Pregnant women are often excluded from randomized controlled trials testing the effects of medications. This is because of concerns about adverse effects of the drug on the developing fetus and uncertainty about the implications of physiological changes in pregnancy on medication dosing. That means that women, their partners and health professionals often do not know the best care management path for women who are already on medication when they start pregnancy. It also means that new drugs that could improve the treatment of pregnancy complications, such as pre-eclampsia and fetal growth restriction, are rarely developed.

We are triangulating evidence from different methods to improve our understanding of the safety and efficacy of medications used during pregnancy, including genetic methods (e.g. Mendelian randomization and genetic colocalization), target trial emulation, within-sibling comparisons, and instrumental variable analyses. We are also integrating genetic and molecular data to identify potential new drug targets, or existing drugs that could be repurposed, to manage pregnancy complications.

Current tools for identifying which women are at risk of pregnancy complications, such as gestational diabetes, pre-eclampsia, fetal growth restriction and preterm birth, rely largely on stratifying women based on established risk factors. However, many women who do not have these risk factors go on to have a complicated pregnancy. Also, some prediction tools rely on information from previous pregnancy, which is not applicable to women in their first pregnancy.

It has been suggested that ‘omics biomarkers, such as genomic, proteomic, metabolomic and epigenomic measures, could address some of these gaps, as they would not rely on history of previous pregnancies and could be measured in blood samples routinely collected in antenatal care. Studies to date that have explored this have rarely validated their prediction models, compared how accurate they are in relation to current tools, or tested whether prediction can be improved by using large-scale complete pre-natal and antenatal data. We are applying machine learning to ‘omics and electronic health records data to test whether we can better identify women at high risk of pregnancy complications.

Women who are diagnosed with gestational diabetes and hypertensive disorders of pregnancy are more likely to have diabetes and/or heart disease later in their lives. It is not fully understood whether this is because pregnancy increases the future risk of developing these diseases or whether women with high risk of cardiometabolic disease before pregnancy are more likely to be diagnosed with such conditions due to antenatal monitoring assessment of blood pressure measures and screening for diabetes, or physiological changes of pregnancy unmasking this risk.

Either way it makes sense to monitor glucose and blood pressure in these women after pregnancy. Currently, this is done at the 6 weeks post-natal check but only ~50% of women who have had gestational diabetes or hypertensive disorders of pregnancy have the postnatal glucose or blood pressure checks. Women from more deprived areas are particularly not likely to have them. This could be because the evidence base for testing these at 6 weeks is lacking. Furthermore, current practice does not take account of pre-pregnancy glucose or blood pressure measures or how these have changed across pregnancy. We are using repeat measures of biomarkers, such as glucose and blood pressure, to identify women with different change patterns from before, during and after pregnancy and explore how different trajectories predict and/or influence future cardiometabolic health. We are using that information to identify which women, at what times and with what measures, should be assessed postnatally.

An increasing number of women start pregnancy with multimorbidity, typically defined as having two or more chronic diseases. This reflects the increasing age of women when they start their family and changes such as the obesity epidemic. We do not know how best to support women with multimorbidity during and after their pregnancies.

We are exploring the causes and consequences of different types of multimorbidity in pregnancy (e.g. predominantly driven by diseases affecting specific organs or systems, such as cardiovascular, neurological, muscular skeletal systems, or mixed systems), as well as comparing findings with different definitions of multimorbidity and with having just one chronic condition.

We have found a causal role for maternal pre-/early-pregnancy BMI on adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications.

Borges MC, et al. Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes. BMC Med. 2024;22(1):32. doi: https://doi.org/10.1186/s12916-023-03167-0

We have found that reproductive markers across women’s lifespan are associated with distinct metabolic signatures in later life. Age at menarche, parity and age at natural menopause are related to numerous metabolic measures, representing multiple dimensions of metabolism, including amino acids, fatty acids, glucose, ketone bodies, and lipoprotein metabolism.

Clayton GL, Borges MC, Lawlor DA. The impact of reproductive factors on the metabolic profile of females from menarche to menopause. Nat Commun. 2024;15(1):1103. doi: https://doi.org/10.1038/s41467-023-44459-6

See Elhakeem A, et al. Long-term cardiometabolic health in people born after assisted reproductive technology: a multi-cohort analysis. Eur Heart J. 2023;44(16):1464-1473. doi: https://doi.org/10.1093/eurheartj/ehac726

See Cornish RP, et al. Maternal pre-pregnancy body mass index and risk of preterm birth: a collaboration using large routine health datasets. BMC Med. 2024;22(1):10. doi: https://doi.org/10.1186/s12916-023-03230-w

See McBride N, et al. Effects of the maternal and fetal proteome on birth weight: a Mendelian randomization analysis. medRxiv [Preprint]. 2023:2023.10.20.23297135. doi: https://doi.org/10.1101/2023.10.20.23297135

The Network is composed of placental and developmental biologists, physician-scientists, cardiovascular biologists, and epidemiologists. It is led by Profs Ananth Karumanchi (Cedar-Sinai Medical Centre, LA, USA) and Didier Stainier (Max Plank Institute for Heart & Lung Research, Germany) and includes Profs Zolt Arany and Mark Kahn (University of Pennsylvania), Myriam Hemberger (University of Calgary) and Jose Luis de la Pompa (Centro Nacional de Investigaciones Cardiovasculares, Madrid). Prof Abigail Fraser is the Bristol and epidemiological lead for this award that aims to identify cellular and molecular mechanisms by which the placenta is connected to maternal and fetal cardiovascular function. This interdisciplinary team has expertise in diverse approaches using both animal models and human data.