Assessing Glycemic Control Using CGM for Women with Diabetes in Pregnancy

Analysis glycemic variability in pregnant women with various type of hyperglycemia

OBJECTIVE

The study primarily aims to compare alterations in the daily patterns of glucose fluctuations across individuals with different kinds of diabetes in pregnancy and secondly investigate influencing factors that may react with glucose variations.

METHODS

We conducted a retrospective cohort study of 776 pregnant women in Shanghai General Hospital. We grouped participants who were exposed to gestational hyperglycemia into 5 sub-groups [Type 1 diabetes (T1DM), Type 2 diabetes (T2DM), Overt diabetes, Gestational diabetes (GDMA1 and GDMA2). Demographic variables and GV parameters were compared among 5 groups through ANOVA-test and Chi-square test. We estimated odd ratios (ORs) for the association between glucose coefficient of variation (CV) and possible influencing variables.

RESULTS

A final total of 776 pregnant women were analyzed. The proportion of pregnant women with pre-gestational diabetes was 31.83% (T1DM: 3.35%,T2DM: 28.48%), ODM 26.68%, and GDM was 41.49% (GDMA1:18.04%, GDMA2: 23.45%). T1DM group performed greatest glucose fluctuations with a CV value 35.02% whereas the number in all the other groups was no more than 22.82% (ODM group). In terms of achieving glycemic control target, only 57.70% participants hit the goal while all the other groups achieved the standard with at least a percentage of 94.20% (ODM group). Other parameters (GMI < 6.0%, GA < 15.70% and HbA1c < 6.0%) showed similar trends in each group. On multivariate logistic regression analysis of possible factors influencing CV, only body mass index (BMI) (OR: 0.754, 95% CI: 0.585-0.971; P = 0.029), HOMA- β (OR:0.969, 95%CI: 0.959-0.976; P = 0.037) and fasting plasma glucose (FPG) (OR: 1.832, 95% CI: 1.170-2.870; P = 0.008) reached statistical significance.

CONCLUSIONS

Pregnant women with type 1 or type 2 diabetes exhibit significantly greater glycemic variability compared to those with gestational diabetes, with the ODM group showing intermediate variability, and BMI, HOMA-β, and FPG identified as independent risk factors for unstable glucose variability.

A Review of Third-Trimester Complications in Pregnancies Complicated by Diabetes Mellitus

Pregnancies affected by both pregestational and gestational diabetes mellitus carry an increased risk of adverse maternal and neonatal outcomes. While the risks associated with diabetes in pregnancy have been well documented and span across all trimesters, maternal and neonatal morbidity have been associated with select third-trimester complications. Further, modifiable risk factors have been identified that can help improve pregnancy outcomes. This review aims to examine the relationship between select third-trimester complications (large for gestational age, intrauterine fetal demise, hypertensive disorders of pregnancy, preterm birth, perineal lacerations, shoulder dystocia, and cesarean delivery) and the aforementioned modifiable risk factors, specifically glycemic control, blood pressure control, and gestational weight gain. It also highlights how early optimization of these modifiable risk factors can reduce adverse maternal, fetal, and neonatal outcomes. KEY POINTS: · Diabetes mellitus in pregnancy increases the risk of third-trimester complications.. · Modifiable risk factors exist for these complications.. · Optimizing these modifiable risk factors improves maternal and neonatal outcomes..

Continuous Glucose Monitoring Profiles in Pregnancies With and Without Gestational Diabetes Mellitus

OBJECTIVE

To determine whether continuous glucose monitoring (CGM)-derived glycemic patterns can characterize pregnancies with gestational diabetes mellitus (GDM) as diagnosed by standard oral glucose tolerance test at 24-28 weeks' gestation compared with those without GDM.

RESEARCH DESIGN AND METHODS

The analysis includes 768 individuals enrolled from two sites prior to 17 weeks' gestation between June 2020 and December 2021 in a prospective observational study. Participants wore blinded Dexcom G6 CGMs throughout gestation. Main outcome of interest was a diagnosis of GDM by oral glucose tolerance test (OGTT). Glycemic levels in participants with GDM versus without GDM were characterized using CGM-measured glycemic metrics.

RESULTS

Participants with GDM (n = 58 [8%]) had higher mean glucose (109 ± 13 vs. 100 ± 8 mg/dL [6.0 ± 0.7 vs. 5.6 ± 0.4 mmol/L], P < 0.001), greater glucose SD (23 ± 4 vs. 19 ± 3 mg/dL [1.3 ± 0.2 vs. 1.1 ± 0.2 mmol/L], P < 0.001), less time in range 63-120 mg/dL (3.5-6.7 mmol/L) (70% ± 17% vs. 84% ± 8%, P < 0.001), greater percent time >120 mg/dL (>6.7 mmol/L) (median 23% vs. 12%, P < 0.001), and greater percent time >140 mg/dL (>7.8 mmol/L) (median 7.4% vs. 2.7%, P < 0.001) than those without GDM throughout gestation prior to OGTT. Median percent time >120 mg/dL (>6.7 mmol/L) and time >140 mg/dL (>7.8 mmol/L) were higher as early as 13-14 weeks of gestation (32% vs. 14%, P < 0.001, and 5.2% vs. 2.0%, P < 0.001, respectively) and persisted during the entire study period prior to OGTT.

CONCLUSIONS

Prior to OGTT at 24-34 weeks' gestation, pregnant individuals who develop GDM have higher CGM-measured glucose levels and more hyperglycemia compared with those who do not develop GDM.

Use of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come?

BACKGROUND

Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly.

METHODS

PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use.

RESULTS

Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use.

CONCLUSIONS

Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future.

An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions-Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes

Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts.