Investigation of a Novel Predictive Biomarker Profile for the Outcome of Periodontal Treatment

An ability to predict the response to conventional non-surgical treatment of a periodontal site would be advantageous. However, biomarkers or tests devised to achieve this have lacked sensitivity. The aim of this study is to assess the ability of a novel combination of biomarkers to predict treatment outcome of patients with chronic periodontitis.Gingival crevicular fluid (GCF) and subgingival plaque were collected from 77 patients at three representative sites, one healthy (probing depth [PD] ≤3 mm) and two diseased (PD ≥6 mm), at baseline and at 3 and 6 months after treatment. Patients received standard non-surgical periodontal treatment at each time point as appropriate. The outcome measure was improvement in probing depth of ≥2 mm. Concentrations of active enzymes (matrix metalloproteinase [MMP]-8, elastase, and sialidase) in GCF and subgingival plaque levels of Porphyromonas gingivalis, Tannerella forsythia, and Fusobacterium nucleatum were analyzed for prediction of the outcome measure.Using threshold values of MMP-8 (94 ng/μL), elastase (33 ng/μL), sialidase (23 ng/μL), and levels of P. gingivalis (0.23%) and T. forsythia (0.35%), receiver operating characteristic curves analysis demonstrated that these biomarkers at baseline could differentiate healthy from diseased sites (sensitivity and specificity ≥77%). Furthermore, logistic regression showed that this combination of these biomarkers at baseline provided accurate predictions of treatment outcome (≥92%).The "fingerprint" of GCF enzymes and bacteria described here offers a way to predict the outcome of non-surgical periodontal treatment on a site-specific basis.