Dataset Information

Predictors of tooth loss due to periodontal disease in patients following long-term periodontal maintenance.

ABSTRACT: AIM:To analyse patient-related factors (PRFs) and tooth-related factors (TRFs) associated with tooth loss due to periodontal disease (TLPD) in patients undergoing periodontal maintenance (PM). MATERIAL AND METHODS:The sample consisted of 500 patients (mean follow-up of 20 years). The impact of PRFs on TLPD was analysed with Poisson regression and multivariate logistic regression. The simultaneous impact of PRFs and TRFs was analysed with multilevel logistic regression and Cox regression. RESULTS:Tooth loss due to periodontal disease was 515 (mean 0.05 patient/year). The significant PRFs were severe periodontitis (p < 0.001), aggressive periodontitis (p < 0.001), smoking (p = 0.018), bruxism (p = 0.022) and baseline number of teeth (p = 0.001). These PRFs allowed characterizing patients losing more teeth. The whole TRFs analysed were significant, depending on the type of tooth and the category of each factor (e.g. mobility 0, 1, 2, and 3). The significant PRFs increased the risk of TLPD by 2 to 3 times while TRFs increased the risk to a higher extent. Mobility was the main TRF. CONCLUSIONS:Severe periodontitis, aggressive periodontitis, smoking, bruxism and baseline number of teeth, as well as the whole TRFs analysed, were associated with TLPD.

SUBMITTER: Martinez-Canut P

PROVIDER: S-EPMC4737315 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Predictors of tooth loss due to periodontal disease in patients following long-term periodontal maintenance.

Martinez-Canut Pedro P

Journal of clinical periodontology 20151129 12

<h4>Aim</h4>To analyse patient-related factors (PRFs) and tooth-related factors (TRFs) associated with tooth loss due to periodontal disease (TLPD) in patients undergoing periodontal maintenance (PM).<h4>Material and methods</h4>The sample consisted of 500 patients (mean follow-up of 20 years). The impact of PRFs on TLPD was analysed with Poisson regression and multivariate logistic regression. The simultaneous impact of PRFs and TRFs was analysed with multilevel logistic regression and Cox regr ...[more]

PMID: 26498672

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Project description: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.

Project description:In this review we critically summarize the evidence base and the progress to date regarding the genomic basis of periodontal disease and tooth morbidity (ie, dental caries and tooth loss), and discuss future applications and research directions in the context of precision oral health and care. Evidence for these oral/dental traits from genome-wide association studies first emerged less than a decade ago. Basic and translational research activities in this domain are now under way by multiple groups around the world. Key departure points in the oral health genomics discourse are: (a) some heritable variation exists for periodontal and dental diseases; (b) the environmental component (eg, social determinants of health and behavioral risk factors) has a major influence on the population distribution but probably interacts with factors of innate susceptibility at the person-level; (c) sizeable, multi-ethnic, well-characterized samples or cohorts with high-quality measures on oral health outcomes and genomics information are required to make decisive discoveries; (d) challenges remain in the measurement of oral health and disease, with current periodontitis and dental caries traits capturing only a part of the health-disease continuum, and are little or not informed by the underlying biology; (e) the substantial individual heterogeneity that exists in the clinical presentation and lifetime trajectory of oral disease can be identified and leveraged in a precision medicine framework or, if unappreciated, can hamper translational efforts. In this review we discuss how composite or biologically informed traits may offer improvements over clinically defined ones for the genomic interrogation of oral diseases. We demonstrate the utility of the results of genome-wide association studies for the development and testing of a genetic risk score for severe periodontitis. We conclude that exciting opportunities lie ahead for improvements in the oral health of individual patients and populations via advances in our understanding of the genomic basis of oral health and disease. The pace of new discoveries and their equitable translation to practice will largely depend on investments in the education and training of the oral health care workforce, basic and population research, and sustained collaborative efforts..

Dataset Information

Predictors of tooth loss due to periodontal disease in patients following long-term periodontal maintenance.

Publications

Predictors of tooth loss due to periodontal disease in patients following long-term periodontal maintenance.

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