Thiago Morelli1,2, Kevin L Moss2, John S Preisser3,4, James D Beck2,4, Kimon Divaris5,6, Di Wu1, Steven Offenbacher1,2. 1. Department of Periodontology, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC. 2. Center for Oral and Systemic Diseases, School of Dentistry, University of North Carolina at Chapel Hill. 3. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill. 4. Department of Dental Ecology, School of Dentistry, 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: Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. METHODS: The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). RESULTS: Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. CONCLUSIONS: These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived personalized propensity scores provide clinical periodontal definitions that reflect disease patterns in the population and offer a useful system for patient stratification that is predictive for disease progression and tooth loss.
BACKGROUND: Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. METHODS: The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). RESULTS: Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. CONCLUSIONS: These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived personalized propensity scores provide clinical periodontal definitions that reflect disease patterns in the population and offer a useful system for patient stratification that is predictive for disease progression and tooth loss.
Authors: Steven Offenbacher; Kimon Divaris; Silvana P Barros; Kevin L Moss; Julie T Marchesan; Thiago Morelli; Shaoping Zhang; Steven Kim; Lu Sun; James D Beck; Matthias Laudes; Matthias Munz; Arne S Schaefer; Kari E North Journal: Hum Mol Genet Date: 2016-03-08 Impact factor: 6.150
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: 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: Lubaina T Arsiwala; Yejin Mok; Chao Yang; Junichi Ishigami; Elizabeth Selvin; James D Beck; Matthew A Allison; Gerardo Heiss; Ryan T Demmer; Kunihiro Matsushita Journal: J Periodontol Date: 2021-11-17 Impact factor: 4.494
Authors: J T Marchesan; K Moss; T Morelli; F R Teles; K Divaris; M Styner; A A Ribeiro; J Webster-Cyriaque; J Beck Journal: J Dent Res Date: 2021-04-27 Impact factor: 6.116