Immune Changes in Pregnancy: Associations with Pre-existing Conditions and Obstetrical Complications at the 20th Gestational Week - A Prospective Cohort Study
Authors: Westergaard David , Lundgaard Agnete Troen , Vomstein Kilian , Fich Line , Hviid Kathrine Vauvert Römmelmayer , Egerup Pia , Christiansen Ann-Marie Hellerung , Nielsen Josefine Reinhardt , Lindman Johanna , Holm Peter Christoffer , Hartwig Tanja Schlaikjær , Jørgensen Finn Stener , Zedeler Anne , Kolte Astrid Marie , Westh Henrik , Jørgensen Henrik Løvendahl , Freiesleben Nina la Cour , Banasik Karina , Brunak Søren , Nielsen Henriette Svarre 
Published: August 2023

AbstractBackgroundPregnancy is a complex biological process and serious complications can arise when the delicate balance between the maternal immune system and the semi-allogeneic fetal immune system is disrupted or challenged. Gestational diabetes mellitus (GDM), pre-eclampsia, preterm birth, and low birth weight, pose serious threats to maternal and fetal health. Identification of early biomarkers through an in-depth understanding of molecular mechanisms is critical for early intervention.MethodsWe analyzed the associations between 47 proteins involved in inflammation, chemotaxis, angiogenesis, and immune system regulation, maternal and neonatal health outcomes, and the baseline characteristics and pre-existing conditions (diseases and obstetric history) of the mother in a prospective cohort of 1,049 pregnant women around the 20th gestational week. Bayesian linear regression models were used to examine the impact of risk factors on biomarker levels and Bayesian cause-specific parametric proportional hazards models were used to analyze the effect of biomarkers on maternal and neonatal health outcomes. Finally, we evaluated the predictive value of baseline characteristics and the 47 proteins using machine-learning models. Shapley additive explanation (SHAP) scores were used to dissect the machine learning models to identify biomarkers most important for predictions.ResultsAssociations were identified between specific inflammatory markers and existing conditions, including maternal age and pre-pregnancy BMI, chronic diseases, complications from prior pregnancies, and COVID-19 exposure. Smoking during pregnancy significantly affected GM-CSF and 9 other biomarkers. Distinct biomarker patterns were observed for different ethnicities. In obstetric complications, IL-6 inversely correlated with pre-eclampsia risk, while acute cesarean section and birth weight to gestational age ratio were linked to markers such as VEGF or PlGF. GDM was associated with IL-1RA, IL-17D, and Eotaxin-3. Severe PPH correlated with CRP and proteins of the IL-17 family. Predictive modeling using MSD biomarkers yielded ROC-AUC values of 0.708 and 0.672 for GDM and pre-eclampsia, respectively. Significant predictive biomarkers for GDM included IL-1RA and Eotaxin-3, while pre-eclampsia prediction yielded highest predictions when including MIP-1β, IL-1RA, and IL-12p70.ConclusionOur study provides novel insights into the interplay between preexisting conditions and immune dysregulation in pregnancy. These findings contribute to our understanding of the pathophysiology of obstetric complications and the identification of novel biomarkers for early intervention(s) to improve maternal and fetal health.