Janet S Kinney1, Thiago Morelli1,2, Min Oh1, Thomas M Braun3, Christoph A Ramseier4, Jim V Sugai1, William V Giannobile1,5. 1. Department of Periodontics and Oral Medicine & Michigan Center for Oral Health Research, University of Michigan School of Dentistry, Ann Arbor, MI, USA. 2. Currently, Department of Periodontology, School of Dentistry, University of North Carolina, Chapel Hill, NC, USA. 3. Biostatistics Department, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA. 4. Currently, Department of Periodontology, School of Dental Medicine, University of Berne, Freiburgstrasse 7, CH-3010 Bern, Switzerland. 5. Department of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, USA.
Abstract
AIM: Assess the ability of a panel of gingival crevicular fluid (GCF) biomarkers as predictors of periodontal disease progression (PDP). MATERIALS AND METHODS: In this study, 100 individuals participated in a 12-month longitudinal investigation and were categorized into four groups according to their periodontal status. GCF, clinical parameters and saliva were collected bi-monthly. Subgingival plaque and serum were collected bi-annually. For 6 months, no periodontal treatment was provided. At 6 months, patients received periodontal therapy and continued participation from 6 to 12 months. GCF samples were analysed by ELISA for MMP-8, MMP-9, Osteoprotegerin, C-reactive Protein and IL-1β. Differences in median levels of GCF biomarkers were compared between stable and progressing participants using Wilcoxon Rank Sum test (p = 0.05). Clustering algorithm was used to evaluate the ability of oral biomarkers to classify patients as either stable or progressing. RESULTS: Eighty-three individuals completed the 6-month monitoring phase. With the exception of GCF C-reactive protein, all biomarkers were significantly higher in the PDP group compared to stable patients. Clustering analysis showed highest sensitivity levels when biofilm pathogens and GCF biomarkers were combined with clinical measures, 74% (95% CI = 61, 86). CONCLUSIONS: Signature of GCF fluid-derived biomarkers combined with pathogens and clinical measures provides a sensitive measure for discrimination of PDP (ClinicalTrials.gov NCT00277745).
AIM: Assess the ability of a panel of gingival crevicular fluid (GCF) biomarkers as predictors of periodontal disease progression (PDP). MATERIALS AND METHODS: In this study, 100 individuals participated in a 12-month longitudinal investigation and were categorized into four groups according to their periodontal status. GCF, clinical parameters and saliva were collected bi-monthly. Subgingival plaque and serum were collected bi-annually. For 6 months, no periodontal treatment was provided. At 6 months, patients received periodontal therapy and continued participation from 6 to 12 months. GCF samples were analysed by ELISA for MMP-8, MMP-9, Osteoprotegerin, C-reactive Protein and IL-1β. Differences in median levels of GCF biomarkers were compared between stable and progressing participants using Wilcoxon Rank Sum test (p = 0.05). Clustering algorithm was used to evaluate the ability of oral biomarkers to classify patients as either stable or progressing. RESULTS: Eighty-three individuals completed the 6-month monitoring phase. With the exception of GCF C-reactive protein, all biomarkers were significantly higher in the PDP group compared to stable patients. Clustering analysis showed highest sensitivity levels when biofilm pathogens and GCF biomarkers were combined with clinical measures, 74% (95% CI = 61, 86). CONCLUSIONS: Signature of GCF fluid-derived biomarkers combined with pathogens and clinical measures provides a sensitive measure for discrimination of PDP (ClinicalTrials.gov NCT00277745).
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