Journal of Oral Science Research ›› 2026, Vol. 42 ›› Issue (1): 1-7.DOI: 10.13701/j.cnki.kqyxyj.2026.01.001

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Advances in Salivary Microbes as Biomarkers in Early Childhood Caries Risk Prediction Models

MAO Jing, HU Tao*   

  1. State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Frontier Innovation Center for Dental Medicine Plus West China Hospital of Stomatology, Sichuan University & Department of Preventive Dentistry, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Received:2025-01-08 Online:2026-01-28 Published:2026-01-23

Abstract: Dental caries is a chronic infectious disease that predominantly affects the hard tissues of the teeth and represents one of the most prevalent global health problems in children. The process of dental caries treatment is often painful and challenging for children, imposing a significant burden on both families and society. Therefore, the prevention of early childhood caries (ECC) is particularly important. Modern caries prevention and control focus on managing caries risk factors, with oral microbiota playing a crucial role in the development and progression of the disease. Many caries risk assessment tools now consider oral microbiota as a key risk factor. For children, saliva samples serve as an ideal testing medium due to their non-invasive and easy-to-handle nature. Using salivary microorganisms as biomarkers in caries risk assessment not only improves the convenience of evaluation but also offers valuable evidence to support early intervention efforts. This article reviews the cariogenic role of salivary microorganisms and their potential as functional biomarkers for ECC risk assessment, discusses the latest progress in their application within ECC risk prediction models, and provides a theoretical foundation for constructing a more comprehensive ECC risk prediction model.

Key words: salivary microorganisms, early childhood caries, caries risk assessment tool, caries risk prediction model, machine learning