Diagnostic Accuracy of Microbiome-Derived Biomarkers in Periodontitis: Systematic Review and Meta-Analysis.

Aim: To evaluate the diagnostic accuracy of microbiome-derived biomarkers for periodontitis in oral fluids (saliva and subgingival samples).

Methods: This systematic review followed PRISMA guidelines. Electronic searches were performed across multiple databases from December 2022 to November 2024. Subgroup analyses, divided into saliva and subgingival samples, were performed using the Random Effects Model (REM), while individual biomarker sensitivity and specificity were evaluated through the Bivariate Random-Effects Model (BREM).

Results: Ten studies were included, stratified by sample type. In the saliva group, Porphyromonas gingivalis, Tannerella forsythia and Prevotella intermedia demonstrated the highest diagnostic accuracy, with sensitivities reaching 89.2%, 89.2% and 86.5%, and specificities of 94.6%, 86.5% and 83.8%, respectively, achieving AUC values above 0.80. Porphyromonas gingivalis was further analysed using BREM, with the Summary Receiver Operating Characteristic (SROC) curve indicating a combined sensitivity and specificity of 84.2% and 85.4%, with an AUC of 0.864. In the subgingival group, biomarkers such as endotoxin activity and combined bacterial biomarkers (5 bacterial species) displayed the highest diagnostic performance, with sensitivities of 90.6% and 85.1% and specificities of 87.9% and 100%, respectively, and AUC values of 0.93 and 0.88.

Conclusion: Microbiome-derived biomarkers show good clinical utility for improving diagnoses of periodontitis, offering high specificity and sensitivity. Future research should focus on standardising methodologies, increasing sample sizes, and including diverse populations to validate these findings, thereby improving diagnostic precision and facilitating the screening methods for the onset of periodontitis and dysbiotic activity.