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Background <p>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.</p> Methods <p>Methods: This study was a nested case-control study. 346 participants were randomly allocated to the training set (<i>n</i> = 242) and the validation set (<i>n</i> = 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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

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  • 发表日期：

    2026年3月12日

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  • Jiajia Chen,
  • Shanshan Yin,
  • Shuling Wang,
  • Shu Li,
  • Ru Feng,
  • Xianqi Wang,
  • Xiao Hao,
  • Xia Zhang,
  • Qing Zhang,
  • Guijuan Zhang,
  • Linlin Hua

摘要

Background

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

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.

Results

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.

Conclusions

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.