Despite their prevalence, little is known about the causes of adverse pregnancy outcomes. These include pregnancy loss (miscarriage or stillbirth), hypertensive disorders of pregnancy (HDP), gestational diabetes (GD), poor perinatal mental health, preterm birth (PTB), small for gestational age (SGA) and large for gestational age (LGA). Over 50% of pregnancies are affected by any of these outcomes, however we are unable to accurately predict their occurrence. Consequently, antenatal care currently stratifies women for different intensity of antenatal monitoring based on antenatal history, age and parity. Beyond early delivery (e.g. to prevent pregnancy loss or severe morbidity in mother or child), and stepwise treatment (from lifestyle through oral hypoglycaemic medication to insulin) for GD, there are few effective treatments to prevent these outcomes. Recent evidence showing that glycaemia-related differences in foetal over-growth predate the time in gestation of diagnostic tests highlights the need for better early prediction tools.
By identifying women at a high risk of pregnancy-related disease, we can maximise healthcare facilities, resources and screening strategies to allow for earlier diagnosis of these disorders. More sensitive and accurate predictive and stratification measures can assist with alleviating the burden on mothers and healthcare providers by redistributing maternal care resources. Incorporating genomics, epigenomics and metabolomics approaches will contribute towards a more cogent understanding of reproductive and perinatal health and disease11.