North Carolina Physician-Based Preventive Oral Health Services Improve Access And Use Among Young Medicaid Enrollees

To combat disparities in oral health and access to dental care among infants and toddlers, most state Medicaid programs now reimburse physician-based preventive oral health services, such as fluoride varnish applications. We used geospatial data to examine the distribution of dental and medical Medicaid providers of pediatric oral health services throughout North Carolina to determine if these services have improved access to care for Medicaid enrollees younger than three years old. We then used claims data to examine the association between distance from these practices and use of dental services for a cohort of approximately 1,000 young children. Among 100 counties, four counties had no physician-based preventive oral health services and nine counties had no dental practice. While children who lived further from the nearest dental practice were less likely to make dental visits, distance from physician-based preventive oral health services did not predict use. For young Medicaid enrollees, oral health services provided in medical offices can improve access and increase use

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome–metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite–species and species–species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome–metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies

Comparison of Bacterial Community Composition of Primary and Persistent Endodontic Infections Using Pyrosequencing

Elucidating the microbial ecology of endodontic infections (EI) is a necessary step in developing effective intra-canal antimicrobials. The aim of the present study was to investigate the bacterial composition of symptomatic and asymptomatic primary and persistent infections in a Greek population, using high throughput sequencing methods

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

Reaching the unreached: de-mystifying the role of ICT in the process of doctoral research

Information and Communication Technology (ICT) has become a necessary element of academic practice in higher education today. Under normal circumstances, PhD students from all disciplines have to use ICT in some form throughout the process of their research, including the preparation, fieldwork, analysis and writing phases of their studies. Nevertheless, there has been little research to date that explores PhD students’ first-hand experiences of using various ICT to support their research practices. This paper brings together the findings and the key points from a review of significant parts of the existing literature associated with the role played by ICT in the processes PhD students use in doctoral research. The review is based on 27 papers appearing in international peer-reviewed journals published from 2005 to 2014. The study seeks to address the under-researched area in the current literature of how ICT plays a role in the processes of doctoral research. While there are many contributions taking the ‘institutional’ or ‘teaching’ perspectives, papers focusing on ‘student’ perspective, or the viewpoint of engaging ICT in daily study routine, are relatively fewer. As far as research methodology is concerned, this review found that many of the papers that were examined were mostly based on perception data such as surveys or interviews, while actual practice data were rarely present. With their ready access to technologies, PhD students are well positioned to take advantage of a range of technologies in order to carry out their research efficiently (in terms of means to an end) and effectively (in terms of reaching goals within a task). This review reveals that in the literature, this important area is under-represented

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.Peer reviewe

Using genetics to test the causal relationship of total adiposity and periodontitis: Mendelian randomization analyses in the Gene-Lifestyle Interactions and Dental Endpoints (GLIDE) Consortium

Background: The observational relationship between obesity and periodontitis is widely known, yet causal evidence is lacking. Our objective was to investigate causal associations between periodontitis and body mass index (BMI).Methods: We performed Mendelian randomization analyses with BMI-associated loci combined in a genetic risk score (GRS) as the instrument for BMI. All analyses were conducted within the Gene-Lifestyle Interactions and Dental Endpoints (GLIDE) Consortium in 13 studies from Europe and the USA, including 49 066 participants with clinically assessed (seven studies, 42.1% of participants) and self-reported (six studies, 57.9% of participants) periodontitis and genotype data (17 672/31 394 with/without periodontitis); 68 761 participants with BMI and genotype data; and 57 871 participants (18 881/38 990 with/without periodontitis) with data on BMI and periodontitis.Results: In the observational meta-analysis of all participants, the pooled crude observational odds ratio (OR) for periodontitis was 1.13 [95% confidence interval (CI): 1.03, 1.24] per standard deviation increase of BMI. Controlling for potential confounders attenuated this estimate (OR = 1.08; 95% CI:1.03, 1.12). For clinically assessed periodontitis, corresponding ORs were 1.25 (95% CI: 1.10, 1.42) and 1.13 (95% CI: 1.10, 1.17), respectively. In the genetic association meta-analysis, the OR for periodontitis was 1.01 (95% CI: 0.99, 1.03) per GRS unit (per one effect allele) in all participants and 1.00 (95% CI: 0.97, 1.03) in participants with clinically assessed periodontitis. The instrumental variable meta-analysis of all participants yielded an OR of 1.05 (95% CI: 0.80, 1.38) per BMI standard deviation, and 0.90 (95% CI: 0.56, 1.46) in participants with clinical data.Conclusions: Our study does not support total adiposity as a causal risk factor for periodontitis, as the point estimate is very close to the null in the causal inference analysis, with wide confidence intervals

Exploring the genetic basis of chronic periodontitis: a genome-wide association study

Chronic periodontitis (CP) is a common oral disease that confers substantial systemic inflammatory and microbial burden and is a major cause of tooth loss. Here, we present the results of a genome-wide association study of CP that was carried out in a cohort of 4504 European Americans (EA) participating in the Atherosclerosis Risk in Communities (ARIC) Study (mean age—62 years, moderate CP—43% and severe CP—17%). We detected no genome-wide significant association signals for CP; however, we found suggestive evidence of association (P < 5 × 10−6) for six loci, including NIN, NPY, WNT5A for severe CP and NCR2, EMR1, 10p15 for moderate CP. Three of these loci had concordant effect size and direction in an independent sample of 656 adult EA participants of the Health, Aging, and Body Composition (Health ABC) Study. Meta-analysis pooled estimates were severe CP (n = 958 versus health: n = 1909)—NPY, rs2521634 [G]: odds ratio [OR = 1.49 (95% confidence interval (CI = 1.28–1.73, P = 3.5 × 10−7))]; moderate CP (n = 2293)—NCR2, rs7762544 [G]: OR = 1.40 (95% CI = 1.24–1.59, P = 7.5 × 10−8), EMR1, rs3826782 [A]: OR = 2.01 (95% CI = 1.52–2.65, P = 8.2 × 10−7). Canonical pathway analysis indicated significant enrichment of nervous system signaling, cellular immune response and cytokine signaling pathways. A significant interaction of NUAK1 (rs11112872, interaction P = 2.9 × 10−9) with smoking in ARIC was not replicated in Health ABC, although estimates of heritable variance in severe CP explained by all single nucleotide polymorphisms increased from 18 to 52% with the inclusion of a genome-wide interaction term with smoking. These genome-wide association results provide information on multiple candidate regions and pathways for interrogation in future genetic studies of CP

GWAS for Interleukin-1β levels in gingival crevicular fluid identifies IL37 variants in periodontal inflammation

There is no agnostic GWAS evidence for the genetic control of IL-1β expression in periodontal disease. Here we report a GWAS for “high” gingival crevicular fluid IL-1β expression among 4910 European-American adults and identify association signals in the IL37 locus. rs3811046 at this locus (p = 3.3 × 10−22) is associated with severe chronic periodontitis (OR = 1.50; 95% CI = 1.12–2.00), 10-year incident tooth loss (≥3 teeth: RR = 1.33; 95% CI = 1.09–1.62) and aggressive periodontitis (OR = 1.12; 95% CI = 1.01–1.26) in an independent sample of 4927 German/Dutch adults. The minor allele at rs3811046 is associated with increased expression of IL-1β in periodontal tissue. In RAW macrophages, PBMCs and transgenic mice, the IL37 variant increases expression of IL-1β and IL-6, inducing more severe periodontal disease, while IL-37 protein production is impaired and shows reduced cleavage by caspase-1. A second variant in the IL37 locus (rs2708943, p = 4.2 × 10−7) associates with attenuated IL37 mRNA expression. Overall, we demonstrate that IL37 variants modulate the inflammatory cascade in periodontal disease