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Aims/hypothesis <p>Women with previous gestational diabetes mellitus (GDM) have an increased risk of developing both type 1 and type 2 diabetes. However, predicting the risk on a personal level is challenging. Polygenic scores (PGSs), which capture an individual's genetic susceptibility to disease, may improve risk prediction. We aimed to examine the utility of PGSs for type 1 diabetes (T1D-PGS) and type 2 diabetes (T2D-PGS) to predict overt diabetes in women with previous GDM, with a median follow-up time of 30 years after index pregnancy.</p> Methods <p>We conducted an observational study including 370 women diagnosed with GDM between 1978 and 1996. Development of type 1 and type 2 diabetes was identified using national registry data comprising ICD-10 diagnosis codes on diabetes until 2019, combined with a glucose tolerance test performed in 2000–2002. The predictive ability of T1D-PGS and T2D-PGS and clinical risk factors during index pregnancy for type 1 and type 2 diabetes, including maternal age at delivery, pre-pregnancy BMI, fasting plasma glucose, insulin treatment and family history of diabetes, was evaluated using receiver operating characteristic area under the curve (AUC).</p> Results <p>During a median follow-up of 30 years, 200 (54.1%) women were diagnosed with diabetes: 30 (8.1%) with type 1 diabetes, 157 (42.4%) with type 2 diabetes and 13 (3.5%) carried monogenic diabetes variants. The mean T1D-PGS was higher in women with type 1 diabetes compared with those without (13.6 vs 11.5, <i>p</i>&lt;0.001), while the T2D-PGS was similar between women with and without type 2 diabetes (52.9 vs 52.8, <i>p</i>=0.16). The T1D-PGS and clinical risk factors during pregnancy were discriminative of type 1 diabetes with AUCs of 0.788 and 0.839, respectively, while a combined model yielded an AUC of 0.887. The T2D-PGS and clinical risk factors during pregnancy yielded AUCs of 0.539 and 0.745, respectively. The combined model had an AUC of 0.752.</p> Conclusions/interpretation <p>In women with previous GDM, the T1D-PGS showed good predictive ability for the development of type 1 diabetes, especially when combined with clinical risk factors during pregnancy, and may have potential to guide targeted follow-up and early treatment interventions. The T2D-PGS showed no predictive ability for type 2 diabetes.</p> Graphical Abstract <p></p>

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Predicting future risk of type 1 and type 2 diabetes after gestational diabetes mellitus using polygenic scores: a cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Isabella L. Jørgensen,
  • Peter Damm,
  • Mette K. Andersen,
  • Pauline K. Reim,
  • Lana R. F. Madsen,
  • Mie K. W. Crusell,
  • Louise Kelstrup,
  • Lene Ringholm,
  • Elisabeth R. Mathiesen,
  • Jeannet Lauenborg,
  • Torben Hansen,
  • Anne C. B. Thuesen

摘要

Aims/hypothesis

Women with previous gestational diabetes mellitus (GDM) have an increased risk of developing both type 1 and type 2 diabetes. However, predicting the risk on a personal level is challenging. Polygenic scores (PGSs), which capture an individual's genetic susceptibility to disease, may improve risk prediction. We aimed to examine the utility of PGSs for type 1 diabetes (T1D-PGS) and type 2 diabetes (T2D-PGS) to predict overt diabetes in women with previous GDM, with a median follow-up time of 30 years after index pregnancy.

Methods

We conducted an observational study including 370 women diagnosed with GDM between 1978 and 1996. Development of type 1 and type 2 diabetes was identified using national registry data comprising ICD-10 diagnosis codes on diabetes until 2019, combined with a glucose tolerance test performed in 2000–2002. The predictive ability of T1D-PGS and T2D-PGS and clinical risk factors during index pregnancy for type 1 and type 2 diabetes, including maternal age at delivery, pre-pregnancy BMI, fasting plasma glucose, insulin treatment and family history of diabetes, was evaluated using receiver operating characteristic area under the curve (AUC).

Results

During a median follow-up of 30 years, 200 (54.1%) women were diagnosed with diabetes: 30 (8.1%) with type 1 diabetes, 157 (42.4%) with type 2 diabetes and 13 (3.5%) carried monogenic diabetes variants. The mean T1D-PGS was higher in women with type 1 diabetes compared with those without (13.6 vs 11.5, p<0.001), while the T2D-PGS was similar between women with and without type 2 diabetes (52.9 vs 52.8, p=0.16). The T1D-PGS and clinical risk factors during pregnancy were discriminative of type 1 diabetes with AUCs of 0.788 and 0.839, respectively, while a combined model yielded an AUC of 0.887. The T2D-PGS and clinical risk factors during pregnancy yielded AUCs of 0.539 and 0.745, respectively. The combined model had an AUC of 0.752.

Conclusions/interpretation

In women with previous GDM, the T1D-PGS showed good predictive ability for the development of type 1 diabetes, especially when combined with clinical risk factors during pregnancy, and may have potential to guide targeted follow-up and early treatment interventions. The T2D-PGS showed no predictive ability for type 2 diabetes.

Graphical Abstract