RESUMO
BACKGROUND AND OBJECTIVE: Untargeted, high-throughput proteomics methodologies have great potential to aid in identifying biomarkers for the diagnosis of periodontal disease. The application of such methods to the discovery of candidate biomarkers for the resolution of periodontal inflammation after periodontal therapy has been investigated. MATERIAL AND METHODS: Gingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis at baseline and 1, 5, 9 and 13 weeks after completion of mechanical periodontal treatment. Clinical indices of periodontal disease, including probing depth, recession, clinical attachment level and bleeding on probing, were recorded at baseline and 13 weeks. Samples were analyzed using an online liquid chromatography-nanoelectrospray-hybrid ion trap-Orbitrap mass spectrometer. Spectra were processed with the PILOT_PROTEIN proteomics software suite. RESULTS: Clinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test, P < .05). From the substantial number of identified proteins, a small subset was extracted by filter methods that included temporal pattern matching, logistic function fitting and mixed-integer linear optimization. This subset includes azurocidin, lysozyme C and myosin-9 as candidate biomarkers prominent at baseline and alpha-smooth muscle actin as prominent 13 weeks after treatment. Cross-validation studies yielded average predictive accuracy and area under the curve of 0.900 and 0.930, respectively. CONCLUSION: High-throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.