Fasting Metabolite Panel for OGTT-Free Diagnosis of Gestational Diabetes Mellitus: A Machine Learning Approach Validated in Dual Cohorts.

Objective: This study aimed to develop a fasting serum metabolite-based method for screening and risk assessment of gestational diabetes mellitus (GDM), potentially reducing dependence on the oral glucose tolerance test (OGTT). Methods: Using a retrospective discovery cohort (n = 435; April-May 2021) with prospective validation (n = 473; November 2018-May 2021) design, 1,053 pregnant women completing standard 75g OGTT were initially enrolled. Fasting serum samples underwent targeted metabolomic profiling. A diagnostic model was constructed using machine learning (random forest) in combination with univariate analysis and rigorous validation protocols. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC). Results: Eight metabolites demonstrated significant differential expression between GDM and non-GDM groups (FDR <0.05). Based on the feature importance rankings, we developed a multivariate logistic regression model incorporating seven metabolites: 2-hydroxybutyric acid, 1,5-anhydroglucitol, glycine, 3-methyl-2-oxobutyric acid, 3-methyl-2-oxovaleric acid, tyrosine, and oleic acid. The composite model (fasting glucose + risk factors + metabolites) demonstrated significantly higher discriminative performance in the discovery cohort (AUC = 0.78) compared to fasting glucose alone (AUC = 0.62), with sustained performance in external validation (AUC = 0.71). Conclusion: This fasting metabolite detection protocol demonstrates promising potential for GDM screening and risk stratification, offering the prospect of reducing reliance on OGTT in specific clinical settings.