Thiago Morelli1,2, Kevin L Moss2, James Beck3, John S Preisser4, Di Wu1, Kimon Divaris5,6, Steven Offenbacher1,2. 1. Department of Periodontology, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC. 2. Department of Dental Ecology, School of Dentistry, University of North Carolina at Chapel Hill. 3. Center for Oral and Systemic Diseases, School of Dentistry, University of North Carolina at Chapel Hill. 4. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill. 5. Department of Pediatric Dentistry, School of Dentistry, University of North Carolina at Chapel Hill. 6. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.
Abstract
BACKGROUND: The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss. METHODS: Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals). RESULTS: The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts. CONCLUSIONS: The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.
BACKGROUND: The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss. METHODS: Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals). RESULTS: The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts. CONCLUSIONS: The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.
Authors: Paul I Eke; Bruce A Dye; Liang Wei; Gary D Slade; Gina O Thornton-Evans; Wenche S Borgnakke; George W Taylor; Roy C Page; James D Beck; Robert J Genco Journal: J Periodontol Date: 2015-02-17 Impact factor: 6.993
Authors: Cary S Agler; Dmitry Shungin; Andrea G Ferreira Zandoná; Paige Schmadeke; Patricia V Basta; Jason Luo; John Cantrell; Thomas D Pahel; Beau D Meyer; John R Shaffer; Arne S Schaefer; Kari E North; Kimon Divaris Journal: Methods Mol Biol Date: 2019
Authors: J T Marchesan; K M Byrd; K Moss; J S Preisser; T Morelli; A F Zandona; Y Jiao; J Beck Journal: J Dent Res Date: 2020-04-22 Impact factor: 6.116
Authors: Thiago Morelli; Kevin L Moss; John S Preisser; James D Beck; Kimon Divaris; Di Wu; Steven Offenbacher Journal: J Periodontol Date: 2018-02-22 Impact factor: 6.993
Authors: Cary S Agler; Kevin Moss; Kamaira H Philips; Julie T Marchesan; Miguel Simancas-Pallares; James D Beck; Kimon Divaris Journal: Adv Exp Med Biol Date: 2019 Impact factor: 2.622
Authors: Souvik Sen; Lauren D Giamberardino; Kevin Moss; Thiago Morelli; Wayne D Rosamond; Rebecca F Gottesman; James Beck; Steven Offenbacher Journal: Stroke Date: 2018-01-15 Impact factor: 7.914