Grant: Team Grant: ERA-Net PerMed

The frequency of twin pregnancies has risen globally due to increased use of assisted conception and increasing maternal age. Twin pregnancies have 5-9 times higher frequencies of fetal chromosomal and structural abnormalities, often deliver preterm, and have high prevalence of gestational diabetes mellitus (GDM), preeclampsia (PE), and intrauterine growth restriction compared to singletons. Twins have unique complications such as the twin-to twin transfusion syndrome in monochorionic (MC) twins. Consequently, more twins are born prematurely, suffer from low birth weight, require admission to newborn intensive care units, and are at greater risk of developing motor and cognitive disorders and cerebral palsy in later life. These impose a huge burden on individuals, families, and societies. We aim to develop a multi-marker, personalized, prenatal diagnostic model to predict feto-maternal complications in twin pregnancies. A multi-national network will recruit a statistically powered cohort of 1,200 twins and follow them through pregnancy until delivery using biochemical, endocrine, imaging, immunologic and cell free DNA (cfDNA) parameters to modify a priori risks. Initial cohorts (Prof Maymon, Israel), demonstrated good prediction of Down syndrome, PE, and GDM using ultrasound and serum markers. We will use these markers and evaluate structural and chromosomal abnormalities [developed by Prof Kagan (Tubingen, Germany)], standardised imaging for MC twins [Prof's Brown and Miron (McGill, Canada)], and endocrine, sonographic, and biophysical markers [Prof Borrell (Barcelona, Spain), and Dr Geipel (Bonn, Germany)]. Prof Nicolaides (London, UK) will contribute his twin cohort data. Prof. Cuckle (Israel) will perform modelling using this large dataset to identify risk stratification algorithms for twins, offering personalized, preventive clinical care pathways for feto-maternal disorders. This would enable a paradigm shift in prenatal personalized care for twin pregnancy.