Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study. (17th October 2022)

• Record Type: Journal Article
• Authors: Di Filippo, Daria; Ahmadzai, Marrwah; Chang, Melissa Han Yiin; Horgan, Ksana; Ong, Ru Min; Darling, Justine; Akhtar, Mahmood; Henry, Amanda; Welsh, Alec
• Other Names: Foti Daniela Academic Editor.
• Is Part Of: Journal of diabetes research. Volume 2022(2022)
• Publication Date: 2022-10-17
• Subjects: Diabetes -- Periodicals; Diabetes -- Pathophysiology -- Periodicals; Diabetes -- Prevention -- Periodicals; Diabetes -- Etiology -- Periodicals; Diabetes -- Epidemiology -- Periodicals; Diabetes -- Pathogenesis -- Periodicals; 616.462005
• DOI: 10.1155/2022/5142918
• Languages: English
• ISSNs: 2314-6745

Abstract

Abstract : Background. Gestational diabetes mellitus (GDM) is diabetes first diagnosed in pregnancy. GDM, together with its short- and long-term negative outcomes, is increasing in incidence all over the world. The current diagnostic method for GDM, the oral glucose tolerance test (OGTT), is dated and has been reported as inconvenient for women as well as poorly reproducible and reliable. Aims. We aimed at assessing the acceptability, feasibility, and accuracy of continuous glucose monitoring (CGM) as a diagnostic test for GDM and explore its correlation with the OGTT and risk factors for GDM. Methods . In this prospective cohort study, pregnant women due for or having completed OGTT underwent CGM for seven days, performing daily finger-prick blood glucose levels before completing an acceptability questionnaire. Data on GDM risk factors and CGM variability were analyzed and compared with OGTT results. Results . Seventy-three women completed CGM (40 GDM, 33 normal glucose tolerances); 34 concurrently underwent OGTT. CGM was acceptable and generally well-tolerated, with skin irritation/itchiness the only adverse event (11 mild, one severe). CGM and OGTT strongly correlated for fasting glucose values (r = 0.86, p < 0.05 ) only. Triangulating GDM risk factors, OGTT results and CGM variability parameters with the application of machine learning highlighted the possibility of unmasking false positive (11 showed low CGM variability and demographic risks but positive OGTT) and false-negative OGTT diagnoses (1 showed high CGM variability and demographic risks but negative OGTT). Conclusions . CGM was well-tolerated, showing poorer glycaemic control in GDM, and revealing potential misdiagnosis of the OGTT when combined with GDM risk factors. Future research is needed to determine cut-off values for CGM-defined and OGTT-independent screening criteria for GDM.