Periodontal medicine is a term used to describe how periodontal infection/inflammation may impact extraoral health. Periodontitis has been linked to over 50 systemic diseases and conditions. As part of the Journal of Dental Research’s Centennial Celebration, this narrative review discusses periodontal medicine research done over the past 100 y, with particular focus on the effects of periodontal disease on 3 pathological conditions: cardiovascular disease, diabetes mellitus, and adverse pregnancy outcomes. We selected 29 total studies that were the “first” of their kind, as they provided novel observations or contributed to shifting paradigms as well as important studies that made strong contributions to progress in understanding relationships to the systemic conditions. These studies were organized in an overview timeline and broken down into timelines by topic: cardiovascular disease ( n = 10), diabetes ( n = 12), and adverse pregnancy outcomes ( n = 7). Overall, the majority of cross-sectional, case-control, and longitudinal studies have revealed positive associations between poor periodontal status and cardiovascular disease, diabetes metabolic control, and a number of adverse pregnancy outcomes, and these associations are upheld in systematic reviews. Findings from randomized controlled trials testing the effects of periodontal therapy on systemic health outcomes were conflicting and inconsistent. While there has been a great deal of progress, we highlight lessons learned and make comments and suggestions on a number of key aspects, including the heterogeneity of case definitions of periodontal disease across studies, accounting for features of the periodontal phenotype that are most relevant to the biological link between periodontitis and systemic outcomes, the role of other comorbid inflammatory conditions, selection of study participants, and timing and intensity of the periodontal intervention.
YA survivors are interested in receiving an MSC videoconference intervention. Feasibility, acceptance, and potential psychosocial benefits of the intervention were demonstrated. Findings can be applied toward the design of an efficacy randomized controlled trial to improve quality of life for YA survivors in transition after cancer treatment.
Fewer endometrial abnormalities occurred during 2 years treatment with anastrozole compared with tamoxifen although statistical significance was not reached in this sub-protocol analysis.
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.
The genetic basis of oral health has long been theorized but little information exists on the heritable variance in common oral and dental disease traits explained by the human genome. We sought to add to the evidence base of heritability of oral and dental traits using high-density genotype data in a well-characterized community-based cohort of middle-age adults. We used genome-wide association (GWAS) data superimposed to clinical and biomarker information generated in the context of the Dental Atherosclerosis Risk In Communities (ARIC) cohort. Genotypes comprised SNPs directly typed on the Affymetrix Genome-Wide Human SNP Array 6.0 chip with minor allele frequency of >5% (n=656,292) or imputed using HapMap II-CEU (n=2,104,905). We investigated 30 traits including 'global' [e.g., number of natural teeth (NT) and incident tooth loss], clinically defined (e.g., dental caries via the DMFS index, periodontitis via the CDC/AAP and WW17 classifications), and biologically informed ones (e.g., subgingival pathogen colonization and 'complex' traits). Heritability (i.e., variance explained; h 2) was calculated using Visscher's Genome-wide Complex Trait Analysis (GCTA) approach, using a random-effects mixed linear model and restricted maximum likelihood (REML) regression adjusting for ancestry (10 principal components), age and sex. H 2 estimates were modest for clinical traits-NT=0.11 (se=0.07), severe chronic periodontitis (CDC/AAP)=0.22 (se=0.19), WW17 Stage 4 vs. 1/2=0.15 (se=0.11). "Severe gingival index" and "high red complex colonization" had h 2 >0.50, while a periodontal complex trait defined by high IL-1β GCF expression and Aggregatibacter actinomycetemcomitans subgingival colonization had the highest h 2 =0.72 (se=0.32). Our results indicate that all GWAS SNPs explain modest levels of the observed variance in clinical oral and dental measures. Subgingival bacterial colonization and complex phenotypes encompassing both bacterial colonization and local inflammatory response had the highest heritability, suggesting that these biologically informed traits are promising targets for the conduct of genomics investigations, according to the notion of precision oral health.
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