Endocrinology and Metabolism

Review Article

Diabetes, obesity and metabolism
The Benefits Of Continuous Glucose Monitoring In Pregnancy: Jee Hee Yoo, Jae Hyeon Kim; Endocrinol Metab. 2023;38(5):472-481. Published online October 11, 2023; DOI: https://doi.org/10.3803/EnM.2023.1805

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Previous studies have consistently demonstrated the positive effects of continuous glucose monitoring (CGM) on glycemic outcomes and complications of diabetes in people with type 1 diabetes. Guidelines now consider CGM to be an essential and cost-effective device for managing type 1 diabetes. As a result, insurance coverage for it is available. Evidence supporting CGM continues to grow and expand to broader populations, such as pregnant people with type 1 diabetes, people with type 2 diabetes treated only with basal insulin therapy, and even type 2 diabetes that does not require insulin treatment. However, despite the significant risk of hyperglycemia in pregnancy, which leads to complications in more than half of affected newborns, CGM indications and insurance coverage for those patients are unresolved. In this review article, we discuss the latest evidence for using CGM to offer glycemic control and reduce perinatal complications, along with its cost-effectiveness in pregestational type 1 and type 2 diabetes and gestational diabetes mellitus. In addition, we discuss future prospects for CGM coverage and indications based on this evidence.

Citations

Citations to this article as recorded by

Wearable devices for glucose monitoring: A review of state-of-the-art technologies and emerging trends
Mohammad Mansour, M. Saeed Darweesh, Ahmed Soltan
Alexandria Engineering Journal.2024; 89: 224. CrossRef

Original Article

Diabetes, Obesity and Metabolism
How Can We Adopt the Glucose Tolerance Test to Facilitate Predicting Pregnancy Outcome in Gestational Diabetes Mellitus?: Kyeong Jin Kim, Nam Hoon Kim, Jimi Choi, Sin Gon Kim, Kyung Ju Lee; Endocrinol Metab. 2021;36(5):988-996. Published online October 15, 2021; DOI: https://doi.org/10.3803/EnM.2021.1107

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Abstract PDF Supplementary Material PubReader ePub

Background
We investigated how 100-g oral glucose tolerance test (OGTT) results can be used to predict adverse pregnancy outcomes in gestational diabetes mellitus (GDM) patients.
Methods
We analyzed 1,059 pregnant women who completed the 100-g OGTT between 24 and 28 weeks of gestation. We compared the risk of adverse pregnancy outcomes according to OGTT patterns by latent profile analysis (LPA), numbers to meet the OGTT criteria, and area under the curve (AUC) of the OGTT graph. Adverse pregnancy outcomes were defined as a composite of preterm birth, macrosomia, large for gestational age, low APGAR score at 1 minute, and pregnancy-induced hypertension.
Results
Overall, 257 participants were diagnosed with GDM, with a median age of 34 years. An LPA led to three different clusters of OGTT patterns; however, there were no significant associations between the clusters and adverse pregnancy outcomes after adjusting for confounders. Notwithstanding, the risk of adverse pregnancy outcome increased with an increase in number to meet the OGTT criteria (P for trend=0.011); odds ratios in a full adjustment model were 1.27 (95% confidence interval [CI], 0.72 to 2.23), 2.16 (95% CI, 1.21 to 3.85), and 2.32 (95% CI, 0.66 to 8.15) in those meeting the 2, 3, and 4 criteria, respectively. The AUCs of the OGTT curves also distinguished the patients at risk of adverse pregnancy outcomes; the larger the AUC, the higher the risk (P for trend=0.007).
Conclusion
The total number of abnormal values and calculated AUCs for the 100-g OGTT may facilitate tailored management of patients with GDM by predicting adverse pregnancy outcomes.

Citations

Citations to this article as recorded by

Risk factors combine in a complex manner in assessment for macrosomia
Yi-Wen Wang, Yan Chen, Yong-Jun Zhang
BMC Public Health.2023;[Epub] CrossRef
Association of the Severity of Hypertensive Disorders in Pregnancy with Birthweight, Childhood Obesity, and Blood Pressure at Age 7
Yan Chen, Yiwen Wang, Yanjun Li, Guodong Ding, Yongjun Zhang
Nutrients.2023; 15(14): 3104. CrossRef

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