Integrated Clinical and Molecular Profiling of Fetal Growth Disorders in the First Trimester

This prospective study evaluated first-trimester markers in pregnancies with isolated and combined forms of fetal growth disorders and gestational diabetes mellitus (GDM). Among 1869 screened women, the analysis included 83 controls, 55 GDM, 22 isolated intrauterine growth restriction (iIUGR), and 33 isolated large-for-gestational-age (iLGA) cases, with GDM subgroups stratified by fetal growth (GDM with normal fetal weight, GDM + IUGR, and GDM + LGA). First-trimester clinical and routine biochemical parameters were recorded, and serum concentrations of 80 proteins were measured using targeted LC-MRM-MS proteomics. Different trajectories emerged: IUGR phenotypes showed low PAPP-A/PlGF and high TSH (p < 0.01), indicating early placental insufficiency, while macrosomia showed opposite trends. GDM + IUGR represented the most severe "double hit" phenotype (lowest PlGF, earliest delivery), whereas GDM + LGA showed increased umbilical artery resistance despite excessive growth, suggesting endothelial dysfunction. Targeted proteomics revealed characteristic signatures: iIUGR featured low complement (C4A|C4B) and IGF proteins (IGFALS, IGFBP3) versus GDM and iLGA (p < 0.001); GDM + IUGR showed elevated PZP and CD5L versus iIUGR (p < 0.05); GDM + LGA was marked by high C4BPA and low RBP4, SERPINA7 versus iLGA (p < 0.05). Complement and IGF pathways were consistently implicated. Machine learning achieved 77% sensitivity for IUGR prediction using clinical parameters and 88% sensitivity for LGA prediction using proteomic data. These findings demonstrate that fetal growth disorders represent pathophysiologically unique entities detectable in the first trimester, enabling early risk stratification and personalized management.