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.

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.

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-1ß with IL-6 (83.0%/83.7%); IL-1ß with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1ß, IL-6 and MMP-8 (81.8%/84.3%); and IL-1ß, 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-1ß, 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.

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.