Accurate risk prediction of adverse pregnancy outcomes (APOs) has the potential to improve antenatal care by identifying high-risk women, who may benefit from more intense monitoring and intervention, and low-risk women, where we can minimise unnecessary invasive procedures and costs to health services. Currently, the UK National Institute of Health and Care Excellence (NICE) guidelines recommend screening women with certain risk factors (e.g., maternal age, parity, BMI, ethnicity) for some APOs, such as gestational diabetes (GD) and gestational hypertension (GHT). However, many women without risk factors go on to develop these complications, emphasising the need for better prediction tools. For other APOs, such as preterm births (PTB), prediction based on traditional risk factors is particularly challenging.
Polygenic risk scores (PRS) have proven valuable for prediction of multiple complex diseases (1-4) and could identify women at risk of APOs before pregnancy, because germline genetic variants are determined at birth and remain unchanged throughout life. We have previously shown the potential clinical utility of using metabolomics to improve prediction of GD and delivering a large for gestational age (LGA) baby (5, 6), underscoring the potential of using deep molecular phenotyping data measured in pregnancy to improve prediction and early diagnosis of APOs.
Aims and Objectives
To explore the potential of PRS and deep molecular phenotyping to improve prediction and early diagnosis of common APOs (e.g. GD, GHT, preeclampsia, PTB, LGA, and fetal growth restriction) beyond traditional clinical models.
Explore the potential of using PRS to improve prediction of APOs using three approaches:
- Develop and validate PRS for prediction of APOs using data from the MR-PREG collaboration (>400,000 pregnancies)
- Test the utility of PRS for related clinical traits (e.g. BMI, diabetes and hypertension predisposition) to improve prediction of APOs
- Apply ‘reverse gear Mendelian Randomisation’ (MR) by using PRS for APOs to detect blood biomarkers, especially proteins, that are influenced by early stages of these conditions, and could be good clinical predictors of APOs.
Explore the potential of using deep molecular phenotyping to identify blood-based molecular predictors of APOs, including proteomic and metabolomic markers measured in early pregnancy, following on from our previous studies (5, 6).
Prediction, omics, genetics, polygenic risk scores, Mendelian randomization
How to apply for this project
This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.
Please visit the Faculty of Health Sciences website for details of how to apply