Clinical Profile, Risk Factors, and Microbial Dysbiosis in Periodontitis: Findings from an Adult Cohort and Microbiome-Based Predictive Models

Background/Objective: Periodontitis is a chronic inflammatory disease influenced by clinical, behavioral, and microbial determinants. However, the contribution of these factors to disease remains a topic of debate, particularly in untreated adult populations. This study aims to characterize the clinical, epidemiological, and microbial features associated with periodontitis in an adult cohort and to compare the discriminatory performance of microbiome-based predictive models with conventional clinical–behavioral models. Methods: A cross-sectional study was conducted in 943 adults. Periodontal status was determined by experienced clinicians according to the 2017 World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. Clinical variables, behavioral factors (smoking, bruxism, diet), intraoral conditions (caries and malocclusion), and systemic comorbidities were recorded. The oral microbiome was analyzed using targeted PCR for selected periodontal bacteria. Predictive models were constructed using logistic regression and least absolute shrinkage and selection operator (LASSO) variable selection. Results: Periodontitis was diagnosed in 47.2% of participants. Age, smoking, and bruxism were significantly associated with periodontitis. Malocclusion was the only significant intraoral predictor (OR = 2.00). Individuals with periodontitis exhibited increased levels of periodontopathogens, including P. gingivalis, T. forsythia, and E. corrodens, along with reduced levels of S. mutans. Microbiome-based models demonstrated superior discriminatory performance (AUC = 0.76, LASSO). E. corrodens and C. sputigena were independently associated with greater probing depth (p < 0.001). Conclusions: Microbiome-based predictive models, particularly at the species level, showed better discriminatory performance than conventional clinical–behavioral models. These findings support the potential utility of salivary microbial signatures as adjunctive, non-invasive biomarkers of periodontal inflammatory status.