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Article

Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

1
Department of Histology and Embryology, Poznan University of Medical Sciences, 6 Swiecickiego Street, 60-781 Poznan, Poland
2
Department of Neonatology, Biophysical Monitoring and Cardiopulmonary Therapies Research Unit, Poznan University of Medical Sciences, 33 Polna Street, 60-535 Poznan, Poland
3
Department of Risk Group Dentistry, Chair of Pediatric Dentistry, Poznan University of Medical Sciences, 70 Bukowska Street, 60-812 Poznan, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Natalia Wawrusiewicz-Kurylonek
Genes 2021, 12(4), 462; https://doi.org/10.3390/genes12040462
Received: 5 February 2021 / Revised: 11 March 2021 / Accepted: 21 March 2021 / Published: 24 March 2021
(This article belongs to the Special Issue Molecular Risk Factors of Complex Diseases)
Background: Several genes and single nucleotide polymorphisms (SNPs) have been associated with early childhood caries. However, they are highly age- and population-dependent and the majority of existing caries prediction models are based on environmental and behavioral factors only and are scarce in infants. Methods: We examined 6 novel and previously analyzed 22 SNPs in the cohort of 95 Polish children (48 caries, 47 caries-free) aged 2–3 years. All polymorphisms were genotyped from DNA extracted from oral epithelium samples. We used Fisher’s exact test, receiver operator characteristic (ROC) curve and uni-/multi-variable logistic regression to test the association of SNPs with the disease, followed by the neural network (NN) analysis. Results: The logistic regression (LogReg) model showed 90% sensitivity and 96% specificity, overall accuracy of 93% (p < 0.0001), and the area under the curve (AUC) was 0.970 (95% CI: 0.912–0.994; p < 0.0001). We found 90.9–98.4% and 73.6–87.2% prediction accuracy in the test and validation predictions, respectively. The strongest predictors were: AMELX_rs17878486 and TUFT1_rs2337360 (in both LogReg and NN), MMP16_rs1042937 (in NN) and ENAM_rs12640848 (in LogReg). Conclusions: Neural network prediction model might be a substantial tool for screening/early preventive treatment of patients at high risk of caries development in the early childhood. The knowledge of potential risk status could allow early targeted training in oral hygiene and modifications of eating habits. View Full-Text
Keywords: early childhood caries; single nucleotide polymorphisms; artificial neural network; early prediction model; complex trait early childhood caries; single nucleotide polymorphisms; artificial neural network; early prediction model; complex trait
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MDPI and ACS Style

Zaorska, K.; Szczapa, T.; Borysewicz-Lewicka, M.; Nowicki, M.; Gerreth, K. Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks. Genes 2021, 12, 462. https://doi.org/10.3390/genes12040462

AMA Style

Zaorska K, Szczapa T, Borysewicz-Lewicka M, Nowicki M, Gerreth K. Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks. Genes. 2021; 12(4):462. https://doi.org/10.3390/genes12040462

Chicago/Turabian Style

Zaorska, Katarzyna, Tomasz Szczapa, Maria Borysewicz-Lewicka, Michał Nowicki, and Karolina Gerreth. 2021. "Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks" Genes 12, no. 4: 462. https://doi.org/10.3390/genes12040462

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