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

<b>Aim: </b>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.<b>Materials and Methods:</b> 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.<b>Results:</b> Bacteria showed better predictive value than individual salivary proteins. Two bacteria (<i>Porphyromonas gingivalis</i> and <i>Mycoplasma faucium</i>) yielded sensitivities &gt;90% and <i>Treponema socranskii</i> demonstrated the top AUC (0.86). A combination of bacteria (<i>Selenomonas sputigena</i>, <i>P. gingivalis</i>, <i>Prevotella nigrescens</i>, <i>Pr. dentalis</i>) 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.<b>Conclusions:</b> A salivary panel using bacteria and ratios of host-response biomarkers accurately discriminated oral health from periodontitis in Type 2 diabetics.