HEALTHx2: patient-centered approaches for studying the effectiveness and reproductive safety of antidepressant medication in perinatal women

The aim of this project is to determine the effectiveness of antidepressant treatment in pregnant and postpartum women, as well as the longer-term metabolic safety of these drugs in pregnancy on the offspring.

The project augments observational data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) linked to national registries, with genetic data in mother-father-child trios (MoBaGenetics) and novel biobank markers of mental illness (i.e., neurosteroids). The project is highly computational, and combines epidemiological methods for causal inference with Bayesian approach to polygenic risk scoring (i.e., LDpred2) and Mendelian Randomization Methods. The ultimate goal is to quantify antidepressant effectiveness in pregnant women, according to genetic variants of drug response, moving towards personalized medicine in the pregnant population. Similarly, our goal is to determine unbiased causal effects of prenatal antidepressant exposure on offspring metabolic risks later in life.

Tags: Bayesian inference, Causality, Databases, Non-parametric and semi-parametric methods, Unsupervised learning, Statistical methods
Published July 4, 2023 10:07 AM - Last modified Oct. 23, 2023 11:49 AM