comparable ratios, with overlapping ranges (stage I: 2.918–5.320; stage III: 2.580–6.563). The significantly higher variability in the control group (coefficient of variation: 63.9%) compared to stage I (16.9%) and stage III (27.7%).

The results in Table 5 illustrated the RANKL/OPG ratios across the stage I periodontitis and stage III periodontitis, and control groups, demonstrating significant differences (p=0.000). The control group exhibited a notably higher mean ratio (33.567±20.630) compared to the stage I (18.116±3.575) and stage III (16.839±5.079) groups. The stage I group had a slightly higher ratio than the stage III group, with narrower ranges (stage I: 11.415–25.165; stage III: 10.735–30.750). The control group, with the widest range (7.789–91.283) and the highest variability (coefficient of variation: 61.5%).

Table 5.

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Ratio statistics for RANKL/OPG in all groups

Group	Mean %	±SD	Sig.	Coefficient of variation
Group	Mean %	±SD	Sig.	Mean centered
Stage I periodontitis	18.116	3.575	0.000	19.7%
Stage III periodontitis	16.839	5.079		30.2%
Control	33.567	20.630		61.5%

The results in Table 6 illustrated the RANK/RANKL ratios between the stage I periodontitis and stage III periodontitis, and control groups, showing non-significant differences (p=0.720). The mean RANK/RANKL ratio was comparable among the groups, with values of 0.230±0.040 for stage I, 0.250±0.047 for stage III, and 0.240±0.037 for the control group. The range of values was similar among the groups, with stage I (0.139–0.319), stage III (0.151–0.323), and control (0.146–0.311). The coefficients of variation were also relatively low for all groups (stage I: 17.2%, stage III: 18.9%, control: 15.3%).

Table 6.

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Ratio statistics for RANK/RANKL in all groups

Group	Mean %	±SD	Sig.	Coefficient of variation
Group	Mean %	±SD	Sig.	Mean centered
Stage I periodontitis	0.230	0.040	0.000	17.2%
Stage III periodontitis	0.250	0.047		18.9%
Control	0.240	0.037		15.3%

Discussion

This research tested the biomarkers RANK, RANKL, and OPG to distinguish stage I periodontitis and stage III periodontitis compared with control group.

Studies in this domain mostly include fundamental data like patients’ age, sex, and overall health state, yielding a non-significant outcome in the present research. A research conducted by Llena et al. indicated that the age of patients whose teeth underwent full healing was much lower than that of patients with no healing.^[25] All of these findings align with our research, since there are no significant variations related to age, and thus, it cannot be regarded as a predictive factor for the healing of periapical lesions.

This research found no significant correlation between sex and the healing of periapical lesions. Nevertheless, several studies have shown a correlation, notably suggesting a preference for women^[26], while others found no association^[27]. Authors ascribe this trend to women’s heightened interest in their health and their more frequent attendance at check-up appointments.^[28]

Only one diseased tooth for each patient was selected. The statistical analysis was made more straightforward by avoiding potential clustering, which could have occurred if the study had included multiple teeth from the same individual. By focusing on just one tooth per participant, the study ensured that each result was independent, making the analysis more direct and reducing complexity in interpreting the outcomes.^[29]

The present research revealed that OPG had the best accuracy in diagnosing stage I and stage III periodontitis when comparing biomarker levels. Furthermore, RANK and RANKL were capable of differentiating only between stage I periodontitis and control groups. These tendencies were not seen when comparing stage III periodontitis to the control group and stage I periodontitis to stage III periodontitis. Consequently, OPG demonstrated the capability to differentiate between various forms of periodontitis and healthy dentition. Identifying biomarkers that facilitate the diagnosis of various forms of periodontitis is very beneficial to clinical practice.

OPG is a crucial regulator of bone resorption that modifies the function and viability of mature osteoclasts. It functions both locally and systemically by binding to RANKL, obstructing its interaction with RANK to impede osteoclast differentiation. The interaction among these molecules is crucial for controlling osteoclast formation and alveolar bone resorption. While all three markers were identified in both stage I and stage III periodontitis, only OPG in the stage I periodontitis cohort exhibited statistically significant elevations relative to the stage III periodontitis and control groups. The plausible reason may be that only symptomatic lesions signify an active phase of periodontitis, as shown by concurrently high levels of OPG and RANKL.^[30] Moreover, activation mechanisms distinct from RANK may be triggered in stage III periodontitis, leading to an insignificant disparity in RANK levels across the research groups. The reduced levels of RANKL and OPG in the control group may indicate proper bone homeostasis.^[31]

It has been suggested that the proinflammatory cytokines play essential role in the stage III periodontitis and stage I and in periapical bone destruction via the generation of RANKL, whereas OPG formation is believed to cause reduction of lesion extension.^[32] Previous studies attempted to detect the actual role of RANKL and OPG in the progression of periapical granuloma, showing their higher levels when compared to healthy periapical tissue, which reinforces their active role in lesion progression and expansion.^[33]

The results of the current study are in disagreement with previously reported studies that documented observations after one year of root canal treatment. They showed a significant decrease in the RANK, RANKL, and OPG levels in the periapical tissues, which reflects the resolution of inflammation and cessation of active bone resorption.^[34]

Furthermore, earlier published studies indicated that the biological effects of OPG are contrary to those mediated by RANKL, since OPG functions as a soluble inhibitor that prevents the binding of RANKL with its receptor RANK, hence inhibiting further activation. Consequently, in vivo investigations in mice have shown that aberrant or deficient production of RANKL, RANK, or OPG manifests both extremities of skeletal morphologies, such as osteoporosis (OPG knockout) and osteopetrosis (OPG transgenic, RANKL knockout, and RANK knockout). Furthermore, they determined that RANKL, RANK, and OPG constitute a new cytokine network and serve as critical regulators of bone metabolism and osteoclast biology.^[34]

Recent studies have highlighted the pivotal role of RANK/RANKL/OPG signaling in the pathogenesis of radicular jaw cysts and other odontogenic lesions. The imbalance of RANKL and OPG in periapical cysts promotes excessive osteoclastic resorption, leading to progressive jawbone destruction. Evidence suggests that RANKL expression is upregulated in cystic epithelium and inflammatory infiltrates, particularly in radicular cysts, while OPG expression remains suppressed, further facilitating lesion expansion.^[15,16]

Interestingly, the RANKL/OPG ratio in radicular cysts mirrors patterns observed in advanced periodontitis, suggesting common regulatory mechanisms in bone loss across these conditions. Additionally, higher RANKL levels in periapical granulomas compared to cysts indicate a more active osteolytic process in granulomas, while cysts exhibit a more stabilized bone resorption state.^[17,18] These findings emphasize the need for further investigation into targeting the RANKL/OPG pathway as a therapeutic approach for managing both periodontitis and periapical cystic lesions.

Conclusion

This study underscores the pivotal role of the RANK/RANKL/OPG system in the etiology of stage I and stage III periodontitis, given the substantial variability in biomarker levels and ratios across different disease severities. In stage I periodontitis, RANKL and OPG levels were significantly elevated; hence, the increased RANK/OPG and RANKL/OPG ratios in this cohort suggest that these biomarkers may serve as effective diagnostic instruments for distinguishing between early and severe periodontal disease. The oscillations in the biomarker expression, driven by an imbalance between pro-resorptive (RANKL) and anti-resorptive (OPG) factors, demonstrate the progressive nature of alveolar bone resorption. These insights enhance our understanding of periodontal disease mechanisms and underscore the potential for targeted therapeutic efforts aimed at modulating the RANK/RANKL/OPG axis.

Funding

The authors have no funding to report.

Competing interests

The authors have declared that no competing interests exist.

Acknowledgements

The authors have no support to report.

References

1. Taha GI, Talib EQ, Abed FB, et al. A review of microbial pathogens and diagnostic techniques in children’s oral health. Sri Lanka Journal of Child Health 2024; 53(4):355–9.

2. Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Periodontol 2018; 89:S159–72.

3. Graetz C, Plaumann A, Schlattmann P, et al. Long‐term tooth retention in chronic periodontitis – results after 18 years of a conservative periodontal treatment regimen in a university setting. J Clin Periodontol 2017; 44(2):169–77.

4. Taha GI. Involvement of IL-10 gene polymorphism (rs1800896) and IL-10 level in the development of periimplantitis. Minerva Dent Oral Sci 2024; 73(5):264–71.

5. Talib EQ, Taha GI. Involvement of interleukin-17A (IL-17A) gene polymorphism and interleukin-23 (IL-23) level in the development of peri-implantitis. BDJ Open [Internet]. 2024; 10(1):12. Available from: https://www.nature.com/articles/s41405-024-00193-9

6. Tonetti MS, Sanz M. Implementation of the new classification of periodontal diseases: Decision‐making algorithms for clinical practice and education. J Clin Periodontol [Internet]. 2019; 46(4):398–405. Available from: https://onlinelibrary.wiley.com/doi/10.1111/jcpe.13104

7. Zupan J, Jeras M, Marc J. Osteoimmunology and the influence of pro-inflammatory cytokines on osteoclasts. Biochem Med (Zagreb) 2013; 23(1):43–63.

8. Galler KM, Weber M, Korkmaz Y, et al. Inflammatory response mechanisms of the dentine-pulp complex and the periapical tissues. Int J Mol Sci 2021 ;22(3):1480.

9. Sun L, Wang X, Saredy J, et al. Innate-adaptive immunity interplay and redox regulation in immune response. Redox Biol 2020; 37:101759.

10. Boyce BF, Xing L. Functions of RANKL/RANK/OPG in bone modeling and remodeling. Arch Biochem Biophys 2008; 473(2):139–46.

11. Khosla S. Minireview: The OPG/RANKL/RANK System. Endocrinology 2001; 142(12):5050–5.

12. De Moraes M, De Lucena HF, De Azevedo PRM, et al. Comparative immunohistochemical expression of RANK, RANKL and OPG in radicular and dentigerous cysts. Arch Oral Biol 2011; 56(11):1256–63.

13. Belibasakis GN, Rechenberg DK, Zehnder M. The receptor activator of NF‐κ B ligand‐osteoprotegerin system in pulpal and periapical disease. Int Endod J 2013; 46(2):99–111.

14. Da Silva TA, Batista AC, Mendonça EF, et al. Comparative expression of RANK, RANKL, and OPG in keratocystic odontogenic tumors, ameloblastomas, and dentigerous cysts. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008; 105(3):333–41.

15. Cavalla F, Letra A, Silva RM, et al. Determinants of periodontal/periapical lesion stability and progression. J Dent Res 2021; 100(1):29–36.

16. Armada L, Dos Santos Marotta P, Pires FR, et al. Expression and distribution of receptor activator of nuclear factor kappa B, receptor activator of nuclear factor kappa B ligand, and osteoprotegerin in periradicular cysts. J Endod 2015; 41(8):1281–7.

17. Grant MM, Taylor JJ, Jaedicke K, et al. Discovery, validation, and diagnostic ability of multiple protein‐based biomarkers in saliva and gingival crevicular fluid to distinguish between health and periodontal diseases. J Clin Periodontol 2022; 49(7):622–32.

18. Romano F, Iaderosa G, Corana M, et al. Comparing ionic profile of gingival crevicular fluid and saliva as distinctive signature of severe periodontitis. Biomedicines 2022; 10(3):687.

19. Khurshid Z, Mali M, Naseem M, et al. Human gingival crevicular fluids (GCF) proteomics: an overview. Dent J (Basel) 2017; 5(1):12.

20. Ababneh KT, Maslamani MJ, Abbadi MS, et al. Risk indicators of aggressive periodontitis in a Jordanian population. BMC Oral Health 2019; 19(1):155.

21. Lang NP, Joss A, Orsanic T, et al. Bleeding on probing. A predictor for the progression of periodontal disease? J Clin Periodontol 1986; 13(6):590–6.

22. Armitage GC. Periodontal diagnoses and classification of periodontal diseases. Periodontol 2000 2004; 34:9–21.

23. Cairo F, Nieri M, Cincinelli S, et al. The interproximal clinical attachment level to classify gingival recessions and predict root coverage outcomes: an explorative and reliability study. J Clin Periodontol 2011; 38(7):661–6.

24. Bhardwaj S, Prabhuji MLV. Comparative volumetric and clinical evaluation of peri-implant sulcular fluid and gingival crevicular fluid. J Periodontal Implant Sci 2013; 43(5):233–42.

25. Llena C, Nicolescu T, Perez S, et al. Outcome of root canal treatments provided by endodontic postgraduate students. A retrospective study. J Clin Med 2020; 9(6):1994.

26. Marquis VL, Dao T, Farzaneh M, et al. Treatment outcome in endodontics: the Toronto study. Phase III: initial treatment. J Endod 2006; 32(4):299–306.

27. Vengerfeldt V, Mändar R, Nguyen MS, et al. Apical periodontitis in southern Estonian population: prevalence and associations with quality of root canal fillings and coronal restorations. BMC Oral Health 2017; 17(1):147.

28. Jiménez‐Pinzón A, Segura‐Egea JJ, Poyato‐Ferrera M, et al. Prevalence of apical periodontitis and frequency of root‐filled teeth in an adult Spanish population. Int Endod J 2004; 37(3):167–73.

29. Hannigan A, Lynch CD. Statistical methodology in oral and dental research: Pitfalls and recommendations. J Dent 2013; 41(5):385–92.

30. Fan F, Shi P, Liu M, et al. Lactoferrin preserves bone homeostasis by regulating the RANKL/RANK/OPG pathway of osteoimmunology. Food Funct 2018; 9(5):2653–60.

31. Sojod B, Chateau D, Mueller CG, et al. RANK/RANKL/OPG signalization implication in periodontitis: new evidence from a RANK transgenic mouse model. Front Physiol 2017; 8:338.

32. Goldman M, Pearson AH, Darzenta N. Endodontic success – Who’s reading the radiograph? Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1972; 33(3):432–7.

33. Menezes R, Garlet TP, Letra A, et al. Differential patterns of receptor activator of nuclear factor kappa B ligand/osteoprotegerin expression in human periapical granulomas: possible association with progressive or stable nature of the lesions. J Endod 2008; 34(8):932–8.

34. Cavalla F, Biguetti CC, Garlet TP, et al. Inflammatory pathways of bone resorption in periodontitis. In: Pathogenesis of Diseases Periodontal. Cham: Springer International Publishing; 2018. p. 59–85.