Risk stratification in early pregnancy for women at increased risk of gestational diabetes

  • C.L. Harrison
    Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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  • C.B. Lombard
    Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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  • C. East
    Monash Women's Maternity Services, Monash Health, Melbourne, Australia
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  • J. Boyle
    Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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  • H.J. Teede
    Corresponding author at: School of Public Health and Preventive Medicine—Monash University Level 1, 43–51 Kanooka Grove, Clayton VIC 31168, Australia. Tel.: +61 3 9594 7532; fax: +61 3 9594 7550.
    Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

    Diabetes and Vascular Medicine Unit, Monash Health, Melbourne, Australia
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Published:September 29, 2014DOI:


      • A two-step GDM risk prediction strategy is explored in high-risk women.
      • When applied with a validated risk prediction tool, fasting glucose predicts GDM.
      • A 10 fold increased risk is demonstrated on IADPSG criteria.
      • Results present an accurate, feasible strategy for early pregnancy risk detection.



      To evaluate the addition of fasting glucose and lipids to a simple, validated risk prediction tool for gestational diabetes (GDM) applied in early pregnancy.


      Women at risk of developing GDM on a validated risk prediction tool were recruited in early pregnancy into a large randomised controlled trial. Outcome measures included fasting biochemical markers (glucose, lipids) at 12–15 weeks gestation and GDM diagnosis (28 weeks gestation). Multivariable logistic regression was used to identify additional predictive biochemical variables for GDM, with corresponding receiver operator characteristic (ROC) curves generated. Unadjusted and adjusted models were derived for both the Australasian Diabetes in Pregnancy (ADIPS) and the International Association for Diabetes in Pregnancy Study Group (IADPSG) GDM diagnostic criteria.


      51 (23%) Women were diagnosed with GDM based on ADIPS criteria, with 60 (30%) diagnosed based on IADPSG criteria. In all four regression models, fasting glucose was the strongest predictor for GDM development with an odds ratio range of 4.7–6.3 (ADIPS) and 8.8–10 (IADPSG). ROC curves revealed an area under the curve of 0.79 (95% CI: 0.72–0.86) for ADIPS criteria and 0.83 (95% CI: 0.77–0.90) for IADPSG criteria for adjusted models.


      In a two-step approach, when applied with a validated risk prediction tool, fasting glucose in early pregnancy was predictive of GDM and incrementally improved risk identification, presenting potential for an early pregnancy, GDM risk screening strategy for streamlining of pregnancy care and opportunity for preventive intervention.


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