title: Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis.
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.
authors.affiliation: Division of Biomedical Informatics, Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, United States of America.
authors.forename: Radhakrishnan
authors.initials: R
authors.lastname: Nagarajan
authors.affiliation: Center for Oral Health Research, College of Dentistry, University of Kentucky, Lexington, KY, United States of America; Division of Oral Medicine, Department of Oral Health Practice, College of Dentistry, University of Kentucky, Lexington, KY, United States of America.
authors.forename: Craig S
authors.initials: CS
authors.lastname: Miller
authors.affiliation: Center for Oral Health Research, College of Dentistry, University of Kentucky, Lexington, KY, United States of America; Division of Periodontics, Department of Oral Health Practice, College of Dentistry, University of Kentucky, Lexington, KY, United States of America.
authors.forename: Dolph
authors.initials: D
authors.lastname: Dawson
authors.affiliation: Division of Periodontics, Department of Oral Health Practice, College of Dentistry, University of Kentucky, Lexington, KY, United States of America.
authors.forename: Mohanad
authors.initials: M
authors.lastname: Al-Sabbagh
authors.affiliation: Center for Oral Health Research, College of Dentistry, University of Kentucky, Lexington, KY, United States of America.
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Ebersole
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D016022:Case-Control Studies; D016207:Cytokines / Q000096:biosynthesis*; D005260:Female; D005786:Gene Expression Regulation; D005891:Gingivitis / Q000378:metabolism* / Q000473:pathology; D006801:Humans; D008297:Male; D010518:Periodontitis / Q000378:metabolism* / Q000473:pathology; D012463:Saliva / Q000378:metabolism*
chemical_list: D015415:Biomarkers; D016207:Cytokines