SUMMARY Early childhood caries (ECC) is the most common chronic childhood disease. Although largely preventable, ECC affects one third of socioeconomically disadvantaged and racial/ethnic minority preschool children in the US. Effectively reducing ECC disparity requires a better understanding of its biological factors from birth to early childhood including identifying differential exposure to risk factors by race and socioeconomic status. While ECC is an infectious disease initiated by the oral microbiota (bacteria and fungi), the interplay between host, environment, and oral microbiota affects the onset and severity of ECC. However, to date, few studies have examined the early-life longitudinal development of oral microbiota in underserved children, and none have utilized comprehensive methods to examine multiplatform (host and environmental) factors that contribute to the establishment of cariogenic microflora and onset of ECC among the underserved children. Oral Microbiome in Early Infancy (OMEI) study will address this urgent need to understand biological factors related to ECC among underserved racial/ethnic minority groups. The OMEI leverages a recently archived birth cohort that compromises 160 low-income minority infants (primarily Black/African American) and a comprehensive collection of medical/oral health records and ~1760 salivary/supragingival samples (obtained via NIDCR KL2TR001999 and K23DE027412, PI: Xiao). The OMEI builds upon our previous work that 1) revealed racial background is associated with early-life oral microbiome development in the context of ECC; 2) demonstrated oral bacterial-fungal cross-kingdom interactions and their associations with ECC; 3) identified human genes related to Host-S. mutans-Dental caries interactions; and 4) developed machine-learning prediction models for ECC. In Aim 1, we will use metagenomic analysis to define the critical assembly and functional development of the oral microbiome (bacteria and fungi) in early infancy (birth to two years) among all infants and their respective racial groups. In Aim 2, we will use computational modeling to identify determinants (maternal, genetic, and immune factors) of infants’ oral microbiome development. In Aim 3, we will use high- dimensional statistical machine learning approaches to integrate multi-platform (maternal, microbial, genetic, immune, and environmental) data to identify biological factors underlying ECC etiopathogenesis and develop ECC prediction models. The OMEI will be the first study that examines the early-life biological factors underlying ECC disparity from an infants’ oral microbiome perspective. Risk factors revealed from OMEI could be used as targets for ECC early prediction and prevention specifically suitable for underserved children. An integrated health disparities research team with investigators from multiple disciplines (microbiome, perinatal oral health, metagenomic sequencing, high-dimensional biostatistics, genetics, and health disparities), together with an outstanding internal-external advisory committee, will ensure the success of the proposed OMEI project.

NARRATIVE The federal agencies and professional organizations have mounted a significant effort to address an increasingly recognized public health problem, Early Childhood Caries (ECC), among the underserved population. A better understanding of the biological factors related to this pediatric oral health disparity is urgently needed. Leveraging an archived underserved birth cohort, our multidisciplinary team proposes using cutting- edge metagenomic sequencing and analysis tool, combined with a high-dimensional statistical machine learning approach, to examine the early-life oral microbiome development and identify its multilevel determinant and association to ECC. ECC risk factors revealed via prediction models could be used as targets for ECC early prediction and prevention specifically suitable for underserved racial minority children.