What does this research mean for the field?

A multidimensional risk prediction model for gestational diabetes mellitus (GDM) can effectively identify women at elevated risk during early pregnancy. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop a prediction model to identify women at high risk for gestational diabetes mellitus in early pregnancy.

March 14, 2026Open Access

Development and validation of a multidimensional indicator-based risk prediction model for gestational diabetes mellitus: a nested case-control study

Key Points

The aim is to develop a prediction model to identify women at high risk for gestational diabetes mellitus in early pregnancy.
Conducted a nested case-control study with 346 women
Applied LASSO regression for variable selection
Developed a nomogram based on selected risk factors
Evaluated model performance with calibration and ROC analysis
Used decision curve analysis for clinical utility assessment
The model achieved an AUC of 0.85 in the training set and 0.73 in the validation set
Factors such as family history of diabetes and pre-pregnancy BMI were linked to increased GDM risk
Higher activated partial thromboplastin time correlated with decreased GDM risk
The model showed satisfactory calibration and high net benefit from decision curve analysis

Abstract

Gestational diabetes mellitus (GDM) could contribute to significant health risks in both mothers and their offspring. Therefore, this study aims to construct a prediction model to identify women at elevated risk for GDM in early pregnancy. Methods: This study was a nested case-control study. 346 participants were randomly allocated to the training set (n = 242) and the validation set (n = 104) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most significant factors among candidate variables. A GDM risk prediction model was further established based on the risk factors chosen by the LASSO. The model’s calibration, discrimination, and clinical use were assessed using the calibration analysis, area under the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Finally, the model was presented with a nomogram. In the training set, a simple GDM risk prediction model was developed by using family history of diabetes, pre-pregnancy body mass index (BMI), progesterone, aspartate transaminase (AST), activated partial thromboplastin time (APTT), and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C). Among them, family history of diabetes, higher pre-pregnancy BMI, progesterone, AST, and TG/HDL-C levels were associated with increased GDM risk, while higher APTT level was associated with decreased GDM risk. The calibration curve indicated satisfactory accuracy. The ROC curve demonstrated excellent discrimination, with the area under the curve (AUC) of 0.85 (95% confidence interval CI, 0.80–0.91) and 0.73 (95%CI, 0.62–0.83) for the training and validation set, respectively. The DCA curve demonstrated high net benefit. Furthermore, internal validation with excellent performance demonstrated the generalizability of the model. The present study developed a model with excellent performance for predicting GDM. Furthermore, a nomogram was constructed to visualize the model. Therefore, this model can serve as an effective GDM prediction tool.

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Cite This Study

Chen et al. (Thu,) studied this question.

synapsesocial.com/papers/69b4fc0eb39f7826a300ca1b https://doi.org/https://doi.org/10.1186/s12902-026-02227-9

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