Association between periodontitis and heart failure in the general population

Oral health as a modifiable risk factor for cardiovascular diseases

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality worldwide with a high socioeconomic burden. Increasing evidence supports a convincing connection with increased cardiovascular risk of periodontal diseases (PD), a group of widespread, debilitating, and costly dysbiotic relapsing-remitting inflammatory diseases of the tissues supporting the teeth. Herein, we ensembled the best available evidence on the connection between CVDs and PD to review the recently emerging concept of the latter as a non-traditional risk factor for CVDs. We focused on oral dysbiosis, inflammation-associated molecular and cellular mechanisms, and epigenetic changes as potential causative links between PD and CVDs. The available evidence on the effects of periodontal treatment on cardiovascular risk factors and diseases was also described.

A novel nomogram for predicting risk of hypertension in US adults with periodontitis: National Health and Nutrition Examination Survey (NHANES) 2009-2014

The goal of our study was to create a nomogram to predict the risk of developing hypertension in patients with periodontitis. Our study used data from a total of 3196 subjects from the National Health and Nutrition Examination Survey 2009 to 2014 who had ever been diagnosed with periodontitis. The data set was randomly divided into a training set and a validation set according to a 7:3 ratio. The data from the training set was utilized to build the prediction model, while the validation set were used to validate the model. To identify the risk variables, stepwise regression was used to perform successive univariate and multivariate logistic regression analysis. The predictive ability of the nomogram model was evaluated using receiver operating characteristic curve. Calibration plots were used to assess the consistency of the prediction model. The clinical value of the model was evaluated using decision curve analysis and clinical impact curve. A nomogram for the risk of hypertension in subjects with periodontitis was constructed in accordance with the 8 predictors identified in this study. The areas under the receiver operating characteristic curve values for the training set and validation set were 0.922 (95% confidence interval: 0.911-0.933) and 0.918 (95% confidence interval: 0.900-0.935), respectively, indicating excellent discrimination. The decision curve analysis and clinical impact curve suggested that the model has significant clinical applications, and the calibration plots of the training set and validation set demonstrated good consistency. The nomogram can effectively predict the risk of hypertension in patients with periodontitis and help clinicians make better clinical decisions.

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.

A systematic comparison of machine learning algorithms to develop and validate prediction model to predict heart failure risk in middle-aged and elderly patients with periodontitis (NHANES 2009 to 2014)

Periodontitis is increasingly associated with heart failure, and the goal of this study was to develop and validate a prediction model based on machine learning algorithms for the risk of heart failure in middle-aged and elderly participants with periodontitis. We analyzed data from a total of 2876 participants with a history of periodontitis from the National Health and Nutrition Examination Survey (NHANES) 2009 to 2014, with a training set of 1980 subjects with periodontitis from the NHANES 2009 to 2012 and an external validation set of 896 subjects from the NHANES 2013 to 2014. The independent risk factors for heart failure were identified using univariate and multivariate logistic regression analysis. Machine learning algorithms such as logistic regression, k-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron were used on the training set to construct the models. The performance of the machine learning models was evaluated using 10-fold cross-validation on the training set and receiver operating characteristic curve (ROC) analysis in the validation set. Based on the results of univariate logistic regression and multivariate logistic regression, it was found that age, race, myocardial infarction, and diabetes mellitus status were independent predictors of the risk of heart failure in participants with periodontitis. Six machine learning models, including logistic regression, K-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron, were built on the training set, respectively. The area under the ROC for the 6 models was obtained using 10-fold cross-validation with values of 0 848, 0.936, 0.859, 0.889, 0.927, and 0.666, respectively. The areas under the ROC on the external validation set were 0.854, 0.949, 0.647, 0.933, 0.855, and 0.74, respectively. K-nearest neighbor model got the best prediction performance across all models. Out of 6 machine learning models, the K-nearest neighbor algorithm model performed the best. The prediction model offers early, individualized diagnosis and treatment plans and assists in identifying the risk of heart failure occurrence in middle-aged and elderly patients with periodontitis.