Effects of Positive Airway Pressure Therapy on Glycemic Variability in Patients With Type 2 Diabetes and OSA: A Randomized Controlled Trial

Design of the Continuous Glucose Monitoring (CGM) Study in the Dietary Approaches to Stop Hypertension for Diabetes Trial (DASH4D-CGM)

BACKGROUND

The Dietary Approaches to Stop Hypertension (DASH) diet is recommended for adults with diabetes. However, the effects of the DASH diet on glucose control and variability have not been thoroughly investigated.

STUDY AIM

To study the effects of a modified DASH diet on glycemic control and glucose variability assessed by continuous glucose monitoring (CGM) in individuals with hypertension and type 2 diabetes.

METHODS

We conducted an ancillary study to add CGM to the Dietary Approaches to Stop Hypertension for Diabetes (DASH4D) Trial. The DASH4D Trial was designed to determine the effects, alone and combined, of a modified DASH diet (vs a diet typical of what Americans with diabetes eat) and lower (vs higher) sodium intake on systolic blood pressure among people with type 2 diabetes and elevated blood pressure. Participants were fed each of the 4 study diets for 5 weeks in a random order. We invited all participants enrolling in the DASH4D Trial to wear a masked CGM sensor for up to 14 days during screening and during each of the four 5-week feeding periods. The DASH4D-CGM Study primarily aimed to evaluate the effects of the modified DASH diet (vs comparison diet) on glycemic control (mean glucose and time-in-range) and glucose variability (coefficient of variation).

CONCLUSION

Combining CGM technology and a controlled feeding design, the DASH4D-CGM Study will generate robust data on the effects of diet on glycemic control and variability. These findings will inform dietary recommendations for adults with type 2 diabetes.

Bidirectional interplay of sleep apnea syndrome and cardio-vascular disorders in diabetes

Although often overlooked sleep apnea has emerged as a significant public health concern. Obstructive sleep apnea (OSA) and diabetes commonly co-exist with a vicious cycle worsening the incidence and severity of both conditions. OSA has many implications including cardiometabolic disorders and impaired cardiovascular (CV) prognosis. OSA combined with diabetes generates a cumulative effect on CV outcomes. The association of OSA with several comorbidities including CV disease and heart failure is bi-directional meaning that some of them are likely to contribute to OSA. In patients with diabetes, OSA treatment should be integrated in a holistic strategy of prevention of CV and microvascular complications. This article provides some clues to advance the understanding of the interplay between OSA and CV disorders in diabetes and to consider the role of some CV risk markers like cardiac autonomic neuropathy and artery stiffness and of novel metrics for hypoxic-related events in CV risk stratification, and offers a discussion on the effects of medical approaches including weight loss strategies, GLP1-receptor agonists and sodium-glucose cotransporter 2 inhibitors. It provides a guidance to improve screening and diagnosis of OSA, and adherence to OSA treatment in patients with diabetes.

Obstructive sleep apnea: Overlooked comorbidity in patients with diabetes

In this review article, we explore the interplay between obstructive sleep apnea (OSA) and type 2 diabetes mellitus (T2DM), highlighting a significant yet often overlooked comorbidity. We delve into the pathophysiological links between OSA and diabetes, specifically how OSA exacerbates insulin resistance and disrupts glucose metabolism. The research examines the prevalence of OSA in diabetic patients and its role in worsening diabetes-related complications. Emphasizing the importance of comprehensive management, including weight control and positive airway pressure therapy, the study advocates integrated approaches to improve outcomes for patients with T2DM and OSA. This review underscores the necessity of recognizing and addressing OSA in diabetes care to ensure more effective treatment and better patient outcomes.

The Glucose Management Indicator: Time to Change Course?

Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.

Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability

BACKGROUND

Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management.

METHODS

We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study.

RESULTS

The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders.

CONCLUSION

We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.