Metagenomic profiling and predictive modeling of the gut microbiome reveal signatures of gestational disease

Abstract

Gut microbial dysbiosis has been implicated in pregnancy complications, yet most studies rely on 16S rRNA sequencing, which limits resolution and functional insight. Here, using shotgun metagenomic sequencing and machine learning, we identified robust microbial taxonomic and functional signatures that distinguish gestational hypertension and gestational diabetes from healthy pregnancies. A combined feature set enabled accurate classification of disease status, with overlapping features between statistical and predictive frameworks underscoring biological relevance. Altogether, our study defines high-resolution microbiome signatures with translational potential as predictive biomarkers for maternal health, while also providing an open, reproducible analysis pipeline to support future investigations.