The estimation of oral microbiome (OM) taxonomic composition in periodontally healthy individuals can often be biased because the clinically periodontally healthy subjects for evaluation can already experience dysbiosis. Usually, they are included just based on the absence of clinical signs of periodontitis. Additionally, the age of subjects is used to be higher to correspond well with tested groups of patients with chronic periodontitis, a disorder typically associated with aging. However, the dysbiosis of the OM precedes the clinical signs of the disease by many months or even years. The absence of periodontal pockets thus does not necessarily mean also good periodontal health and the obtained image of “healthy OM” can be distorted.To overcome this bias, we taxonomically characterized the OM in almost a hundred young students of dentistry with precise oral hygiene and no signs of periodontal disease. We compared the results with the OM composition of older periodontally healthy individuals and also a group of patients with severe periodontitis (aggressive periodontitis according to former classification system). The clustering analysis revealed not only two compact clearly separated clusters corresponding to each state of health, but also a group of samples forming an overlap between both well-pronounced states. Additionally, in the cluster of periodontally healthy samples, few outliers with atypical OM and two major stomatotypes could be distinguished, differing in the prevalence and relative abundance of two main bacterial genera: Streptococcus and Veillonella. We hypothesize that the two stomatotypes could represent the microbial succession from periodontal health to starting dysbiosis. The old and young periodontally healthy subjects do not cluster separately but a trend of the OM in older subjects to periodontitis is visible. Several bacterial genera were identified to be typically more abundant in older periodontally healthy subjects.

The dysbiosis of oral microbiome (OM) precedes the clinical signs of periodontal disease. Its simple measure thus could indicate individuals at risk of periodontitis development; however, such a tool is still missing. Up to now, numerous microbial taxa were associated with periodontal health or periodontitis. The outputs of most studies could, nevertheless, be slightly biased from following two reasons: First, the healthy group is often characterized only by the absence of the disease, but the individuals could already suffer from dysbiosis without any visible signs. Second, the healthy/diseased OM characteristics are frequently determined based on average data obtained for whole groups of periodontally healthy persons versus patients. Especially in smaller sets of tested individuals the typical individual variability can thus complicate the unambiguous assignment of oral taxa to respective state of health. In this work the taxonomic composition of OM was evaluated for 20 periodontally healthy individuals and 15 patients with chronic periodontitis. The narrowed selection set of the most diseased patients (confirmed by clinical parameters) and the most distant group of healthy individuals with the lowest probability of dysbiosis was determined by clustering analysis and used for identification of marker taxa. Based on their representation in each individual oral cavity we proposed the numeric index of periodontal health called R/G value. Its diagnostic potential was further confirmed using independent set of 20 periodontally healthy individuals and 20 patients with periodontitis with 95 percent of samples assigned correctly. We also assessed the individual temporal OM dynamics in periodontal health and we compared it to periodontitis. We revealed that the taxonomic composition of the system changes dynamically but generally it ranges within values typical for periodontal health or transient state, but far from values typical for periodontitis. R/G value tool, formulated from individually evaluated data, allowed us to arrange individual OMs into a continuous series, instead of two distinct groups, thus mimicking the gradual transformation of a virtual person from periodontal health to disease. The application of R/G value index thus represents a very promising diagnostic tool for early prediction of persons at risk of developing periodontal disease.