ArticleEndocrine2025
Establishment of an accurate prediction system for gestational diabetes mellitus based on the characteristics of metabolic kinetics in early pregnancy: a prospective two-center cohort study in population according to IOM criteria.
Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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18 authors.
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Abstract
objectiveTo develop an accurate prediction system for gestational diabetes mellitus (GDM) in women adhering to Institute of Medicine (IOM) weight gain criteria by analyzing early-pregnancy metabolic kinetics and identifying independent risk factors.
methodsA prospective two-center cohort study enrolled 1,031 pregnant women meeting IOM guidelines. Clinical, anthropometric, and metabolic parameters (pre-pregnancy BMI, lipid profiles, inflammatory markers) were collected at 6–12 weeks of gestation. Machine learning models were trained on seven core variables identified via logistic regression, with performance evaluated by AUC, sensitivity, and specificity.
resultsThe GDM group (n = 279) exhibited significantly higher pre-pregnancy weight, BMI, triglycerides (1.21 vs. 0.96 mmol/L, p < 0.001), and inflammatory markers. Multivariate analysis identified parity ≥ 2 (OR = 4.37), family diabetes history (OR = 1.64), third-trimester weight (OR = 1.13), and triglycerides (OR = 1.49) as independent predictors. A neural network model achieved the highest AUC (0.732) with 65.1% sensitivity and 68.9% specificity, outperforming logistic regression and other algorithms. Dynamic weight trajectories and lipid dysregulation were critical risk drivers, even in IOM-compliant women.
conclusionIn pregnant women adhering to IOM weight gain standards, early-pregnancy metabolic dysfunctionand specific weight trajectories persist as pivotal GDM predictors. Our neural network model integrating seven core variables—including triglyceride levels, dynamic weight patterns (6–10 weeks and 24–28 weeks), and clinical risk factors—achieved superior prediction accuracy compared to conventional weight-centric approaches. This system enables early risk stratification and personalized interventions during the first trimester, addressing a critical gap in prenatal care for guideline-compliant populations at residual GDM risk.
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