权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10612957
负责人：
STEVEN R. GILL
金额：
$ 71.73万
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/10612957
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.

总结幼儿龋病是最常见的儿童慢性疾病。虽然在很大程度上幼儿保育是可以预防的，影响到三分之一的社会经济弱势群体和少数种族/族裔学龄前儿童。美国的孩子。有效地减少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影响 status.虽然ECC是一种由口腔微生物群（细菌和真菌）引发的传染病，宿主、环境和口腔微生物群之间的相互作用影响ECC的发病和严重程度。然而，迄今为止，研究已经检查了服务不足儿童口腔微生物群的早期纵向发展，没有人利用综合方法来检查多平台（宿主和环境）因素，有助于在服务不足的儿童中建立致龋微生物菌群和ECC的发病。早期婴儿口腔微生物组（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，与幼儿保育有关的生物因素。OMEI利用最近存档的出生队列，包括160名低收入少数民族婴儿（主要是黑人/非洲人美国）和全面收集的医疗/口腔健康记录和~1760唾液/龈上样品（通过NIDCR KL 2 TR 001999和K23 DE 027412获得，PI：Xiao）。OMEI建立在我们以前的 1）揭示种族背景的工作与早期生活中口腔微生物组的发育有关 2）证实了口腔细菌-真菌的跨王国相互作用及其与ECC的关联; 3）发现了与宿主S相关的人类基因。突变体-龋齿相互作用;以及4）开发的机器学习 ECC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组装，在婴儿早期（出生到两岁）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）影响婴儿口腔微生物组发育。在目标3中，我们将使用高- 多维统计机器学习方法来整合多平台（母体，微生物，遗传，免疫和环境）数据，以鉴定ECC发病机理和发展的生物学因素 ECC预测模型OMEI将是第一个研究早期生命的生物因素，从婴儿口腔微生物组的角度来看ECC差异。OMEI中揭示的风险因素可用作专为服务不足的儿童制定的幼儿保育早期预测和预防目标。集成健康差异研究小组与来自多个学科（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），以及出色的内部和外部咨询委员会，将确保拟议的OMEI计划取得成功。