Christine Austin<\/a><\/h5>\nDevelopment of biomarkers in deciduous teeth of children with FASD that predict neurobehavioral performance<\/strong> 1UH2AA029062-01<\/span> | University of California San Diego<\/p>\nPrenatal alcohol exposure sequelae, collectively termed Fetal Alcohol Spectrum Disorders (FASD), are varied and present in as many as 10% of elementary school children. They affect cognitive functioning, as well as physical and mental health, and represent a significant public health issue. Timely and appropriate treatment can positively impact a child\u2019s developmental trajectory and prevent secondary disabilities. In the majority of cases, diagnosis of FASD requires documentation of prenatal alcohol exposure. A biomarker of prenatal alcohol exposure would allow diagnosis of children where maternal self-report is unavailable. Prevention and treatment efforts would benefit from biomarkers able to noninvasively assess the magnitude and gestational time of exposures. Additionally, research into the etiology of FASD would benefit from such markers and the refining of our current understanding of mechanisms. We propose to develop biomarkers in dental tissue to quantitatively measure exposures, allowing the documentation of prenatal alcohol exposure in naturally shed deciduous (baby) teeth and the linking of prenatal exposures to neurobehavioral deficits. We will optimize our techniques to be able to detect exposures by month of second and third trimester. We will test whether our novel biomarkers predict neurobehavioral performance. In teasing out associations among exposures and outcomes, our study benefits from the well-characterized CIFASD cohort where consistently gathered, already existing data may be accessed and analyzed in conjunction with novel biomarker findings. This study will provide preliminary data for an R01 or U01 application to incorporate additional outcome data existing within the array of CIFASD data, such as neuroimaging, assess the effects of co-exposures, and further calibrate and improve our predictive capacity within the larger sample.<\/p>\n