Machine learning reveals microbiome differences by periodontitis severity.

Abstract

Periodontitis is a chronic inflammatory disease driven by microbial dysbiosis, yet the microbial signatures associated with severity remain incompletely understood. This study investigated changes in subgingival microbial composition across clinically defined severity groups and evaluated the performance of microbiome-based machine-learning models for classifying periodontitis severity. Subgingival plaque samples from 84 patients were analyzed using 16S rRNA gene sequencing. Microbial diversity showed a modest decreasing trend with increasing severity, although differences were not statistically significant. Five machine learning models were applied to classify periodontitis. Random Forest and XGBoost achieved AUC values of 0.98, indicating statistically significant classification performance (p < 0.05) after feature selection. Validation using three external cohorts demonstrated substantial performance variability across populations, reflecting differences in oral microbiome composition, sample type, and periodontal status definitions. Feature importance analysis identified Fusobacterium, Campylobacter, Stomatobaculum, Leptotrichia and Segatella as key contributors to periodontitis severity classification, consistent with their established roles in periodontal dysbiosis. These findings highlight the potential of microbiome-based models for classifying periodontitis severity while underscoring the need to incorporate diverse populations and robust feature-selection strategies to enhance generalizability.