Alassy, Hatem2021-08-162021-08-162021-05https://hdl.handle.net/11299/223103University of Minnesota M.S. thesis. May 2021. Major: Dentistry. Advisor: Massimo Costalonga. 1 computer file (PDF); x, 94 pages.Background: Peri-implant diseases, peri-implantitis (PI) and peri-implant mucositis (PIM), are highly prevalent in subjects with dental implants. Despite this prevalence, diagnosing peri-implant disease (PID) remains challenging because of lack of accuracy and precision of periodontal probing and dental radiographs. Furthermore, these diagnostic tools document history of disease rather than current disease activity. There is no current model to predict the progression of PID. Biomarkers are commonly used in medicine to objectively determine disease state, or responses to a therapeutic intervention. Biomarkers in peri-implant crevicular fluid (PICF) show promise in their diagnostic and prognostic value. Metabolomic analysis of PICF quantifies molecules associated with host and bacterial metabolism may reflect on pathophysiology of peri-implantitis. Un-targeted metabolomics allows the discovery of unknown biomarkers without bias to correlate them with peri-implantitis and its progression. Aim: We hypothesize that the simple metabolites in PICF are predictors of future peri-implantitis progression. We aim to define the unique set of metabolites in the PICF that establish a reliable method for early prediction of bone-loss progression in peri-implantitis. Methods: Clinical and radiographic examinations and PICF samples were collected from 130 implants in 71 subjects at baseline, 6, 12, 18 and 24 months. At baseline, 59 implants were healthy (bone loss < 2mm; PD  4mm), 33 implants had PI (bone loss ≥ 3mm; PD ≥ 6mm) and 38 other implants had bone loss ≥ 2mm and <3mm and PD 5mm. Radiographic bone level changes of 112 implants and relative metabolites in PICF samples were measured at each 6 months interval using proton nuclear magnetic resonance (H-NMR) spectroscopy. MetaboAnalyst 5.0 software correlated metabolite levels with radiographic bone changes of ≥ 1mm within a 6-month interval. Results: In the cross-sectional component at baseline, univariate ROC curve analysis demonstrated that the Cadaverine/Lysine signature was significantly correlated with peri-implantitis (AUC= 0.76; 95% CI 0.658-0.855, p< 0.000) versus healthy implants. While alpha ketoglutarate was significantly correlated with healthy implants (AUC= 0.706; 95% CI 0.593-0.819; p= 0.002). In the longitudinal component, the metabolite levels in PICF of untreated diseased implants that demonstrated progressive radiographic bone loss of ≥ 1mm within a 6-month interval (group A, n=6) were compared to the metabolites of healthy-non-progressing implants (group B, n=26) and to diseased-non-progressing implants (group C, n=8). Proline and 1-3-diaminopropane levels could predict future bone loss of ≥ 1mm (AUC= 0.917 and 0.854 respectively) whereas glucose and arginine levels could predict the absence of bone loss in group C and group B respectively (AUC= 0.896 and 0.801) although statistical significance was not reached for all 4 metabolites. Biotin and propionate levels were higher in group C compared group A and group B (ANOVA p< 0.001; AUC biotin= 0.889; AUC propionate= 0.87). Valine levels were higher in both group A and group C compared to group B (p= 0.002; AUC= 0.841). Conclusions: PICF metabolites identified using H-NMR spectroscopy mapped a specific metabolomic profile able to identify implants with peri-implantitis versus healthy implants with moderate accuracy. Furthermore, specific metabolites discriminated between progressive disease versus non-progressing disease status and health.enBiomarkersBone-LossMetabolomicsPeri-implantitisPrognosisThesis or Dissertation