The aim of this study was to determine whether the metabolic glucose profile, based on glycaemic control and insulin requirements, was different in women with gestational diabetes mellitus (GDM) and intrahepatic cholestasis of pregnancy (ICP) compared to women with only GDM. Methods: This retrospective cohort study comprised women with GDM and ICP matched with women with only GDM was undertaken at Aarhus University hospital, Denmark, from 2012 to 2019. A total of 46 cases and 184 controls were compared in relation to glycaemic control during pregnancy. Women with GDM and ICP were further divided into subgroups according to the severity of ICP: mild ICP (fasting bile salts 10-39 μmol/L) and moderate/severe ICP (bile salts ≥40 μmol/L). Results: No statistically significant differences were observed in baseline 2-h oral glucose tolerance test values, second and third trimester HbA 1c values, or maximum insulin requirements during pregnancy between women with GDM with and without ICP. Significantly more women with ICP developed preeclampsia during pregnancy: 23.9% (11/46) versus 7.6% (14/184); p = 0.003. Conclusions: This study is the first to address the course of pregnancy in women with GDM with and without ICP in a clinical setting. Under the current treatment guidelines, ICP is not associated with clinically significant changes in glycaemic control in GDM. Significantly more women with both GDM and ICP developed preeclampsia.

Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes including large for gestational age infants. Individualizing the management of women with GDM based on the likelihood of needing insulin may improve pregnancy outcomes. The aim of this study is to identify characteristics associated with a need for insulin in women with GDM, and to develop a predictive model for insulin requirement. A historical cohort study was conducted among all women with GDM in a singleton pregnancy at Aarhus University Hospital from 2012 to 2017. Variables associated with insulin treatment were identified through multivariable logistic regression. The variables were dichotomized and included in a point scoring system aiming to predict the likelihood of needing insulin. Seven variables were associated with needing insulin: family history of diabetes, current smoker, multiparity, prepregnancy body mass index, gestational age at the oral glucose tolerance test (OGTT), 2-h glucose value at the OGTT and hemoglobin A1c at diagnosis. A risk score was calculated assigning one point to each variable. On ROC analysis, a cut-off value of ≥3 points optimally predicted a requirement for insulin. This prediction model may be clinically useful to predict requirement for insulin treatment after further validation.