In Utero Exposure to Medications for Cardiometabolic Conditions and Risk of Congenital Malformations - Project Summary/Abstract Given increases in recent decades to the prevalence of obesity among women of reproductive age and to maternal ages, a substantial number of women are entering pregnancy with poor cardiometabolic health, leading to adverse maternal and infant outcomes. While effective medications are available for both weight loss and for the cardiometabolic comorbidities of obesity, the safety of many of these medications in pregnancy is uncertain. There may be possible harm to the fetus due to teratogenic effects, but equally there can be adverse consequences to women of reproductive age of forgoing beneficial medications due to uncertain risk to the fetus. The objective of this project is to quantify the risk of congenital malformations with maternal cardiometabolic condition and with in utero exposure during the first trimester to three medications that are used for common cardiometabolic conditions: angiotensin-converting enzyme (ACE) inhibitors, statins, and glucagon-like peptide-1 (GLP-1) receptor agonists. For ACE inhibitors and statins, medications used to lower blood pressure and reduce cholesterol, there is some evidence of increased risk of malformations, but previous research studies have produced conflicting findings. Limitations in earlier studies may be responsible for the observed associations. While for GLP-1 agonists, a medication used to treat type 2 diabetes and for weight loss, there is a relative lack of data from human studies, but increased risk of visceral and skeletal malformations observed in animal studies among rats and rabbits. Research aims include 1) characterization of the multifactorial burden of malformations among pregnant women by BMI and pre-pregnancy values of HbA1c, cholesterol and blood pressure; and estimation of the effect on malformations of cardiometabolic medications prescribed in the first trimester, namely of 2) ACE inhibitors, 3) statins, and 4) GLP-1 agonists. To quantify the relative risk of malformations, cohort studies will be conducted using a large cohort of pregnancies identified in Clinical Practice Research Datalink (CPRD) Aurum, a database of UK electronic health records that has measurements on the potential confounders of BMI, HbA1c, blood pressure, and blood cholesterol. Propensity score methods and quantitative bias analyses, incorporating machine learning, will be used to robustly estimate associations and quantify uncertainty due to both random and possible systematic error. To enable the transition to an independent investigator, Dr. Brown will undertake mentored training at the Harvard T.H. Chan School of Public Health towards his goals of improved understanding of maternal health and perinatal care, increased skills in advanced statistical and causal methods, and better research leadership, management, and communication skills. By quantifying risk of malformations with these medications, this project is expected to provide valuable information to enable prescribers and women of reproductive age to make more informed treatment choices balancing possible teratogenic risk with the known benefits of these medications, and thereby is anticipated to lead to improved maternal and infant outcomes.
