Fragestellung
Gestational diabetes mellitus (GDM) is a significant contributor to adverse maternal and offspring outcomes, with an increased prevalence in pregnancies following assisted reproductive technology (ART). Current screening strategies identify GDM only during mid-pregnancy, potentially missing critical early preventive windows. This study hypothesized that metabolic alterations predisposing to GDM are detectable even before conception. We aimed to determine if combined analysis of systemic (serum) and ovarian (follicular fluid) metabolic profiles could improve early risk prediction.

Methodik
In a propensity score–matched cohort study, 50 women who later developed GDM and 50 normoglycemic controls undergoing IVF were analyzed. Matching included maternal age, BMI, and ovarian reserve markers. Untargeted LC–MS metabolomics was performed in paired serum and follicular fluid samples obtained prior to embryo transfer. Multivariate modeling, machine learning–based biomarker selection, ROC analysis, and pathway enrichment analysis were applied.

Ergebnisse
Clinical baseline characteristics were comparable between groups. Metabolomic profiling revealed distinct preconception metabolic signatures associated with later GDM. Eighty-four metabolites were altered in follicular fluid and 53 in serum, with clear separation between groups in multivariate models.
A four-metabolite follicular fluid panel predicted GDM with an AUC of 1.00.
A five-metabolite serum panel achieved an AUC of 0.976.
An integrated multi-matrix model combining both systemic and ovarian data also achieved an AUC of 1.00 with high internal stability. Cross-matrix analyses suggested early metabolic decoupling between systemic and ovarian compartments prior to pregnancy.

Schlussfolgerung/en
Metabolic alterations associated with GDM are detectable before pregnancy in women undergoing IVF. Combined serum and follicular fluid metabolomic profiling may enable early risk stratification and supports the concept that GDM pathophysiology begins prior to conception. These findings open perspectives for preconception risk assessment and targeted prevention strategies in high-risk populations.