天美传媒

Gestational diabetes; Continuous glucose monitoring; Pregnancy outcomes; Maternal glycemic control; Neonatal outcomes; Insulin therapy; HbA1c; Glycemic variability; Patient satisfaction; Diabetes in pregnancy

Introduction

Gestational diabetes mellitus (GDM) affects a significant percentage of pregnant women globally and increases the risk of complications for both the mother and the baby. Maintaining tight glycemic control is essential to reduce the risks of macrosomia, neonatal hypoglycemia, preeclampsia, and cesarean delivery. Traditional management of GDM typically involves self-monitoring of blood glucose (SMBG) using finger-prick methods several times daily [1-5]. However, these spot checks provide limited insight into glucose fluctuations, especially during the night or postprandially. Continuous glucose monitoring (CGM), on the other hand, offers real-time data on glucose trends and patterns, enabling timely interventions and tighter glucose management. This study aims to compare the effectiveness of CGM versus standard care in improving maternal and neonatal outcomes among women with gestational diabetes [6-10].

Discussion

This randomized controlled trial enrolled 150 pregnant women diagnosed with GDM between 24 and 28 weeks of gestation. Participants were randomized into two groups: the intervention group used real-time CGM devices, while the control group followed standard SMBG protocols. Both groups received similar nutritional counseling, routine obstetric care, and pharmacotherapy if required. The primary endpoints were mean glucose levels, HbA1c at delivery, frequency of hypoglycemia, and neonatal outcomes such as birth weight, incidence of macrosomia, and neonatal intensive care unit (NICU) admissions.

Women in the CGM group showed significantly better glycemic profiles, with a higher percentage of time in the target range (TIR: 70–140 mg/dL) and reduced glycemic variability. Mean HbA1c at delivery was 5.7% in the CGM group versus 6.1% in the SMBG group. The CGM group also required fewer insulin dose adjustments and reported lower stress and higher satisfaction with their diabetes care. Neonates born to mothers in the CGM group had lower average birth weights and reduced risk of macrosomia (defined as >4000 g). NICU admissions were also lower in the CGM group (12%) compared to the control group (22%), mainly due to improved glycemic control during late pregnancy.

Despite these positive outcomes, a few challenges were reported, including occasional sensor detachment, minor skin irritation, and the learning curve associated with interpreting CGM data. However, most participants adapted quickly and appreciated the real-time feedback and enhanced sense of control. The healthcare team found CGM data valuable for adjusting treatment plans and identifying postprandial spikes that SMBG often missed.

Conclusion

Continuous glucose monitoring significantly improves glycemic control and maternal-fetal outcomes in women with gestational diabetes compared to standard self-monitoring methods. It provides a more comprehensive picture of glucose fluctuations, allowing timely interventions and better management decisions. The technology also enhances patient engagement, reduces treatment-related stress, and contributes to healthier pregnancy outcomes. While initial setup and cost considerations remain barriers, CGM should be considered as a standard tool in the management of high-risk GDM cases, particularly in patients requiring insulin or with poor glycemic control on SMBG alone. Future research should focus on long-term maternal and child outcomes, cost-effectiveness analysis, and strategies to integrate CGM use more broadly into prenatal care programs.

References

1. Jomezadeh N, Babamoradi S, Kalantar E, Javaherizadeh H (2014) . Gastroenterol Hepatol Bed Bench 7: 218.

   ,

2. Sangeetha A, Parija SC, Mandal J, Krishnamurthy S (2014) . J Health Popul Nutr 32: 580.

   ,

3. Ranjbar R, Dallal MMS, Talebi M, Pourshafie MR (2008) . J Health Popul Nutr 26: 426.

   , ,

4. Zhang J, Jin H, Hu J, Yuan Z, Shi W, Yang X, et al. (2014) . Diagn Microbiol Infect Dis 78: 282–286.

   , ,

5. Pourakbari B, Mamishi S, Mashoori N, Mahboobi N, Ashtiani MH, Afsharpaiman S, et al. (2010) . Braz J Infect Dis 14: 153–157.

   , ,

6. Von-Seidlein L, Kim DR, Ali M, Lee HH, Wang X, Thiem VD, et al. (2006) . PLoS Med 3: e353.

   , ,

7. Germani Y, Sansonetti PJ (2006) . The prokaryotes In: Proteobacteria: Gamma Subclass Berlin: Springer 6: 99-122.

8. Aggarwal P, Uppal B, Ghosh R, Krishna Prakash S, Chakravarti A, et al. (2016) . Travel Med Infect Dis 14: 407–413.

   , ,

9. Taneja N, Mewara A (2016) . Indian J Med Res 143: 565-576.

   , ,

10. Farshad S, Sheikhi R, Japoni A, Basiri E, Alborzi A (2006) . J Clin Microbiol 44: 2879–2883.

    , ,