Abstract
This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM). Our findings demonstrate that performance measures (sensitivity, specificity and accuracy) of traditional classification as single classifier were comparable to that of the SVA counterparts using clinical labels of the samples as ground truth. However, unlike traditional single classifier approaches, the normalized ensemble vote-counts from SVA revealed varying proclivity of the subjects for each of the disease groups. More importantly, the SVA identified a subset of gingivitis and periodontitis samples that demonstrated a biological proclivity commensurate with the other clinical group. This subset was confirmed across SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM. Heatmap visualization of their ensemble sets revealed lack of consensus between these subsets and the rest of the samples within the respective disease groups indicating the unique nature of the patients in these subsets. While the source of variation is not known, the results presented clearly elucidate the need for novel approaches that accommodate inherent heterogeneity and personalized variations within disease groups in diagnostic characterization. The proposed approach falls within the scope of P4 medicine (predictive, preventive, personalized, and participatory) with the ability to identify unique patient profiles that may predict specific disease trajectories and targeted disease management.
Highlights
Movement towards an era of personalized medicine and individualized clinical decisions in periodontology requires significant improvement in our ability to define risk and predict disease progression
A recent report by Lang et al [2] focusing on gingivitis as a risk factor in periodontal disease identified that approximately 37% of teeth with consistent gingivitis progressed to periodontitis and tooth loss, while non-inflamed teeth showed a 99.5% survival
The heterogeneity of these disease populations is evident in that various treatment studies of both gingivitis and periodontitis have shown that a portion of the gingivitis population does not respond well to standard mechanical therapy [9] nor do they return to biological health in a timely manner [10], and a portion of the periodontitis population remains somewhat refractory to treatment using standard approaches [9]
Summary
Movement towards an era of personalized medicine and individualized clinical decisions in periodontology requires significant improvement in our ability to define risk and predict disease progression. Current practice in differential diagnosis and treatment options in dentistry often stops at the clinical and radiographic presentation of the patient, despite the expansion of information available from biological fluids, plaque and tissue. Gingivitis is a reversible condition associated with bacterial biofilms that generally resolves clinically in about one week after the reinstitution of oral hygiene procedures [1]. It is an oftenoverlooked disease, despite being the “gateway” to periodontitis for a significant portion of the population [2,3,4,5]. The heterogeneity of these disease populations is evident in that various treatment studies of both gingivitis and periodontitis have shown that a portion of the gingivitis population does not respond well to standard mechanical therapy [9] nor do they return to biological health in a timely manner [10], and a portion of the periodontitis population remains somewhat refractory to treatment using standard approaches [9]
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