Aims/hypothesis. This study analysed the relationship between congenital malformations (CM) and severity of gestational diabetes mellitus. Methods. A cohort of 2060 infants of mothers with gestational diabetes was studied. Universal screening and 3 rd Workshop-Conference criteria were used to diagnose gestational diabetes. The severity of diabetes was assessed on the basis of previous hyperglycaemia, blood glucose values in diagnostic OGTT, area under the glucose curve, gestational age and HbA 1 c at diagnosis, insulin requirements during pregnancy, and OGTT after delivery. Potentially confounding variables (age, pre-pregnancy BMI, smoking) were considered. The relationship of potential predictors with CM was analysed with several multivariate logistic regression analyses.Results. The rate of CM was 6% for minor and 3.8% for major malformations (1.4% heart, 0.8% renal/urinary, 0.7% skeletal, 0.3% hypospadias, 0.2% central nervous system, 0.2% cleft lip/palate, 0.1% digestive tract, 0.3% other). In the final models, forward logistic regression analysis identified pre-pregnancy BMI as the predictor of CM (area under receiver operating characteristic curve 0.616); in the backward analysis additional predictors were 1-h blood glucose in diagnostic OGTT and gestational age at diagnosis (area under receiver operating characteristic curve 0.646). Both BMI and severity of gestational diabetes were predictors of heart and minor CM, whereas BMI predicted renal/urinary CM and severity of diabetes predicted skeletal CM. Conclusions/interpretation. In these infants of mothers with gestational diabetes, severity of diabetes and pre-pregnancy BMI were predictors of CM, in accordance with the well-documented pathogenic role of BMI (in the general population) and hyperglycaemia (in diabetic pregnancy). BMI was the main predictor of more prevalent CM. [Diabetologia (2004) 47:509-514]

The DIABTel system, a telemedicine system that includes a wireless personal assistant for remote treatment advising, allows better glycemic control in pump-treated patients with type 1 diabetes. To our knowledge, this is the first study that demonstrates improved glycemic control with the use of a telemedicine system that incorporates insulin delivery data.