Protein networks are influenced by maternal BMI and differentiate preterm birth types

Preterm birth remains a leading cause of neonatal morbidity and mortality. It is classified as spontaneous, characterized by the unexpected onset of labor, or medically indicated, resulting from obstetric intervention due to pregnancy complications. The mechanisms underlying each subtype are incompletely understood, and obesity further modulates preterm birth risk through unclear biological pathways. This study aims to identify second trimester maternal plasma proteomic signatures distinguishing spontaneous and medically-indicated preterm birth and to determine how body mass index modifies these profiles.

In 100 pregnant individuals (30 spontaneous preterm birth, 30 medically-indicated preterm birth, 40 uncomplicated term deliveries), second trimester plasma was profiled using 7 K SomaScan v4.1 aptamer-based proteomic assay. Multivariate modeling and pathway analyses identified protein signatures distinguishing preterm birth subtypes, and computational network modeling with in silico perturbation analysis defined protein intermediates linking body mass index and preterm birth subtypes.

Here we show distinct proteomic signatures among spontaneous preterm birth, medically-indicated preterm birth, and term deliveries. Supervised modeling achieves clear separation and identifies key discriminatory proteins including SIGLEC6, DHFR, UBASH3A, and PHB2. Early pregnancy body mass index substantially contributes to proteomic variance and modifies preterm birth associated expression of inflammatory (PROK2, IL36A), vascular (F11R) and oxidative stress (GLX1) proteins. Network perturbation identifies FABP4, CRP, UBE2G2, and LRP8 as critical intermediates linking body mass index and preterm birth.

Distinct proteomic profiles characterize spontaneous and medically-indicated preterm birth. Body mass index emerges as a key modifier of these molecular signatures, offering insight into the obesity-associated pathways underlying preterm birth.