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A novel classification of glucose profile in pregnancy based on continuous glucose monitoring data. | LitMetric

AI Article Synopsis

  • The study aimed to explore the glucose profiles in pregnant women with and without gestational diabetes by using continuous glucose monitoring (CGM) data over two weeks.
  • Three distinct clusters of glucose profiles were identified: a low glucose variability group, a moderate glucose variability group, and a high glucose variability group, with higher instances of gestational diabetes in the high glucose group.
  • The findings suggest that thorough analysis of CGM data can help identify women at risk for gestational diabetes based on their glucose patterns and other characteristics.

Article Abstract

Aim: To investigate the glucose profile of women with and without gestational diabetes mellitus (GDM) by simultaneously analyzing several factors of continuous glucose monitoring (CGM) data.

Methods: CGM was conducted for 2 weeks in the second trimester of pregnant women whose random blood glucose level was ≥100 mg/dl. A 75-g oral glucose tolerance test was performed around day 7, and the index of hyperglycemia, relative hypoglycemia, and indices of glucose variability were extracted from CGM data. Unsupervised hierarchical clustering was performed to categorize glucose profiles of the participants.

Results: CGM data were obtained from 29 women. Glucose profiles were categorized into three clusters: low glucose levels with less glucose variability group (L group, n = 7); moderate glucose levels with moderate-to-high glucose variability group (M group, n = 18); and high glucose levels with high glucose variability group (H group, n = 4). The waveforms of the glucose profiles were very different among the three groups. Women with GDM tended to be more frequent in the H group than in the M and L groups (75.0%, 16.7%, and 14.3%, respectively; p = 0.053). Maternal age was significantly higher and the proportion of multiparous women was significantly larger in the H group compared to L group (p = 0.002 and 0.015, respectively).

Conclusions: A comprehensive analysis of CGM data could help us extract a subgroup of women with characteristics of GDM.

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Source
http://dx.doi.org/10.1111/jog.14677DOI Listing

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