Metabolomics Insights in Early Childhood Caries

Abstract

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 log₂-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 × 10^-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10^-6) and N-acetylneuraminate (p = 6.8 × 10^-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10^-6) and epicatechin (P = 2.9 × 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.

Keywords: biofilm; children; dental caries; machine learning; microbiome; risk assessment.

Figure 1.

Visual representations of the early childhood caries (ECC) traits that were defined and used in metabolomics analyses according to 3 different sets of tooth surfaces and summary of the significantly associated metabolites. (A) Entire dentition (88 surfaces, continuous and binary traits for 2 International Caries Detection and Classification System [ICDAS] thresholds and untreated disease). (B) All surfaces of teeth from which biofilm was sampled (22 surfaces), green highlighted. (C) The specific 5 facial/buccal surfaces from which biofilm was sampled, green highlighted. (D) Venn diagram and UpSet plot of metabolites significantly associated with more than 1 dental caries trait (linear regression of log₂-transformed metabolite values with false discovery rate correction, q < 0.05). Arrows indicate the direction of the association. Catechin, epicatechin, fucose, and N-acetylneuraminate were associated with 2 localized ICDAS ≥1 disease traits, and creatine was associated with a localized binary ICDAS ≥1 trait and the ECC person-level case definition at the ICDAS ≥3 detection threshold.

Figure 2.

Feature importance plot for the top 50 metabolites in the best-fitting tree-based pipeline optimization tool (TPOT) AutoML model. The metabolites are presented in order of descending “feature importance” in the AutoML model. The result for the top metabolite, catechin, can be interpreted as 1.1% relative classification performance decrease if catechin values are permuted in the TPOT prediction model.