ArticleBMC oral health2025
Comparison of salivary statherin and beta-defensin-2 levels, oral health behaviors, and demographic factors in children with and without early childhood caries.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- A Comparative Analysis of Differences in Salivary hBD-2 Levels and Their Correlation with Dental Caries and Unstimulated Saliva pH in Children with Primary and Permanent Dentition.Diagnostics (Basel, Switzerland) · 2026Article
- Association between childhood obesity, salivary adiponectin, and total dental caries experience (dmft + DMFT) in children: a cross-sectional study.Scientific reports · 2026Article
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Authors and funding
6 authors.
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Abstract
backgroundEarly childhood caries (ECC) is a widespread pediatric dental condition that is influenced by a combination of biological, behavioral, and demographic factors. Salivary biomarkers, including beta-defensin-2 (BD-2) and statherin (STATH), offer potential as non-invasive tools for detecting and assessing the risk of ECC. This study aims to compare the levels of salivary statherin and beta-defensin-2, alongside oral health behaviors and demographic factors, in children both with and without early childhood caries.
methodsThis case-control study involved 75 children diagnosed with ECC and 75 age- and gender-matched caries-free controls. Unstimulated saliva samples were obtained and analyzed via ELISA to quantify the levels of beta-defensin-2 and statherin. Demographic and behavioral data were gathered through structured interviews with parents. Statistical analyses included t-tests, logistic regression, and machine learning models to predict the risk of ECC.
resultsSalivary beta-defensin-2 levels were significantly higher in children with ECC (9.25 ± 2.89 ng/mL) compared to caries-free controls (6.41 ± 2.45 ng/mL, p = 0.003), indicating its potential as a diagnostic biomarker. Statherin levels, although lower in the ECC group, did not differ significantly between groups (p = 0.08). Behavioral factors such as regular dental visits and parental education levels were strongly associated with ECC prevalence. Machine learning models demonstrated high accuracy in predicting ECC, with the Gradient Boosting and CatBoost achieving the highest performance.
conclusionsSalivary beta-defensin-2 is a promising ECC risk assessment biomarker, while statherin is less effective as an independent predictor. Behavioral and demographic factors significantly influence ECC prevalence. Machine learning models integrating clinical, demographic, and salivary data provide a robust tool for detection and targeted prevention strategies. Comprehensive approaches combining salivary biomarkers and behavioral interventions are critical to managing ECC, particularly in resource-limited settings.
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