Glycemic variability and indices of glycemic control among pregnant women with type 1 diabetes (T1D) based on the use of continuous glucose monitoring share technology

Abstract

Background

Pregnancies complicated by type 1 diabetes (T1D) experience high levels of glycemic variability, which may be associated with adverse maternal and neonatal outcomes. Therefore, strategies that help pregnant women with T1D manage their glycemic control are of great interest.

Methods

We examined associations with or without remote monitoring of Continuous Glucose Monitor (CGM) data by friends and family with indices of glycemic control and glycemic variability during pregnancies complicated by T1D in a pilot non-randomized trial (n = 28). During preconception or the first trimester, participants were placed in one of two groups based on device compatibility: (1) CGM Alone (n = 13): women without iPhone, iPad or iPod Touch; or (2) CGM Share (n = 15): women with iPhone, iPad, or iPod Touch and followers with devices compatible for data viewing. Linear mixed models were used to compare indices of glycemic control and glycemic variability over time between groups.

Results

Participants using CGM Share had lower estimated HbA1c levels over time (p = .028), glucose management index (p = .041), and fewer glucose excursions >200 mg/dL in each trimester (p = .022) compared to those using CGM Alone. Participants using CGM Alone had higher high blood glucose index (p = .020), mean area under the curve (p = .026), and standard deviation (p = .046) compared to those using CGM Share. Other measures of glycemic variability did not differ between groups.

Conclusion

In this non-randomized pilot study, use of CGM Share was associated with improvements in several indices of glycemic control and glycemic variability.

Keywords:

• Type 1 diabetes
• glucose monitoring
• glycemic variability
• pregnancy
• continuous glucose monitoring

Acknowledgements

The authors thank the study participants. We thank Mary Voelmle for assistance with study procedures and Satish Garg, MD for assistance with original study design. We thank Tim Vigers for the computer programming contributions to the CGM data analysis.

Author contributions

JS wrote the manuscript draft and edited the final document. RG curated data and reviewed the manuscript. JKD curated data, validated data, and reviewed the manuscript. PJ curated data, validated data, and reviewed the manuscript. JKSB curated data, validated data, reviewed, and edited the manuscript. LP curated data, performed statistical analyses, contributed to manuscript writing, reviewed and edited the manuscript. SP wrote the protocol, curated data, analyzed some of the data, contributed to manuscript writing, reviewed and edited the manuscript. SP is the guarantor of this work and has access to the data.

Disclosure statement

S.P. reports research funding from DexCom, Inc., Eli Lilly, JDRF, Leona & Helsmley Charitable Trust, NIDDK, and Sanofi; medical advisory board for Medtronic MiniMed, Inc.; and consulting for the JAEB center and diaTribe. J.S., R.G., L.P., P.J., J.D. and J.S.B reported no potential conflicts of interest relevant to this article.

Additional information

Funding

This was an investigator-initiated study in part supported by Dexcom, Inc. through the Board of Regents at the University of Colorado Denver. Dexcom, Inc is the manufacturer of the CGM device and the Share software assessed here. This study was supported by NIH/NCRR Colorado CTSI Grant Number UL1 RR025780. Its contents are the authors’ sole responsibility and do not necessarily represent Dexcom, Inc. or official NIH views. The funders did not have roles in data collection, data analysis, publication decisions, or manuscript preparation.