The building blocks of precision oral health in early childhood: the ZOE 2.0 study

Guardians' Self-Reported Fair/Poor Oral Health Is Associated with Their Young Children's Fair/Poor Oral Health and Clinically Determined Dental Caries Experience

In this cross-sectional, community-based study among a multi-ethnic sample of preschool-age children in North Carolina, United States, we sought to quantify the association between guardians' self-reported oral health and their children's oral health and determine whether race/ethnicity and education level modify these associations. We used questionnaire (n = 7852) responses about caregivers' and their children's oral health and clinical examination-derived (n = 6243) early childhood caries (ECC) status defined at the ICDAS ≥ 3 caries lesion detection threshold. We used multi-level mixed-effects generalized linear models to examine the associations between the guardians' reported oral health and their children's reported and clinically determined oral health among the entire sample and within strata of race/ethnicity, guardians' education, and children's dental home. The guardians' and their children's reported fair/poor oral health (FPOH) were 32% and 15%, respectively, whereas 54% of the children had ECC and 36% had unrestored disease. The guardians' FPOH was strongly associated with their children's FPOH (average marginal effect (AME) = +19 percentage points (p.p.); 95% CI = 17-21), and this association was most pronounced among Hispanics, lower-educated guardians, and children without a dental home. Similar patterns, but smaller-in-magnitude associations, were found for the guardians' FPOH and their children's clinically determined ECC (AME = +9 p.p.; 95% CI = 6-12) and unrestored disease (AME = +7 p.p.; 95% CI = 4-9). The study's findings support a strong association between guardians' and their children's reported and clinically determined oral health and implicate ethnicity, education, and having a dental home as factors possibly modifying the magnitude of these associations.

PARTITIONING AROUND MEDOIDS CLUSTERING AND RANDOM FOREST CLASSIFICATION FOR GIS-INFORMED IMPUTATION OF FLUORIDE CONCENTRATION DATA

Community water fluoridation is an important component of oral health promotion, as fluoride exposure is a well-documented dental caries-preventive agent. Direct measurements of domestic water fluoride content provide valuable information regarding individuals' fluoride exposure and thus caries risk; however, they are logistically challenging to carry out at a large scale in oral health research. This article describes the development and evaluation of a novel method for the imputation of missing domestic water fluoride concentration data informed by spatial autocorrelation. The context is a state-wide epidemiologic study of pediatric oral health in North Carolina, where domestic water fluoride concentration information was missing for approximately 75% of study participants with clinical data on dental caries. A new machine-learning-based imputation method that combines partitioning around medoids clustering and random forest classification (PAMRF) is developed and implemented. Imputed values are filtered according to allowable error rates or target sample size, depending on the requirements of each application. In leave-one-out cross-validation and simulation studies, PAMRF outperforms four existing imputation approaches-two conventional spatial interpolation methods (i.e., inverse-distance weighting, IDW and universal kriging, UK) and two supervised learning methods (k-nearest neighbors, KNN and classification and regression trees, CART). The inclusion of multiply imputed values in the estimation of the association between fluoride concentration and dental caries prevalence resulted in essentially no change in PAMRF estimates but substantial gains in precision due to larger effective sample size. PAMRF is a powerful new method for the imputation of missing fluoride values where geographical information exists.

Guardian Reports of Children's Sub-optimal Oral Health Are Associated With Clinically Determined Early Childhood Caries, Unrestored Caries Lesions, and History of Toothaches

Background: Parents'/guardians' perceptions of their children's oral health are useful proxies of their clinically determined caries status and are known to influence dental care-seeking behavior. In this study, we sought to examine (1) the social and behavioral correlates of fair/poor child oral health reported by guardians and (2) quantify the association of these reports with the prevalence of early childhood caries (ECC), unrestored caries lesions and toothaches. Methods: We used guardian-reported child oral health information (dichotomized as fair/poor vs. excellent/very good/good) obtained via a parent questionnaire that was completed for n = 7,965 participants (mean age = 52 months; range = 36-71 months) of a community-based, cross-sectional epidemiologic study of early childhood oral health in North Carolina between 2016 and 2019. Social, demographic, oral health-related behavioral data, and reports on children's history of toothaches (excluding teething) were collected in the same questionnaire. Unrestored ECC (i.e., caries lesions) was measured via clinical examinations in a subset of n = 6,328 children and was defined as the presence of one or more tooth surfaces with an ICDAS ≥ 3 caries lesion. Analyses relied on descriptive and bivariate methods, and multivariate modeling with average marginal effect (A.M.E.) estimation accounting for the clustered nature of the data. Estimates of association [prevalence ratios (PR) and adjusted marginal effects (AME) with 95% confidence intervals (CI)] were obtained via multilevel generalized linear models using Stata's svy function and accounting for the clustered nature of the data. Results: The prevalence of fair/poor oral health in this sample was 15%-it increased monotonically with children's age, was inversely associated with parents' educational attainment, and was higher among Hispanics (21%) and African Americans (15%) compared to non-Hispanic whites (11%). Brushing less than twice a day, not having a dental home, and frequently consuming sugar-containing snacks and beverages were significantly associated with worse reports (P < 0.0005). Children with fair/poor reported oral health were twice as likely to have unrestored caries lesions [prevalence ratio (PR) = 2.0; 95% confidence interval (CI) = 1.8-2.1] and 3.5 times as likely to have experienced toothaches [PR = 3.5; 95% CI = 3.1-3.9] compared to those with better reported oral health. Conclusions: Guardian reports of their children's oral health are valuable indicators of clinical and public health-important child oral health status. Those with fair/poor guardian-reported child oral health have distinguishing characteristics spanning socio-demographics, oral-health related practices, diet, and presence of a dental home.

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

A Web-Based Rendering Application for Communicating Dental Conditions

The importance of visual aids in communicating clinical examination findings or proposed treatments in dentistry cannot be overstated. Similarly, communicating dental research results with tooth surface-level precision is impractical without visual representations. Here, we present the development, deployment, and two real-life applications of a web-based data visualization informatics pipeline that converts tooth surface-level information to colorized, three-dimensional renderings. The core of the informatics pipeline focuses on texture (UV) mapping of a pre-existing model of the human primary dentition. The 88 individually segmented tooth surfaces receive independent inputs that are represented in colors and textures according to customizable user specifications. The web implementation SculptorHD, deployed on the Google Cloud Platform, can accommodate manually entered or spreadsheet-formatted tooth surface data and allows the customization of color palettes and thresholds, as well as surface textures (e.g., condition-free, caries lesions, stainless steel, or ceramic crowns). Its current implementation enabled the visualization and interpretation of clinical early childhood caries (ECC) subtypes using latent class analysis-derived caries experience summary data. As a demonstration of its potential clinical utility, the tool was also used to simulate the restorative treatment presentation of a severe ECC case, including the use of stainless steel and ceramic crowns. We expect that this publicly available web-based tool can aid clinicians and investigators deliver precise, visual presentations of dental conditions and proposed treatments. The creation of rapidly adjustable lifelike dental models, integrated to existing electronic health records and responsive to new clinical findings or planned for future work, is likely to boost two-way communication between clinicians and their patients.

An Automated Machine Learning Classifier for Early Childhood Caries

Purpose: The purpose of the study was to develop and evaluate an automated machine learning algorithm (AutoML) for children's classification according to early childhood caries (ECC) status. Methods: Clinical, demographic, behavioral, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used. ECC prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication. Results: A parsimonious model including two terms (i.e., children's age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviors, fluoride exposure, and dental home had worse performance (AUC equals 0.66, Se equals 0.54, PPV equals 0.61). Conclusions: Parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for ECC screening. The classifier can accommodate biological information that can help improve its performance in the future.

Metabolomics Insights in Early Childhood Caries

Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log₂-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10^-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10^-6) and N-acetylneuraminate (p = 6.8 × 10^-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10^-6) and epicatechin (P = 2.9 × 10^-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

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.

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.

Traumatic dental injuries in preschool-age children: Prevalence and risk factors

This study examined the prevalence, socio-demographic correlates, and clinical predictors of traumatic dental injuries (TDIs) in the primary dentition among a community-based sample of preschool-age children. The sample comprised 1,546 preschool-age children (mean age 49 [range: 24-71] months) in North Carolina public preschools, enrolled in a population-based investigation among young children and their parents in North Carolina. Information on socio-demographic, extraoral, and intraoral characteristics was collected and analyzed with bivariate and multivariate methods, including logistic regression modeling and marginal effects estimation. The prevalence of dental trauma was 47% and 8% of TDI cases were "severe" (pulp exposure, tooth displacement, discolored or necrotic tooth, or tooth loss). In bivariate analyses, overjet and lip incompetence were significantly associated with TDI. Overjet remained positively associated with severe trauma in multivariate analysis, OR = 1.4, 95% confidence interval (CI) [1.2, 1.6], corresponding to an absolute 1.3%, 95% CI [0.7, 1.8], increase in the likelihood of severe trauma, per millimeter of overjet. Children with increased overjet (>3 mm) were 3.8, 95% CI [2.0, 7.4], times as likely to have experienced severe TDI compared with those with ≤3 mm. Overjet is a strong risk factor for TDIs in the primary dentition. Incorporating and operationalizing this information may help TDI prevention and related anticipatory guidance for families of preschool-age children.