Understand biological factors underlying early childhood caries disparity from the oral microbiome in early infancy

从婴儿早期口腔微生物组了解儿童早期龋齿差异背后的生物因素

• 批准号: 10765136
• 负责人: STEVEN R. GILL
• 金额: $ 17.89万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10765136
• 关键词: Address; Advisory Committees; Affect; African American; Archives; Bacteria; Biological; Biological Factors; Biometry; Birth; Black race; Caries prevention; Child; Child Development; Childhood; Chronic; Collection; Communicable Diseases; Communities; Computer Models; Data; Data Sources; Dental; Dental caries; Development; Disadvantaged minority; Discipline; Disease; Disparity; Ensure; Environment; Environmental Risk Factor; Exposure to; Genes; Genetic; Goals; Health; Health Disparities Research; Human; Immune; Immunologic Factors; Immunologic Markers; Infant; Infant Development; Life; Life Cycle Stages; Low income; Machine Learning; Medical; Metagenomics; Methods; Minority; Modeling; Mothers; Mycoses; National Institute of Dental and Craniofacial Research; Oral; Oral health; Pattern; Perinatal; Preschool Child; Prevention; Professional Organizations; Public Health; Race; Research Personnel; Risk Factors; Saliva; Salivary; Sampling; Severities; Shapes; Socioeconomic Status; Streptococcus mutans; Testing; Time; Training; Underserved Population; Work; cohort; craniofacial; early childhood; early onset; ethnic minority; ethnic minority population; fungus; health disparity; health record; high dimensionality; infancy; innovation; machine learning prediction; metagenomic sequencing; microbial; microbiome; microbiome composition; microbiota; minority children; multidisciplinary; oral bacteria; oral microbial community; oral microbiome; predictive modeling; prenatal; racial disparity; racial minority; racial minority population; racial population; socioeconomic disadvantage; socioeconomic disparity; statistical and machine learning; success; tool; transmission process

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.

摘要 儿童早期龋病(ECC)是儿童最常见的慢性疾病。尽管很大程度上 可预防的，ECC影响到三分之一的社会经济弱势群体和种族/少数民族学龄前儿童 美国的儿童。有效缩小ECC差距需要更好地了解其生物学因素 从出生到儿童早期，包括按种族和社会经济确定不同的风险因素暴露 状态。虽然ECC是一种由口腔微生物区系(细菌和真菌)引发的传染病，但这种相互作用 宿主、环境和口腔微生物区系之间的关系影响ECC的发生和严重程度。然而，到目前为止，几乎没有人 研究已经检查了缺乏服务的儿童早期口腔微生物区系的纵向发展，以及 没有人利用全面的方法来检查多平台(主机和环境)因素 有助于在未得到充分服务的儿童中建立起致龋性微生物区系，并使儿童患上ECC。 早期婴幼儿口腔微生物组(OMEI)研究将解决这一迫切需要了解的问题 在服务不足的种族/少数民族群体中，与ECC有关的生物学因素。OMEI利用 最近存档的出生队列危及160名低收入少数族裔婴儿(主要是黑人/非洲人 和全面的医疗/口腔健康记录和~1760份唾液/牙龈上液 样品(通过NIDCR KL2TR001999和K23DE027412获得，PI：肖)。OMEI建立在我们之前的基础上 研究表明，种族背景与早期口腔微生物群的发育有关 2)展示了口腔细菌-真菌的跨界相互作用及其与ECC的关系；3) 鉴定出与宿主S相关的人类基因。变种人与龋齿的相互作用；4)发展了机器学习 ECC的预测模型。在目标1中，我们将使用元基因组分析来定义关键组装和 婴幼儿早期(出生至两岁)口腔微生物组(细菌和真菌)的功能发育 婴儿和他们各自的种族群体。在目标2中，我们将使用计算建模来确定决定因素 (母体、遗传和免疫因素)对婴儿口腔微生物群发育的影响。在目标3中，我们将使用HIGH- 维度统计机器学习方法，用于集成多平台(母体、微生物、遗传、 免疫和环境)数据，以确定ECC发病机制和发展的生物学因素 ECC预测模型。OMEI将是第一项检查早期生命生物学因素的研究 从婴儿口腔微生物组的角度看ECC差异。OMEI揭示的风险因素可以作为 ECC早期预测和预防的目标特别适用于服务不足的儿童。一个完整的 健康差异研究团队由来自多个学科的研究人员组成(微生物组、围产期口腔健康、 元基因组测序、高维生物统计学、遗传学和健康差异)，以及 优秀的内部和外部咨询委员会将确保拟议的OMEI项目取得成功。