4.7 Article

Oral Microbiota Composition Predicts Early Childhood Caries Onset

Journal

JOURNAL OF DENTAL RESEARCH
Volume 100, Issue 6, Pages 599-607

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/0022034520979926

Keywords

dental caries; risk assessment; receiver operating characteristic curve; machine learning; biomarkers; 16S rRNA

Funding

  1. National Institute of Dental Research, National Institutes of Health, Department of Health and Human Services, under National Institutes of Health (NIH)/National Institute of Dental and Craniofacial Research (NIDCR) [R01 DE024985-04A1, R01 DE013683-11A1]

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Early childhood caries (ECC) is the most common chronic disease in preschool children in the United States, with a significant impact on quality of life and societal burden, especially among those living in poverty. Analysis of oral microbiota may serve as a predictive tool for ECC onset.
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. Rothia mucilaginosa, Streptococcus sp., and Veillonella parvula 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.

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