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

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.