Periodontitis, especially in its chronic form, is one of the leading causes of tooth loss, significantly affecting the quality of life in the modern era of aging society. Recent studies have revealed a potential correlation between periodontitis and various systemic diseases, including cardiovascular diseases and Alzheimer’s dementia (AD). With the body of epidemiologic evidence that links these separate disease entities, several lines of hypotheses have been postulated to provide mechanistic understandings that mostly comprises abnormal regulation of immunologic and inflammatory signaling. In this review, we revisit the experimental findings that describe virulence factors derived from Porphyromonas gingivalis, including gingipains and lipopolysaccharides, as well as their roles in the pathophysiology of AD. In addition, we address potential immunologic challenges imposed by this bacterial pathogen contributing to progression of AD.

Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in Ada- Boost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.

The purpose of this study was to synthetically examine the relationship between systemic diseases and periodontal diseases. The data of the Fifth Korea National Health and Nutrition Examination Survey were used. SPSS 18.0 for Windows was applied for statistical analysis. The surveyed data were analyzed by using independent sample t-test for the difference between Body Mass Index and clinical test according to the existence of periodontal disease, and X2test for the relationship between periodontal disease and systemic disease. Multiple logistic regression analysis was used in order to figure out the influence upon the existence of periodontal disease prevalence among general characteristics and relevant factors to systemic disease. As results, the value of high density lipoprotein (HDL) and HBA1C were statistically significant, depending on the presence of periodontal disease. As for the relationship between periodontal disease and systemic disease, hypertension (odds ratio 1.362, p<.05), cardiovascular disease (odds ratio 2.118, p<.05), arthritis (odds ratio 1.289, p<.05) and cirrhosis (odds ratio 6.124, p<.05) were statistically significant. In conclusion, the majority of the systemic diseases such as cardiovascular system, diabetes, and liver diseases have been considered to have relationship with periodontal disease. To strengthen oral health education is needed to enhance systemic health as well as oral health. Moreover, basic biological research should be followed to support this surveyed study.

The purpose of this study was to synthetically examine the relationship between systemic diseases and periodontal diseases. The data obtained from the Fifth Korea National Health and Nutrition Examination Survey were used. SPSS 18.0 for Windows was applied for statistical analysis. The surveyed data were analyzed by using independent sample t-test for the difference between Body Mass Index and clinical test according to the existence of periodontal disease, and X2 test for the relationship between periodontal disease and systemic disease. Multiple logistic regression analysis was used in order to figure out the influence upon the periodontal disease prevalence among general characteristics and systemic diseases. As results, the values of high density lipoprotein (HDL) and HBA1C were statistically significant, depending on the presence of periodontal disease. As for the relationship between periodontal disease and systemic disease, hypertension (odds ratio 1.362, p<.05), cardiovascular disease (odds ratio 2.118, p<.05), arthritis (odds ratio 1.289, p<.05) and cirrhosis (odds ratio 6.124, p<.05) were statistically significant. According to Multiple logistic regression analysis, gender (odds ratio 1.24, p<.05), alcohol intake (odds ratio 1.25, p<.05), cardiovascular diseases (odds ratio 1.56, p<.05), and liver cirrhosis (odds ratio 1.17, p<.05) were related to the prevalence of periodontal diseases. In conclusion, the systemic diseases such as cardiovascular system, diabetes, and liver diseases revealed to have relationship with periodontal disease. To strengthen oral health education is needed to enhance systemic health as well as oral health. Moreover, basic biological research should be followed to support this surveyed study.

This study was conducted in order to examine the effects of alcohol-free cetylpyridinium chloride drinking water additive and oral gel on clinical parameters related to periodontal disease in beagle dogs. This study was conducted with healthy 15 beagle dogs. Following a professional teeth cleaning procedure, dogs were divided into three groups. Dogs in the control group received nothing, those in the drinking water additive (DWA) group received 800 ml water with 15 ml of alcohol-free cetylpyridinium chloride drinking water additive daily, and those in the Oral gel (OG) group were treated with oral gel containing alcohol-free cetylpyridinium chloride and 0.05% chlorhexidine gluconate daily. Clinical parameters, including plaque index (PI), calculus index (CI), and gingivitis index (GI) were evaluated at two and four weeks. Dogs in the DWA and OG groups had significantly less plaque than dogs in the control group at two and four weeks (P<0.01, P<0.05). And, at four weeks, CI was significantly lower in the OG group compared to the control group (P<0.05). On GI, similar scores were recorded for all groups during the experimental period. No significant difference was observed between the DWA group and the OG group. The effect of alcohol-free cetylpyridinium chloride drinking water additive was similar to the result for alcohol containing cetylpyridinium chloride mouthwash reported in a previous study. The effect in control of periodontal disease was better in the OG group because of additional chlorhexidine gluconate. However, use of drinking water additive will be more convenient for owners; thus, it will be more effective for achievement of long-term results.

Because of the irreversible nature of periodontal disease, early diagnosis is an important aspect of management of patients with periodontal disease. Human saliva is an attractive medium for disease diagnosis because its collection is noninvasive and simple. Analysis of saliva may be especially beneficial in the determination of current periodontal status and serve as means for the screening of periodontal disease. In the present study, we investigated potential biochemical markers in whole saliva samples for the screening of periodontal disease using proteomics technique. We enrolled five subjects each from four different groups on the basis of measures of periodontal health (healthy group, gingivitis group, chronic periodontitis group and aggressive periodontitis group). Eleven proteins in whole saliva samples were identified as differentially expressed proteins between the healthy and periodontal disease groups using 2-dimensional electrophoresis and matrix-assisted laser desorption/ionization time-of-flight / time-of-flight mass spectrophotometry (MADLI-TOF/TOF MS) approaches. Although the diagnostic value of oral fluid has been recognized for some time and potential biomarkers of periodontal disease have been identified in saliva, this, to our knowledge, is one of the first studies to examine large-scale proteomic profiling to identify the extent of periodontal destruction. Thus, this work provides an important framework for future efforts aimed at understanding salivary responses to periodontal destruction and predicting the future disease progression.

The purpose of this study is to investigate the relationship of periodontal disease to self-reported history of stroke in the elderly(60 years of age and older) with a special emphasis on elderly women. Data from the Third National Health and Nutrition Examination Survey(NHANES III), a large population-based cross-sectional survey of the United States, were utilized for this study. Since we have 1,563 edentulous subjects from a total of 5,123 subjects and periodontal disease is a major cause of tooth loss, it was necessary to account for this in our statistical analysis. Hence, we developed a new index called the Periodontal Health Status(PHS) index. In the logistic regression models with stratification by gender, males did not show statistically significant relationship between Periodontal Health Status(PHS) and stroke history. In contrast, females showed some marginal association between Periodontal Health Status(PHS) and stroke history. Further longitudinal intervention studies need to be conducted to determine the temporal relationship between periodontal disease and stroke

The purpose of this study was to evaluate the bioflavonoids naringin on dental caries and periondontal disease in the albinorat. Twenty-five-day-old-male rats were fed the experimental diets for 42 days in this work at the end of the 42-day experimental period. The tooth surfaces were examined under a dissecting microscope. The sulcular caries lesions were recorded : the first molars were more affected than the second and third molars. Alveolar bone loss was measured on the buccal and lingual aspects of each molar: three site measurements (mesialpoint midpoint and distalpoint) were taken on the first molars. The results showed that the bioflavonoids, naringin had beneficial effects in the prevention of periodontal disease.