Diagnostic accuracy for apical and chronic periodontitis biomarkers in gingival crevicular fluid: an exploratory study.

AIM: The aim of this study was to assess the levels and diagnostic accuracy of a set of potential biomarkers of periodontal tissue metabolism in gingival crevicular fluid (GCF) from patients with chronic periodontitis (CP) and asymptomatic apical periodontitis ( AAP). MATERIALS AND METHODS: Thirty one GCF samples from 11 CP patients, 44 GCF samples from 38 AAP patients and 31 GCF samples from 13 healthy volunteers were obtained (N = 106). Matrix metalloproteinases (MMPs) -2 and -9 were determined by zymography; levels of MMP-8 by ELISA and IFMA and MPO by ELISA. IL-1, IL-6, TNFα, DKK-1, Osteonectin, Periostin, TRAP-5 and OPG were determined by a multiplex quantitative panel. Statistical analysis was performed using linear mixed-effects models. RESULTS: The MMP-9 and MMP-8 were higher in CP, followed by AAP, versus healthy individuals (p < 0.05). ProMMP-2, MPO, IL-1, IL-6, PTN, TRAP-5 and OPG were significantly higher in CP when compared with AAP and healthy patients (p < 0.05). The highest diagnostic accuracies were observed for ProMMP-2, ProMMP-9, MMP-8 and TRAP-5 (AUC > 0.97) in CP, and for the active form of MMP-9 and MMP-8 (AUC > 0.90) in AAP. CONCLUSION: Gingival crevicular fluid composition is modified by CP and AAP. MMP-9 and MMP-8 show diagnostic potential for CP and AAP, whereas MMP-2 and TRAP-5 are useful only for CP.

Hierarchical modeling of agreement.

Kappa-like agreement indexes are often used to assess the agreement among examiners on a categorical scale. They have the particularity of correcting the level of agreement for the effect of chance. In the present paper, we first define two agreement indexes belonging to this family in a hierarchical context. In particular, we consider the cases of a random and fixed set of examiners. Then, we develop a method to evaluate the influence of factors on these indexes. Agreement indexes are directly related to a set of covariates through a hierarchical model. We obtain the posterior distribution of the model parameters in a Bayesian framework. We apply the proposed approach on dental data and compare it with the generalized estimating equations approach.