Machine learning methods to assess risk for prenatal and neonatal iron deficiency anemia from maternal stress exposure

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PROJECT SUMMARY This K99 application aims to determine biological and behavioral pathways by which maternal psychosocial stress in pregnancy impacts risk for maternal and infant iron deficiency anemia (IDA). IDA is one of the most common causes of anemia worldwide, and around 20% of women in the US experience a stressful life event throughout their pregnancy. Due to the increased iron demands of pregnancy, pregnancy itself poses a significant risk of IDA, especially for low-income and racially- and ethnically-minoritized women. IDA increases the risk of adverse pregnancy outcomes and can negatively impact the iron status of the neonate that may cause irreversible harm to neurodevelopment. There is growing concern that oral vitamin supplementation might not be enough to counteract the risks of IDA in the context of systemic inflammation, including inflammation produced by chronic psychosocial stress and subsequent neuroendocrine dysregulation. Maternal psychosocial stress has been associated with infant iron status previously, but the potential biological mechanisms are not yet characterized despite the