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

A systematic review and meta-analysis on early-childhood-caries global data

OBJECTIVES

The present study systematically reviewed and provided a meta-analysis on early childhood caries (ECC) global prevalence and its association with socioeconomic indicators, both geographical and regarding unemployment rate, national income as well as income inequalities.

METHODS

Only cross-sectional or cohort studies covering ECC prevalence and experience in children younger than 71 months, reporting sample size, diagnostic criteria and conducted in urban and rural communities were considered. No language restriction was selected. Studies published from 2011 to 2022 available in PubMed, Web of Science, Embase and Open Grey literature were retrieved by ad hoc prepared search strings. The meta-analyses were conducted for both overall ECC prevalence and experience stratified by country of publication as well as measures of socioeconomic indicators using a random effects model using STATA 18®.

RESULTS

One hundred publications reporting ECC data from 49 countries (published from 2011 to 2022) were included and summarized by meta-analysis. The lowest prevalence was reported in Japan (20.6%) and Greece (19.3%). The global estimated random-effect pooled prevalence of ECC was 49% (95%CI: 0.44-0.55). The random-effect pooled caries prevalence (ECC) was 34% (95%CI: 02.20-0.48) (Central/South America), 36% (95%CI: 0.25-0.47) (Europe), 42% (95%CI: 0.32-0.53) (Africa), 52% (95%CI: 0.45-0.60) (Asia-Oceania), 57% (95%CI: 0.36-0.77) (North America) and 72% (95%CI: 0.58-0.85) (Middle East). When stratified by gross national income (GNI) the ECC prevalence ranged from 30% ($20,000-$39,999) to 57% in countries with the lowest GNI (<$5000). Stratification by inequality index (Gini index) resulted in an ECC prevalence range of 39% (low inequality) to 62% (no inequality), while for life expectancy the ECC prevalence ranged from 28% in countries with the highest life expectancy (< 80 years) to 62% in countries with 71-75 years life expectancy.

DISCUSSION

Within the limitations of this study (lack of certainty about the results as many countries are not represented and lack of uniformity in prevalence and experience data represented), results from 49 different countries reported a wide range of ECC prevalence. These reports indicated persisting high worldwide distribution of the disease. Both ECC prevalence and experience were associated with geographical areas and GNI.

REGISTRATION

PROSPERO: CRD-42,022,290,418.

Improvement of parents' oral health knowledge by a school-based oral health promotion for parents of preschool children: a prospective observational study

BACKGROUND

Parents of preschool children have inadequate oral health knowledge in Hong Kong. Parents play a critical role in preschool children's dietary patterns and oral health behaviors. A school-based oral health promotion (OHP) for parents of preschoolers was developed and investigated.

OBJECTIVES

The objective of this study was to evaluate effects of the school-based OHP for parents of preschool children on parents' oral health knowledge and preschool children's early childhood caries (ECC).

MATERIALS AND METHODS

This was a quasi-experimental study. Parents of preschool children were divided into the intervention group (IG) and the control group (CG) according to their own selection. Parents in the IG participated in a structured school-based OHP workshop, while those in the CG did not attend the OHP workshop. Parents in both groups were invited to complete a questionnaire assessing their oral health knowledge before (T0), one month after (T1), and twelve months after (T2) the OHP workshop. Preschool children's caries was examined via dmft score at T0 and T2.

RESULTS

Parents' oral health knowledge was negatively correlated with preschool children's dmft scores (R = -0.200, P < 0.001). Oral health knowledge was significantly improved in IG (P < 0.001) but not in CG (P = 0.392) at T1. Both groups experienced a significant improvement in oral health knowledge from T0 to T2 (P < 0.001). Parents' oral health knowledge in the IG was significantly higher compared to the CG at T1 (P < 0.001), but difference in the scores at T2 between the two groups showed no significant difference (P = 0.727). No significant difference was found in changes in children's dmft score from T0 to T2 between the IG and CG (p = 0.545).

CONCLUSION

Preschool children's high ECC is associated with the limited oral health knowledge of their parents. The school-based OHP workshop for parents increased parents' oral health knowledge within one month. This positive effect was maintained for twelve months and can be extended to a larger scale in the school setting.

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries

Streptococcus mutans has been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity of Selenomonas sputigena, Prevotella salivae and Leptotrichia wadei, either individually or with S. mutans. We show that S. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulating S. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability of S. sputigena to colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected with S. mutans, S. sputigena causes extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

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.

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 disease, 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.

Understanding the Predictive Potential of the Oral Microbiome in the Development and Progression of Early Childhood Caries

BACKGROUND

Early childhood caries (ECC) is the most common chronic disease in young children and a public health problem worldwide. It is characterized by the presence of atypical and fast progressive caries lesions. The aggressive form of ECC, severe early childhood caries (S-ECC), can lead to the destruction of the whole crown of most of the deciduous teeth and cause pain and sepsis, affecting the child's quality of life. Although the multifactorial etiology of ECC is known, including social, environmental, behavioral, and genetic determinants, there is a consensus that this disease is driven by an imbalance between the oral microbiome and host, or dysbiosis, mediated by high sugar consumption and poor oral hygiene. Knowledge of the microbiome in healthy and caries status is crucial for risk monitoring, prevention, and development of therapies to revert dysbiosis and restore oral health. Molecular biology tools, including next-generation sequencing methods and proteomic approaches, have led to the discovery of new species and microbial biomarkers that could reveal potential risk profiles for the development of ECC and new targets for anti-caries therapies. This narrative review summarized some general aspects of ECC, such as definition, epidemiology, and etiology, the influence of oral microbiota in the development and progression of ECC based on the current evidence from genomics, transcriptomic, proteomic, and metabolomic studies and the effect of antimicrobial intervention on oral microbiota associated with ECC.

CONCLUSION

The evaluation of genetic and proteomic markers represents a promising approach to predict the risk of ECC before its clinical manifestation and plan efficient therapeutic interventions for ECC in its initial stages, avoiding irreversible dental cavitation.

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.

Children's oral health-related behaviours and early childhood caries: A latent class analysis

OBJECTIVE

In this cross-sectional study in a large community-based sample of preschool-age children, we sought to identify distinct clusters of modifiable early childhood oral health-related behaviours (OHBs) and quantify their association with clinical and parent-reported measures of early childhood oral health.

METHODS

We relied upon a questionnaire (n = 8033; 11% in Spanish) and clinical oral health data (n = 6404; early childhood caries [ECC] prevalence = 54%] collected in the context of an epidemiologic study of early childhood oral health among 3- to 5-year-old children in North Carolina. Latent class analysis was used to identify clusters of modifiable OHBs based on parents' responses to 6 questionnaire items pertaining to their children's oral hygiene, diet and dental home. The optimal number of clusters was determined based on measures of model fit and interpretability. We examined associations of OHB clusters with clinical and parent-reported child oral health status (ie, ECC prevalence, severity and proportion with untreated disease) using bivariate association tests and multivariable regression modelling with marginal effects estimation accounting for clustered data. We used Mplus v.8.6 (Muthén & Muthén, Los Angeles, CA, USA) and Stata v.16.1 (StataCorp, College Station, TX, USA) for data analyses.

RESULTS

We identified 2 OHB clusters, a favourable (74%) and an unfavourable (26%) one. Children in the favourable OHB cluster had better oral hygiene practices (ie, tooth brushing frequency and fluoridated toothpaste use), lower consumption frequency of sugar-containing snacks and beverages, less frequent reports of night-time bottle-feeding history and a higher likelihood of a dental home. Children in the unfavourable cluster had significantly higher ECC prevalence (57% vs 53%), caries burden (mean dmfs = 9.3 vs 7.6), untreated disease (43% vs 33%) and worse parent-reported oral health status than the favourable cluster.

CONCLUSIONS

Our findings demonstrate the importance and utility of clustering common, modifiable ECC risk factors in population studies - health promotion efforts may centre on groups of people rather than individual behavioural risk factors.

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.