Salivary biomarker combination prediction model for the diagnosis of periodontitis in a Taiwanese population

Abstract

Background/Purpose: This study aimed at screening the diagnostic potential of salivary biomarkers and pairing them to establish a prediction model with higher accuracy in diagnosing periodontitis in the Taiwanese population. Methods: Fifty-seven participants were divided into a non-periodontitis group and a periodontitis group. Salivary biomarkers CRP, IL-6, IL-8, IL-1β, IL-1ra, lactoferrin, MMP-8, MMP-9, PDGF-BB, TNF-α, and VEGF, were analyzed. The potential and reliability of the biomarkers for diagnosing periodontitis were analyzed dichotomously. The correlation of individual biomarkers with periodontitis was assessed using the Spearman rank correlation coefficient with logistic regression. The combinational prediction model was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Regarding individual biomarkers, IL-1β and MMP-9 levels were significantly higher, and TNF-α was significantly lower in the periodontitis group. IL-1β, MMP-8, and MMP-9 exhibited a greater odds ratio with statistical significance in the dichotomous table. The combination of three biomarkers yielded AUCs of 0.821–0.853, and the combination of IL-1β, IL-1ra, and MMP-9 exhibited the highest AUC (0.853), with high sensitivity (73.3%) and specificity (88.9%). Conclusion: Regarding individual biomarkers, IL-1β, MMP-8, and MMP-9 showed potential for identifying patients with periodontitis. The combination of IL-1β, IL-1ra, and MMP-9 might be feasible for developing a future point-of-care device for diagnosing periodontitis. Keywords: Diagnosis, Saliva, Observational study, Cytokines, Point-of-care systems, Regression analysis