Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, United States.
Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States.
Front Cell Infect Microbiol. 2022 May 23;12:881899. doi: 10.3389/fcimb.2022.881899. eCollection 2022.
Early childhood caries (ECC) is not only the most common chronic childhood disease but also disproportionately affects underserved populations. Of those, children living in Thailand have been found to have high rates of ECC and severe ECC. Frequently, the cause of ECC is blamed on a handful of cariogenic organisms, such as and . However, ECC is a multifactorial disease that results from an ecological shift in the oral cavity from a neutral pH (~7.5) to an acidic pH (<5.5) environment influenced by the host individual's biological, socio-behavioral, and lifestyle factors. Currently, there is a lack of understanding of how risk factors at various levels influence the oral health of children at risk. We applied a statistical machine learning approach for multimodal data integration (parallel and hierarchical) to identify caries-related multiplatform factors in a large cohort of mother-child dyads living in Chiang Mai, Thailand (N=177). Whole saliva (1 mL) was collected from each individual for DNA extraction and 16S rRNA sequencing. A set of maternal and early childhood factors were included in the data analysis. Significantly, vaginal delivery, preterm birth, and frequent sugary snacking were found to increase the risk for ECC. The salivary microbial diversity was significantly different in children with ECC or without ECC. Results of linear discriminant analysis effect size (LEfSe) analysis of the microbial community demonstrated that , , and were significantly enriched in ECC children. Whereas was less abundant among caries-free children, suggesting its potential to be a candidate biomarker for good oral health. Based on the multimodal data integration and statistical machine learning models, the study revealed that the mode of delivery and snack consumption outrank salivary microbiome in predicting ECC in Thai children. The biological and behavioral factors may play significant roles in the microbial pathobiology of ECC and warrant further investigation.
婴幼儿龋（ECC）不仅是最常见的儿童慢性疾病，而且不成比例地影响服务不足的人群。在这些人群中，生活在泰国的儿童被发现患有高龋率和严重的 ECC。通常，ECC 的原因归咎于少数几种致龋生物，如 和 。然而，ECC 是一种多因素疾病，是由口腔内的生态环境从中性 pH（~7.5）转变为酸性 pH（<5.5）引起的，受宿主个体的生物学、社会行为和生活方式因素的影响。目前，人们对不同层次的风险因素如何影响高危儿童的口腔健康还缺乏了解。我们应用了一种统计机器学习方法进行多模态数据整合（平行和分层），以在一个生活在泰国清迈的大型母婴对子队列（N=177）中识别与龋病相关的多平台因素。从每个人身上采集 1 毫升全唾液用于 DNA 提取和 16S rRNA 测序。在数据分析中包括了一组母婴和幼儿期因素。阴道分娩、早产和频繁食用含糖零食被发现会增加 ECC 的风险。患有 ECC 或无 ECC 的儿童的唾液微生物多样性有显著差异。微生物群落的线性判别分析效应量（LEfSe）分析结果表明， 、 和 在 ECC 儿童中明显富集。而 在无龋儿童中丰度较低，提示其可能成为口腔健康良好的候选生物标志物。基于多模态数据整合和统计机器学习模型，该研究表明，分娩方式和零食消费在预测泰国儿童 ECC 方面比唾液微生物组更为重要。生物学和行为因素可能在 ECC 的微生物病理生物学中发挥重要作用，值得进一步研究。
