Aim: To determine whether combinations of enzymes in gingival crevicular fluid (GCF) can act as improved biomarkers compared with single enzymes for predicting the outcome of treatment and also for diagnosing the clinical status of sites.
Methods: Thirty subjects with chronic periodontitis were recruited to a 12-month longitudinal pilot study. GCF samples from three representative sites: healthy (≤3 mm), deep non-bleeding (NB) (≥6 mm) and deep bleeding (DB) (≥6 mm) sites and clinical data were collected at baseline, 3 months, 6 months and 12 months following periodontal treatment. Active enzyme levels (MMP-8, cathepsin G, elastase, trypsin-like activity and sialidase) in GCF samples were assessed. The enzyme profiles and clinical data of each site were analysed for correlation and logistic regression was performed to find the predictive value of the active enzyme levels regarding the outcome of treatment.
Results: Twenty-two individuals completed the study. All active enzyme levels were significantly higher in diseased sites than healthy sites. Logistic regression showed that the combination of MMP8, elastase and sialidase provided accurate predictions of treatment outcome (88% for NB and 86% for DB), which was significantly better than each enzyme alone (61%).
Conclusion: This pilot has suggested that combined active enzyme profiling could provide significant prediction of outcome of treatment.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)