BACKGROUND: Gestational diabetes mellitus (GDM) is a pregnancy-related metabolic complication. Despite optimal glycemic control from self-monitoring blood glucose (SMBG) in non-insulin-dependent GDM, variations in pregnancy outcomes persist. Glycemic variability is believed to be a factor that causes adverse pregnancy outcomes. Continuous glucose monitoring system (CGMS) detects interstitial glucose values every 5 minutes, and glycemic variability data from CGMS during the third trimester may be a predictor of fetal birth weight and pregnancy outcomes. The aim of this study was to investigate correlation between third trimester glycemic variability in non-insulin-dependent GDM and fetal birth weight. METHOD: This prospective study was conducted in 55 pregnant volunteers with non-insulin-dependent GDM that were recruited at 28 to 32 weeks' gestation from the outpatient clinic of the Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital during the study period of August 1 to December 31, 2016. Patients had CGMS installed for at least 72 hours and glycemic variability data were analyzed. RESULTS: Of 55 enrolled volunteers, the data from 47 women were included in the analysis. Mean CGMS duration was 85.5 ± 12.83 hours. No statistically significant correlation was identified between glycemic variability in third trimester and birth weight percentiles, or between third trimester CGMS parameters and pregnancy outcomes in the study. CONCLUSION: Based on these findings, third trimester glycemic variability data from CGMS are not a predictor of fetal birth weight percentile, and no significant association was found between CGMS parameters and adverse pregnancy outcomes; thus, CGMS is not necessary in non-insulin-dependent GDM.
BACKGROUND:Gestational diabetes mellitus (GDM) is a pregnancy-related metabolic complication. Despite optimal glycemic control from self-monitoring blood glucose (SMBG) in non-insulin-dependent GDM, variations in pregnancy outcomes persist. Glycemic variability is believed to be a factor that causes adverse pregnancy outcomes. Continuous glucose monitoring system (CGMS) detects interstitial glucose values every 5 minutes, and glycemic variability data from CGMS during the third trimester may be a predictor of fetal birth weight and pregnancy outcomes. The aim of this study was to investigate correlation between third trimester glycemic variability in non-insulin-dependent GDM and fetal birth weight. METHOD: This prospective study was conducted in 55 pregnant volunteers with non-insulin-dependent GDM that were recruited at 28 to 32 weeks' gestation from the outpatient clinic of the Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital during the study period of August 1 to December 31, 2016. Patients had CGMS installed for at least 72 hours and glycemic variability data were analyzed. RESULTS: Of 55 enrolled volunteers, the data from 47 women were included in the analysis. Mean CGMS duration was 85.5 ± 12.83 hours. No statistically significant correlation was identified between glycemic variability in third trimester and birth weight percentiles, or between third trimester CGMS parameters and pregnancy outcomes in the study. CONCLUSION: Based on these findings, third trimester glycemic variability data from CGMS are not a predictor of fetal birth weight percentile, and no significant association was found between CGMS parameters and adverse pregnancy outcomes; thus, CGMS is not necessary in non-insulin-dependent GDM.
Authors: Daphne N Voormolen; J Hans DeVries; Arie Franx; Ben W J Mol; Inge M Evers Journal: BMC Pregnancy Childbirth Date: 2012-12-27 Impact factor: 3.007
Authors: M G Dalfrà; N C Chilelli; G Di Cianni; G Mello; C Lencioni; S Biagioni; M Scalese; G Sartore; A Lapolla Journal: Int J Endocrinol Date: 2013-11-11 Impact factor: 3.257
Authors: Claire L Meek; Diana Tundidor; Denice S Feig; Jennifer M Yamamoto; Eleanor M Scott; Diane D Ma; Jose A Halperin; Helen R Murphy; Rosa Corcoy Journal: Diabetes Care Date: 2021-01-25 Impact factor: 19.112
Authors: Martina Gáborová; Viera Doničová; Ivana Bačová; Mária Pallayová; Martin Bona; Igor Peregrim; Soňa Grešová; Judita Štimmelová; Barbora Dzugasová; Lenka Šalamonová Blichová; Viliam Donič Journal: Int J Environ Res Public Health Date: 2021-03-25 Impact factor: 3.390