BACKGROUND: An ability to predict the response to conventional non-surgical treatment of a periodontal site would be advantageous. However, so far the biomarkers or tests devised to achieve this have lacked sensitivity. The aim of this study was to assess the ability of a novel combination of biomarkers to predict the outcome of treatment of patients with chronic periodontitis. METHODS: GCF and subgingival plaque were collected from 77 patients at 3 representative sites, 1 healthy (≤ 3mm) and 2 diseased (≥ 6mm) at baseline and at 3 and 6-months post treatment. Patients received standard nonsurgical periodontal treatment at each time point as appropriate. The outcome measure was improvement in pocket depth of ≥2mm. Concentrations of active enzymes (MMP 8, elastase and sialidase) in GCF and subgingival plaque levels of Porphyromonas gingivalis, Tannerella forsythia and Fusobacterium nucleatum were analysed for prediction of the outcome measure. RESULTS: Using threshold values of MMP8 (94ng/μl), elastase (33ng/μl), sialidase (23ng/μl), 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 the above biomarkers at baseline provided accurate predictions of treatment outcome (≥ 92%). CONCLUSION: The 'finger print' of GCF enzymes and bacteria described here offers a way to predict the outcome of non-surgical periodontal treatment on a site-specific basis.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)