Metabolomics Insights in Early Childhood Caries
Metabolomics Insights in Early Childhood Caries
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DOI：
10.1177/0022034520982963
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发表时间：
2021-06-01
影响因子：
7.6
通讯作者：
Divaris, K.
中科院分区：
文献类型：
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作者：
Heimisdottir, L. H.;Lin, B. M.;Divaris, K.
龋齿的特征是在生物膜-牙齿表面界面发生了生态失调，但生物膜的全面生化特征还很缺乏。我们使用代谢组学来鉴定与早期儿童龋齿（ECC）患病率和严重程度相关的龈上生物膜的生化特征。该研究的分析样本包括289名3至5岁的儿童（51%患有ECC），他们在北卡罗来纳州的公立幼儿园上学，并参加了一项以社区为基础的儿童早期口腔健康横断面研究。临床检查由经过校准的检查员在社区地点使用国际龋齿检测和分类系统（ICDAS）标准进行。采用超高效液相色谱-串联质谱法对左上象限所有乳牙面/颊表面牙菌斑进行分析。个体代谢物与18个临床特征（基于不同的ECC定义和牙齿表面组）之间的关联使用布朗距离相关（dCor）和对数(2)转换值的线性回归模型进行量化，应用错误发现率多重测试校正。使用基于树的管道优化工具(TPOT)-机器学习过程来确定最适合的ECC分类代谢物模型。鉴定出503种已命名的代谢物，包括微生物、宿主和外源生化物质。大多数显著的ecc代谢物呈阳性（即上调/富集）。局部ECC病例定义（ICDAS >= 1）与代谢组的相关性最强（dcop = 8 × 10(-3)）。经过多次测试校正后，16种代谢物与ECC显著相关，包括焦糖（P = 3.0 × 10(-6)）和n -乙酰神经胺酸酯（P = 6.8 × 10(-6)）具有较高的ECC患病率，儿茶素（P = 4.7 × 10(-6)）和表儿茶素（P = 2.9 × 10(-6)）具有较低的ECC患病率。儿茶素、表儿茶素、丙酸咪唑、focal、9,10- dihome和n -乙酰神经胺酸酯在自动TPOT模型中ECC分类重要性排名前15位。这些龈上生物膜代谢物的发现为ECC生物学提供了新的见解，可以作为疾病活动或风险评估措施发展的基础。
Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log(2)-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS >= 1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 x 10(-3)). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 x 10(-6)) and N-acetylneuraminate (p = 6.8 x 10(-6)) with higher ECC prevalence, as well as catechin (P = 4.7 x 10(-6)) and epicatechin (P = 2.9 x 10(-6)) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.