OBJECTIVES: The aim of the present investigation was to determine the profile of peri-implant crevicular fluid (PICF) biomarkers combined with microbial profiles from implants with healthy peri-implant tissues and peri-implantitis to assess real-time disease activity. MATERIAL AND METHODS: Sixty-eight patients were included in this cross-sectional study. They were divided into two groups: 34 patients with at least one healthy implant (control) and 34 with at least one peri-implantitis affected implant (test). Total DNA content and qPCR analysis for periodontal bacteria obtained from subgingival plaque samples (Aggregatibacter actinomycetemcomitans, Prevotella intermedia, Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola) and a PICF analysis for IL-1β, VEGF, MMP-8, TIMP-2, and OPG were performed. The individual and combined diagnostic ability of each biomarker for peri-implantitis and target bacterial species were analyzed. RESULTS: The mean concentration of IL-1β (44.6 vs. 135.8 pg/ml; P < 0.001), TIMP-2 (5488.3 vs. 9771.8 pg/ml; P = 0.001), VEGF (59.1 vs. 129.0 pg/ml; P = 0.012), and OPG (66.5 vs. 111.7 pg/ml; P = 0.050) was increased in the peri-implantitis patients. The mean expression of MMP-8 (6029.2 vs. 5943.1 pg/ml; P = 0.454) and did not reveal a meaningful difference among groups. Total bacterial DNA of selected microorganisms was associated with a threefold or greater increase in peri-implantitis although no statistical significant difference. The ability to diagnose diseased sites was enhanced by T. denticola combined with IL-1β, VEGF, and TIMP-2 PICF levels. CONCLUSION: The present data suggest that the increased levels of the selected PICF-derived biomarkers of periodontal tissue inflammation, matrix degradation/regulation, and alveolar bone turnover/resorption combined with site-specific microbial profiles may be associated with peri-implantitis and could have potential as predictors of peri-implant diseases.
OBJECTIVES: The aim of the present investigation was to determine the profile of peri-implant crevicular fluid (PICF) biomarkers combined with microbial profiles from implants with healthy peri-implant tissues and peri-implantitis to assess real-time disease activity. MATERIAL AND METHODS: Sixty-eight patients were included in this cross-sectional study. They were divided into two groups: 34 patients with at least one healthy implant (control) and 34 with at least one peri-implantitis affected implant (test). Total DNA content and qPCR analysis for periodontal bacteria obtained from subgingival plaque samples (Aggregatibacter actinomycetemcomitans, Prevotella intermedia, Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola) and a PICF analysis for IL-1β, VEGF, MMP-8, TIMP-2, and OPG were performed. The individual and combined diagnostic ability of each biomarker for peri-implantitis and target bacterial species were analyzed. RESULTS: The mean concentration of IL-1β (44.6 vs. 135.8 pg/ml; P < 0.001), TIMP-2 (5488.3 vs. 9771.8 pg/ml; P = 0.001), VEGF (59.1 vs. 129.0 pg/ml; P = 0.012), and OPG (66.5 vs. 111.7 pg/ml; P = 0.050) was increased in the peri-implantitispatients. The mean expression of MMP-8 (6029.2 vs. 5943.1 pg/ml; P = 0.454) and did not reveal a meaningful difference among groups. Total bacterial DNA of selected microorganisms was associated with a threefold or greater increase in peri-implantitis although no statistical significant difference. The ability to diagnose diseased sites was enhanced by T. denticola combined with IL-1β, VEGF, and TIMP-2 PICF levels. CONCLUSION: The present data suggest that the increased levels of the selected PICF-derived biomarkers of periodontal tissue inflammation, matrix degradation/regulation, and alveolar bone turnover/resorption combined with site-specific microbial profiles may be associated with peri-implantitis and could have potential as predictors of peri-implant diseases.
Authors: Mia Rakic; Pablo Galindo-Moreno; Alberto Monje; Sandro Radovanovic; Hom-Lay Wang; David Cochran; Anton Sculean; Luigi Canullo Journal: Clin Oral Investig Date: 2017-12-07 Impact factor: 3.573
Authors: Chin-Wei Wang; Yuning Hao; Riccardo Di Gianfilippo; James Sugai; Jiaqian Li; Wang Gong; Kenneth S Kornman; Hom-Lay Wang; Nobuhiko Kamada; Yuying Xie; William V Giannobile; Yu Leo Lei Journal: Theranostics Date: 2021-05-03 Impact factor: 11.556