A two-step screening algorithm including fasting plasma glucose measurement and a risk estimation model is an accurate strategy for detecting gestational diabetes mellitus.

AIMS/HYPOTHESIS: It is currently not clear how to construct a time- and cost-effective screening strategy for gestational diabetes mellitus (GDM). Thus, we elaborated a simple screening algorithm combining (1) fasting plasma glucose (FPG) measurement; and (2) a multivariable risk estimation model focused on individuals with normal FPG levels to decide if a further OGTT is indicated. METHODS: A total of 1,336 women were prospectively screened for several risk factors for GDM within a multicentre study conducted in Austria. Of 714 women (53.4%) who developed GDM using recent diagnostic guidelines, 461 were sufficiently screened with FPG. A risk prediction score was finally developed using data from the remaining 253 women with GDM and 622 healthy women. The screening algorithm was validated with a further 258 pregnant women. RESULTS: A risk estimation model including history of GDM, glycosuria, family history of diabetes, age, preconception dyslipidaemia and ethnic origin, in addition to FPG, was accurate for detecting GDM in participants with normal FPG. Including an FPG pretest, the receiver operating characteristic AUC of the screening algorithm was 0.90 (95% CI 0.88, 0.91). A cut-off value of 0.20 was able to differentiate between low and intermediate risk for GDM with a high sensitivity. Comparable results were seen with the validation cohort. Moreover, we demonstrated an independent association between values derived from the risk estimation and macrosomia in offspring (OR 3.03, 95% CI 1.79, 5.19, p < 0.001). CONCLUSIONS/INTERPRETATION: This study demonstrates a new concept for accurate but cheap GDM screening. This approach should be further evaluated in different populations to ensure an optimised diagnostic algorithm.

Non-esterified fatty acid dynamics during oral glucose tolerance test in women with former gestational diabetes.

AIMS: Women with former gestational diabetes are at increased risk of Type 2 diabetes, which likely relates to hyperlipidaemia and ectopic lipid storage, mainly in the liver. Here, we examined the response of non-esterified fatty acid dynamics to oral glucose loading (oral glucose tolerance test). METHODS: We studied women with former gestational diabetes with normal glucose tolerance (n = 60) or impaired glucose metabolism (n = 12) and compared them with healthy women after normal pregnancy (control subjects, n = 15). During a 3-h oral glucose tolerance test, glucose, insulin and non-esterified fatty acid were frequently measured to compute the area under the non-esterified fatty acid curve and parameters of ß-cell function and insulin sensitivity. Through mathematical modelling, we assessed insulin sensitivity of lipolysis inhibition and the fractional non-esterified fatty acid turnover rate. We also measured some serum liver enzymes. RESULTS: Women with former gestational diabetes were slightly older and had greater body mass than control subjects. Subjects with impaired glucose metabolism had lower oral glucose insulin sensitivity, but higher fasting insulin and area under the non-esterified fatty acid curve, which inversely related to oral glucose insulin sensitivity and independently determined mean glycaemia. Model-derived non-esterified fatty acid parameters were lower in subjects with impaired glucose metabolism than in control subjects, particularly sensitivity of non-esterified fatty acid inhibition to insulin (2.50 ± 0.52 vs. 1.06 ± 0.20 · 10(-2) ml/µU). Also, subjects with impaired glucose metabolism had higher liver transaminases. However, all non-esterified fatty acid parameters showed only modest inverse correlation with liver transaminases. CONCLUSIONS: Despite greater insulinaemia, circulating non-esterified fatty acids are higher in women with former gestational diabetes than in control subjects, which likely results from reduced sensitivity of lipolysis inhibition to insulin. This parameter may serve as indicator of an early metabolic derangement in this population at risk for diabetes.