Lightning Talk + Poster ESA-SRB-ANZOS 2025 in conjunction with ENSA

Pregnancy complications leave a proteomic signature of future health risk. (128561)

Lucy A Bartho 1 2 , Sue P Walker 1 2 , Teresa M MacDonald 1 2 , Danica Idzes 1 2 , Ping Cannon 1 2 , Vi Nguyen 1 2 , Gordon CS Smith 3 4 , Stephen Tong 1 2 , Tu'uhevaha J Kaitu'u-Lino 1 2
  1. Obstetrics, Gynaecology and Newborn Health, University of Melbourne, Melbourne, Victoria, Australia
  2. Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
  3. Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
  4. Department of Physiology, Development and Neuroscience, Centre for Trophoblast Research (CTR), University of Cambridge, Cambridge, United Kingdom

Epidemiological studies have identified associations between pregnancy complications, fetal growth restriction (FGR) and preeclampsia, and increased risk of maternal chronic disease. However, no blood tests can identify women at risk. Using a novel aptamer-based proteomics platform, machine learning algorithms (SomaSignal tests) have identified protein signatures associated with chronic disease risk1,2. We sought to assess whether these algorithms could identify disease risk during pregnancy.

 

Employing a case cohort design, we measured 7,000 plasma proteins using the aptamer-based proteomics platform. We measured samples collected at 36 weeks’ gestation: an Australian study (n=115 <3rd birthweight centile/FGR n=92 preeclampsia, n=177 cohort), and a United Kingdom study (n=80 <3rd birthweight centile/FGR, n=172 cohort). Samples were collected prior to diagnosis of term fetal growth restriction or preeclampsia. SomaSignal test algorithms were applied to assess protein signatures associated with long-term chronic disease risk.

 

In the Australian study, women who later delivered a fetal growth restricted infant showed protein signatures associated with reduced heart function (within 6 and 12 months, p=0.001), and 33% increased risk of cardiovascular events (p=7.6x10-6) compared to the cohort. These protein signatures were also associated with increased risk of mid-life dementia (p=1.5x10-9), greater visceral fat (p=4.06 x10-6) and body fat percentage (p=0.003). These findings were validated in the United Kingdom cohort.

 

Women who later developed term preeclampsia showed more pronounced chronic disease-associated protein signatures, supporting existing epidemiological data. This included protein signatures associated with reduced heart function (within 6 and 12 months, p=0.001) and increased cardiovascular (p=5.54x10-6), hepatic (p=0.001) and renal (p=0.0007) disease risk relative to the cohort. Protein signatures for these women were also associated with increased mid-life dementia risk (p=1.61x10-8), glucose intolerance (p=0.001), and visceral fat (p=5.35x10-10).

 

Here, we demonstrate the potential to identify chronic disease risk during pregnancy. This may aid improved post-partum care, offering an earlier window for intervention strategies.