What question did this study set out to answer?

This research aims to develop a predictive model for gestational diabetes risk using BMI trajectories and biomarkers.

Dual‐center prospective validation of an early second‐trimester predictive model integrating BMI trajectories and pathophysiological biomarkers for gestational diabetes risk stratification

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Prospective validation identifies predictive markers of gestational diabetes risk, suggesting early intervention improves outcomes.

This research aims to develop a predictive model for gestational diabetes risk using BMI trajectories and biomarkers.
Dual-center prospective cohort study with 1,450 pregnant women enrolled before 12 weeks gestation.
Analyzed variables include BMI at various gestational weeks and metabolic biomarkers like fasting glucose and C-peptide.
GDM was diagnosed using a 75 g OGTT, and model performance was assessed by ROC curve analysis.
GDM incidence was 26.1% among participants.
Model predictors include age, BMI, fasting glucose, and fasting C-peptide with notable odds ratios for each.
The final model displays moderate discrimination with AUC values around 0.684, indicating its predictive capability.

Gao et al. (2025) studied this question.

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