As many as 1 in 4 women experience a perinatal mental health problem. In Québec this corresponds to approximately 20,000 women every year. Maternal antenatal mental health associates with adverse maternal health outcomes but also negatively impacts child neurodevelopment, effects that are clinically significant and can persist until adulthood. We currently lack clinical prediction models to accurately identify women, and their children, who are at risk for adverse mental health outcomes.
My research program will focus on complementary themes that converge on understanding individual differences in risk for adverse mental health outcomes in both mother and child.
Theme 1: Building better (biologically-informed) prediction models. Prediction models based on conventional psychosocial cannot accurately identify women at risk for mental health problems. We will address these limitations by drawing on both existing and a new cohort to carry out the largest genetically-informed analysis of maternal mental health to date.
Theme 2: Individual differences in treatment efficacy of perinatal interventions. Evidence-based perinatal interventions reduce maternal anxiety and depression, however not all women respond equally. I will integrate measures of genomic risk (identified in Theme 1) to understand individual differences in maternal treatment response to intervention.
Theme 3: Biomarkers that reflect the impact of perinatal adversity at the level of the individual child.
We have contributed to the development of a new epigenetic clock for children, the Pediatric Clock. We use three independent cohorts to test if the Pediatric Clock can be used to assess the impact of maternal mental health on child development.
The knowledge gained from this research program will contribute to more effective screening strategies to better identify, and treat, women and children who are at risk from the negative effects of perinatal mental health problems.