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.

Periodontal disease (PD) and atherosclerosis are both polymicrobial and multifactorial and although observational studies supported the association, the causative relationship between these two diseases is not yet established. Polymicrobial infection-induced periodontal disease is postulated to accelerate atherosclerotic plaque growth by enhancing atherosclerotic risk factors of orally infected Apolipoprotein E deficient (ApoE^null) mice. At 16 weeks of infection, samples of blood, mandible, maxilla, aorta, heart, spleen, and liver were collected, analyzed for bacterial genomic DNA, immune response, inflammation, alveolar bone loss, serum inflammatory marker, atherosclerosis risk factors, and aortic atherosclerosis. PCR analysis of polymicrobial-infected (Porphyromonas gingivalis [P. gingivalis], Treponema denticola [T. denticola], and Tannerella forsythia [T. forsythia]) mice resulted in detection of bacterial genomic DNA in oral plaque samples indicating colonization of the oral cavity by all three species. Fluorescent in situ hybridization detected P. gingivalis and T. denticola within gingival tissues of infected mice and morphometric analysis showed an increase in palatal alveolar bone loss (p<0.0001) and intrabony defects suggesting development of periodontal disease in this model. Polymicrobial-infected mice also showed an increase in aortic plaque area (p<0.05) with macrophage accumulation, enhanced serum amyloid A, and increased serum cholesterol and triglycerides. A systemic infection was indicated by the detection of bacterial genomic DNA in the aorta and liver of infected mice and elevated levels of bacterial specific IgG antibodies (p<0.0001). This study was a unique effort to understand the effects of a polymicrobial infection with P. gingivalis, T. denticola and T. forsythia on periodontal disease and associated atherosclerosis in ApoE^null mice.

Periodontitis is a chronic and multifactorial inflammatory disease that can lead to tooth loss. At present, the diagnosis for periodontitis is primarily based on clinical examination and radiographic parameters. Detecting the periodontal pathogens at the subgingival plaque requires skilled professionals to collect samples. Periodontal pathogens are also detected on various mucous membranes in patients with periodontitis. In this study, we characterized the oral microbiome profiles from buccal mucosa and supragingival space in a total of 272 healthy subjects as a control group, and periodontitis patients as a disease group. We identified 13 phyla, 193 genera, and 527 species and determined periodontitis-associated taxa. Porphyromonas gingivalis, Tannerella forsythia, Treponema denticolar, Filifactor alocis, Porphyromonas endodontalis, Fretibacterium fastiosum and Peptostreptococcus species were significantly increased in both the buccal mucosa and the supragingival space in periodontitis patients. The identified eight periodontitis-associated bacterial species were clinically validated in an independent cohort. We generated the prediction model based on the oral microbiome profiles using five machine learning algorithms, and validated its capability in predicting the status of patients with periodontitis. The results showed that the oral microbiome profiles from buccal mucosa and supragingival space can represent the microbial composition of subgingival plaque and further be utilized to identify potential microbial biomarkers for the diagnosis of periodontitis. Besides, bacterial community interaction network analysis found distinct patterns associated with dysbiosis in periodontitis. In summary, we have identified oral bacterial species from buccal and supragingival sites which can predict subgingival bacterial composition and can be used for early diagnosis of periodontitis. Therefore, our study provides an important basis for developing easy and noninvasive methods to diagnose and monitor periodontitis.

Kim HJ, et al. J Periodontal Implant Sci. 2022 Aug;52:e27. https://doi.org/10.5051/jpis.2202080104

Aim: Periodontitis is caused by dysbiosis of oral microbes and is associated with increased cognitive decline in Alzheimer's disease (AD), and recently, a potential functional link was proposed between oral microbes and AD. We compared the oral microbiomes of patients with or without AD to evaluate the association between oral microbes and AD in periodontitis.Materials and Methods: Periodontitis patients with AD (n = 15) and cognitively unimpaired periodontitis patients (CU) (n = 14) were recruited for this study. Each patient underwent an oral examination and neuropsychological evaluation. Buccal, supragingival and subgingival plaque samples were collected, and microbiomes were analysed by next‐generation sequencing. Alpha diversity, beta diversity, linear discriminant analysis effect size, analysis of variance‐like differential expression analysis and network analysis were used to compare group oral microbiomes.Results: All 29 participants had moderate to severe periodontitis. Group buccal and supragingival samples were indistinguishable, but subgingival samples demonstrated significant alpha and beta diversity differences. Differential analysis showed subgingival samples of the AD group had higher prevalence of Atopobium rimae, Dialister pneumosintes, Olsenella sp. HMT 807, Saccharibacteria (TM7) sp. HMT 348 and several species of Prevotella than the CU group. Furthermore, subgingival microbiome network analysis revealed a distinct, closely connected network in the AD group comprised of various Prevotella spp. and several anaerobic bacteria.Conclusions: A unique microbial composition was discovered in the subgingival region in the AD group. Specifically, potential periodontal pathogens were found to be more prevalent in the subgingival plaque samples of the AD group. These bacteria may possess a potential to worsen periodontitis and other systemic diseases. We recommend that AD patients receive regular, careful dental check‐ups to ensure proper oral hygiene management.

Ahn DH, et al. J Periodontal Implant Sci. 2019 Dec;49(6):397-405. https://doi.org/10.5051/jpis.2019.49.6.397