Biologic modelling of periodontal disease progression

Abstract

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.

Microbiome Profiles of Ligature-Induced Periodontitis in Nonhuman Primates across the Life Span

“…Finally, it is clear that standard nonsurgical therapy of scaling and root planing lowers the microbial burden in periodontitis and decreases inflammation as an approach to controlling the disease (44,62,64). However, while the majority of the population responds well to this treatment, a portion of the population continues to demonstrate disease, with little predictive understanding of who constitutes this "nonresponding" group (70)(71)(72)(73). In humans, it is difficult to implement a prospective periodontitis protocol, and the longitudinal dynamics of the microbiome have generally been defined by comparing disease microbiomes to those posttreatment.…”

The combined use of salivary biomarkers and clinical parameters to predict the outcome of scaling and root planing: A cohort study

“…showed potential in predicting treatment sensitivity (sensitivity and specificity: 71%). A study by Nagarajan et al (2019) showed that the baseline levels of IL-1β and MMP-8 in saliva were higher in those with progression of periodontitis after SRP or oral hygiene only.…”

“…Using saliva as a diagnostic fluid for point-of-care analysis for periodontal diseases is advantageous because sample collection is easy and non-invasive, and there is access to plentiful biological fluid (Korte & Kinney, 2016). Some biomarkers in saliva have been shown to be related to the treatment response to SRP (Lee et al, 2018;Nagarajan et al, 2019;Sexton et al, 2011) and could potentially be used to estimate the treatment response to SRP.…”

Salivary biomarkers for discriminating periodontitis in the presence of diabetes

“…Diagnostic advancements in periodontitis continue to benefit from integration of salivary biomarker information with findings from clinical and radiographic assessments (Barnes et al, 2014; de Lima et al, 2016; Miyoshi et al, 2018; Nagarajan et al, 2015). Biomolecules found in saliva have the advantage of early noninvasive detection of host defence and inflammatory events that likely precede clinical and radiographic evidence of periodontal tissue damage (Chan et al, 2012; Glimvall et al, 2012; Nagarajan et al, 2019; Salminen et al, 2014; Taylor, 2014). Acquisition of this biological information creates an opportunity for addressing critical needs for more personalized approaches to improve periodontal health, which is the basis for P4 medicine (Flores et al, 2013; Miller et al, 2010; Sagner et al, 2017).…”