IADR Abstract Archives
Investigation of Prognostic Biomarkers of Periodontal Treatment
Objectives: The aim of this study was to investigate the use of the putative salivary and serum biomarkers MMP-8, MMP-9 and S100-A8 to predict outcomes of periodontal treatment.
Methods: 36 patients with moderate or severe chronic periodontitis received a standardised course of nonsurgical periodontal treatment. Patient treatment responses were assessed according to the percentage of deep sites preoperatively that showed a reduction following treatment. In addition subjects were dichotomised as “responders” and “non-responders” according to the percentage of non-responding deep sites (≥50%) seen in each patient. Unstimulated saliva and serum samples were collected pre- and post-operatively. Concentrations of MMP-8, MMP-9 and S100-A8 in all samples were determined by ELISA. Predictive value was assessed by ROC and correlations calculated with Pearsons Correlation test.
Results: Saliva concentrations of MMP-8 (pre-op mean:572.4ng/ml, SD:418.2, post –op mean:431.5ng/ml, SD:465.1, P<0.01) and MMP-9 (pre-op mean:485.3ng/ml, SD:305.2, , post –op mean:369.9ng/ml, S.D:239.3, P<0.05) significantly reduced following treatment. There was no significant change in saliva concentrations of S100-A8 or with any of the serum analytes.
Using ROC analysis, Pre-op salivary MMP-9 showed a modest predictive value for treatment outcome (Area under the Curve 0.677) .
Salivary MMP-8 and -9 concentrations from the same patient were significantly correlated in both pre-op ( R2 = 0.477, P<0.01) and post-op ( R2 = 0.663, P<0.01) samples. S100A8 concentrations did not correlate with MMP concentrations. In addition there were no significant correlations seen between salivary and serum samples of any analyte.
In non-responders only the percentage change of serum MMP-8 (R2=-0.588) & MMP-9 (R2= -0.595)was significantly inversely correlated (P<0.05) with the percentage change of deep sites.
Conclusions: In conclusion, these putative biomarkers showed little evidence of value as prognostic biomarkers. The wide variations in individual responses emphasise both the large variability and complexity of regulation of these factors in disease.
Methods: 36 patients with moderate or severe chronic periodontitis received a standardised course of nonsurgical periodontal treatment. Patient treatment responses were assessed according to the percentage of deep sites preoperatively that showed a reduction following treatment. In addition subjects were dichotomised as “responders” and “non-responders” according to the percentage of non-responding deep sites (≥50%) seen in each patient. Unstimulated saliva and serum samples were collected pre- and post-operatively. Concentrations of MMP-8, MMP-9 and S100-A8 in all samples were determined by ELISA. Predictive value was assessed by ROC and correlations calculated with Pearsons Correlation test.
Results: Saliva concentrations of MMP-8 (pre-op mean:572.4ng/ml, SD:418.2, post –op mean:431.5ng/ml, SD:465.1, P<0.01) and MMP-9 (pre-op mean:485.3ng/ml, SD:305.2, , post –op mean:369.9ng/ml, S.D:239.3, P<0.05) significantly reduced following treatment. There was no significant change in saliva concentrations of S100-A8 or with any of the serum analytes.
Using ROC analysis, Pre-op salivary MMP-9 showed a modest predictive value for treatment outcome (Area under the Curve 0.677) .
Salivary MMP-8 and -9 concentrations from the same patient were significantly correlated in both pre-op ( R2 = 0.477, P<0.01) and post-op ( R2 = 0.663, P<0.01) samples. S100A8 concentrations did not correlate with MMP concentrations. In addition there were no significant correlations seen between salivary and serum samples of any analyte.
In non-responders only the percentage change of serum MMP-8 (R2=-0.588) & MMP-9 (R2= -0.595)was significantly inversely correlated (P<0.05) with the percentage change of deep sites.
Conclusions: In conclusion, these putative biomarkers showed little evidence of value as prognostic biomarkers. The wide variations in individual responses emphasise both the large variability and complexity of regulation of these factors in disease.