| Title: |
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, la Cour Freiesleben, Nina, Banasik, Karina, Brunak, Søren, Nielsen, Henriette Svarre |
| Source: |
BMC Medicine; 12/18/2024, Vol. 22 Issue 1, p1-15, 15p |
| Subject Terms: |
GESTATIONAL diabetes, NEONATOLOGY, LOW birth weight, MATERNAL age, MEDICAL sciences, TEENAGE pregnancy |
| Abstract: |
Background: Pregnancy is a complex biological process and serious complications can arise when the delicate balance between the maternal and semi-allogeneic fetal immune systems 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. Methods: We 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 of the mother in a prospective cohort of 1049 pregnant women around the 20th gestational week. We used Bayesian linear regression models to examine the impact of risk factors on biomarker levels and Bayesian cause-specific parametric proportional hazards models to analyze the effect of biomarkers on maternal and neonatal outcomes. We evaluated the predictive value of baseline characteristics and 47 proteins using machine-learning models and identified the most predictive biomarkers using Shapley additive explanation scores. Results: Associations were identified between specific inflammatory markers and several conditions, including maternal age and pre-pregnancy body mass index, chronic diseases, complications from prior pregnancies, and COVID-19 exposure. Smoking during pregnancy affected GM-CSF and 9 other biomarkers. Distinct biomarker patterns were observed for different ethnicities. Within obstetric complications, IL-6 inversely correlated with pre-eclampsia risk, while birth weight to gestational age ratio was linked to markers including VEGF and PlGF. GDM was associated with IL-1RA, IL-17D, and eotaxin-3. Severe postpartum hemorrhage correlated with CRP, IL-13, and proteins of the IL-17 family. Predictive modeling yielded area under the receiver operating characteristic curve 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 the highest predictions when including MIP-1β, IL-1RA, and IL-12p70. Conclusions: Our 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
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