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Published in: Acta Diabetologica 3/2016

01-06-2016 | Original Article

Development of a simple tool to predict the risk of postpartum diabetes in women with gestational diabetes mellitus

Authors: M. Köhler, A. G. Ziegler, A. Beyerlein

Published in: Acta Diabetologica | Issue 3/2016

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Abstract

Aims

Women with gestational diabetes mellitus (GDM) have an increased risk of diabetes postpartum. We developed a score to predict the long-term risk of postpartum diabetes using clinical and anamnestic variables recorded during or shortly after delivery.

Methods

Data from 257 GDM women who were prospectively followed for diabetes outcome over 20 years of follow-up were used to develop and validate the risk score. Participants were divided into training and test sets. The risk score was calculated using Lasso Cox regression and divided into four risk categories, and its prediction performance was assessed in the test set.

Results

Postpartum diabetes developed in 110 women. The computed training set risk score of 5 × body mass index in early pregnancy (per kg/m2) + 132 if GDM was treated with insulin (otherwise 0) + 44 if the woman had a family history of diabetes (otherwise 0) − 35 if the woman lactated (otherwise 0) had R 2 values of 0.23, 0.25, and 0.33 at 5, 10, and 15 years postpartum, respectively, and a C-Index of 0.75. Application of the risk score in the test set resulted in observed risk of postpartum diabetes at 5 years of 11 % for low risk scores ≤140, 29 % for scores 141–220, 64 % for scores 221–300, and 80 % for scores >300.

Conclusions

The derived risk score is easy to calculate, allows accurate prediction of GDM-related postpartum diabetes, and may thus be a useful prediction tool for clinicians and general practitioners.
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Literature
3.
go back to reference Kim C, Newton KM, Knopp RH (2002) Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care 25(10):1862–1868CrossRefPubMed Kim C, Newton KM, Knopp RH (2002) Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care 25(10):1862–1868CrossRefPubMed
5.
go back to reference Füchtenbusch M, Ferber K, Standl E, Ziegler A-G (1997) Prediction of type 1 diabetes postpartum in patients with gestational diabetes mellitus by combined islet cell autoantibody screening: a prospective multicenter study. Diabetes 46(9):1459–1467CrossRefPubMed Füchtenbusch M, Ferber K, Standl E, Ziegler A-G (1997) Prediction of type 1 diabetes postpartum in patients with gestational diabetes mellitus by combined islet cell autoantibody screening: a prospective multicenter study. Diabetes 46(9):1459–1467CrossRefPubMed
6.
go back to reference Löbner K, Knopff A, Baumgarten A et al (2006) Predictors of postpartum diabetes in women with gestational diabetes mellitus. Diabetes 55(3):792–797CrossRefPubMed Löbner K, Knopff A, Baumgarten A et al (2006) Predictors of postpartum diabetes in women with gestational diabetes mellitus. Diabetes 55(3):792–797CrossRefPubMed
8.
go back to reference Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395CrossRefPubMed Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395CrossRefPubMed
10.
go back to reference Gerds TA, Kattan MW, Schumacher M, Yu C (2013) Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring. Stat Med 32(13):2173–2184. doi:10.1002/sim.5681 CrossRefPubMed Gerds TA, Kattan MW, Schumacher M, Yu C (2013) Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring. Stat Med 32(13):2173–2184. doi:10.​1002/​sim.​5681 CrossRefPubMed
11.
go back to reference Steyerberg E (2010) Clinical prediction models: a practical approach to development, validation, and updating. Springer, New York Steyerberg E (2010) Clinical prediction models: a practical approach to development, validation, and updating. Springer, New York
12.
go back to reference Team RC (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Team RC (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
13.
go back to reference Lee AJ, Hiscock RJ, Wein P, Walker SP, Permezel M (2007) Gestational diabetes mellitus: clinical predictors and long-term risk of developing type 2 diabetes: a retrospective cohort study using survival analysis. Diabetes Care 30(4):878–883. doi:10.2337/dc06-1816 CrossRefPubMed Lee AJ, Hiscock RJ, Wein P, Walker SP, Permezel M (2007) Gestational diabetes mellitus: clinical predictors and long-term risk of developing type 2 diabetes: a retrospective cohort study using survival analysis. Diabetes Care 30(4):878–883. doi:10.​2337/​dc06-1816 CrossRefPubMed
16.
go back to reference Noctor E, Crowe C, Carmody LA et al (2015) ATLANTIC-DIP: prevalence of metabolic syndrome and insulin resistance in women with previous gestational diabetes mellitus by International Association of Diabetes in Pregnancy Study Groups criteria. Acta Diabetol 52(1):153–160. doi:10.1007/s00592-014-0621-z CrossRefPubMed Noctor E, Crowe C, Carmody LA et al (2015) ATLANTIC-DIP: prevalence of metabolic syndrome and insulin resistance in women with previous gestational diabetes mellitus by International Association of Diabetes in Pregnancy Study Groups criteria. Acta Diabetol 52(1):153–160. doi:10.​1007/​s00592-014-0621-z CrossRefPubMed
18.
21.
go back to reference Schaefer-Graf UM, Buchanan TA, Xiang AH, Peters RK, Kjos SL (2002) Clinical predictors for a high risk for the development of diabetes mellitus in the early puerperium in women with recent gestational diabetes mellitus. Am J Obstet Gynecol 186(4):751–756. doi:10.1067/mob.2002.121895 CrossRefPubMed Schaefer-Graf UM, Buchanan TA, Xiang AH, Peters RK, Kjos SL (2002) Clinical predictors for a high risk for the development of diabetes mellitus in the early puerperium in women with recent gestational diabetes mellitus. Am J Obstet Gynecol 186(4):751–756. doi:10.​1067/​mob.​2002.​121895 CrossRefPubMed
22.
go back to reference de Seymour JV, Conlon CA, Sulek K et al (2014) Early pregnancy metabolite profiling discovers a potential biomarker for the subsequent development of gestational diabetes mellitus. Acta Diabetol 51(5):887–890. doi:10.1007/s00592-014-0626-7 CrossRefPubMed de Seymour JV, Conlon CA, Sulek K et al (2014) Early pregnancy metabolite profiling discovers a potential biomarker for the subsequent development of gestational diabetes mellitus. Acta Diabetol 51(5):887–890. doi:10.​1007/​s00592-014-0626-7 CrossRefPubMed
25.
go back to reference Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW (2009) Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med 150(11):741–751CrossRefPubMed Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW (2009) Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med 150(11):741–751CrossRefPubMed
Metadata
Title
Development of a simple tool to predict the risk of postpartum diabetes in women with gestational diabetes mellitus
Authors
M. Köhler
A. G. Ziegler
A. Beyerlein
Publication date
01-06-2016
Publisher
Springer Milan
Published in
Acta Diabetologica / Issue 3/2016
Print ISSN: 0940-5429
Electronic ISSN: 1432-5233
DOI
https://doi.org/10.1007/s00592-015-0814-0

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