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Title: Investigation into the potential for key biomarkers to predict the outcome of periodontal treatment
Author: Gul, Sarhang Gul
ISNI:       0000 0004 5918 7519
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2016
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Various biomarkers in gingival crevicular fluid (GCF) have been examined in an attempt to obtain a specific and sensitive marker for periodontal disease progression and response to treatment. To date none have been found to be particularly reliable on their own. Therefore, the aim of this longitudinal clinical study was to determine whether key host enzymes (Matrix metalloproteinase-8, Cathepsin G, Elastase) and bacterial enzymes (Trypsin-like activity, Sialidase) detectable in GCF plus levels of key bacteria (Porphyromonas gingivalis, Tannerella forsythia and Fusobacterium nucleatum) detectable in subgingival plaque can be used in combination to provide an improved prognostic “finger print” for the outcome of treatment in patients with chronic periodontitis. Methods: 89 subjects were recruited to a longitudinal study. At baseline, 3-month and 6-month samples of GCF and subgingival plaque were collected from 3 representative sites: healthy (≤ 3mm), deep non-bleeding (DNB) (≥ 6mm) and deep bleeding (DB) (≥ 6mm) using a Periopaper® strips and a periodontal curette. In addition full mouth clinical data (pocket probing depth, clinical attachment loss, plaque index and bleeding on probing) were also recorded and patients received standard non-surgical periodontal treatment. GCF samples were assayed for each of the above enzyme activities using colourimetric/fluorometric substrates and subgingival plaque samples were assayed for the levels of each of the above bacterial species by qPCR. Data were analysed on a site-by- site basis using logistic regression for enzyme and bacterial profiles predictive of ≥ 2mm improvement in pocket probing depth (PPD) 6 months after treatment. Results: 77 individuals completed the 6-month interval phase. Full mouth clinical data showed statistically significant reduction in response to treatment, however, one third of DNB and DB sites showed less than 2mm improvement in pocket depth. The average levels of all biomarkers (enzymes and bacteria) were significantly higher in diseased sites than healthy sites and overall they decreased through the course of the study except Fusobacterium nucleatum. Matrix metalloproteinase-8 (MMP8), elastase, sialidase, Porphyromonas gingivalis and Tannerella forsythia showed the greatest reductions. The levels of MMP8, elastase and sialidase at baseline significantly correlated with the initial PPD as follows: MMP8 (r= 0.58), elastase (r=0.51) and sialidase (r=0.5). Using threshold enzyme levels that were the values with the highest sensitivity and specificity MMP8 (94ng/μl), elastase (33ng/μl) sialidase (2.3ng/μl), Porphyromonas gingivalis (0.23%) and Tannerella forsythia (0.35%), ROC curve analysis demonstrated that the baseline levels of these five biomarkers at sites are reliable diagnostic biomarkers as they differentiated healthy sites from diseased sites with degree of sensitivity and specificity above 77%. Furthermore, logistic regression showed that the combination of MMP8, elastase and sialidase provided accurate predictions of treatment outcome (81.3% for DNB, 80.3% for DB), which was significantly better than each enzyme alone (62.5%). When combined with the levels of Porphyromonas gingivalis and Tannerella forsythia the prediction value increased to 92% for DNB sites and 93.3% for DB sites. The biomarker values of MMP8, elastase and sialidase were also shown to be reliable after validating with an independent cohort. Preliminary tests showed that these three enzymes can be translated in to a simple chair-side test but this requires further development. Conclusion: Combined profiles of the above biomarkers offer a significantly improved indication of a site’s likely response to non-surgical periodontal treatment.
Supervisor: Rawlinson, Andrew ; Douglas, Ian ; Griffiths, Gareth Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available