Several biomarkers are used to measure iron deficiency (ID) during pregnancy, but the prevalence of ID and its association with adverse birth outcomes shows inconsistent results. The aim of this study was to examine the prevalence of ID in third trimester using multiple indicators of iron status and the relationship with birth outcomes in Chinese population. We conducted a retrospective observational cohort study of 11,581 pregnant women between 2016 and 2017 in Changzhou City, Jiangsu Province, China. We obtained the data (maternal characteristics and birth outcomes) and the concentrations of ID biomarkers from our hospitalization information system and laboratory information system, respectively. Using serum ferritin (SF), serum transferrin (ST) and their ratio as criteria of ID, we investigated associations between birth outcomes and late pregnancy ID. The prevalence of ID in our study was 51.82% as defined by low SF (< 12 μg/L), 54.43% as defined by high ST (> 4 g/L) and 53.90% as defined by high ratio of ST/SF (Log 10 transform > 5.52). Maternal ST/SF ratio was associated with higher mean birth weight (97.04 g; 95% CI, 74.28, 119.81 for the highest vs. lowest quartile). Third trimester maternal ID, defined by ST/SF ratio, was associated with lower risks of preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA) infants, higher risks of macrosomia and large for gestational age (LGA) babies (for PTB: OR = 0.53, 95% CI, 0.36–0.77; for LBW: OR = 0.44, 95% CI, 0.31–0.62; for SGA: OR = 0.69, 95% CI, 0.57–0.83; for macrosomia: OR = 1.39, 95% CI, 1.13–1.70; for LGA: OR = 1.20, 95% CI, 1.04–1.39). ID in the third-trimester of pregnancy are frequent in Chinese women. Our findings suggest that the ratio of ST/SF measured in late pregnancy could be useful as a significant predictor of unfavorable birth outcomes.

Gestational diabetes mellitus (GDM) is an increasingly prevalent pregnancy complication with significant maternal-fetal health impacts. Triclosan (TCS), a ubiquitous environmental endocrine disruptor, has been epidemiologically linked to GDM risk, yet its molecular mechanisms remain poorly understood. We employed an integrated bioinformatics approach combining network toxicology, transcriptomics, and machine learning to identify potential molecular targets connecting TCS exposure with GDM. Common target genes were identified by intersecting TCS-related targets from multiple databases with GDM-associated genes, followed by core target screening through differential expression analysis of GDM placental tissue (GSE70493) and feature selection using machine learning algorithms. Target validation was conducted via immune cell infiltration analysis, single-cell expression profiling, Western blot and molecular docking. We identified 210 shared TCS-GDM target genes significantly enriched in metabolic, inflammatory, and hormonal pathways. Five core genes (CXCL12, FABP4, HSD11B1, SDHA, and PRKAG2) emerged as critical molecular links, validated through protein-level expression analysis. These targets demonstrated significant associations with diverse immune cell populations and exhibited pronounced expression alterations in GDM placentas with high collective diagnostic performance. Molecular docking confirmed favorable TCS binding affinities with all core target proteins. This study systematically revealed the potential molecular mechanisms underlying TCS influence on GDM through core targets regulating lipid metabolism, glucocorticoid signaling, energy homeostasis, and immune responses, providing new insights for understanding environmental endocrine disruptor health risks and offering potential biomarkers for early GDM diagnosis and intervention.