Radhakrishnan Nagarajan1, Mohanad Al-Sabbagh2, Dolph Dawson2,3, Jeffrey L Ebersole2,3. 1. Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA. 2. Division of Periodontics, College of Dentistry, University of Kentucky, Lexington, KY, USA. 3. Center for Oral Health Research, College of Dentistry, University of Kentucky, Lexington, KY, USA.
Abstract
BACKGROUND: In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. AIM: The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). MATERIAL & METHODS: The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. RESULTS: Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. CONCLUSIONS: Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient-specific risk profiles and tailored interventions.
BACKGROUND: In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. AIM: The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). MATERIAL & METHODS: The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. RESULTS: Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. CONCLUSIONS: Host adaptive immune responses to the oral colonization/infectioncontribute significantly to creating the profiles specific for periodontitispatients with potential to assist in defining patient-specific risk profiles and tailored interventions.
Authors: Jeffrey L Ebersole; David Cappelli; Erik C Mathys; Michelle J Steffen; Robert E Singer; Michael Montgomery; Glen E Mott; M John Novak Journal: Ann Periodontol Date: 2002-12
Authors: Luciana Saraiva; Estela S Rebeis; Eder de S Martins; Ricardo T Sekiguchi; Ellen S Ando-Suguimoto; Carlos Eduardo S Mafra; Marinella Holzhausen; Giuseppe A Romito; Marcia P A Mayer Journal: J Clin Periodontol Date: 2014-08-27 Impact factor: 8.728
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