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Purpose: To determine whether periodontal disease is positively associated with incident diabetes across the continuum of body mass levels (BMI) and test the hypothesis that the periodontal risk for incident diabetes is modified by BMI.Methods: We included 5569 diabetes-free participants from Visit 4 (1996-1998) of the Atherosclerosis Risk in Communities (ARIC) Study and followed them until 2018. Periodontal disease status was classified by periodontal profile class (PPC)-Stages and incident diabetes was based on participant report of physician diagnosis. We estimated the hazard ratios (HR) for diabetes using a competing risk model for each PPC-Stage. We assessed multiplicative interactions between periodontal disease and BMI (as a continuous variable) on risk of diabetes.Results: During a median time of 19.4 years of follow-up, 1,348 incident diabetes cases and 1,529 deaths occurred. Compared to "healthy/incidental disease" Stage , participants with PPC-"severe periodontal disease" or "severe tooth loss" Stage and lower BMI had elevated risk for diabetes adjusting for demographic, smoking, education and biological variables when accounting for death as a competing risk with HRs 1.76 (95%CI 1.10-2.80) and 2.11 (95% CI 1.46-3.04), respectively. The interaction between PPC-stages and BMI was significant (p= 0.01). No significant associations of PPC-Stages with incident diabetes were present when BMI was above 31 kg/m 2.Conclusion: Periodontal disease was associated with incident diabetes, especially in non-obese participants. Dentists should be aware that periodontal disease is associated with incident diabetes but the association may be modified for patient's at higher BMI levels. [ABSTRACT FROM AUTHOR]

The concept of precision dentistry as it relates to precision medicine is relatively new to the field of oral health. Precision dentistry is a contemporary, multifaceted, data-driven approach to oral health care that uses individual characteristics to stratify similar patients into phenotypic groups. The objective is to provide clinicians with the information that will allow them to improve treatment planning and a patient's response to treatment. Providers that use a precision oral health approach would move away from using an "average treatment" for all patients with a particular diagnosis and move toward more specific treatments for patients within each diagnostic subgroup. Precision dentistry requires a method or a model that places each individual in a subgroup where each member is the same as every other member in relation to the disease of interest. Precision dentistry is a paradigm shift that requires a new way of thinking about diagnostic categories. This approach uses patients' risk factor data (including, but not limited to, genetic, environmental, and health behavioral), rather than expert opinion or clinical presentation alone, to redefine traditional categories of health and disease. We review aspects of current efforts to allow precision dentistry to be realized and focus on one of the major innovations that may help precision dentistry to be practiced by periodontists, the World Workshop Model. Another approach is the Periodontal Profile Class system. These two approaches represent examples of supervised and unsupervised learning systems, respectively. This review compares and contrasts these two learning systems for their ability to classify patients into homogeneous disease and risk groups, as well as their feasibility at achieving the objective of enabling precision dentistry. We conclude that: (a) the World Workshop Model concept of stages and grades works as expected, in that periodontal status appears to be more serious in each successive stage. In addition, the seriousness and the complexity of the disease are greater as the grade increases within each stage. Stages and grades are important for precision dentistry because they consider the risk of future disease and the prognosis, and enable practitioners to use more signs, symptoms, and other associated factors when placing a patient in a diagnostic category; (b) the assignment of stages and grades using unsupervised learning systems is superior to using supervised learning systems for the prediction of 10-year tooth loss and 3-year attachment loss progression. In addition, the unsupervised learning approach (Periodontal Profile Class stages) results in stronger associations between the periodontal phenotypes and systemic diseases and conditions (prevalent diabetes, C-reactive protein, and incident stroke). This probably occurs because an unsupervised learning model produces more data-driven, mutually exclusive, homogeneous groups than a supervised learning model. [ABSTRACT FROM AUTHOR]