- author = "L. H. Heimisdottir and B. M. Lin and H. Cho and A. Orlenko and A. A. Ribeiro and A. Simon-Soro and J. Roach and D. Shungin and J. Ginnis and M. A. Simancas-Pallares and H. D. Spangler and A. G. {Ferreira Zandona} and J. T. Wright and P. Ramamoorthy and J. H. Moore and H. Koo and D. Wu and K. Divaris",
- title = "Metabolomics Insights in Early Childhood Caries",
- journal = "Journal of Dental Research",
- year = "2021",
- month = "9 " # jan,
- note = "Epub ahead of print",
- keywords = "genetic algorithms, genetic programming, TPOT, children, biofilm, dental caries, microbiome, machine learning, risk assessment",
- ISSN = "0022-0345",
-
DOI = "
doi:10.1177/0022034520982963",
- abstract = "Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterisations 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 analytical sample comprised 289 children ages 3 to 5 (51percent 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 analysed 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 ge 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.",
-
notes = "Division of Pediatric and Public Health, Adams School
of Dentistry, University of North Carolina, Chapel
Hill, NC, USA
PMID: 33423574",
