Microbial biomarkers as a predictor of periodontal treatment response: A systematic review.

To evaluate the prognostic accuracy of microbial biomarkers and their associations with the response to active periodontal treatment (APT) and supportive periodontal therapy (SPT). Microbial dysbiosis plays a crucial role in the disease processes of periodontitis. Biomarkers based on microbial composition may offer additional prognostic value, supplementing the limitations of current clinical parameters. While these microbial biomarkers have been clinically evaluated, there is a lack of consensus regarding their prognostic accuracy. A structured search strategy was applied to MEDLINE (PubMed), Cochrane Library, and Embase on 1/11/2022 to identify relevant publications. Prospective clinical studies involving either APT or SPT, with at least 3-month follow-up were included. There were no restrictions on the type of microbial compositional analysis. 1918 unique records were retrieved, and 13 studies (comprising 943 adult patients) were included. Heterogeneity of the studies precluded a meta-analysis, and none of the included studies had performed the sequence analysis of the periodontal microbiome. Seven and six studies reported on response to APT and SPT, respectively. The prognostic accuracy of the microbial biomarkers for APT and SPT was examined in only two and four studies, respectively. Microbial biomarkers had limited predictive accuracy for APT and inconsistent associations for different species across studies. For SPT, elevated abundance of periodontal pathogens at the start of SPT was predictive of subsequent periodontal progression. Similarly, persistent high pathogen loads were consistently associated with progressive periodontitis, defined as an increased pocket probing depth or clinical attachment loss. While there was insufficient evidence to support the clinical use of microbial biomarkers as prognostic tools for active periodontal treatment outcomes, biomarkers that quantify periodontal pathogen loads may offer prognostic value for predicting progressive periodontitis in the subsequent supportive periodontal therapy phase. Additional research will be required to translate information regarding subgingival biofilm composition and phenotype into clinically relevant prognostic tools.

Quantifying periodontitis-associated oral dysbiosis in tongue and saliva microbiomes-An integrated data analysis.

BACKGROUND: Periodontitis is primarily driven by subgingival biofilm dysbiosis. However, the quantification and impact of this periodontal dysbiosis on other oral microbial niches remain unclear. This study seeks to quantify the dysbiotic changes in tongue and salivary microbiomes resulting from periodontitis by applying a clinically relevant dysbiosis index to an integrated data analysis. METHODS: The National Center for Biotechnology Information (NCBI) database was searched to identify BioProjects with published studies on salivary and tongue microbiomes of healthy and periodontitis subjects. Raw sequence datasets were processed using a standardized bioinformatic pipeline and categorized by their ecological niche and periodontal status. The subgingival microbial dysbiosis index (SMDI), a dysbiosis index originally developed using the subgingival microbiome, was computed at species and genus levels and customized for each niche. Its diagnostic accuracy for periodontitis was evaluated using receiver operating characteristic curves. RESULTS: Four studies, contributing 328 microbiome samples, were included. At both species and genus levels, periodontitis samples had a higher SMDI, but the differences were only significant for subgingival biofilm and saliva (p < 0.001). However, SMDI showed good diagnostic accuracy for periodontitis status for all three niches (area under curve ranging from 0.76 to 0.90, p < 0.05). The dysbiosis index of subgingival biofilm was positively correlated with saliva consistently (p < 0.001) and with the tongue at the genus level (p = 0.036). CONCLUSIONS: While the impact on the tongue microbiome requires further investigation, periodontitis-associated dysbiosis affects the salivary microbiome and is quantifiable using the dysbiosis index. The diagnostic potential of salivary microbial dysbiosis as a convenient periodontal biomarker for assessing periodontal status has potential public health and clinical applications. PLAIN LANGUAGE SUMMARY: Periodontitis, a severe inflammation of the gums which causes bone loss, is a disease caused by an imbalance of good and bad bacteria under the gums. However, it is unclear how this bacterial imbalance in the gums affects the bacterial balance of other distinct parts of the mouth, such as the saliva and tongue. This study uses bacteria datasets of four previously published studies, contributing a total of 328 bacterial samples. The data were processed using a uniform data analysis workflow, and a bacterial score, the subgingival microbial dysbiosis index (SMDI), previously shown to capture periodontitis-associated bacteria imbalance, was calculated separately for samples from under the gums, the saliva, and the tongue. The SMDI was able to distinguish between health and periodontitis within each oral location, and in general, the scores were higher for periodontitis samples, though this difference was significant only for bacteria under the gums and in saliva. Saliva scores were also consistently correlated with bacteria under the gums. This study shows that periodontitis-associated bacterial imbalances are observed in oral locations beyond just under the gums, particularly the saliva. Thus, saliva bacteria may be used as a convenient biomarker for assessing gum disease, allowing for potential public health and clinical applications.

Development of a dental diet-tracking mobile app for improved caries-related dietary behaviours: Key features and pilot evaluation of quality.

Objective: Diet significantly contributes to dental decay (caries) yet monitoring and modifying patients' diets is a challenge for many dental practitioners. While many oral health and diet-tracking mHealth apps are available, few focus on the dietary risk factors for caries. This study aims to present the development and key features of a dental-specific mobile app for diet monitoring and dietary behaviour change to prevent caries, and pilot data from initial user evaluation. Methods: A mobile app incorporating a novel photo recognition algorithm and a localised database of 208,718 images for food item identification was developed. The design and development process were iterative and incorporated several behaviour change techniques commonly used in mHealth. Pilot evaluation of app quality was assessed using the end-user version of the Mobile Application Rating Scale (uMARS). Results: User feedback from the beta-testing of the prototype app spurred the improvement of the photo recognition algorithm and addition of more user-centric features. Other key features of the final app include real-time prompts to drive actionable behaviour change, goal setting, comprehensive oral health education modules, and visual metrics for caries-related dietary factors (sugar intake, meal frequency, etc.). The final app scored an overall mean (standard deviation) of 3.6 (0.5) out of 5 on the uMARS scale. Conclusion: We developed a novel diet-tracking mobile app tailored for oral health, addressing a gap in the mHealth landscape. Pilot user evaluations indicated good app quality, suggesting its potential as a useful clinical tool for dentists and empowering patients for self-monitoring and behavioural management.

Impact of income and financial subsidies on oral health care utilization among persons with disabilities in Singapore.

BACKGROUND/AIM(S): Globally, studies have shown that the dental disease burden among persons with intellectual and/or developmental disabilities (IDD) is high and can be attributed to lower utilization levels of dental services. The aim of the study was to assess the influence of income and financial subsidies on the utilization of dental care services among persons with IDD in Singapore. METHODS: Between August 2020 and August 2021, a cross-sectional study was conducted via centres offering Early Intervention Programme for Infants and Children, special education schools and adult associations in Singapore serving persons with IDD. A sample of 591 caregivers of children and adults with IDD completed the survey. Data on sociodemographic information, oral health behaviours and dental utilization were collected. Financial subsidy status was assessed by the uptake of a government-funded, opt-in Community Health Assist Scheme (CHAS) for low-income families that provided a fixed subsidy amount for dental services in the primary care setting. Statistical analysis was carried out using univariable, multiple logistic regression and modified Poisson regression. Propensity score matching was carried out in R version 4.0.2 to assess the impact of financial subsidies on oral health care utilization among persons with IDD. RESULTS: Compared to those with lower gross monthly household incomes, the adjusted prevalence ratios of having at least one dental visit in the past year, having at least one preventive dental visit in the past year, and visiting the dentist at least once a year for persons with IDD with gross monthly household incomes of above SGD$4000 were 1.28 (95% CI 1.08-1.52), 1.48 (95% CI 1.14-1.92) and 1.36 (95% CI 1.09-1.70), respectively. Among those who were eligible for CHAS Blue subsidies (247 participants), 160 (62.0%) took up the CHAS Blue scheme and 96 (35.4%) visited the dentist at least yearly. There was no statistically significant difference in the utilization of dental services among individuals enrolled in the CHAS Blue subsidy scheme among those eligible for CHAS Blue subsidies. CONCLUSION: Higher household income was associated with a higher prevalence of dental visits in the past year, preventive dental visits in the past year, and at least yearly dental visits. CHAS Blue subsidies alone had limited impact on dental utilization among persons with IDD who were eligible for subsidies.