Prediction of Future Caries in 1-Year-Old Children via the Salivary Microbiome

Author:

Raksakmanut R.1ORCID,Thanyasrisung P.2ORCID,Sritangsirikul S.34,Kitsahawong K.5,Seminario A.L.6,Pitiphat W.7,Matangkasombut O.28ORCID

Affiliation:

1. Graduate Program in Oral Biology and Center of Excellence on Oral Microbiology and Immunology, Faculty of Dentistry, Chulalongkorn University, Wang-Mai, Pathumwan, Bangkok, Thailand

2. Department of Microbiology and Center of Excellence on Oral Microbiology and Immunology, Faculty of Dentistry, Chulalongkorn University, Wang-Mai, Pathumwan, Bangkok, Thailand

3. Department of Pediatric Dentistry, Faculty of Dentistry, Chulalongkorn University, Wang-Mai, Pathumwan, Bangkok, Thailand

4. PhD Program in Oral Sciences, Faculty of Dentistry, Khon Kaen University, Muang District, Khon Kaen, Thailand

5. Division of Pediatric Dentistry, Department of Preventive Dentistry, Faculty of Dentistry, Khon Kaen University, Muang District, Khon Kaen, Thailand

6. Department of Pediatric Dentistry, School of Dentistry, University of Washington, WA, USA

7. Division of Dental Public Health, Department of Preventive Dentistry, Faculty of Dentistry, Khon Kaen University, Muang District, Khon Kaen, Thailand

8. Research Laboratory of Biotechnology, Chulabhorn Research Institute, Laksi, Bangkok, Thailand

Abstract

Dental caries is the most common chronic disease in children that causes negative effects on their health, development, and well-being. Early preventive interventions are key to reduce early childhood caries prevalence. An efficient strategy is to provide risk-based targeted prevention; however, this requires an accurate caries risk predictor, which is still lacking for infants before caries onset. We aimed to develop a caries prediction model based on the salivary microbiome of caries-free 1-y-old children. Using a nested case-control design within a prospective cohort study, we selected 30 children based on their caries status at 1-y follow-up (at 2 y old): 10 children who remained caries-free, 10 who developed noncavitated caries, and 10 who developed cavitated caries. Saliva samples collected at baseline before caries onset were analyzed through 16S rRNA gene sequencing. The results of β diversity analysis showed a significant difference in salivary microbiome composition between children who remained caries-free and those who developed cavitated caries at 2 y old (analysis of similarities, Benjamini-Hochberg corrected, P = 0.042). The relative abundance of Prevotella nanceiensis, Leptotrichia sp. HMT 215, Prevotella melaninogenica, and Campylobacter concisus in children who remained caries-free was significantly higher than in children who developed cavitated caries (Wilcoxon rank sum test, P = 0.024, 0.040, 0.049, and 0.049, respectively). These taxa were also identified as biomarkers for children who remained caries-free (linear discriminant analysis effect size, linear discriminant analysis score = 3.69, 3.74, 3.53, and 3.46). A machine learning model based on these 4 species distinguished between 1-y-old children who did and did not develop cavitated caries at 2 y old, with an accuracy of 80%, sensitivity of 80%, and specificity of 80% (area under the curve, 0.8; 95% CI, 44.4 to 97.5). Our findings suggest that these salivary microbial biomarkers could assist in predicting future caries in caries-free 1-y-old children and, upon validation, are promising for development into an adjunctive tool for caries risk prediction for prevention and monitoring.

Funder

Faculty of Dentistry, Chulalongkorn University

National Institutes of Health

Publisher

SAGE Publications

Subject

General Dentistry

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