Predicting Early Childhood Caries Through an Integrated Model of Oral Microbiome, Dietary Sugar Exposure, Salivary Characteristics, and Social Determinants

Authors

  • Leila Sadat Kamali Department of Endodontics, Tabriz University of Medical Sciences, Tabriz, Iran
  • Reza Safari Majd Department of Endodontics, Tabriz University of Medical Sciences, Tabriz, Iran Author

Keywords:

early childhood caries, oral microbiome, sugar intake, social determinants, saliva, machine learning, caries prediction

Abstract

Early childhood caries is among the most prevalent preventable oral diseases worldwide and results from complex interactions among microbial ecology, dietary behavior, host susceptibility, and socioeconomic conditions. Current caries-risk instruments have limited capacity to capture these multidimensional interactions and frequently demonstrate modest predictive performance at the individual level. This prospective study proposes to develop and validate an integrated prediction model for early childhood caries using oral microbiome characteristics, objectively assessed dietary sugar exposure, salivary properties, behavioral factors, and social determinants of health. Caries-free children aged 12–36 months will be recruited from community dental and pediatric health services and followed for 24 months. Dental examinations will use standardized caries detection criteria, while supragingival plaque and saliva samples will undergo microbial sequencing to characterize taxonomic diversity and cariogenic functional pathways. Salivary flow, buffering capacity, pH, and selected host-defense markers will be quantified. Dietary exposures will be assessed through repeated 24-hour dietary recalls supplemented by ecological momentary recording of sugar-containing foods and beverages. Household income, parental education, oral-health literacy, fluoride exposure, toothbrushing practices, dental attendance, and neighborhood-level deprivation will also be examined. Regularized regression, gradient boosting, and interpretable machine-learning models will be developed and externally validated. Predictive value will be assessed using discrimination, calibration, reclassification, and decision-curve analysis. Explainability methods will identify modifiable combinations of biological, behavioral, and social factors most strongly associated with incident caries. Interaction analyses will determine whether favorable salivary or microbial profiles attenuate the effect of frequent sugar consumption and whether socioeconomic disadvantage modifies biological susceptibility. Rather than treating childhood caries as a predominantly microbial or behavioral condition, this investigation adopts an integrated precision-prevention framework. The resulting model could enable risk stratification before visible cavitation, guide individualized preventive intensity, and identify intervention targets extending beyond conventional oral-hygiene counseling. The study may also clarify how biological susceptibility interacts with structural and behavioral determinants during the earliest stages of caries development.

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How to Cite

Kamali, L. S., & Safari Majd, R. (2026). Predicting Early Childhood Caries Through an Integrated Model of Oral Microbiome, Dietary Sugar Exposure, Salivary Characteristics, and Social Determinants. Journal of Oral and Dental Health Nexus, 3(2), 1-13. https://jodhn.com/index.php/jodhn/article/view/36