Identification and validation of gestational diabetes subgroups by data-driven cluster analysis

Early prediction of gestational diabetes mellitus: the role of the pregnancy-specific triglycerides-glucose index and other fasting parameters in combination with dynamic testing

The identification of mothers at risk for gestational diabetes mellitus (GDM) at start of pregnancy may be beneficial to improve perinatal outcomes. This study aims evaluating the predictive performance of fasting and dynamic indices of glucose metabolism at first trimester and their association with later GDM development. A cohort of 198 women received detailed metabolic assessment at median gestational age (13 weeks) including 75-g oral glucose tolerance test (OGTT) with assessment of glucose, insulin and C-peptide, and biochemical markers (including triglycerides) to calculate different indices of insulin sensitivity either at fasting and in the OGTT dynamic conditions. Moreover, parameters of β-cell function were assessed. A second OGTT was performed between 24 and 28 gestational weeks (GW) to identify women with GDM. We found that 28 women developed GDM, and, in univariable analysis, this was fairly predicted by several first trimester indices, both at fasting and in dynamic conditions. However, fasting indices containing maternal triglycerides showed better accuracy as compared to traditional indices (even the dynamic ones). In multivariable analysis, the best predictive model of GDM development included fasting and OGTT glucose values, HbA1c, and an insulin sensitivity marker that includes triglycerides (e.g. the improved triglyceride-glucose index, TyGIS). β-Cell function was not included in such predictive model, but at 24-28 GW it showed remarkable impairment in women with GDM. In conclusion, both fasting and dynamic parameters of glucose homeostasis at early pregnancy showed fair predictive accuracy for later GDM, with TyGIS showing excellent performance. β-Cell dysfunction role needs being further elucidated.

Availability of Open Dynamic Glycemic Data in the Field of Diabetes Research: A Scoping Review

BACKGROUND

Poor data availability and accessibility characterizing some research areas in biomedicine are still limiting potentialities for increasing knowledge and boosting technological advancement. This phenomenon also characterizes the field of diabetes research, in which glycemic data may serve as a basis for different applications. To overcome this limitation, this review aims to provide a comprehensive analysis of the publicly available data sets related to dynamic glycemic data.

METHODS

Search was performed in four different sources, namely scientific journals, Google, a comprehensive registry of clinical trials and two electronic databases. Retrieved data sets were analyzed in terms of their main characteristics and on the typology of data provided.

RESULTS

Twenty-five data sets were identified including data from challenge tests (5 of 25) or data from Continuous Glucose Monitoring (CGM, 20 of 25). As for the data sets including challenge tests, all of them were freely downloadable; most of them (80%) related only to oral glucose tolerance test (OGTT) with standard duration (2 h), but varying for timing and number of collected blood samples, and variables collected in addition to glucose levels (with insulin levels being the most common); the remaining 20% of them also included intravenous glucose tolerance test (IVGTT) data. As for the data sets related to CGM, 7 of 20 were freely downloadable, whereas the remaining 13 were downloadable upon completion of a request form.

CONCLUSIONS

This review provided an overview of the readily usable data sets, thus representing a step forward in fostering data access in diabetes field.