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

基本信息

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
项目类别：
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

项目摘要

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.

概括儿童早期龋齿（ECC）是最常见的慢性儿童疾病。 ECC 是可以预防的，它影响了三分之一的社会经济弱势群体和少数种族/族裔学前班有效减少美国儿童的 ECC 差异需要更好地了解其生物学因素。从出生到幼儿期，包括确定种族和社会经济因素对风险因素的不同暴露程度虽然 ECC 是一种由口腔微生物群（细菌和真菌）引发的传染病，但它们之间相互作用。宿主、环境和口腔微生物群之间的关系会影响 ECC 的发生和严重程度，但迄今为止，研究很少。研究调查了服务不足的儿童生命早期口腔微生物群的纵向发展，以及没有人利用综合方法来检查多平台（主机和环境）因素有助于在服务不足的儿童中建立致龋菌群和 ECC 的发生。婴儿早期口腔微生物组 (OMEI) 研究将解决这一迫切需要 OMEI 利用与服务不足的少数族裔群体中的 ECC 相关的生物因素。最近存档的出生队列涉及 160 名低收入少数族裔婴儿（主要是黑人/非洲人）美国）以及全面收集的医疗/口腔健康记录和约 1760 份唾液/龈上样本样本（通过 NIDCR KL2TR001999 和 K23DE027412 获得，PI：Xiao）。 OMEI 建立在我们之前的基础上。 1）揭示种族背景与早期口腔微生物群发展相关的工作 ECC；2) 经口腔证实的细菌-真菌跨界相互作用及其与 ECC 的关联；确定了与 Host-S 和龋齿相互作用相关的人类基因；4) 开发了机器学习； ECC 的预测模型在目标 1 中，我们将使用宏基因组分析来定义关键的组装和婴儿早期（出生至两岁）口腔微生物组（细菌和真菌）的功能发育在目标 2 中，我们将使用计算模型来确定婴儿及其各自的种族群体。（母体、遗传和免疫因素）婴儿口腔微生物组发育的影响在目标 3 中，我们将使用高- 多维统计机器学习方法集成多平台（母体、微生物、遗传、免疫和环境）数据来识别 ECC 发病机制背后的生物因素并开发 ECC 预测模型将是第一个研究早期生命生物学因素的研究。从婴儿口腔微生物组的角度来看 ECC 差异可以用作 OMEI 揭示的风险因素。特别适合服务不足的儿童的 ECC 早期预测和预防目标。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、宏基因组测序、高维生物统计学、遗传学和健康差异），以及优秀的内部外部咨询委员会将确保拟议的 OMEI 项目的成功。