Dataset Information

Periodontal profile classes predict periodontal disease progression and tooth loss.

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.

SUBMITTER: Morelli T

PROVIDER: S-EPMC6125718 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Periodontal profile classes predict periodontal disease progression and tooth loss.

Morelli Thiago T Moss Kevin L KL Preisser John S JS Beck James D JD Divaris Kimon K Wu Di D Offenbacher Steven S

Journal of periodontology 20180222 2

<h4>Background</h4>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 Periodonta ...[more]

PMID: 29520822

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Project description:Precise classification of periodontal disease has been the objective of concerted efforts and has led to the introduction of new consensus-based and data-driven classifications. The purpose of this study was to characterize the microbiological signatures of a latent class analysis (LCA)-derived periodontal stratification system, the Periodontal Profile Class (PPC) taxonomy. We used demographic, microbial (subgingival biofilm composition), and immunological data (serum IgG antibody levels, obtained with checkerboard immunoblotting technique) for 1,450 adult participants of the Dental Atherosclerosis Risk in Communities (ARIC) study, with already generated PPC classifications. Analyses relied on t tests and generalized linear models with Bonferroni correction. Men and African Americans had higher systemic antibody levels against most microorganisms compared to women and Caucasians (P < 0.05). Healthy individuals (PPC-I) had low levels of biofilm bacteria and serum IgG levels against most periodontal pathogens (P < 0.05). Subjects with mild to moderate disease (PPC-II to PPC-III) showed mild/moderate colonization of multiple biofilm pathogens. Individuals with severe disease (PPC-IV) had moderate/high levels of biofilm pathogens and antibody levels for orange/red complexes. High gingival index individuals (PPC-V) showed moderate/high levels of biofilm Campylobacter rectus and Aggregatibacter actinomycetemcomitans. Biofilm composition in individuals with reduced periodontium (PPC-VI) was similar to health but showed moderate to high antibody responses. Those with severe tooth loss (PPC-VII) had significantly high levels of multiple biofilm pathogens, while the systemic antibody response to these microorganisms was comparable to health. The results support a biologic basis for elevated risk for periodontal disease in men and African Americans. Periodontally healthy individuals showed a low biofilm pathogen and low systemic antibody burden. In the presence of PPC disease, a microbial-host imbalance characterized by higher microbial biofilm colonization and/or systemic IgG responses was identified. These results support the notion that subgroups identified by the PPC system present distinct microbial profiles and may be useful in designing future precise biological treatment interventions.

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

Periodontal profile classes predict periodontal disease progression and tooth loss.

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Periodontal profile classes predict periodontal disease progression and tooth loss.

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