Genomics of periodontal disease and tooth morbidity

Decorin in the spatial control of collagen mineralization

Understanding the molecular mechanism by which the periodontal ligament (PDL) is maintained uncalcified between two mineralized tissues (cementum and bone) may facilitate the functional repair and regeneration of the periodontium complex, disrupted in the context of periodontal diseases. However, research that explores the control of type I collagen (COL I) mineralization fails to clarify the detailed mechanism of regulating spatial collagen mineralization, especially in the periodontium complex. In the present study, decorin (DCN), which is characterized as abundant in the PDL region and rare in mineralized tissues, was hypothesized to be a key regulator in the spatial control of collagen mineralization. The circular dichroism results confirmed that DCN regulated the secondary structure of COL I, and the surface plasmon resonance results indicated that COL I possessed a higher affinity for DCN than for other mineralization promoters, such as DMP-1, OPN, BSP and DSPP. These features of DCN may contribute to blocking intrafibrillar mineralization in COL I fibrils during the polymer-induced liquid-precursor mineralization process when the fibrils are cross-linked with DCN. This effect was more remarkable when the fibrils were phosphorylated by sodium trimetaphosphate, as shown by the observation of a tube-like morphology via TEM and mineral sheath via SEM. This study enhances the understanding of the role of DCN in mineralization regulation among periodontal tissues. This provides insights for the development of biomaterials for the regeneration of interfaces between soft and hard tissues.

Occlusion dysfunction and Alzheimer's disease: Mendelian randomization study

Aim

Occlusion dysfunction (OD) is increasingly linked to Alzheimer's disease (AD). This study aimed to elucidate the causal relationship between OD and AD using Mendelian randomization (MR) analysis.

Materials and methods

Genome-wide association study (GWAS) meta-analysis data obtained from FinnGen, IEU Open GWAS, and UK Biobank (UKBB) was represented as instrumental variables. We validated the causal relationship between periodontal disease (PD), loose teeth (PD & occlusion dysfunction), dentures restoration (occlusion recovery), and AD.

Results

According to the MR analysis, PD and AD have no direct causal relationship (P = 0.395, IVW). However, loose teeth significantly increased the risk of AD progression (P = 0.017, IVW, OR = 187.3567, 95%CI = 2.54E+00-1.38E+04). These findings were further supported by the negative causal relationship between dentures restoration and AD (P = 0.015, IVW, OR = 0.0234, 95%CI = 1.13E-03-0.485).

Conclusion

The occlusion dysfunction can ultimately induce Alzheimer's disease. Occlusion function was a potentially protective factor for maintaining neurological health.

The promise and challenges of genomics-informed periodontal disease diagnoses

Recent advances in human genomics and the advent of molecular medicine have catapulted our ability to characterize human and health and disease. Scientists and healthcare practitioners can now leverage information on genetic variation and gene expression at the tissue or even individual cell level, and an enormous potential exists to refine diagnostic categories, assess risk in unaffected individuals, and optimize disease management among those affected. This review investigates the progress made in the domains of molecular medicine and genomics as they relate to periodontology. The review summarizes the current evidence of association between genomics and periodontal diseases, including the current state of knowledge that approximately a third of the population variance of periodontitis may be attributable to genetic variation and the management of several monogenic forms of the disease can be augmented by knowledge of the underlying genetic cause. Finally, the paper discusses the potential utility of polygenic risk scores and genetic testing for periodontitis diagnosis now and in the future, in light of applications that currently exist in other areas of medicine and healthcare.

The spatial transcriptomic landscape of human gingiva in health and periodontitis

The gingiva is a key oral barrier that protects oral tissues from various stimuli. A loss of gingival tissue homeostasis causes periodontitis, one of the most prevalent inflammatory diseases in humans. The human gingiva exists as a complex cell network comprising specialized structures. To understand the tissue-specific pathophysiology of the gingiva, we applied a recently developed spatial enhanced resolution omics-sequencing (Stereo-seq) technique to obtain a spatial transcriptome (ST) atlas of the gingiva in healthy individuals and periodontitis patients. By utilizing Stereo-seq, we identified the major cell types present in the gingiva, which included epithelial cells, fibroblasts, endothelial cells, and immune cells, as well as subgroups of epithelial cells and immune cells. We further observed that inflammation-related signalling pathways, such as the JAK-STAT and NF-κB signalling pathways, were significantly upregulated in the endothelial cells of the gingiva of periodontitis patients compared with those of healthy individuals. Additionally, we characterized the spatial distribution of periodontitis risk genes in the gingiva and found that the expression of IFI16 was significantly increased in endothelial cells of inflamed gingiva. In conclusion, our Stereo-seq findings may facilitate the development of innovative therapeutic strategies for periodontitis by mapping periodontitis-relevant genes and pathways and effector cells.

Assessing the association between tea intake and risk of dental caries and periodontitis: a two-sample Mendelian randomization study

Tea is an indispensable beverage in people's daily life. However, the relationship between tea intake and dental caries and periodontitis is controversial. We extracted datasets for tea intake and oral diseases from genome-wide association studies (GWASs) conducted by the UK Biobank and the Gene Lifestyle Interactions in Dental Endpoints consortium. We selected 38 single-nucleotide polymorphisms (SNPs) significantly associated with tea intake as instrumental variables (IVs) (P < 5.0 × 10-8). Mendelian randomization (MR) was performed to investigate the potential causality between tea intake and caries and periodontitis. Multivariable Mendelian randomization (MVMR) analyses were utilized to estimate causal effects of tea intake on risk of caries and periodontitis after adjusting for smoking, body mass index (BMI), and socioeconomic factors. The results showed that higher tea intake was suggestively associated with fewer natural teeth (β = - 0.203; 95% CI = 0.680 to 0.980; P = 0.029) and higher risk of periodontitis (OR = 1.622; 95% CI = 1.194 to 2.205; P = 0.002). After Bonferroni correction, the causality of tea intake on periodontitis remained significant. The significance of periodontitis disappeared after adjusting for the socioeconomic factors in MVMR (OR = 1.603; 95% CI = 0.964 to 2.666; P = 0.069). Tea intake had no association with risk of caries. Statistical insignificance of the heterogeneity test and pleiotropy test supported the validity of the MR study. Our results provide insight into the potential relationship between tea intake and oral diseases from a dietary lifestyle perspective, which may help prevent oral diseases.

Oral microbiota, co-evolution, and implications for health and disease: The case of indigenous peoples

Evidence indicates that oral microbiota plays a crucial role in human health and disease. For instance, diseases with multifactorial etiology, such as periodontitis and caries, which cause a detrimental impact on human well-being and health, can be caused by alterations in the host-microbiota interactions, where non-pathogenic bacteria give way to pathogenic orange/red-complex bacterial species (a change from a eubiotic to dysbiotic state). In this scenario, where thousands of oral microorganisms, including fungi, archaea, and phage species, and their host are co-evolving, a set of phenomena, such as the arms race and Red or Black Queen dynamics, are expected to operate. We review concepts on the subject and revisit the nature of bacterial complexes linked to oral health and diseases, as well as the problem of the bacterial resistome in the face of the use of antibiotics and what is the impact of this on the evolutionary trajectory of the members of this symbiotic ecosystem. We constructed a 16SrRNA tree to show that adaptive consortia of oral bacterial complexes do not necessarily rescue phylogenetic relationships. Finally, we remember that oral health is not exempt from health disparity trends in some populations, such as Native Americans, when compared with non-Indigenous people.

Gene sequencing applications to combat oral-cavity related disorders: a systematic review with meta-analysis

Gene sequencing (GS) has numerous applications in combatting oral-cavity related disorders, including identifying genetic risk factors for diseases, developing targeted therapies, and improving diagnostic methods. It can help identify specific genetic mutations or variations that increase the risk of developing oral-cavity related disorders, such as oral cancer, periodontal disease, and cleft lip and palate. By the means of the following investigation, our primary objective was to assess the impact of GS technique in diagnosing and potentially treating diseases of the oral cavity by the means of a systematic review and meta-analysis. We commenced by defining the terms "gene sequencing," "oral cavity," and "disorders" as the important elements in our investigation's subject. Next, relevant databases like PubMed, Scopus, Embase, Web of Science, and Google Scholar were searched using keywords and synonyms for each concept, such as "genomic sequencing," "DNA sequencing," "oral health," "oral diseases," "dental caries," "periodontal disease," "oral cancer," and "salivary gland disorders." We combined several search terms, such as "gene sequencing AND oral disorders AND periodontal disease" or "oral cancer OR genomic sequencing," to further hone your search results using Boolean operators like "AND" and "OR." The oral cavity analysis obtained by CS in the selected articles revealed that most of the disorders were, in fact, a direct causal event influenced by the oral microbiome. Moreover, each sampled oral cavity evidenced a different microbial community, which predicted the precipitation of benign as well as malignant conditions, though not on a definitive basis. In the last ten years, genomic sequencing had advanced remarkably as majority of our selected studies observed, making it possible to diagnose and treat a variety of oral and maxillofacial disorders, including cancer. It was also used to ascertain a person's genetic make-up as well as to spot numerous genetic abnormalities that can predispose individuals to diseases. Understanding the different sequencing techniques and the resulting genetic anomalies may help with their clinical application and lead to an improvement in illness diagnosis and prognosis as a whole in the field of dentistry.

Precision periodontal care: from omics discoveries to chairside diagnostics

The interface of molecular science and technology is guiding the transformation of personalized to precision healthcare. The application of proteomics, genomics, transcriptomics, and metabolomics is shaping the suitability of biomarkers for disease. Prior validation of such biomarkers in large and diverse patient cohorts helps verify their clinical usability. Incorporation of molecular discoveries into routine clinical practice relies on the development of customized assays and devices that enable the rapid delivery of analytical data to the clinician, while the patient is still in session. The present perspective review addresses this topic under the prism of precision periodontal care. Selected promising research attempts to innovate technological platforms for oral diagnostics are brought forward. Focus is placed on (a) the suitability of saliva as a conveniently sampled biological specimen for assessing periodontal health, (b) proteomics as a high-throughput approach for periodontal disease biomarker identification, and (c) chairside molecular diagnostic assays as a technological funnel for transitioning from the laboratory benchtop to the clinical point-of-care.

Dental Diseases Increase Risk of Aortic Arch Calcification Independent of Renal Dysfunction in Older Adults: Shenzhen Community Cohort Study

Many studies have documented that dental diseases were associated with an increased risk of cardiovascular diseases. Aortic arch calcification (AoAC) is a powerful predictor of cardiovascular diseases. However, whether the status of dental health is associated with AoAC is still unknown. 9463 participants over the age of 60 from Shenzhen community centers were included in the cross-sectional analysis. Physical examination data, blood biochemical tests, and AoAC scores calculated by chest radiography were collected and analyzed. Among them, 2630 participants were followed up for AoAC progression up to 36 months. Participants with AoAC suffered more tooth loss than those without AoAC (77.62% vs. 72.91%; p < 0.001). Association rule analysis suggested a strong association between dental diseases and AoAC. Tooth loss or decay increased the risk of AoAC progression (HR 1.459; 95%CI 1.284−1.658) after adjusting other risk factors including renal dysfunction. Dental diseases are potential predictors for AoAC in elderly people, which are independent of renal dysfunction.

Phenotype Harmonization in the GLIDE2 Oral Health Genomics Consortium

Genetic risk factors play important roles in the etiology of oral, dental, and craniofacial diseases. Identifying the relevant risk loci and understanding their molecular biology could highlight new prevention and management avenues. Our current understanding of oral health genomics suggests that dental caries and periodontitis are polygenic diseases, and very large sample sizes and informative phenotypic measures are required to discover signals and adequately map associations across the human genome. In this article, we introduce the second wave of the Gene-Lifestyle Interactions and Dental Endpoints consortium (GLIDE2) and discuss relevant data analytics challenges, opportunities, and applications. In this phase, the consortium comprises a diverse, multiethnic sample of over 700,000 participants from 21 studies contributing clinical data on dental caries experience and periodontitis. We outline the methodological challenges of combining data from heterogeneous populations, as well as the data reduction problem in resolving detailed clinical examination records into tractable phenotypes, and describe a strategy that addresses this. Specifically, we propose a 3-tiered phenotyping approach aimed at leveraging both the large sample size in the consortium and the detailed clinical information available in some studies, wherein binary, severity-encompassing, and "precision," data-driven clinical traits are employed. As an illustration of the use of data-driven traits across multiple cohorts, we present an application of dental caries experience data harmonization in 8 participating studies (N = 55,143) using previously developed permanent dentition tooth surface-level dental caries pattern traits. We demonstrate that these clinical patterns are transferable across multiple cohorts, have similar relative contributions within each study, and thus are prime targets for genetic interrogation in the expanded and diverse multiethnic sample of GLIDE2. We anticipate that results from GLIDE2 will decisively advance the knowledge base of mechanisms at play in oral, dental, and craniofacial health and disease and further catalyze international collaboration and data and resource sharing in genomics research.

Interferon activated gene 204 protects against bone loss in experimental periodontitis

BACKGROUND

Periodontal destruction can be the result of different known and yet-to-be-discovered biological pathways. Recent human genetic association studies have implicated interferon-gamma inducible protein 16 (IFI16) and absent in melanoma 2 (AIM2) with high periodontal interleukin (IL)-1β levels and more destructive disease, but mechanistic evidence is lacking. Here, we sought to experimentally validate these observational associations and better understand IFI16 and AIM2's roles in periodontitis.

METHODS

Periodontitis was induced in Ifi204-/- (IFI16 murine homolog) and Aim2-/- mice using the ligature model. Chimeric mice were created to identify the main source cells of Ifi204 in the periodontium. IFI16-silenced human endothelial cells were treated with periodontal pathogens in vitro. Periodontal tissues from Ifi204-/- mice were evaluated for alveolar bone (micro-CT), cell inflammatory infiltration (MPO+ staining), Il1b (qRT-PCR), and osteoclast numbers (cathepsin K+ staining).

RESULTS

Ifi204-deficient mice> exhibited >20% higher alveolar bone loss than wild-type (WT) (P < 0.05), while no significant difference was found in Aim2-/- mice. Ifi204's effect on bone loss was primarily mediated by a nonbone marrow source and was independent of Aim2. Ifi204-deficient mice had greater neutrophil/macrophage trafficking into gingival tissues regardless of periodontitis development compared to WT. In human endothelial cells, IFI16 decreased the chemokine response to periodontal pathogens. In murine periodontitis, Ifi204 depletion elevated gingival Il1b and increased osteoclast numbers at diseased sites (P < 0.05).

CONCLUSIONS

These findings support IFI16's role as a novel regulator of inflammatory cell trafficking to the periodontium that protects against bone loss and offers potential targets for the development of new periodontal disease biomarkers and therapeutics.

The roles of ANRIL polymorphisms in periodontitis: a systematic review and meta-analysis

OBJECTIVES

The aim of this study is to investigate the potential role of ANRIL polymorphisms in susceptibility to periodontitis.

METHODS

The authors searched Pubmed, Web of Science, and Scopus up to April 2021 to identify all published studies without any language restriction on the association between ANRIL and periodontitis. A meta-analysis of all ANRIL variants replicated by three or more studies was performed by testing multiple genetic models of association. Pooled odds ratios and 95% confidence intervals (CI) were used to estimate associations. Tests for sensitivity and publication bias were performed.

RESULTS

Twenty-two variants in the ANRIL gene were examined for their potential association with the risk of periodontitis. However, only 4 (rs1333048, rs1333042, rs2891168, rs496892) are replicated at least three or more studies. The ANRIL rs1333048 was the most replicated polymorphisms with five articles, seven different populations comprising of 1331 cases, and 2624 controls. The pooled overall analysis showed that rs1333048, rs1333042, rs2891168, and rs496892 polymorphisms were associated with susceptibility to periodontitis in the whole population in allele contrast and dominant models. Moreover, similar to the overall analysis, rs1333048 polymorphism showed a significant association with grade C periodontitis (known as aggressive periodontitis in 1999 classification) in allele contrast (OR = 1.16) and dominant models (1.19). Interestingly, subgroup analysis also showed rs1333048 polymorphism might influence predisposition to a slowly progressive form of periodontitis (known as chronic periodontitis in 1999 classification).

CONCLUSION

Our findings suggest that the ANRIL rs1333048, rs1333042, rs2891168, and rs496892 polymorphisms might influence predisposition to periodontitis, particularly in Caucasians.

CLINICAL SIGNIFICANCE

ANRIL gene may represent a potential risk marker for periodontitis.

Distinct Microbial Signatures between Periodontal Profile Classes

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.

Identification of novel genome-wide pleiotropic associations with oral inflammatory traits

Oral inflammatory diseases (OIDs) are a group of dental diseases with multiple clinical manifestations that impact the majority of the world's population. Many studies have investigated the associations between individual OID traits and genomic variants, but whether pleiotropic loci are shared by oral inflammatory traits remains poorly understood. Here, we conducted multitrait joint analyses based on the summary statistics of genome-wide association studies (GWASs) of five dental traits from the UK Biobank. Among these genome-wide significant loci, two were novel for both painful gums and toothache. We identified causal variants at each novel locus, and functional annotation based on multiomics data suggested IL10 and IL12A/TRIM59 as potential candidate genes at the novel pleiotropic loci. Subsequent analyses of pathway enrichment and protein-protein interaction networks suggested the involvement of the candidate genes in immune regulation. In conclusion, our results uncover novel pleiotropic loci for OID traits and highlight the importance of immune regulation in the pathogenesis of OIDs. These findings will enhance our understanding of the pathogenesis of OIDs and be beneficial for risk screening, prevention, and the development of novel drugs targeting the immune regulation of OIDs.

Human oral mucosa cell atlas reveals a stromal-neutrophil axis regulating tissue immunity

The oral mucosa remains an understudied barrier tissue. This is a site of rich exposure to antigens and commensals, and a tissue susceptible to one of the most prevalent human inflammatory diseases, periodontitis. To aid in understanding tissue-specific pathophysiology, we compile a single-cell transcriptome atlas of human oral mucosa in healthy individuals and patients with periodontitis. We uncover the complex cellular landscape of oral mucosal tissues and identify epithelial and stromal cell populations with inflammatory signatures that promote antimicrobial defenses and neutrophil recruitment. Our findings link exaggerated stromal cell responsiveness with enhanced neutrophil and leukocyte infiltration in periodontitis. Our work provides a resource characterizing the role of tissue stroma in regulating mucosal tissue homeostasis and disease pathogenesis.

Association of short sleep with risk of periodontal disease: A meta-analysis and Mendelian randomization study

AIM

The aim of this meta-analysis and Mendelian randomization (MR) study was to assess the association between short sleep and periodontal disease, including tooth loss and periodontitis.

MATERIALS AND METHODS

The meta-analysis of relevant studies was conducted to assess the association between short sleep and periodontal disease. MR analyses were conducted with the inverse-variance-weighted (IVW) method, weighted median method, MR-Egger method, and MR-Robust Adjusted Profile Score (RAPS) method to assess the causal effect of short sleep on tooth loss and periodontitis.

RESULTS

Seven cross-sectional studies involving 40,196 individuals were included in the meta-analysis. The association between short sleep and periodontal disease was not statistically significant (odds ratios (OR) =1.13, 95% confidence interval (CI): 0.99, 1.28; p = 0.076). In the MR analysis, we did not observe statistically significant causal associations of genetically determined short sleep with tooth loss (β: -0.056; 95% CI: -0.181 to 0.068; p = 0.376) and periodontitis (β: -0.112; 95% CI: -0.340 to 0.117; p = 0.339).

CONCLUSIONS

Short sleep is not associated with the risk of periodontal disease according to current evidence. Future studies need to pay attention to the measurement of sleep duration, the choice of statistical models, and other domains of sleep health.

Using national register data to estimate the heritability of periodontitis

AIM

To identify whether periodontal traits derived from electronic dental records are biologically informative and heritable.

MATERIALS AND METHODS

The study included 11,974 adult twins (aged 30-92 years) in the Swedish Twin Registry. Periodontal records from dental examinations were retrieved from a national register and used to derive continuous measures of periodontal health. A latent class approach was used to derive categorial measures of periodontal status. The correlation patterns in these traits were contrasted in monozygotic and dizygotic twin pairs using quantitative genetic models to estimate the heritability of the traits.

RESULTS

For continuous traits, heritability estimates ranged between 41.5% and 48.3% with the highest estimates for number of missing tooth surfaces and rate of change in number of deep periodontal pockets (≥6 mm). For categorial traits, the latent class approach identified three classes (good periodontal health, mild periodontitis signs and severe signs of periodontitis) and there was a clear difference in the hazard for subsequent tooth loss between these three classes. Despite this, the class allocations were only slightly more heritable than a conventional dichotomous disease definition (45.2% vs. 42.6%).

CONCLUSIONS

Periodontitis is a moderately heritable disease. Quantitative periodontal traits derived from electronic records are an attractive target for future genetic association studies.

Cohort Profile: ZOE 2.0-A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health

Early childhood caries (ECC) is an aggressive form of dental caries occurring in the first five years of life. Despite its prevalence and consequences, little progress has been made in its prevention and even less is known about individuals' susceptibility or genomic risk factors. The genome-wide association study (GWAS) of ECC ("ZOE 2.0") is a community-based, multi-ethnic, cross-sectional, genetic epidemiologic study seeking to address this knowledge gap. This paper describes the study's design, the cohort's demographic profile, data domains, and key oral health outcomes. Between 2016 and 2019, the study enrolled 8059 3-5-year-old children attending public preschools in North Carolina, United States. Participants resided in 86 of the state's 100 counties and racial/ethnic minorities predominated-for example, 48% (n = 3872) were African American, 22% white, and 20% (n = 1611) were Hispanic/Latino. Seventy-nine percent (n = 6404) of participants underwent clinical dental examinations yielding ECC outcome measures-ECC (defined at the established caries lesion threshold) prevalence was 54% and the mean number of decayed, missing, filled surfaces due to caries was eight. Nearly all (98%) examined children provided sufficient DNA from saliva for genotyping. The cohort's community-based nature and rich data offer excellent opportunities for addressing important clinical, epidemiologic, and biological questions in early childhood.

Biologically informed stratification of periodontal disease holds the key to achieving precision oral health

Medicine and dentistry need to treat the individual not the "average patient." This personalized or precision approach to health care involves correctly diagnosing and properly classifying people to effectively customize prevention, diagnosis, and treatment. This is not a trivial undertaking. Achieving precision health requires making sense of big data, both at the population level and at the molecular level. The latter can include genetic, epigenetic, transcriptomic, proteomic, metabolomic data, and microbiome data. This biological information can augment established clinical measurements and supplement data on socioeconomic status, lifestyle, behaviors, and environmental conditions. Here, the central thesis is that, with sufficient data and appropriate methods, it is possible to segregate symptom-based and phenotypically based categories of patients into clinically and biologically similar groups. These groups are likely to have different clinical trajectories and benefit from different treatments. Additionally, such groups are optimal for investigations seeking to unveil the genomic basis of periodontal disease susceptibility. Analysis of these complex data to produce actionable and replicable health and disease categories requires appropriately sophisticated bioinformatics approaches and thorough validation in diverse patient samples and populations. Successful research programs will need to consider both population-level and well-controlled deep phenotyping approaches. Biologically informed stratification of periodontal disease is both feasible and desirable. Ultimately, this approach can accelerate the development of precision health through improvements in research and clinical applications.

Genomics of periodontal disease and tooth morbidity

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..

Searching Deep and Wide: Advances in the Molecular Understanding of Dental Caries and Periodontal Disease

During the past decades, remarkable progress has been made in the understanding of the molecular basis of the 2 most common oral diseases, dental caries and periodontal disease. Improvements in our knowledge of the diseases' underlying biology have illuminated previously unrecognized aspects of their pathogenesis. Importantly, the key role of the oral (supragingival and subgingival) microbiome is now well recognized, and both diseases are now best understood as dysbiotic. From a host susceptibility standpoint, some progress has been made in dissecting the "hyperinflammatory" trait and other pathways of susceptibility underlying periodontitis, and novel susceptibility loci have been reported for dental caries. Nevertheless, there is a long road to the translation of these findings and the realization of precision oral health. There is promise and hope that the rapidly increasing capacity of generating multiomics data layers and the aggregation of study samples and cohorts comprising thousands of participants will accelerate the discovery and translation processes. A first key element in this process has been the identification and interrogation of biologically informed disease traits-these "deep" or "precise" traits have the potential of revealing biologically homogeneous disease signatures and genetic susceptibility loci that might present with overlapping or heterogeneous clinical signs. A second key element has been the formation of international consortia with the goals of combining and harmonizing oral health data of thousands of individuals from diverse settings-these "wide" collaborative approaches leverage the power of large sample sizes and are aimed toward the discovery or validation of genetic influences that would otherwise be impossible to detect. Importantly, advancements via these directions require an unprecedented engagement of systems biology and team science models. The article highlights novel insights into the molecular basis of dental caries and chronic periodontitis that have been gained from recent and ongoing studies involving "deep" and "wide" analytical approaches.

Periodontics in the USA: An introduction

The United States continues to be an incubator for new concepts and approaches to the diagnosis, treatment, and prevention of periodontal diseases. This volume of Periodontology 2000 presents some of these newer areas of research and paradigms that have emerged in the United States from both long-established and new investigators. These areas include: (1) more comprehensive approaches to assessing the total periodontal microbiome, including bacteria, viruses, and fungi, and their interactions with both the local and systemic inflammatory and immune responses, as well as with other oral and systemic conditions and diseases; (2) new developments for a more comprehensive characterization of the patient genome, transcriptome, and proteome profiles and the role of these profiles in periodontal disease pathogenesis; (3) new developments in nonsurgical approaches to periodontal diseases, including broad-based lines of attack using natural antimicrobials and host-modulation therapies and more focused approaches that target specific interactions in the host response; and (4) new big data analysis, machine learning, and imaging approaches, both for understanding the pathogenesis of periodontal diseases and for developing improved risk-assessment tools and better treatment outcomes.

Biologically Defined or Biologically Informed Traits Are More Heritable Than Clinically Defined Ones: The Case of Oral and Dental Phenotypes

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 combined with clinical and biomarker information in 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 were 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 (e.g., subgingival pathogen colonization and "complex" traits). Heritability (i.e., variance explained; h²) was calculated using Visscher's Genome-wide Complex Trait Analysis (GCTA), using a random-effects mixed linear model and restricted maximum likelihood (REML) regression adjusting for ancestry (10 principal components), age, and sex. Heritability 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). "High gingival index" and "high red complex colonization" had h² > 0.50, while a periodontal complex trait defined by high IL-1β GCF expression and Aggregatibacter actinomycetemcomitans subgingival colonization had the highest h² = 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 capture aspects of the disease process and are promising targets for genomics investigations, according to the notion of precision oral health.