- Metabolic Risk/Epidemiology
- Glucose Effectiveness from Short Insulin-Modified IVGTT and Its Application to the Study of Women with Previous Gestational Diabetes Mellitus
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Micaela Morettini, Carlo Castriota, Christian Göbl, Alexandra Kautzky-Willer, Giovanni Pacini, Laura Burattini, Andrea Tura
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Diabetes Metab J. 2020;44(2):286-294. Published online January 13, 2020
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DOI: https://doi.org/10.4093/dmj.2019.0016
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Abstract
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- Background
This study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (SG), namely calculated SG (CSG), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to apply it to study women with previous gestational diabetes mellitus (pGDM). MethodsCSG was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from normal tolerance to type 2 diabetes mellitus (T2DM). CSG was then tested on a population of women with pGDM (n=57). Each subject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference SG. ResultsCSG was described as CSG=1.06×10−2+5.71×10−2×KG/Gpeak, KG being the mean slope (absolute value) of loge glucose in 10–25- and 25–50-minute intervals, and Gpeak being the maximum of the glucose curve. Good agreement between CSG and SG in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (<5% observations outside limits of agreement), and by the test for equivalence (equivalence margin not higher than one standard deviation). At baseline, the PROG subgroup showed significantly lower SG and CSG values compared to the NONPROG subgroup (P<0.03). ConclusionCSG is a valid SG predictor. In the pGDM group, glucose effectiveness appeared to be impaired in women progressing to T2DM.
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Citations
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So-Yeon Kim, Young Shin Song, Soo-Kyung Kim, Yong-Wook Cho, Kyung-Soo Kim Diabetes & Metabolism Journal.2022; 46(1): 140. CrossRef - Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques
Ludovica Ilari, Agnese Piersanti, Christian Göbl, Laura Burattini, Alexandra Kautzky-Willer, Andrea Tura, Micaela Morettini Frontiers in Physiology.2022;[Epub] CrossRef - The Clinical Characteristics of Gestational Diabetes Mellitus in Korea: A National Health Information Database Study
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