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Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score

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Abstract

Aims/hypothesis

Women with a history of gestational diabetes mellitus (GDM) are at increased risk of future development of type 2 diabetes. Recently, over 65 genetic variants have been confirmed to be associated with diabetes. We investigated whether this genetic information could improve the prediction of future diabetes in women with GDM.

Methods

This was a prospective cohort study consisting of 395 women with GDM who were followed annually with an OGTT. A weighted genetic risk score (wGRS), consisting of 48 variants, was assessed for improving discrimination (C statistic) and risk reclassification (continuous net reclassification improvement [NRI] index) when added to clinical risk factors.

Results

Among the 395 women with GDM, 116 (29.4%) developed diabetes during a median follow-up period of 45 months. Women with GDM who went on to develop diabetes had a significantly higher wGRS than those who did not (9.36 ± 0.92 vs 8.78 ± 1.07; p < 1.56 × 10−7). In a complex clinical model adjusted for age, prepregnancy BMI, family history of diabetes, blood pressure, fasting glucose and fasting insulin concentration, the C statistic marginally improved from 0.741 without the wGRS to 0.775 with the wGRS (p = 0.015). The addition of the wGRS to the clinical model resulted in a modest improvement in reclassification (continuous NRI 0.430 [95% CI 0.218, 0.642]; p = 7.0 × 10−5).

Conclusions/interpretation

In women with GDM, who are at high risk of diabetes, the wGRS was significantly associated with the future development of diabetes. Furthermore, it improved prediction over clinical risk factors.

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Abbreviations

GDM:

Gestational diabetes mellitus

GRS:

Genetic risk score

GWA:

Genome-wide association

IGT:

Impaired glucose tolerance

IQR:

Interquartile range

IR:

Insulin resistance

IS:

Insulin secretion

NGT:

Normal glucose tolerance

NRI:

Net reclassification improvement

SNP:

Single nucleotide polymorphism

uGRS:

Unweighted genetic risk score

wGRS:

Weighted genetic risk score

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Acknowledgements

This work was supported by the Korea Healthcare Technology R & D Project, Ministry of Health and Welfare (grant no. A111362), and by a grant from the National Project for Personalized Genomic Medicine, Ministry for Health and Welfare (grant no. A111218-GM09), Republic of Korea.

Funding

This work was funded by the National Project for Personalized Genomic Medicine and Korea Healthcare Technology R & D Project, Ministry for Health and Welfare, Republic of Korea.

Duality of interest

The authors declare that there is no duality of interest associated with this study.

Contribution statement

SHK contributed substantially to the conception and design, acquisition, analysis and interpretation of data and drafting and revising the manuscript. SHC, KK, HSJ, YMC, SL and SYK contributed substantially to the conception and design, interpretation of data and revising the manuscript. NHC contributed substantially to the conception and design, acquisition and interpretation of data and revising the manuscript. KSP and HCJ contributed substantially to the conception and design, acquisition, analysis and interpretation of data and revising the manuscript. All authors approved the final version to be published.

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Authors and Affiliations

  1. Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-Ro, Chongno-Gu, Seoul, 110-744, South Korea

    Soo Heon Kwak, Hye Seung Jung, Young Min Cho, Seong Yeon Kim & Kyong Soo Park

  2. Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Chongno-Gu, Seoul, 110-799, South Korea

    Sung Hee Choi, Young Min Cho, Seong Yeon Kim, Kyong Soo Park & Hak C. Jang

  3. Department of Internal Medicine, Seoul National University Bundang Hospital, 82 Gumi-Ro, 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 463-707, South Korea

    Sung Hee Choi, Soo Lim & Hak C. Jang

  4. Department of Statistics, Sookmyung Women’s University, Seoul, South Korea

    Kyunga Kim

  5. Department of Preventive Medicine, Ajou University School of Medicine, Suwon, South Korea

    Nam H. Cho

  6. World Class University Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, 103 Daehak-Ro, Chongno-Gu, Seoul, 110-799, South Korea

    Kyong Soo Park

Authors
  1. Soo Heon Kwak
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  2. Sung Hee Choi
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  3. Kyunga Kim
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  4. Hye Seung Jung
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  5. Young Min Cho
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  6. Soo Lim
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  7. Nam H. Cho
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  8. Seong Yeon Kim
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  9. Kyong Soo Park
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  10. Hak C. Jang
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Corresponding authors

Correspondence to Kyong Soo Park or Hak C. Jang.

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Kwak, S.H., Choi, S.H., Kim, K. et al. Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score. Diabetologia 56, 2556–2563 (2013). https://doi.org/10.1007/s00125-013-3059-x

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  • DOI: https://doi.org/10.1007/s00125-013-3059-x

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