Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score
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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.
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.
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).
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.
KeywordsGenetic risk score Gestational diabetes Risk prediction Type 2 diabetes
Gestational diabetes mellitus
Genetic risk score
Impaired glucose tolerance
Normal glucose tolerance
Net reclassification improvement
Single nucleotide polymorphism
Unweighted genetic risk score
Weighted genetic risk score
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.
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.
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.
- 21.Hosmer DW, Lemeshow S (1989) Applied logistic regression. Wiley, New YorkGoogle Scholar