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.

Short-term anti-plaque effect of a cymenol mouthwash analysed using the DenTiUS Deep Plaque software: a randomised clinical trial

BACKGROUND

The effect of cymenol mouthwashes on levels of dental plaque has not been evaluated thus far.

OBJECTIVE

To analyse the short-term, in situ, anti-plaque effect of a 0.1% cymenol mouthwash using the DenTiUS Deep Plaque software.

METHODS

Fifty orally healthy participants were distributed randomly into two groups: 24 received a cymenol mouthwash for eight days (test group A) and 26 a placebo mouthwash for four days and a cymenol mouthwash for a further four days thereafter (test group B). They were instructed not to perform other oral hygiene measures. On days 0, 4, and 8 of the experiment, a rinsing protocol for staining the dental plaque with sodium fluorescein was performed. Three intraoral photographs were taken per subject under ultraviolet light. The 504 images were analysed using the DenTiUS Deep Plaque software, and visible and total plaque indices were calculated (ClinicalTrials ID NCT05521230).

RESULTS

On day 4, the percentage area of visible plaque was significantly lower in test group A than in test group B (absolute = 35.31 ± 14.93% vs. 46.57 ± 18.92%, p = 0.023; relative = 29.80 ± 13.97% vs. 40.53 ± 18.48%, p = 0.024). In comparison with the placebo, the cymenol mouthwash was found to have reduced the growth rate of the area of visible plaque in the first four days by 26% (absolute) to 28% (relative). On day 8, the percentage areas of both the visible and total plaque were significantly lower in test group A than in test group B (visible absolute = 44.79 ± 15.77% vs. 65.12 ± 16.37%, p < 0.001; visible relative = 39.27 ± 14.33% vs. 59.24 ± 16.90%, p < 0.001; total = 65.17 ± 9.73% vs. 74.52 ± 13.55%, p = 0.007). Accounting for the growth rate with the placebo mouthwash on day 4, the above results imply that the cymenol mouthwash in the last four days of the trial reduced the growth rate of the area of visible plaque (absolute and relative) by 53% (test group A) and 29% (test group B), and of the area of total plaque by 48% (test group A) and 41% (test group B).

CONCLUSIONS

The 0.1% cymenol mouthwash has a short-term anti-plaque effect in situ, strongly conditioning the rate of plaque growth, even in clinical situations with high levels of dental plaque accumulation.

Relationship between dental and periodontal health status and the salivary microbiome: bacterial diversity, co-occurrence networks and predictive models

The present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. Our scale of overall oral health was used to produce a convenience sample of 81 patients from 270 who were initially recruited. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 × 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the phyloseq, DESeq2, Microbiome, SpiecEasi, igraph, MixOmics packages. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or oral disease, especially in high grades. Supragingival dental parameters influence the microbiota’s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that oral health has on the salivary microbiome. The possible keystone OTUs are different in the oral health and disease, and even these vary between dental and periodontal disease: half of them belongs to the core microbiome and are independent of the abundance parameters. The salivary microbiome, involving a considerable number of OTUs, shows an excellent discriminatory potential for distinguishing different grades of dental, periodontal or oral disease; considering the number of predictive OTUs, the best model is that which predicts the combined dental and periodontal status.

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

The objective of the present study was to determine cytokine thresholds derived from predictive models for the diagnosis of chronic periodontitis, differentiating by smoking status. Seventy-five periodontally healthy controls and 75 subjects affected by chronic periodontitis were recruited. Sixteen mediators were measured in gingival crevicular fluid (GCF) using multiplexed bead immunoassays. The models were obtained using binary logistic regression, distinguishing between non-smokers and smokers. The area under the curve (AUC) and numerous classification measures were obtained. Model curves were constructed graphically and the cytokine thresholds calculated for the values of maximum accuracy (ACC). There were three cytokine-based models and three cytokine ratio-based models, which presented with a bias-corrected AUC > 0.91 and > 0.83, respectively. These models were (cytokine thresholds in pg/ml for the median ACC using bootstrapping for smokers and non-smokers): IL1alpha (46099 and 65644); IL1beta (4732 and 5827); IL17A (11.03 and 17.13); IL1alpha/IL2 (4210 and 7118); IL1beta/IL2 (260 and 628); and IL17A/IL2 (0.810 and 1.919). IL1alpha, IL1beta and IL17A, and their ratios with IL2, are excellent diagnostic biomarkers in GCF for distinguishing periodontitis patients from periodontally healthy individuals. Cytokine thresholds in GCF with diagnostic potential are defined, showing that smokers have lower threshold values than non-smokers.

Comparing student and staff perceptions of the "Educational Climate" in Spanish Dental Schools using the Dundee Ready Education Environment Measure

OBJECTIVE

To compare the perceptions of students and teachers of the "Educational Climate" (EC) in Spanish public dental schools.

METHODS

A group of 1064 students and 354 teachers from six Spanish public dental schools responded to the DREEM questionnaire. This has 50 items grouped into five subscales: perception of learning (Learning); perception of teachers (Teachers); academic self-perceptions (Academic); perception of the atmosphere in the faculty (Atmosphere); and social self-perceptions (Social). The DREEM scale provides results for each item, each subscale and the overall EC.

RESULTS

The EC scores were 123.2 (61.6%) for the students and 134.1 (67.0%) for the teachers (P<.001). The scores of the students and teachers for the subscales were, respectively: 27.9 (58.1%) and 30.2 (63.0 %) for Learning (P<.001); 26.8 (60.9%) and 32.6 (74.1%) for Teachers (P<.001); 20.7 (64.7%) and 20.5 (64.0%) for Academic (P=.333); 29.9 (62.3%) and 33.7 (70.3%) for Atmosphere (P<.001); and 17.9 (64.0%) and 16.9 (60.5%) for Social (P<.001). The students identified six problematic items (12.0 %) compared to only two (4.0 %) highlighted by the teachers.

CONCLUSION

The students and teachers considered the EC to be "more positive than negative" in Spanish public dental schools; and the different subscales to be "positive and acceptable." The teachers did, however, evaluate the EC, and specifically the learning-teaching process, more positively than their students, identifying fewer problematic educational aspects. Both groups agreed on the need to: improve support systems for students who suffer from stress and reduce teaching based on "factual learning."

Cytokine-based Predictive Models to Estimate the Probability of Chronic Periodontitis: Development of Diagnostic Nomograms

Although a distinct cytokine profile has been described in the gingival crevicular fluid (GCF) of patients with chronic periodontitis, there is no evidence of GCF cytokine-based predictive models being used to diagnose the disease. Our objectives were: to obtain GCF cytokine-based predictive models; and develop nomograms derived from them. A sample of 150 participants was recruited: 75 periodontally healthy controls and 75 subjects affected by chronic periodontitis. Sixteen mediators were measured in GCF using the Luminex 100™ instrument: GMCSF, IFNgamma, IL1alpha, IL1beta, IL2, IL3, IL4, IL5, IL6, IL10, IL12p40, IL12p70, IL13, IL17A, IL17F and TNFalpha. Cytokine-based models were obtained using multivariate binary logistic regression. Models were selected for their ability to predict chronic periodontitis, considering the different role of the cytokines involved in the inflammatory process. The outstanding predictive accuracy of the resulting smoking-adjusted models showed that IL1alpha, IL1beta and IL17A in GCF are very good biomarkers for distinguishing patients with chronic periodontitis from periodontally healthy individuals. The predictive ability of these pro-inflammatory cytokines was increased by incorporating IFN gamma and IL10. The nomograms revealed the amount of periodontitis-associated imbalances between these cytokines with pro-inflammatory and anti-inflammatory effects in terms of a particular probability of having chronic periodontitis.

Relationship between periodontitis-associated subgingival microbiota and clinical inflammation by 16S pyrosequencing

AIM

To analyse the relationship between the chronic periodontitis-associated subgingival microbiota and clinical inflammation.

MATERIAL AND METHODS

Sixty subjects with generalized chronic periodontitis participated in this study. Patients were divided into two groups according to their bleeding on probing (BOP) scores: BOP-1 group (mean scores ≤50% in sampled sites) and BOP-2 group (mean scores >50%). Subgingival bacterial samples from periodontal patients were studied by pyrosequencing PCR products of the 16S rRNA gene and by real-time PCR.

RESULTS

In all the analysed subgingival samples, 102 bacterial genera and 203 species (from 41 genera of interest) were identified. Rarefaction curves showed a greater number of bacterial species in samples from BOP-2 group compared to BOP-1 group. The BOP-1 group had significantly higher abundance percentages of Anaeroglobus (especifically, A. geminatus), Capnocytophaga (especifically C. gingivalis), TM7 and Veillonella. The BOP-2 had significantly higher abundance percentages of Desulfobulbus (especially D. propionicus), Eubacterium (especially E. saphenum), Filifactor alocis, Streptococcus constellatus, Tannerella (especially, T. forsythia) and Treponema.

CONCLUSION

16S pyrosequencing revealed that increased inflammation, at sites with periodontitis, is associated with a more diverse subgingival microbiota and specific changes in the bacterial composition, involving "established" periopathogens, symbionts and novel low-abundance pathobionts.