Preconception risk of gestational diabetes: Development of a prediction model in nulliparous Australian women. (December 2018)

• Record Type: Journal Article
• Authors: Schoenaker, Danielle A.J.M.; Vergouwe, Yvonne; Soedamah-Muthu, Sabita S.; Callaway, Leonie K.; Mishra, Gita D.
• Is Part Of: Diabetes research and clinical practice. Volume 146(2018)
• Page Start: 48
• Page End: 57
• Publication Date: 2018-12
• Subjects: Gestational diabetes -- Preconception -- Prediction -- Prevention; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2018.09.021
• Languages: English
• ISSNs: 0168-8227

Abstract

Abstract: Aim: To develop a prediction model for preconception identification of women at risk of gestational diabetes mellitus (GDM). Methods: Data from a prospective cohort, the Australian Longitudinal Study on Women's Health, were used. Nulliparous women aged 18–23 who reported a pregnancy up to age 37–42 were included. Preconception predictors of GDM during a first pregnancy were selected using logistic regression. Regression coefficients were multiplied by a shrinkage factor estimated with bootstrapping to improve prediction in external populations. Results: Among 6504 women, 314 (4.8%) developed GDM during their first pregnancy. The final prediction model included age at menarche, proposed age at future first pregnancy, ethnicity, body mass index, diet, physical activity, polycystic ovary syndrome, and family histories of type 1 or 2 diabetes and GDM. The model showed good discriminative ability with a C-statistic of 0.79 (95% CI 0.76, 0.83) after internal validation. More than half of the women (58%) were classified to be at risk of GDM (>2% predicted risk), with corresponding sensitivity and specificity values of 91% and 43%. Conclusions: Nulliparous women at risk of GDM in a future first pregnancy can be accurately identified based on preconception lifestyle and health-related characteristics. Further studies are needed to test our model in other populations.