Grading system for periodontitis by analyzing levels of periodontal pathogens in saliva

Periodontitis is an infectious disease that is associated with microorganisms that colonize the tooth surface. Clinically, periodontal condition stability reflects dynamic equilibrium between bacterial challenge and host response. Therefore, periodontal pathogen assessment can assist in the early detection of periodontitis. Here we developed a grading system called the periodontal pathogen index (PPI) by analyzing the copy numbers of multiple pathogens both in healthy and chronic periodontitis patients. We collected 170 mouthwash samples (64 periodontally healthy controls and 106 chronic periodontitis patients) and analyzed the salivary 16S rRNA levels of nine pathogens using multiplex, quantitative real-time polymerase chain reaction. Except for Aggregatibacter actinomycetemcomitans, copy numbers of all pathogens were significantly higher in chronic periodontitis patients. We classified the samples based on optimal cut-off values with maximum sensitivity and specificity from receiver operating characteristic curve analyses (AUC = 0.91, 95% CI: 0.87-0.96) into four categories of PPI: Healthy (1-40), Moderate (41-60), At Risk (61-80), and Severe (81-100). PPI scores were significantly higher in all chronic periodontitis patients than in the controls (odds ratio: 31.7, 95% CI: 13.41-61.61) and were associated with age, scaling as well as clinical characteristics including clinical attachment level and plaque index. Our PPI grading system can be clinically useful for the early assessment of pathogenic bacterial burden and follow-up monitoring after periodontitis treatment

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified "healthy" and "moderate or severe" periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group

Detection of association between periodontitis and polymorphisms of IL-1 beta+3954 and TNF-alpha-863 in the Korean population after controlling for confounding risk factors

Background and Objective Interleukin (IL)-1 and tumor necrosis factor (TNF)-alpha are inflammatory cytokines that play an important role in periodontitis, and their genetic variations have been suggested to be associated with increased risk of periodontitis. Focusing on three single nucleotide polymorphisms (SNPs) of IL-1 alpha + 4845, IL-1 beta + 3954, and TNF-alpha -863, we aimed to investigate the relationship between periodontitis risk and the polymorphisms of IL-1 alpha/beta and TNF-alpha in Koreans. Material and Methods Mouthwash samples from 548 subjects (135 controls without periodontitis, 387 generalized chronic periodontitis patients, and 26 generalized aggressive periodontitis patients) were collected for isolation of genomic DNA. Genotyping of selected SNPs was performed using real-time PCR. Univariable associations between the polymorphisms and periodontitis were assessed by chi-squared test or Fisher's exact test. To evaluate the association after controlling for confounding effects of various risk factors, we stratified the subjects according to the presence or absence of self-reported diseases and employed multiple logistic regression model to adjust for age, smoking status, and oral hygiene indices and behaviors. Results Significant association of IL-1 beta + 3954 and TNF-alpha -863 polymorphisms with periodontitis was observed after adjusting for the confounding risk factors, but not in univariable association analysis. The significant association between genotype CT of IL-1 beta + 3954 and increased risk of advanced periodontitis was consistently detected regardless of the status of self-reported diseases. In the polymorphism of TNF-alpha -863, the genotype with minor allele (CA + AA) was significantly associated with periodontitis susceptibility, which was observed only in the subjects with self-reported diseases. Conclusion The results suggest that genetic variations of IL-1 beta + 3954 and TNF-alpha -863 are associated with increased risk of periodontitis in Koreans. In addition, our findings underscore the importance of controlling for confounding risk factors to detect significant association between genetic factors and risk of periodontitis. A further well-designed large-scale study is needed to warrant our results

Additional file 1 of The soluble VCAM-1 level is a potential biomarker predicting severe acute graft versus host disease after allogeneic hematopoietic cell transplantation

Additional file 1