Relationship Between PAPP-A Levels in the First Trimester of Pregnancy and Complication Risk

Pregnancy-associated plasma protein A (PAPP-A) is increasingly being recognized as a valuable biochemical marker for identifying pregnant women at risk of experiencing complications during pregnancy. We aimed to determine the association between first-trimester PAPP-A levels and the risk of pregnancy complications.

A prospective cohort study was conducted on 606 pregnant women who were screened for aneuploidy in the first trimester using the double test at the Hai Phong Obstetrics and Gynecology Hospital from February 2023 to February 2024. Odds ratios (ORs) with 95% confidence intervals (CI) were calculated to assess the association between PAPP-A levels and various pregnancy complications, including hypertensive disorders, fetal growth restriction, preterm birth, miscarriage, and stillbirth. A multivariate binary regression model was used to analyze the relationship between PAPP-A levels and these complications, with p-values < 0.05 considered significant.

Among the 606 participants, 86 (14.2%) presented with low PAPP-A levels (< 0.5 multiple of the median (MoM)), of whom 50 (58.1%) experienced pregnancy complications. A significant association was observed between low PAPP-A levels and hypertensive disorders, fetal growth restriction, spontaneous preterm birth, and miscarriage (OR: 5.1 [95% CI: 2.8-9.4]; 2.54 [1.38-4.68]; 7.48 [3.07-18.21]; and 4.5 [1.4-14.6], respectively). Multivariate regression analysis revealed a significant independent association between PAPP-A levels and these complications (OR: 5.05 [95% CI: 2.71-9.42], 2.52 [1.36-4.66], 7.76 [3.16-19.07], and 4.06 [1.17-14.11], respectively).

Our findings suggest that pregnant women with low PAPP-A levels during the first trimester are at an increased risk of developing pregnancy complications, including hypertensive disorders, fetal growth restriction, preterm birth, and miscarriage. Further studies that integrate PAPP-A with other parameters in the predictive model are necessary to reach definitive conclusions.