Combining Suspect Screening with Large Language Model-Based Text Mining to Comprehensively Characterize Organic Compounds in Human Milk Associated with Pregnancy Complications

Chemical
exposure contributes to maternal pregnancy complications
like gestational hypertension (GH), anemia, and gestational diabetes
mellitus (GDM). However, current studies remain fragmented due to
limited analysis of compounds, impeding mechanistic insights. Here,
we present a novel framework that integrates high-throughput analysis
and large language model-based text mining to identify organic compounds
while leveraging existing massive data, thereby enabling a comprehensive
understanding of pregnancy complication mechanisms and establishing
an exposure atlas. Using this approach, we identified five compounds
in human milk for the first time, including carbazole and 4,4′-diphenoxybenzophenone,
and 35 additional compounds not previously linked to pregnancy complications.
We further employed text mining to comprehensively uncover disease-specific
chemical signatures based on global data: GH with polycyclic aromatic
hydrocarbons (PAHs) and derivatives (e.g., 2-methylnaphthalene and
acenaphthene), anemia with nitrogen-containing compounds (e.g., 4-methoxyformanilide),
and GDM with long-chain carboxylic acids (e.g., 2,4,7,9-tetramethyldec-5-yne-4,7-diol).
Further analysis revealed pathogenic mechanisms: PAHs and derivatives
promoted oxidative stress in GH, nitrogen-containing compounds damaged
red blood cells in anemia, and long-chain carboxylic acids interfered
with mitochondrial function in GDM. These findings construct an atlas
of organic compounds associated with pregnancy complications and offer
new leads for understanding their environmental origins.