Dynamic Multi-Omics Profiling during the OGTT Reveals Impaired Metabolic Responses, Lipid Structural Remodeling, and Environmental Exposure Associations in Gestational Diabetes Mellitus

Plasma was sampled at fasting (0 h) and at 1 h and 2 h post-glucose load and analyzed by untargeted liquid chromatography–mass spectrometry (LC-MS) in both positive and negative ionization modes.Raw data comprise 732 mzXML files organized into two molecular layers: a Metabolome/Exposome layer (374 files: 187 POS + 187 NEG) shared between the two omics, and an independently acquired Lipidome layer (358 files: 183 POS + 175 NEG).

Processed feature matrices include a 1,322-feature metabolome table with metabolite names and KEGG IDs (omics_metabolome.csv; 560 POS + 762 NEG, PQN-normalized), a 258-chemical exposome matrix (omics_exposome.csv), and a 2,207-lipid species matrix with class annotations (omics_lipidome.csv; 159 clinical samples). A per-omics sample metadata table (371 rows) links original and harmonized mzXML filenames, group (GDM / Control / QC), patient identifier, OGTT time point, and omics layer.The cohort consists of 84 GDM pregnancies and 84 healthy controls, with each subject contributing up to three OGTT time points (168 clinical samples in total), plus 19 pooled quality-control samples.  This dataset supports studies on glucose-load metabolic dynamics, GDM pathophysiology, environmental-contaminant exposure in pregnancy, and integrative multi-omics biomarker discovery.