Reliability of oral biomarkers in the prediction and diagnosis of periodontal disease.

Abstract

Introduction: Research has been intensive on the possibility of using the ideal oral fluids in the prediction and diagnosis of periodontal disease.

Objectives: This study aimed to detect the most accurate biomarkers that can be used to predict and diagnose periodontal disease.

Methods: Gingival crevicular fluid (GCF) and subgingival plaque samples were collected. The levels of biomarkers ( Human Cathelicidine LL-37 (LL-37), Matrix Metalloproteinase -8 (MMP-8), Matrix Metalloproteinase -9 (MMP-9), interleukin (IL)-6, interleukin (IL)-1β, tumour necrosis factor-α (TNF-α), osteoprotegerin (OPG), OC and PGE2) were quantified by Enzyme-Linked Immunosorbent Assay (ELISA), while the subgingival periodontal pathogens (T. forsythia, T. denticola, P. gingivalis and A.a) were identified using Real-time Polymerase Chain Reaction (RT-PCR). Cumulative Risk Score (CRS), a new statistical approach, was used to evaluate the accuracy of applying oral biomarkers in the diagnosis of periodontal disease based on three selected biomarkers.

Results: The results of this study showed that MMP-8, IL-1β, PGE2 and IL-6 in GCF are associated with increased count of periodontal pathogens and different clinical periodontal parameters when compared to the other biomarkers. This association increased significantly by using CRS, which had 2 to 3 times higher odds ratios than the use of any selected biomarkers alone.

Conclusions: In conclusion, this study showed that the levels of biomarkers in the GCF, mainly MMP-8, IL-1β, PGE2 and IL-6, if used separately, could be useful in the prediction and diagnosis of periodontal disease to a certain degree of accuracy. The study also showed that the combination of three GCF biomarkers in a single biomarker package to establish the CRS index is more precise in the prediction and diagnosis of periodontal disease than the use of other biomarkers.