The role of nitrate-reducing oral bacteria in the etiology of insulin resistance and elevated blood pressure

Increasing evidence suggests that the oral microbiome is highly relevant to cardiometabolic outcomes. Associations between the oral microbiome and extra-oral outcomes are most commonly hypothesized to result from a chronic inflammatory response to a dysbiotic oral microbiome. However, an alternative mechanism hypothesized to link the oral microbiome to cardiometabolic risk is via the production of nitric oxide, a physiologically important gaseous transmitter. The enterosalivary nitrate-nitrite-nitric oxide pathway of nitric oxide generation is dependent on the presence of nitrate-reducing oral bacteria in the mouth to reduce salivary nitrates to nitrite, which are then swallowed and made systemically bioavailable for further reduction into nitric oxide in the blood vessels and tissues. Thus, this pathway presents a mechanism for oral bacteria to exert a beneficial influence on cardiometabolic health. The overall objective of this dissertation is to advance the understanding of the role of nitrate-reducing oral bacteria in cardiometabolic outcomes in a population setting. This objective was met using three aims. First, a systematic literature review was conducted to identify and assess the associations between nitrate-reducing oral bacteria and insulin resistance, plasma glucose, diabetes, blood pressure and hypertension found in the existing literature. The literature review found no study that explicitly tested the hypothesis of an association between nitrate-reducing oral bacteria and the cardiometabolic outcomes of interest. In addition, there were very few observed associations between nitrate-reducing oral bacteria and these cardiometabolic outcomes, and the findings were inconsistent across studies. Secondly, the associations between nitrate-reducing oral bacteria and insulin resistance, plasma glucose, blood pressure, prediabetes and hypertension were assessed using baseline data from a cohort of diabetes-free participants. Increased levels of oral nitrate-reducing bacteria were associated with lower insulin resistance, plasma glucose and mean systolic blood pressure cross-sectionally, but no associations were found with prediabetes and hypertension. Finally, as dietary nitrate intake influences the level of salivary nitrate in the mouth for the nitrate-reducing oral bacteria to act on, the associations between dietary nitrate and insulin resistance, plasma glucose, blood pressure, prediabetes and hypertension were assessed. No clear associations between higher dietary nitrate intake and the cardiometabolic outcomes were found. However, there was some indication that higher dietary nitrate intake was associated with lower systolic blood pressure. The interaction of dietary nitrate intake with nitrate-reducing oral bacteria was then considered, but no evidence of such interaction was found. Overall, the results of this dissertation suggest that higher levels of nitrate-reducing oral bacteria may confer health benefits across the range of bacterial levels likely observed in human populations. These findings help inform future public health research aimed at utilizing the enterosalivary pathway of nitric oxide generation to improve cardiometabolic health

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.</p

From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore

Singapore has been striving to improve the provision of healthcare services to her people. In this course, the government has taken note of the deficiency in regulating and supervising people's nutrient intake, which is identified as a contributing factor to the development of chronic diseases. Consequently, this issue has garnered significant attention. In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects. To this end, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We further identify the profound meaning of localized food datasets and systematically clean and curate a localized Singaporean food dataset FoodSG-233. To overcome the hurdle in recognition performance brought by Singaporean multifarious food dishes, we propose to integrate supervised contrastive learning into our food recognition model FoodSG-SCL for the intrinsic capability to mine hard positive/negative samples and therefore boost the accuracy. Through a comprehensive evaluation, we present performance results of the proposed model and insights on food-related healthcare applications. The FoodSG-233 dataset has been released in https://foodlg.comp.nus.edu.sg/

Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study

BackgroundPain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. ObjectiveThe aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. MethodsAll posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. ResultsA total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added. ConclusionsThis study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ

Does the teaching of caries risk assessment foster preventive-minded dental students?

10.29060/taps.2022-7-3/sc2766The Asia Pacific Scholar7342-4

When e-Learning takes centre stage amid COVID-19: Dental educators' perspectives and their future impacts.

10.1111/eje.12727Eur J Dent Edu