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.

Explanations as a New Metric for Feature Selection: A Systematic Approach

With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practitioners, while meeting regulatory requirements. Feature Selection (FS) is widely used as a part of a biomedical ML pipeline to significantly reduce the number of variables while preserving as much information as possible. However, the choice of FS methods affects the entire pipeline including the final prediction explanations, whereas very few works investigate the relationship between FS and model explanations. Through a systematic workflow performed on 145 datasets and an illustration on medical data, the present work demonstrated the promising complementarity of two metrics based on explanations (using ranking and influence changes) in addition to accuracy and retention rate to select the most appropriate FS/ML models. Measuring how much explanations differ with/without FS are particularly promising for FS methods recommendation. While reliefF generally performs the best on average, the optimal choice may vary for each dataset. Positioning FS methods in a tridimensional space, integrating explanations-based metrics, accuracy and retention rate, would allow the user to choose the priorities to be given on each of the dimensions. In biomedical applications, where each medical condition may have its own preferences, this framework will make it possible to offer the healthcare professional the appropriate FS technique, to select the variables that have an important explainable impact, even if this comes at the expense of a limited drop of accuracy.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

A Spectral Principal Component Analysis-Based Framework for Composite Hard/Soft Tissue Fluorescence Image Investigation

Traditional thin sectioning microscopy of large bone and dental tissue samples using demineralization may disrupt structure morphologies and even damage soft tissues, thus compromising the histopathological investigation. Here, we developed a synergistic and original framework on thick sections based on wide-field multi-fluorescence imaging and spectral Principal Component Analysis (sPCA) as an alternative, fast, versatile, and reliable solution, suitable for highly mineralized tissue structure sustain and visualization. Periodontal 2-mm thick sections were stained with a solution containing five fluorescent dyes chosen for their ability to discriminate close tissues, and acquisitions were performed with a multi-zoom macroscope for blue, green, red, and NIR (near-infrared) emissions. Eigen-images derived from both standard scaler (Std) and Contrast Limited Adaptive Histogram Equalization (Clahe) pre-preprocessing significantly enhanced tissue contrasts, highly suitable for histopathological investigation with an in-depth detail for sub-tissue structure discrimination. Using this method, it is possible to preserve and delineate accurately the different anatomical/morphological features of the periodontium, a complex tooth-supporting multi-tissue. Indeed, we achieve characterization of gingiva, alveolar bone, cementum, and periodontal ligament tissues. The ease and adaptability of this approach make it an effective method for providing high-contrast features that are not usually available in standard staining histology. Beyond periodontal investigations, this first proof of concept of an sPCA solution for optical microscopy of complex structures, especially including mineralized tissues opens new perspectives to deal with other chronic diseases involving complex tissue and organ defects. Overall, such an imaging framework appears to be a novel and convenient strategy for optical microscopy investigation.

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.

Periodontitis induced by Porphyromonas gingivalis drives periodontal microbiota dysbiosis and insulin resistance via an impaired adaptive immune response

OBJECTIVE

To identify a causal mechanism responsible for the enhancement of insulin resistance and hyperglycaemia following periodontitis in mice fed a fat-enriched diet.

DESIGN

We set-up a unique animal model of periodontitis in C57Bl/6 female mice by infecting the periodontal tissue with specific and alive pathogens like Porphyromonas gingivalis (Pg), Fusobacterium nucleatum and Prevotella intermedia. The mice were then fed with a diabetogenic/non-obesogenic fat-enriched diet for up to 3 months. Alveolar bone loss, periodontal microbiota dysbiosis and features of glucose metabolism were quantified. Eventually, adoptive transfer of cervical (regional) and systemic immune cells was performed to demonstrate the causal role of the cervical immune system.

RESULTS

Periodontitis induced a periodontal microbiota dysbiosis without mainly affecting gut microbiota. The disease concomitantly impacted on the regional and systemic immune response impairing glucose metabolism. The transfer of cervical lymph-node cells from infected mice to naive recipients guarded against periodontitis-aggravated metabolic disease. A treatment with inactivated Pg prior to the periodontal infection induced specific antibodies against Pg and protected the mouse from periodontitis-induced dysmetabolism. Finally, a 1-month subcutaneous chronic infusion of low rates of lipopolysaccharides from Pg mimicked the impact of periodontitis on immune and metabolic parameters.

CONCLUSIONS

We identified that insulin resistance in the high-fat fed mouse is enhanced by pathogen-induced periodontitis. This is caused by an adaptive immune response specifically directed against pathogens and associated with a periodontal dysbiosis.

Spatial and temporal structure of the clinical research based on mesenchymal stromal cells: A network analysis

BACKGROUND AIMS

Using innovative tools derived from social network analysis, the aims of this study were (i) to decipher the spatial and temporal structure of the research centers network dedicated to the therapeutic uses of mesenchymal stromal cells (MSCs) and (ii) to measure the influence of fields of applications, cellular sources and industry funding on network topography.

METHODS

From each trial using MSCs reported on ClinicalTrials.gov, all research centers were extracted. Networks were generated using Cytoscape 3.2.2, where each center was assimilated to a node, and one trial to an edge connecting two nodes.

RESULTS

The analysis included 563 studies. An independent segregation was obvious between continents. Asian, South American and African centers were significantly more isolated than other centers. Isolated centers had fewer advanced phases (P <0.001), completed studies (P = 0.01) and industry-supported studies (P <0.001). Various thematic priorities among continents were identified: the cardiovascular, digestive and nervous system diseases were strongly studied by North America, Europe and Asia, respectively. The choice of cellular sources also affected the network topography; North America was primarily involved in bone-marrow-derived MSC research, whereas Europe and Asia dominated the use of adipose-derived MSCs. Industrial funding was the highest for North American centers (90.5%).

CONCLUSIONS

Strengthening of international standards and statements with institutional, federal and industrial partners is necessary. More connections would facilitate the transfer of knowledge, sharing of resources, mobility of researchers and advancement of trials. Developing partnerships between industry and academic centers seems beneficial to the advancement of trials across different phases and would facilitate the translation of research discoveries.

High-fat diet induces periodontitis in mice through lipopolysaccharides (LPS) receptor signaling: protective action of estrogens

Background

A fat-enriched diet favors the development of gram negative bacteria in the intestine which is linked to the occurrence of type 2 diabetes (T2D). Interestingly, some pathogenic gram negative bacteria are commonly associated with the development of periodontitis which, like T2D, is characterized by a chronic low-grade inflammation. Moreover, estrogens have been shown to regulate glucose homeostasis via an LPS receptor dependent immune-modulation. In this study, we evaluated whether diet-induced metabolic disease would favor the development of periodontitis in mice. In addition, the regulatory role of estrogens in this process was assessed.

Methods

Four-week-old C57BL6/J WT and CD14 (part of the TLR-4 machinery for LPS-recognition) knock-out female mice were ovariectomised and subcutaneously implanted with pellets releasing either placebo or 17β-estradiol (E2). Mice were then fed with either a normal chow or a high-fat diet for four weeks. The development of diabetes was monitored by an intraperitoneal glucose-tolerance test and plasma insulin concentration while periodontitis was assessed by identification of pathogens, quantification of periodontal soft tissue inflammation and alveolar bone loss.

Results

The fat-enriched diet increased the prevalence of periodontal pathogenic microbiota like Fusobacterium nucleatum and Prevotella intermedia, gingival inflammation and alveolar bone loss. E2 treatment prevented this effect and CD14 knock-out mice resisted high-fat diet-induced periodontal defects.

Conclusions/Significance

Our data show that mice fed with a diabetogenic diet developed defects and microflora of tooth supporting-tissues typically associated with periodontitis. Moreover, our results suggest a causal link between the activation of the LPS pathway on innate immunity by periodontal microbiota and HFD-induced periodontitis, a pathophysiological mechanism that could be targeted by estrogens.

Inherited platelet disorders and oral health

Platelets play a key role in thrombosis and hemostasis. Accumulation of platelets at the site of vascular injury is the first step in the formation of hemostatic plugs, which play a pivotal role in preventing blood loss after injury. Platelet adhesion at sites of injury results in spreading, secretion, recruitment of additional platelets, and formation of platelet aggregates. Inherited platelet disorders are rare causes of bleeding syndromes, ranging from mild bruising to severe hemorrhage. The defects can reflect deficiency or dysfunction of platelet surface glycoproteins, granule contents, cytoskeletal proteins, platelet pro-coagulant function, and signaling pathways. For instance, Bernard-Soulier syndrome and Glanzmann thrombasthenia are attributed to deficiencies of glycoprotein Ib/IX/V and GPIIb/IIIa, respectively, and are rare but severe platelet disorders. Inherited defects that impair platelet secretion and/or signal transduction are among the most common forms of mild platelet disorders and include gray platelet syndrome, Hermansky-Pudlak syndrome, and Chediak-Higashi syndrome. When necessary, desmopressin, antifibrinolytic agents, and transfusion of platelets remain the most common treatment of inherited platelet disorders. Alternative therapies such as recombinant activated factor VII are also available for a limited number of situations. In this review, we will discuss the management of patients with inherited platelet disorders in various clinical situations related to dental cares, including surgical intervention.

Radiographic and histologic evaluation of a sinus augmentation with composite bone graft: a clinical case report

BACKGROUND

The purpose of the present study was to present a clinical case report of a sinus augmentation with a composite bone graft and delayed implant placement with a 5-year follow-up.

METHODS

A 40-year-old female in good health required replacement of her partial upper removable denture with a prosthesis supported by implants. Due to insufficient bone, she had to undergo a sinus lift. A block graft of autogenous chin bone, along with anorganic bovine bone covered by collagen, was utilized. Eight months postsurgery, a biopsy core was taken and 3 endosseous implants were placed.

RESULTS

The sinus lift and implantation site are illustrated with clinical, radiological, and histological data. The discussion covers the problems raised by the choice of the surgical technique, grafting material, and immediate or delayed placement of the appropriate implant design.

CONCLUSION

Our results show that a maxillary sinus augmentation with a composite bone graft is a possible method for creating adequate bone volume before implantation.