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.

Science for the Next Century: Deep Phenotyping

“…The presence of caries subtypes has been previously suggested for adults ( Shaffer et al 2013 ). Metabolomic analyses of the supragingival biofilm paired with machine learning–based classifiers provide additional biological informed approaches for better understanding and phenotyping early childhood caries and its subtypes ( Heimisdottir et al 2021 ). Arguably, using deep or granular phenotyping of biologically informed traits in large diverse populations can ultimately provide insights into the molecular basis of dental caries, periodontal disease, and their postulated subtypes ( Divaris 2019 ).…”

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

“…In ZOE 2.0, 87% of metabolites have some missing data whereas 58% have missing values in Lloyd-Price. To address missingness in these two cohorts, we applied a rigorous feature-wise Quantile Regression Imputation of Left-Censored data (QRILC) (18) to impute missing metabolite values and avoid underestimated metabolite-level variance, as in a previous publication (5). All 503 metabolites in ZOE 2.0 have <90% missing data among the 289 included participants.…”

“…A subset of participants' biofilm samples underwent metagenomics, metatranscriptomics, and metabolomics analyses, under the umbrella Trans-Omics for Precision Dentistry and Early Childhood Caries or TOPDECC (accession: phs002232.v1.p1) (11). As such, metagenomics (i.e., shotgun whole genome sequencing or WGS), metatranscriptomics (i.e., RNA-seq), and global metabolomics data (i.e., ultra-performance liquid chromatography-tandem mass spectrometry) (5,15,16) from supragingival biofilm samples of ~300 children, paired with clinical information on ECC are available. After exclusions due to phenotype and metabolite missingness described in a previous publication (5), the joint microbiome-metabolome data include 289 participants.…”

“…Aside from the logical interest in understanding the microbiome's composition, measuring and understanding its associated metabolic activities is arguably of utmost biological relevance. Recent studies have linked the metabolome with several important health conditions including inflammatory bowel disease (IBD) (2), obesity and type II diabetes (3), cholesterol levels (4), and early childhood dental caries (ECC) (5). Despite the rapidly increasing availability of microbiome data in large health cohorts, metabolomics data are still scant.…”

“…Moreover, its accuracy has not been benchmarked and its performance in other microbial communities with distinct ecology and function (e.g., the oral cavity) remains unknown. This is important because measured metabolomes at different body sites may include, besides the products of microbial metabolism, biochemical contributions from the host and the environment (e.g., dietary sugars in the study of dental biofilm (5)). Although an accurate determination of metabolite sources may not always be possible, predictions of these biofilm metabolites using microbiome information are highly desirable.…”