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Biologic modelling of periodontal disease progression
Author(s) -
Nagarajan Radhakrishnan,
Miller Craig S.,
Dawson Dolph,
Ebersole Jeffrey L.
Publication year - 2019
Publication title -
journal of clinical periodontology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.456
H-Index - 151
eISSN - 1600-051X
pISSN - 0303-6979
DOI - 10.1111/jcpe.13064
Subject(s) - tannerella forsythia , saliva , medicine , periodontitis , scaling and root planing , disease , porphyromonas gingivalis , chronic periodontitis , pathology , honeysuckle , alternative medicine , traditional chinese medicine
Aim To investigate the synergistic role of biologic markers from saliva, serum and plaque in modelling periodontitis disease progression. Material and Methods This longitudinal study evaluated characteristics of disease progression in 114 patients with generalized moderate to severe periodontitis. The primary outcome was detection of sites with progressing attachment loss sites over 6 months in patients who received scaling and root planing or oral hygiene only. The predictive potential of 27 biomarkers in serum, whole saliva and subgingival plaque was evaluated using three classification algorithms (Support Vector Machines; Naïve Bayes Classifier; and Linear Discriminant Analysis) within an ensemble predictive modelling framework. Results Disease progression occurred in 24.6% of subjects (28/114). Predictive modelling using Naïve Bayes Classifier identified progressors best with sensitivity of ~89%. The use of the three classification algorithms revealed the concerted role of salivary matrix metalloproteinase‐8, serum biomarkers (serum amyloid P, matrix metalloproteinase 1, bactericidal permeability‐increasing protein, isoprostane) along with levels of Porphryomonas gingivalis and Tannerella forsythia in plaque in predicting progressors. Conclusions Synergistic utility of baseline bacterial and inflammatory biomarkers from saliva, serum and plaque predicted disease progression.