Glycemic profile of women with normoglycemia and gestational diabetes mellitus during early pregnancy using continuous glucose monitoring system

Continuous Glucose Monitoring System Profile of Women Stratified Using Different Levels of Glycated Hemoglobin (HbA1c) in Early Pregnancy: A Cross-sectional Study

AIM

To evaluate the differences in the continuous glucose monitoring system (CGMS) profiles of women in early pregnancy stratified based on different HbA1c levels known to be predictive of gestational diabetes mellitus (GDM) at 24-28 weeks of gestation (≥ 5.2%) and adverse pregnancy outcomes (≥ 5.5%) in Indian women.

METHODS

We enrolled women at 8^+ 0 to 19^+ 6 weeks of gestation (early pregnancy), evaluated the glycaemic parameters of clinical interest using CGMS, and reported them per standard methodology proposed by Hernandez et al. WHO 2013 criteria were used for diagnosis of early GDM.

RESULTS

Ninety-six women were enrolled at 14.0 ± 3.2 weeks of gestation. Of these, 38 were found to have early GDM (diagnosed before 20 weeks of gestation) on evaluation. Of 96 women, 33 (34.4%) had HbA1c value ≥ 5.5% [11 (19.0%) with normoglycaemia and 22 (57.9%) with GDM]. The women with elevated HbA1c differed significantly from those with HbA1c < 5.5% for all evaluated parameters. The differences for overall women were > 10 mg/dl (0.56 mmol/l) for 1-h postprandial glucose (difference of 0.78 mmol/l), 2-h postprandial glucose (difference of 0.59 mmol/l), peak postprandial glucose (difference of 0.75 mmol/l), and 1-h postprandial glucose excursion (difference of 0.59 mmol/l). Of 58 women with normoglycaemia, 29 (50.0%) had an HbA1c value  ≥ 5.2%. In comparison, in the normoglycaemic group of women with and without HbA1c ≥ 5.2% (known to be predictive of future GDM), the results were significant for 1-h (difference of 0.44 mmol/l), 2-h (difference of 0.278 mmol/l), and peak postprandial glucose (difference of 0.35 mmol/l).

CONCLUSIONS

The results suggest that women with elevated HbA1c (≥ 5.5%) in early pregnancy significantly differ from those with HbA1c < 5.5% in all glycaemic parameters evaluated in this study, suggesting that HbA1c at this cut-off has a role to play in early pregnancy.

Continuous Glucose Monitoring System Profile of Women with Gestational Diabetes Mellitus Missed Using Isolated Fasting Plasma Glucose-Based Strategies Alternative to WHO 2013 Criteria: A Cross-Sectional Study

INTRODUCTION

The aim of the study was to evaluate the differences in the continuous glucose monitoring system (CGMS)-based glycemic parameters between women with normoglycemia and early gestational diabetes mellitus (GDM) identified on the basis of mild fasting plasma glucose elevation (FPG, 5.1-5.5 mmol/L) and/or post-load plasma glucose elevation (PLG, 1-h ≥ 10.0 mmol/L or 2-h ≥ 8.5 mmol/L).

METHODS

This cross-sectional study included women with singleton pregnancy (8⁺⁰ to 19⁺⁶ weeks of gestation) and normoglycemia or GDM per World Health Organization (WHO) 2013 criteria. We evaluated the glycemic parameters of clinical interest using blinded CGMS evaluation and reported them per standard methodology proposed by Hernandez et al. RESULTS: A total of 87 women (GDM, n = 38) were enrolled at 28.6 ± 4.5 years. Among women with GDM, 10 (26.3%) had isolated mild FPG elevation (5.1-5.5 mmol/L), 10 (26.3%) had isolated PLG elevation (1-h ≥ 10.0 mmol/L or 2-h ≥ 8.5 mmol/L), and 7 (18.4%) had a combination of both. The remaining 11 (28.9%) had elevated FPG (≥ 5.6 mmol/L) with or without PLG elevation. Thus, when an isolated FPG cutoff ≥ 5.6 mmol/L is used to diagnose GDM, 27 (71.0%) women would be perceived as normoglycemic. Such women had significantly higher CGMS parameters of clinical interest, such as 24-h mean glucose, fasting glucose, 1-h and 2-h postprandial glucose (PPG), 1-h PPG excursion, and peak PPG.

CONCLUSIONS

An isolated FPG threshold, especially the higher cutoff ≥ 5.6 mmol/L, can potentially miss a large proportion of women (nearly three-fourths) diagnosed with GDM per WHO 2013 criteria. Eventually, such women fare significantly differently from normoglycemic women in various CGMS parameters of clinical interest.

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers.

CGMS profile of women diagnosed as GDM by IADPSG criteria and labelled as normoglycemic by alternate criteria in early pregnancy

AIMS/INTRODUCTION

We aimed to evaluate and compare CGMS-based glycemic parameters in women in early pregnancy (<20 weeks of gestation) who were classified as: a) GDM by IADPSG but normoglycemia by alternate (UK NICE, CDA and DIPSI) criteria, and b) normoglycemia by both (IADPSG and alternate) criteria.

MATERIAL AND METHODS

In this cross-sectional study, eligible women underwent standard 75-g OGTT, followed by the placement of a CGMS. Glycemia-related parameters were calculated using the standard approach for CGMS data in pregnancy.

RESULTS

We enrolled 96 women at 14.0 ± 3.2 weeks of gestation. Of the women diagnosed as GDM by IADPSG criteria, 34.2%, 26.3% and 44.7% were classified as normoglycemic by UK NICE, CDA and DIPSI criteria, respectively. Mean 1-h postprandial glucose and time above range were significantly higher in women who were GDM by IADPSG, but normoglycemia by CDA criteria, compared to women with normoglycemia using both criteria. Similarly, mean 1-h postprandial glucose, 2-h postprandial glucose, peak postprandial glucose, 1-hr postprandial glucose excursion and time above range were significantly higher in women who were not identified as GDM by UK NICE criteria. Finally, women missed by DIPSI criteria had significantly higher mean 1-h postprandial glucose, 2-h postprandial glucose, peak postprandial glucose, postprandial glucose excursion, 24-h glucose, and time above range parameters.

CONCLUSIONS

More than a quarter of women diagnosed as GDM by IADPSG criteria are not identified by alternate criteria. Such women are significantly different from normoglycemic women in terms of several CGMS-based glycemic parameters of clinical significance.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study

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.