Bayesian Modeling for Prenatal, Natal and Postnatal Predictors of Developmental Defects of Enamel in Primary Maxillary Central Incisor Teeth

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Project Abstract Developmental defects of enamel or DDE include enamel hypoplasia (EH), opacities (OP) and post-eruptive breakdown (PEB). DDE are defects in the structural integrity of the tooth that happen while the tooth is developing. EH is characterized by an area of less enamel and is a quantitative defect. OP are characterized as an area with a difference in translucency and are a qualitative defect. PEB is the loss of enamel after the tooth erupts into the mouth. DDE are a problem because EH can put the tooth at higher risk for dental caries, and both OP and PEB can be esthetic problems. The goal of our proposed study is to identify clinical and other factors, and the timing of these factors that result in DDE of primary maxillary central incisor teeth. Our hypothesis is that these predictors for DDE are related to calcium homeostasis. This study builds on our prior study which found pregnancy and birth factors related to calcium homeostasis that were predictive for EH. We now propose a more extensive study of the impact of maternal pregnancy factors, birth factors and early infancy factors on the development of DDE in the deciduous maxillary central incisor teeth. We study DDE in these two teeth because they begin calcification at 13-15 weeks of gestation, complete calcification by 4-6 weeks postnatal, and erupt at about 1 year of age. The enamel crowns of these two teeth serve as an irreversible record of pregnancy, birth and early infancy exposures. A best test of the cause-effect of maternal, birth and early infancy related factors is a prospective study of mothers with monthly monitoring of factors during pregnancy, at birth, then at early infancy and also following the children to examine their teeth which erupt into the oral cavity at about 1 year of age. We are uniquely poised to conduct secondary analyses of existing data resources so that we can identify the predictors and timing for these predictors during human tooth development and resultant DDE. We will compare and contrast the predictors for the different DDE outcomes to learn more about their development. We will develop Bayesian models that identify the predictors and their timing for DDE outcomes. The results of this study of existing data resources are designed to close the gap in knowledge about possibly modifiable predictors and also to provide an analytical approach to identify pregnancy, birth and early infancy predictors for DDE in the child. Results will also help with the design of future validation and intervention studies.