Background
Oral microbiome sequencing has revealed key links between microbiome dysfunction and dental caries. However, these efforts have largely focused on Western populations, with few studies on the Middle Eastern communities. The current study aimed to identify the composition and abundance of the oral microbiota in saliva samples of children with different caries levels using machine learning approaches.
Conclusion
The assessment of oral microbiota samples in a representative Saudi Arabian population for high and low metrics of dental caries yields signatures of abundances and diversity.
Methods
Oral microbiota composition and abundance were identified in 250 Saudi participants with high dental caries and 150 with low dental caries using 16 S rRNA sequencing on a NextSeq 2000 SP flow cell (Illumina, CA) using 250 bp paired-end reads, and attempted to build a classifier using random forest models to assist in the early detection of caries.
Results
The ADONIS test results indicate that there was no significant association between sex and Bray-Curtis dissimilarity (p ~ 0.93), but there was a significant association with dental caries status (p ~ 0.001). Using an alpha level of 0.05, five differentially abundant operational taxonomic units (OTUs) were identified between males and females as the main effect along with four differentially abundant OTUs between high and low dental caries. The mean metrics for the optimal hyperparameter combination using the model with only differentially abundant OTUs were: Accuracy (0.701); Matthew's correlation coefficient (0.0509); AUC (0.517) and F1 score (0.821) while the mean metrics for random forest model using all OTUs were:0.675; 0.054; 0.611 and 0.796 respectively.