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学因素

• 批准号: 10443354
• 负责人: STEVEN R. GILL
• 金额: $ 73.13万
• 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/10443354
• 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; Discipline; Disease; 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; Microbial Genetics; 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; 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 and ethnic; racial disparity; racial minority; racial minority 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.

概括 幼儿汽车（ECC）是最常见的慢性儿童疾病。尽管很大程度上 可预防的ECC影响了社会经济处于不利和种族/族裔少数民族学龄前儿童的三分之一 在美国的孩子。有效降低ECC差异需要更好地了解其生物学因素 从出生到幼儿，包括识别种族和社会经济的差异风险因素 地位。虽然ECC是口腔菌群（细菌和真菌）发起的一种传染病，但相互作用 在宿主，环境和口服微生物群之间会影响ECC的发作和严重程度。但是，迄今为止很少 研究检查了服务不足的儿童口服微生物群的早期纵向发展，以及 没有人利用全面的方法来检查多平台（宿主和环境）因素 在服务不足的儿童中有助于建立致癌的菌群和ECC的发作。 婴儿早期（OMEI）研究中的口服微生物组将解决这种迫切需要了解的需求 在服务不足的种族/族裔少数群体中，与ECC有关的生物因素。 Omei利用 最近存档的出生队列损害了160名低收入少数民族婴儿（主要是黑人/非洲人 美国）和医疗/口腔健康记录的全面集合以及〜1760 样品（通过NIDCR KL2TR001999和K23DE027412，PI：Xiao获得）。 Omei建立在我们以前的 1）揭示种族背景与背景下的早期口腔微生物组的发展有关 ecc; 2）表现出口服细菌杂种跨王朝的相互作用及其与ECC的关联； 3） 确定了与宿主-S相关的人类基因。变形式龋齿相互作用； 4）开发机器学习 ECC的预测模型。在AIM 1中，我们将使用宏基因组分析来定义关键组装和 婴儿早期（两年出生至两年）口服微生物组（细菌和真菌）的功能发展 婴儿及其各自的种族群体。在AIM 2中，我们将使用计算建模来识别确定器 婴儿口服微生物组发育的（母体，遗传和免疫因素）。在AIM 3中，我们将使用高 尺寸统计机器学习方法以整合多平台（母体，微生物，遗传， 免疫和环境）数据，以识别ECC病原体的基础生物学因素并发展 ECC预测模型。 Omei将是第一个研究早期生物生物学因素的研究 从婴儿的口服微生物组的角度来看，ECC差异。 OMEI揭示的危险因素可以用作 ECC的早期预测和预防目标，专门适用于服务不足的儿童。一个集成的 健康差异研究团队与来自多个学科的研究人员（微生物组，围产期口腔健康， 宏基因组测序，高维生物统计学，遗传学和健康差异）以及 出色的内部咨询委员会将确保拟议的OMEI项目的成功。