Development and validation of a model integrating clinical and metabolomic markers for gestational diabetes mellitus prediction.

Abstract

Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication. Current diagnostic approaches are inherently retrospective, necessitating the development of effective early prediction models for timely intervention and improved outcomes. This study aimed to develop and validate a prediction model for GDM risk by integrating first-trimester clinical and metabolomic indicators. A retrospective cohort study was conducted involving 342 singleton pregnancies that received routine antenatal care and underwent mid-pregnancy oral glucose tolerance tests (OGTT) from January 2022 to December 2024. Participants were randomly allocated to a training set (n = 239) and a validation set ( n = 103) in a 7:3 ratio. Core predictors were identified through a univariate analysis, LASSO regression, and subsequent multivariable logistic regression. Four machine learning models-Random Forest, Support Vector Machine (SVM), Gradient Boosting Machine, and Logistic Regression-were constructed and compared. Performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve analysis. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) values. A multivariable analysis identified seven independent predictors: pre-pregnancy BMI, first-trimester fasting plasma glucose, triglycerides, C-reactive protein, and the branched-chain amino acid score (risk factors), as well as pregnancy-associated plasma protein-A and 1,5-anhydroglucitol (protective factors). In the validation set, the SVM model achieved optimal performance with an AUC of 0.861 (95% confidence interval (CI): 0.772-0.949). Calibration and decision curve analyses demonstrated good agreement between predicted and observed risks and affirmed clinical utility across a wide threshold probability range. A prediction model integrating first-trimester clinical and metabolomic markers was successfully developed and validated. The model demonstrates favorable predictive accuracy and clinical applicability, offering potential as an auxiliary tool for early risk stratification and personalized GDM management. Future multi-center external validation is warranted to confirm generalizability.