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Maternal Plasma Proteins Associated with Birth Weight: A Longitudinal, Large Scale Proteomic Study
Ina Jungersen Andresen1, Ane Cecilie Westerberg1,2, Marie Cecilie Paasche Roland1,3
1Department of Obstetrics, Division of Obstetrics and Gynecology, Oslo University Hospital, 0372 Oslo, Norway.
Insights
Predicting fetal growth is challenging. Maternal plasma proteins show promise as biomarkers for identifying large for gestational age (LGA) infants, aiding clinical management.
Area of Science:
- Proteomics
- Maternal-fetal medicine
- Biomarker discovery
Background:
- Infants born small (SGA) or large (LGA) for gestational age face increased health risks.
- Current fetal weight prediction via ultrasound is often imprecise.
- Reliable biomarkers are needed for optimal maternal and infant care.
Purpose of the Study:
- To investigate maternal plasma proteins as potential biomarkers for predicting fetal growth deviations.
- To develop predictive models for identifying large for gestational age (LGA) and small for gestational age (SGA) infants.
Main Methods:
- Maternal blood samples collected from 70 pregnant women across three trimesters.
- Quantified nearly 5000 proteins using SomaLogic platform.
- Applied machine learning (Random Forest) with cross-validation for predictive modeling.
Main Results:
- Machine learning models accurately predicted LGA infants (AUC > 0.8) using 20 proteins.
- Prediction of SGA infants was less successful.
- Identified 148 proteins with higher abundance in LGA vs. adequate for gestational age (AGA) pregnancies; only 4 proteins differed in SGA vs. AGA.
Conclusions:
- Maternal plasma proteome contains potential biomarkers for predicting LGA.
- Further research is warranted to validate these biomarkers for clinical application.
Abstract:
Small infants for gestational age (SGA) and large infants for gestational age (LGA) have increased risk of complications during delivery and later in life. Prediction of the fetal weight is currently limited to biometric parameters obtained by ultrasound scans that can be imprecise. Biomarkers of fetal growth would be crucial for tailoring clinical management and optimizing outcomes for the mother and child. Seventy pregnant women participated in the current study, including 58, 7, and 5 giving birth to adequate for gestational age (AGA), SGA, and LGA infants, respectively. Maternal venous blood was drawn at gestational weeks 12-19, 21-27, and 28-34 and quantified for nearly 5000 proteins on the SomaLogic platform. We used machine learning algorithms with leave-one-out cross-validation to construct multiprotein models for prediction of birth weight groups. Random forest models using only 20 predefined proteins (selected by moderated t tests) were able to predict LGA with good discrimination (AUC > 0.8) at all three visits, while prediction of SGA was less successful. Protein differential abundance analysis revealed 148 proteins with higher abundance in LGA compared to AGA pregnancies, while only four proteins were differentially abundant between the SGA and AGA. The principal findings indicate that the maternal plasma proteome may hold potential biomarkers of LGA.
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