The Role of Dysbiotic Oral Microbiota in Cardiometabolic Diseases: A Narrative Review

Over the past decade, there have been significant advancements in the high-flow analysis of "omics," shedding light on the relationship between the microbiota and the host. However, the full recognition of this relationship and its implications in cardiometabolic diseases are still underway, despite advancements in understanding the pathophysiology of these conditions. Cardiometabolic diseases, which include a range of conditions from insulin resistance to cardiovascular disease and type 2 diabetes, continue to be the leading cause of mortality worldwide, with a persistently high morbidity rate. While the link between the intestinal microbiota and cardiometabolic risks has been extensively explored, the role of the oral microbiota, the second-largest microbiota in the human body, and specifically the dysbiosis of this microbiota in causing these complications, remains incompletely defined. This review aims to examine the association between the oral microbiota and cardiometabolic diseases, focusing on the dysbiosis of the oral microbiota, particularly in periodontal disease. Additionally, we will dive into the mechanistic aspects of this dysbiosis that contribute to the development of these complications. Finally, we will discuss potential prevention and treatment strategies, including the use of prebiotics, probiotics, and other interventions.

Gingival Orofacial Granulomatosis Clinical and 2D/3D Microscopy Features after Orthodontic Therapy: A Pediatric Case Report

Orofacial granulomatosis (OFG) represents a heterogeneous group of rare orofacial diseases. When affecting gingiva, it appears as a chronic soft tissue inflammation, sometimes combined with the enlargement and swelling of other intraoral sites, including the lips. Gingival biopsy highlights noncaseating granulomatous inflammation, similar to that observed in Crohn’s disease and sarcoidosis. At present, the etiology of OFG remains uncertain, although the involvement of the genetic background and environmental triggers, such as oral conditions or therapies (including orthodontic treatment), has been suggested. The present study reports the results of a detailed clinical and 2D/3D microscopy investigation of a case of gingival orofacial granulomatosis in an 8-year-old male patient after orthodontic therapy. Intraoral examination showed an erythematous hyperplasia of the whole gingiva with a granular appearance occurring a few weeks after the installation of a quad-helix. Peri-oral inspection revealed upper labial swelling and angular cheilitis. General investigations did not report ongoing extra-oral disturbances with the exception of a weakly positive anti-Saccharomyces cerevicae IgG auto-antibody. Two- and three-dimensional microscopic investigations confirmed the presence of gingival orofacial granulomatosis. Daily corticoid mouthwashes over a period of 3 months resulted in a slight improvement in clinical signs, despite an intermittent inflammation recurrence. This study brings new insights into the microscopic features of gingival orofacial granulomatosis, thus providing key elements to oral practitioners to ensure accurate and timely OFG diagnosis. The accurate diagnosis of OFG allows targeted management of symptoms and patient monitoring over time, along with early detection and treatment of extra-oral manifestations, such as Crohn’s disease.

Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study

Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.