Cytokine Thresholds in Gingival Crevicular Fluid with Potential Diagnosis of Chronic Periodontitis Differentiating by Smoking Status

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Investigating the diagnostic potential of IL-1β, IL-10, and IL-36γ in gingival crevicular fluid in patients with different periodontal conditions

BACKGROUND

The study aimed to analyze cytokine levels, including interleukin (IL)-1β, IL-10, and IL-36γ, to investigate the link between pro- and anti-inflammatory responses in periodontal conditions and assess their potential as diagnostic biomarkers for distinguishing between different types of periodontal conditions.

METHODS

80 systemically healthy non-smokers (25 periodontally healthy, 25 with gingivitis, 30 with periodontitis) were included. Clinical periodontal parameters were recorded, and gingival crevicular fluid (GCF) samples were obtained. Receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic value of cytokines.

RESULTS

IL-36γ had the highest sensitivity for diagnosing periodontitis, although its specificity for identifying those without periodontitis was relatively low. The combination of IL-1β and IL-36γ was the most effective in differentiating periodontitis from periodontal health. IL-10 was found to be an acceptable discriminator for distinguishing gingivitis from healthy conditions. However, its sensitivity and specificity for identifying gingivitis were lower. The combination of the three cytokines showed the highest ability to distinguish between periodontitis and gingivitis.

CONCLUSION

The levels of IL-1β, IL-10, and IL-36γ in GCF may provide insights into periodontal health and disease status. Further studies are needed to validate these results and explore the potential of these cytokines in periodontal disease management.

Accuracy of periodontitis diagnosis obtained using multiple molecular biomarkers in oral fluids: A systematic review and meta-analysis

AIM

To determine the accuracy of biomarker combinations in gingival crevicular fluid (GCF) and saliva through meta-analysis to diagnose periodontitis in systemically healthy subjects.

METHODS

Studies on combining two or more biomarkers providing a binary classification table, sensitivity/specificity values or group sizes in subjects diagnosed with periodontitis were included. The search was performed in August 2022 through PUBMED, EMBASE, Cochrane, LILACS, SCOPUS and Web of Science. The methodological quality of the articles selected was evaluated using the QUADAS-2 checklist. Hierarchical summary receiver operating characteristic modelling was employed to perform the meta-analyses (CRD42020175021).

RESULTS

Twenty-one combinations in GCF and 47 in saliva were evaluated. Meta-analyses were possible for six salivary combinations (median sensitivity/specificity values): IL-6 with MMP-8 (86.2%/80.5%); IL-1β with IL-6 (83.0%/83.7%); IL-1β with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1β, IL-6 and MMP-8 (81.8%/84.3%); and IL-1β, IL-6, MIP-1α and MMP-8 (76.6%/79.7%).

CONCLUSIONS

Two-biomarker combinations in oral fluids show high diagnostic accuracy for periodontitis, which is not substantially improved by incorporating more biomarkers. In saliva, the dual combinations of IL-1β, IL-6 and MMP-8 have an excellent ability to detect periodontitis and a good capacity to detect non-periodontitis. Because of the limited number of biomarker combinations evaluated, further research is required to corroborate these observations.

Gingival crevicular fluid galectin-3 and interleukin-1 beta levels in stage 3 periodontitis with grade B and C

OBJECTIVES

This study aims to evaluate GCF Galectin-3 and Interleukin-1 beta (IL-β) levels in different grades (B and C) of stage 3 periodontitis, concurrently, and also to investigate their discriminative efficiencies in periodontal diseases.

MATERIALS AND METHODS

A total of 80 systemically healthy and non-smoker individuals, 20 stage 3 grade C (S3GC) periodontitis 20 stage 3 grade B (S3GB) periodontitis, 20 gingivitis, and 20 periodontally healthy were enrolled. Clinical periodontal parameters were recorded and GCF Galectin-3 and IL-1β total amounts were measured by ELISA. Receiver operating characteristics curve was used for estimating the area under the curve (AUC).

RESULTS

Galectin-3 and IL-1β were detected in all participants. Both periodontitis groups had significantly higher GCF Galectin-3 total amounts than periodontally healthy controls (p <0.05). S3GC periodontitis group had also significantly higher GCF Galectin-3 levels than gingivitis group (p <0.05). GCF IL-1β levels in periodontitis groups were higher than gingivitis and periodontally healthy groups (p <0.05). Galectin-3 exhibited an AUC value of 0.89 with 95% sensitivity to discriminate S3GC periodontitis from periodontal health, an AUC value of 0.87 with 80% sensitivity to discriminate S3GC periodontitis versus gingivitis, while an AUC value of 0.85 with 95% sensitivity to discriminate S3GB periodontitis from healthy controls.

CONCLUSIONS

GCF Galectin-3 levels are involved in the pathogenesis of periodontal diseases. Galectin-3 showed excellent diagnostic performances to discriminate S3GB and S3GC periodontitis from periodontal health and gingivitis.

CLINICAL RELEVANCE

The present findings suggest that GCF Galectin-3 levels may be useful in the diagnosis of the periodontal diseases.

Three-Dimensional Humanized Model of the Periodontal Gingival Pocket to Study Oral Microbiome

The oral cavity contains distinct microenvironments that serve as oral barriers, such as the non-shedding surface of the teeth (e.g., enamel), the epithelial mucosa and gingival tissue (attached gingiva) where microbial communities coexist. The interactions and balances between these communities are responsible for oral tissue homeostasis or dysbiosis, that ultimately dictate health or disease. Disruption of this equilibrium can lead to chronic inflammation and permanent tissue damage in the case of chronic periodontitis. There are currently no experimental tissue models able to mimic the structural, physical, and metabolic conditions present in the human oral gingival tissue to support the long-term investigation of host-pathogens imbalances. Herein, the authors report an in vitro 3D anatomical gingival tissue model, fabricated from silk biopolymer by casting a replica mold of an adult human mandibular gingiva to recreate a tooth-gum unit. The model is based on human primary cultures that recapitulate physiological tissue organization, as well as a native oxygen gradient within the gingival pocket to support human subgingival plaque microbiome with a physiologically relevant level of microbial diversity up to 24 h. The modulation of inflammatory markers in the presence of oral microbiome indicates the humanized functional response of this model and establishes a new set of tools to investigate host-pathogen imbalances in gingivitis and periodontal diseases.

Electronic Cigarette Use Promotes a Unique Periodontal Microbiome

Electronic cigarettes (e-cigs) have become prevalent as an alternative to conventional cigarette smoking, particularly in youth. E-cig aerosols contain unique chemicals which alter the oral microbiome and promote dysbiosis in ways we are just beginning to investigate. We conducted a 6-month longitudinal study involving 84 subjects who were either e-cig users, conventional smokers, or nonsmokers. Periodontal condition, cytokine levels, and subgingival microbial community composition were assessed, with periodontal, clinical, and cytokine measures reflecting cohort habit and positively correlating with pathogenic taxa (e.g., Treponema, Saccharibacteria, and Porphyromonas). α-Diversity increased similarly across cohorts longitudinally, yet each cohort maintained a unique microbiome. The e-cig microbiome shared many characteristics with the microbiome of conventional smokers and some with nonsmokers, yet it maintained a unique subgingival microbial community enriched in Fusobacterium and Bacteroidales (G-2). Our data suggest that e-cig use promotes a unique periodontal microbiome, existing as a stable heterogeneous state between those of conventional smokers and nonsmokers and presenting unique oral health challenges. IMPORTANCE Electronic cigarette (e-cig) use is gaining in popularity and is often perceived as a healthier alternative to conventional smoking. Yet there is little evidence of the effects of long-term use of e-cigs on oral health. Conventional cigarette smoking is a prominent risk factor for the development of periodontitis, an oral disease affecting nearly half of adults over 30 years of age in the United States. Periodontitis is initiated through a disturbance in the microbial biofilm communities inhabiting the unique space between teeth and gingival tissues. This disturbance instigates host inflammatory and immune responses and, if left untreated, leads to tooth and bone loss and systemic diseases. We found that the e-cig user's periodontal microbiome is unique, eliciting unique host responses. Yet some similarities to the microbiomes of both conventional smokers and nonsmokers exist, with strikingly more in common with that of cigarette smokers, suggesting that there is a unique periodontal risk associated with e-cig use.

Gingival Crevicular Fluid (GCF): A Diagnostic Tool for the Detection of Periodontal Health and Diseases

The methodologies applicable for the evaluation of periodontal associated diseases are constantly evolving to provide quick, realistic, and scientifically proven results. Trends in the past followed a clinical evaluation of periodontal tissues and radiographic-based reports that formed the foundation for detection of diseases involving the structures supporting the teeth. As the confines and limitations of conventional strategies became obvious over the passage of time, hand in hand variety of techniques have evolved and experimentally justified. These improvisations are based on an improved understanding of the periodontal-pathogenic cascade. Periodontal pathogenesis and a paradigm shift from disease understanding to disease prevention and treatment entail few prerequisites that demand the objectivity of diagnostics procedure that includes sensitivity and specificity along with an explanation of the intensity of the disease, Gingival crevicular fluid an oral bio-fluid resides in the close proximity with gingival tissues have been widely used to understand and differentiate the periodontal health and diseased status. The biomarkers present in the GCF can be a reliable tool to detect the minute changes seen in the disease processes. The GCF consists of various host and bacterial-derived products as well as biomarkers which in turn can be evaluated for the diagnosis, prognosis as well as management of the periodontal disease. Thus, the review aims at describing GCF as a potential oral biofluid helpful in differentiating periodontal health and disease status.

Effect of non-surgical periodontal therapy on the serum and crevicular fluid interleukin-10 levels in chronic periodontitis - A systematic review and meta-analysis

OBJECTIVE

The objective of the study was to assess the effect of non-surgical periodontal therapy (NSPT) on the levels of anti-inflammatory cytokine interleukin 10 (IL-10) in serum, gingival crevicular fluid (GCF), and saliva of chronic periodontitis patients.

METHODS

A comprehensive search was made on PubMed, Embase, and Scopus databases from Jan 2000 to Sep 2020. Three focused questions were addressed: Do GCF, serum, saliva and IL-10 levels change significantly after NSPT? Randomized and Non-Randomized clinical trials were included in the study. Results of the meta-analysis are expressed as standardized mean differences and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines.

RESULTS

A total of 709 studies were retrieved and 17 met the inclusion criteria for systematic review, whereas subgroup meta-analysis was performed on seven studies (GCF-5, serum-2). All studies included had low risk of bias. IL-10levelin GCF showed an increase at 3 months after NSPT with subsequent decrease at 6 months. However, increased serum IL-10 level at 6 months was seen after therapy.

CONCLUSION

Within the limitations, there is moderate evidence that NSPT significantly alters the IL-10 levels in body fluids. IL-10 levels increased in GCF at 3 months whereas decrease was seen at 6 months. Increased IL-10 levels were seen in serum at 6 months.

Deep metagenomics examines the oral microbiome during dental caries, revealing novel taxa and co-occurrences with host molecules

Dental caries, the most common chronic infectious disease worldwide, has a complex etiology involving the interplay of microbial and host factors that are not completely understood. In this study, the oral microbiome and 38 host cytokines and chemokines were analyzed across 23 children with caries and 24 children with healthy dentition. De novo assembly of metagenomic sequencing obtained 527 metagenome-assembled genomes (MAGs), representing 150 bacterial species. Forty-two of these species had no genomes in public repositories, thereby representing novel taxa. These new genomes greatly expanded the known pangenomes of many oral clades, including the enigmatic Saccharibacteria clades G3 and G6, which had distinct functional repertoires compared to other oral Saccharibacteria. Saccharibacteria are understood to be obligate epibionts, which are dependent on host bacteria. These data suggest that the various Saccharibacteria clades may rely on their hosts for highly distinct metabolic requirements, which would have significant evolutionary and ecological implications. Across the study group, Rothia, Neisseria, and Haemophilus spp. were associated with good dental health, whereas Prevotella spp., Streptococcus mutans, and Human herpesvirus 4 (Epstein-Barr virus [EBV]) were more prevalent in children with caries. Finally, 10 of the host immunological markers were significantly elevated in the caries group, and co-occurrence analysis provided an atlas of potential relationships between microbes and host immunological molecules. Overall, this study illustrated the oral microbiome at an unprecedented resolution and contributed several leads for further study that will increase the understanding of caries pathogenesis and guide therapeutic development.

Gingival Crevicular Fluid Cytokines in Moderate and Deep Sites of Stage III Periodontitis Patients in Different Rates of Clinical Progression

Clinical criteria are inappropriate to measure the degree of susceptibility to progression of periodontal damage. Thus, the aim of this study was to assess whether gingival crevicular fluid (GCF) levels of cytokines could discriminate patients suffering from stage III periodontitis with moderate (Grade B) and rapid rates of progression (Grade C) prior to and 6 months after non-surgical periodontal treatment. GCF samples were obtained from moderate and deep sites of 20 patients diagnosed as Grade B and 20 patients as grade C stage III periodontitis and analyzed for interleukin (IL)-1β, IL-9, tumor necrosis factor (TNF)-α, and vascular endothelial growth factor (VEGF) using a high-sensitivity Bio-Plex Suspension Array System. At baseline, higher IL-1β but lower IL-9 GCF levels were observed in moderate sites of the grade C compared to the grade B group. In spite of comparable clinical improvement, this difference maintained after treatment, suggesting a residual pro-inflammatory state. In deep sites, no differences were observed between periodontitis groups except for VEGF levels that decreased more in Grade B periodontitis at 6 months post-therapy. A mathematical model was constructed to identify Grade C periodontitis patients based on the subjects’ GCF levels of IL-1β and IL-9, which achieved an area under the receiver-operating characteristic (ROC) curve of 0.94. This study can contribute to the early assessment of risk of future breakdown in periodontitis patients.

Biomarker for Asymptomatic Apical Periodontitis in Gingival Crevicular Fluid: aMMP-8

Objectives  Asymptomatic apical periodontitis (AAP) is one of the most widespread chronic inflammatory diseases in the field of dental medicine. Active matrix metalloproteinase (aMMP-8) previously demonstrated diagnostic potential as a biomarker for AAP in gingival crevicular fluid (GCF). The aim of this study was to determine the levels and diagnostic accuracy of aMMP-8 in GCF from teeth with AAP.

Materials and Methods  In this cross-sectional study, GCF samples were obtained from teeth with AAP (sample group, n = 31) and their contralateral teeth (control group, n = 31). Clinical and cone beam computed tomography (CBCT) parameters were used for the diagnosis and assessment of AAP and the determination of clinically healthy marginal periodontal tissues. One pool GCF sample per tooth was obtained from the whole crevice’s perimeter. aMMP-8 levels were determined by enzyme-linked immunosorbent assay (ELISA).

Statistical Analysis  Wilcoxon signed ranks test and Spearman rank correlation coefficient (rs) were used as statistical tools. The significance level was set at p < .05.

Results  The two groups demonstrated biomarker levels corresponding to a healthy marginal periodontal tissue. aMMP-8 levels were statistically and significantly higher in the samples collected from teeth with AAP. Lesions with greater volume showed correspondingly larger diameters. No statistically significant correlation between aMMP-8 levels and lesions’ volume or diameter was discovered.

Conclusion  GCF composition is modified by AAP only to a minimal extent. Further research is needed to substantiate the utilization of aMMP-8 as a potential biomarker for the diagnosis of the disease as well as to explore its relationship with other biomarkers.

Effect of scaling and root planing on the expression of anti-inflammatory cytokines (IL-4, IL-9, IL-10, and IL-13) in the gingival crevicular fluid of electronic cigarette users and non-smokers with moderate chronic periodontitis

Purpose

The aim of this cross-sectional study was to investigate the effect of scaling and root planing (SRP) on the expression of anti-inflammatory cytokines (interleukin [IL]-4, IL-9, IL-10, and IL-13) in the gingival crevicular fluid (GCF) of electronic cigarette users and non-smokers with moderate chronic periodontitis (CP).

Methods

Electronic cigarette users and non-smokers with CP were included in the study. Full-mouth plaque and gingival indices, probing depth (PD), clinical attachment loss (CAL), and marginal bone loss (MBL) were assessed. The GCF was collected, and its volume and levels of IL-4, IL-9, IL-10, and IL-13 were assessed. These parameters were evaluated at baseline and 3 months after SRP. The sample size was estimated, and comparisons between groups were performed. P<0.05 was considered to indicate statistical significance.

Results

Thirty-six electronic cigarette users (47.7±5.8 years old) and 35 non-smokers (46.5±3.4 years old) with CP were included. At baseline, there were no differences in plaque index (PI), PD, CAL, MBL, and GCF IL-4, IL-9, IL-10, and IL-13 between electronic cigarette users and non-smokers. At the 3-month follow-up, there were no significant differences in PI, gingival index (GI), PD, CAL, and MBL in electronic cigarette users compared to baseline, while there were significant reductions in PI, GI, and PD among non-smokers. At the 3-month follow-up, GCF IL-4, IL-9, IL-10, and IL-13 levels were significantly elevated in both groups (P<0.05) compared to baseline. The increases in GCF IL-4, IL-9, IL-10, and IL-13 levels were significantly higher in non-smokers (P<0.05) than in electronic cigarette users at the 3-month follow-up.

Conclusions

Levels of GCF IL-4, IL-9, IL-10, and IL-13 increased after SRP in electronic cigarette users and non-smokers with CP; however, the anti-inflammatory effect of SRP was more profound in non-smokers than in electronic cigarette users.

Predictive Model of Clinical Attachment Loss and Oral Health-Related Quality of Life through Depressive Symptomatology, Oral Hygiene Habits, and Proinflammatory Biomarkers: A Pilot Study

Subjective aspects such as oral health-related quality of life (OHRQoL) and depression are important aspects in the periodontal care. The objectives of the study were to test a predictive model of clinical attachment loss and OHRQoL in a pooled sample of dental patients with periodontitis and mental health patients with depressive symptomatology, and test the invariance of the model across both types of patients. Three self-report scales were applied to assess depression, OHRQoL and oral hygiene habits, saliva samples were collected for three proinflammatory biomarkers, and the clinical attachment loss was measured in 35 patients with periodontitis and 26 patients with depressive symptomatology. Data were analyzed through structural equation modeling. The one-group analysis revealed a psychosomatic complaint model of disagreement between the complaint and the clinically observable. In the multi-group analysis, the model was not invariant. It was necessary to introduce a singularity in relation to depressive symptomatology for each population. Thus, a good and equivalent fit was achieved between the six nested models in constraints, as well as equivalent parameters between both types of patients. The study of a dental population in conjunction with a mental health population with a psychosomatic risk factor reveals interesting and unexpected results.

Influence of inflammasome NLRP3, and IL1B and IL2 gene polymorphisms in periodontitis susceptibility

The pathogenesis of periodontitis (PD) involves several molecules of the immune system that interact in a network to eliminate the periodontopathogens, yet, they contribute to periodontal tissue destruction. The different mechanisms that lead to periodontal tissue damage are not clear. Despite this, immune response genes have been related to the development of PD previously, such as those involved in inflammasomes which are multiprotein complexes and cytokines including Interleukin-1. The aim of the study was to evaluate the polymorphisms in NLRP3 inflammasome, cytokine and receptor of cytokines genes in the development of periodontitis. This case-control study was conducted in 186 patients with PD (stage II and III and grade B) and 208 controls (localized gingivitis and periodontally healthy individuals). Genotyping was performed using PCR-RFLP for the SNP rs4612666 in NLRP3 and using PCR-SSP for IL1A, IL1B, IL1R, IL1RN, IL4RA, INFG, TGFB1, TNF, IL2, IL4, IL6, and IL10. Cytokine serum levels were measured using Luminex technology. SNPStats and OpenEpi software were used to perform statistical analysis. The higher frequencies of NLRP3 T/C and IL1B -511 T/T genotypes and IL2 (+166, -330) GT haplotype were observed in patients with PD compared to controls. The SNPs in NLRP3, IL1R +1970, IL6–174, TNF -308, IL2 +166 and -330, TGFB1 +869 and +915, IL4RA +1902, IL4–1098 and -590 were associated to PD in men. In conclusion, polymorphisms in NLRP3, IL1B and IL2 genes were associated to PD susceptibility. Men carrying the NLRP3, IL1R, IL6, TNF, IL2, TGFB1, IL4RA and IL4 polymorphisms had greater susceptibility than women for developing PD.

Levels of Myeloperoxidase and Metalloproteinase-9 in Gingival Crevicular Fluid from Diabetic Subjects with and without Stage 2, Grade B Periodontitis

Objective

The present study aimed to compare levels of matrix metalloproteinase-9 (MMP-9) and myeloperoxidase (MPO) in gingival crevicular fluid (GCF) from subjects with controlled and noncontrolled Type 2 Diabetes Mellitus (T2D), with and without stage 2 grade B periodontitis (POD2B) versus healthy (H) subjects.

Methods

The levels of both enzymes, from 80 GCF samples collected with PerioPaper strips, were analyzed by a Multiplex/Luminex assay. Five groups were formed, all current patients at the Institutional Dentistry Service, and distributed as follows: two groups of diabetics (one controlled and one poorly controlled); two groups with the previous conditions and diagnosed with POD2B; and one H group.

Results

The highest concentration of MMP-9 corresponded to the H group, while the lowest corresponded to the T2D controlled group. Regarding MPO levels, the highest levels were associated with the T2D controlled with POD2B group and the lowest with the T2D controlled group.

Conclusions

No apparent relationship between the elevation of MMP-9 and MPO levels was observed among subjects with T2D, with and without POD2B, compared to H subjects.