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.

Early childhood caries (ECC) is a biofilm-mediated disease. Social, environmental and behavioral determinants as well as innate susceptibility are major influences on its incidence; however, from a pathogenetic standpoint, the disease is defined and driven by oral dysbiosis. In other words, the disease occurs when the natural equilibrium between the host and its oral microbiome shifts towards states that promote demineralization at the biofilm-tooth surface interface. Thus, a comprehensive understanding of dental caries as a disease requires the characterization of both the composition and the function or metabolic activity of the supragingival biofilm according to well-defined clinical statuses. However, taxonomic and functional information of the supragingival biofilm is rarely available in clinical cohorts, and its collection presents unique challenges among very young children. This paper presents a protocol and pipelines available for the conduct of supragingival biofilm microbiome studies among children in the primary dentition that has been designed in the context of a large-scale population-based genetic epidemiologic study of ECC. The protocol has been developed for the collection of two supragingival biofilm samples from the maxillary primary dentition, enabling downstream taxonomic (e.g., metagenomics) and functional (e.g., transcriptomics and metabolomics) analyses. The protocol is being implemented in the assembly of a pediatric precision medicine cohort comprising over 5,000 participants to-date, contributing social, environmental, behavioral, clinical and biological data informing ECC and other oral health outcomes.

Aim: This in vivo experimental study investigated bacterial microbiome and metabolome longitudinal changes associated with enamel caries lesion progression and arrest. Methods: We induced natural caries activity in three caries-free volunteers prior to four premolar extractions for orthodontic reasons. The experimental model included placement of a modified orthodontic band on smooth surfaces and a mesh on occlusal surfaces. We applied the cariesinducing protocol for 4-and 6-weeks, and subsequently promoted caries lesion arrest via a 2week toothbrushing period. Lesions were verified clinically and quantitated via micro-CT enamel density measurements. The biofilm microbial composition was determined via 16S rRNA gene Illumina sequencing and NMR spectrometry was used for metabolomics. Results: Biofilm maturation and caries lesion progression were characterized by an increase in Gram-negative anaerobes, including Veillonella and Prevotella. Streptococcus was associated caries lesion progression, while a more equal distribution of Streptococcus, Bifidobacterium, Atopobium, Prevotella, Veillonella, and Saccharibacteria (TM7) characterized arrest. Lactate, acetate, pyruvate, alanine, valine, and sugars were more abundant in mature biofilms compared to newly formed biofilms. Conclusions: These longitudinal bacterial microbiome and metabolome results provide novel mechanistic insights into the role of the biofilm in caries progression and arrest and offer promising candidate biomarkers for validation in future studies.

Summary Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe’s Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.

Measuring gene-gene dependence in single cell RNA sequencing (scRNA-seq) count data is often of interest and remains challenging, because an unidentified portion of the zero counts represent non-detected RNA due to technical reasons. Conventional statistical methods that fail to account for technical zeros incorrectly measure the dependence among genes. To address this problem, we propose a bivariate zero-inflated negative binomial (BZINB) model constructed using a bivariate Poisson-gamma mixture with dropout indicators for the technical (excess) zeros. Parameters are estimated based on the EM algorithm and are used to measure the underlying dependence by decomposing the two sources of zeros. Compared to existing models, the proposed BZINB model is specifically designed for estimating dependence and is more flexible, while preserving the marginal zero-inflated negative binomial distributions. Additionally, it has a simple latent variable framework, allowing parameters to have clear and intuitive interpretations, and its computation is feasible with large scale data. Using a recent scRNA-seq dataset, we illustrate model fitting and how the model-based measures can be different from naive measures. The inferential ability of the proposed model is evaluated in a simulation study. An R package 'bzinb' is available on CRAN.