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.

No abstract