<b>Salivary Biomarker Panel for Detecting Periodontitis</b>

Aim: This study tested the hypothesis that a salivary biomarker panel (i.e., consisting of 2 to 6 biomarker features) could yield diagnostic accuracy of 95% for periodontitis, allowing for earlier disease detection. Materials and Methods: Concentrations of 11 protein biomarkers by immunoassays and oral microbiome species by 16s rRNA sequencing were evaluated in saliva samples from 28 healthy adults and 28 Type 2 diabetic patients with periodontitis. Data were analyzed for discrimination of health or periodontitis using 5-fold cross-validation logistic regression, receiver operator characteristics (ROC), and odds ratios. Results: Bacteria showed better predictive value than individual salivary proteins. Two bacteria (Porphyromonas gingivalis and Mycoplasma faucium) yielded sensitivities >90% and Treponema socranskii demonstrated the top AUC (0.86). A combination of bacteria (Selenomonas sputigena, P. gingivalis, Prevotella nigrescens, Pr. dentalis) with salivary protein ratios (prostaglandin E2/tissue inhibitor of metalloproteinase-1 or macrophage inflammatory protein-1a/tissue inhibitor of metalloproteinase-1) produced robust accuracy (95%) and precision 96.5% for the detection of periodontitis. Conclusions: A salivary panel using bacteria and ratios of host-response biomarkers accurately discriminated oral health from periodontitis in Type 2 diabetics.