Genomics Research Center, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
Department of Psychiatry, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
J Dent Res. 2021 Jun;100(6):599-607. doi: 10.1177/0022034520979926. Epub 2020 Dec 24.
As the most common chronic disease in preschool children in the United States, early childhood caries (ECC) has a profound impact on a child's quality of life, represents a tremendous human and economic burden to society, and disproportionately affects those living in poverty. Caries risk assessment (CRA) is a critical component of ECC management, yet the accuracy, consistency, reproducibility, and longitudinal validation of the available risk assessment techniques are lacking. Molecular and microbial biomarkers represent a potential source for accurate and reliable dental caries risk and onset. Next-generation nucleotide-sequencing technology has made it feasible to profile the composition of the oral microbiota. In the present study, 16S ribosomal RNA (rRNA) gene sequencing was applied to saliva samples that were collected at 6-mo intervals for 24 mo from a subset of 56 initially caries-free children from an ongoing cohort of 189 children, aged 1 to 3 y, over the 2-y study period; 36 children developed ECC and 20 remained caries free. Analyses from machine learning models of microbiota composition, across the study period, distinguished between affected and nonaffected groups at the time of their initial study visits with an area under the receiver operating characteristic curve (AUC) of 0.71 and discriminated ECC-converted from healthy controls at the visit immediately preceding ECC diagnosis with an AUC of 0.89, as assessed by nested cross-validation. sp., and were selected as important discriminatory features in all models and represent biomarkers of risk for ECC onset. These findings indicate that oral microbiota as profiled by high-throughput 16S rRNA gene sequencing is predictive of ECC onset.
作为美国学龄前儿童中最常见的慢性疾病，幼儿龋病（ECC）对儿童的生活质量有深远影响，给社会带来了巨大的人力和经济负担，而且 disproportionately 影响那些生活在贫困中的人。龋病风险评估（CRA）是 ECC 管理的重要组成部分，但现有的风险评估技术的准确性、一致性、可重复性和纵向验证都存在不足。分子和微生物生物标志物代表了准确可靠的龋齿风险和发病的潜在来源。下一代核苷酸测序技术使得分析口腔微生物群落的组成成为可能。在本研究中，16S 核糖体 RNA（rRNA）基因测序应用于唾液样本，这些样本是从正在进行的 189 名 1 至 3 岁儿童队列中的 56 名最初无龋儿童中抽取的，每隔 6 个月收集一次，共收集 24 个月；其中 36 名儿童发生了 ECC，20 名儿童保持无龋。在 2 年的研究期间，对微生物群落组成的机器学习模型分析，在研究初期的就诊时，通过受试者工作特征曲线（ROC）下面积（AUC）为 0.71 区分了受影响和未受影响的组，在 ECC 诊断前的就诊时，通过 AUC 为 0.89 区分了 ECC 转化组和健康对照组，通过嵌套交叉验证进行评估。 sp.，和 被选为所有模型中的重要鉴别特征，代表了 ECC 发病的风险生物标志物。这些发现表明，高通量 16S rRNA 基因测序所描绘的口腔微生物群与 ECC 的发病有关。
