Scientific rational for research
Evidence links several pregnancy-related conditions to long-term health risks, including gestational hypertension and cardiovascular disease, gestational diabetes and metabolic disorders, and perinatal mental health issues with ongoing psychological needs. Despite these well-established associations, there is limited understanding of how pregnancy complications translate into long-term health trajectories and when intervention is most effective.
More precise models for risk prediction, incorporating a range of health and ‘omic factors, could enable earlier identification of high-risk individuals. By leveraging pregnancy as a predictor of long-term health, there is an opportunity to improve preventative strategies, personalise care, and enhance health outcomes across the life course.
Research question
Can the utilisation of ‘omics data incorporated alongside traditional clinical risk factors predict future ill health after pregnancy complications?
Objectives
1. Assess long-term disease risk following pregnancy complications
– Evaluate the progression from gestational diabetes to type 2 diabetes
– Investigate how hypertensive disorders of pregnancy (e.g., preeclampsia, gestational hypertension) influence future cardiovascular disease risk
2. Examine the impact of pre-existing conditions on pregnancy-related disease progression
– Determine how pre-existing cardiometabolic conditions (e.g., obesity, chronic hypertension, type 1 or 2 diabetes) during pregnancy influence long-term health trajectories
3. Utilise longitudinal clinical and biochemical data to define disease trajectories
– Establish patterns of onset and progression of diabetes, cardiovascular disease, and related metabolic conditions
4. Leverage multi-omics data to explore genetic and molecular determinants of disease
– Use whole genome sequencing, exome sequencing, and proteomic data to investigate genetic drivers of metabolic and cardiovascular disease