Features of continuous glycemic profile and glycemic variability in patients with obstructive sleep apnea syndrome

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

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

BACKGROUND

Glycemic variability is associated with increased risk for cardiovascular disease in patients with type 2 diabetes independent of glycosylated hemoglobin A1c (HbA1c) levels. Given the conflicting evidence on the effect of positive airway pressure (PAP) therapy for OSA on HbA1c, elucidating its effect on glycemic variability has value.

RESEARCH QUESTION

Does the use of PAP therapy for OSA improve glycemic variability in patients with type 2 diabetes?

STUDY DESIGN AND METHODS

A randomized controlled trial was conducted in 184 patients with type 2 diabetes and moderate-to-severe OSA. Participants received either 3 months of PAP therapy with lifestyle counseling or lifestyle counseling alone. End points included the SD of glucose levels along with other metrics derived from continuous glucose monitoring and self-monitoring of blood glucose.

RESULTS

No differences were noted in either primary or secondary continuous glucose monitoring end points between the two groups. Average use of PAP therapy was 5.4 h/night (SD, 1.6). Exploratory analyses by sex showed significant differences in the primary and secondary outcomes. In female participants, PAP therapy was associated with improvement in the SD of glucose levels, with a mean difference in change between intervention and control groups of 3.5 mg/dL (P = .02). PAP therapy was also associated with lower post-dinner and bedtime glucose levels: 20.1 mg/dL (P < .01) and 34.6 mg/dL (P < .01), respectively.

INTERPRETATION

PAP therapy did not improve glycemic control or variability in patients with moderate-to-severe OSA and type 2 diabetes. Exploratory analyses suggested that PAP therapy may improve glucose variability in female participants. Post-dinner and bedtime glucose levels were higher in those who did not receive PAP therapy.

TRIAL REGISTRATION

ClinicalTrials.gov; No.: NCT02454153; URL: www.

CLINICALTRIALS

gov.

A Review of the Associations Between Obstructive Sleep Apnea and Gestational Diabetes Mellitus and Possible Mechanisms of Disease

Obstructive sleep apnea (OSA) usually leads to the occurrence of diabetes. Gestational diabetes mellitus (GDM) is a common gestational complication associated with adverse maternal and fetal outcomes. Increasing studies suggest that women with OSA during pregnancy may be at a significantly greater risk of developing GDM. It is crucial to explore the association between OSA and GDM and the mechanisms underlying this association. In this review, we presented a comprehensive literature review of the following: the association between OSA and GDM, the possible mechanisms of this association, and the effects of continuous positive airway pressure (CPAP) on OSA with GDM. The results showed that most authors suggested that there was an association between OSA and GDM. The intermittent hypoxemia (IH) and reduction of slow-wave sleep (SWS) may be the key to this association. IH induces the products of oxidative stress and inflammation as well as dysregulation of the hypothalamic-pituitary-adrenal, which lead to diabetes. In addition, SWS reduction in OSA enhances the inflammation by increasing the inflammatory cytokines, increases the sympathetic activation, and causes changes in leptin level, which result in the development of GDM. Additionally, whether CPAP is beneficial to GDM remains still unclear.

Postprandial glucose and HbA1c are associated with severity of obstructive sleep apnoea in non-diabetic obese subjects

INTRODUCTION

Obstructive sleep apnoea (OSA) is an underdiagnosed condition frequently associated with glycaemic control impairment in patients with type 2 diabetes.

AIM

To assess the relationship between glycometabolic parameters and OSA in obese non-diabetic subjects.

METHODS

Ninety consecutive subjects (mean age 44.9 ± 12 years, mean BMI 42.1 ± 9 kg/m²) underwent polysomnography and a 2-h oral glucose tolerance test (OGTT).

RESULTS

OSA was identified in 75% of subjects, with a higher prevalence of males compared to the group of subjects without OSA (62% vs 32%, p = 0.02). Patients with OSA had comparable BMI (42.8 kg/m² vs 39.4 kg/m²), a higher average HbA1c (5.8% vs 5.4%, p < 0.001), plasma glucose at 120 min during OGTT (2 h-PG; 123 mg/dl vs 97 mg/dl, p = 0.009) and diastolic blood pressure (81.1 mmHg vs 76.2 mmHg, p = 0.046) than obese subjects without OSA. HbA1c and 2 h-PG were found to be correlated with the apnoea-hypopnoea index (AHI; r = 0.35 and r = 0.42, respectively) and with percent of sleep time with oxyhaemoglobin saturation < 90% (ST90; r = 0.44 and r = 0.39, respectively). Further, in a linear regression model, ST90 and AHI were found to be the main determinants of 2 h-PG (β = 0.81, p < 0.01 and β = 0.75, p = 0.02, respectively) after controlling for age, sex, waist circumference, physical activity, and C-reactive protein. Similarly, ST90 and AHI persisted as independent determinants of HbA1c (β = 0.01, p = 0.01 and β = 0.01, p = 0.01, respectively).

CONCLUSION

Beyond the traditional clinical parameters, the presence of a normal-high value of 2 h-PG and HbA1c should raise suspicion of the presence of OSA in obese subjects.

Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes

AIMS/HYPOTHESIS

Suboptimal subjective sleep quality is very common in adults with type 1 diabetes. Reducing glycaemic variability is a key objective in this population. To date, no prior studies have examined the associations between objectively measured sleep quality and overnight glycaemic variability in adults with type 1 diabetes. We aimed to test the hypothesis that poor sleep quality would be associated with greater overnight glycaemic variability.

METHODS

Data were collected in the home setting from 20 adults (ten male and ten female participants) with type 1 diabetes who were current insulin pump users. Simultaneous assessments of objective sleep quality (Zmachine Insight+) and continuous glucose monitoring (CGM) were performed over multiple nights (up to 15 nights) in each participant. Due to the real-life nature of this study, the participants kept their usual CGM alerts for out-of-range glucose values. Sleep quality was categorised as 'good' or 'poor' using a composite of objective sleep features (i.e. sleep efficiency, wake after sleep onset and number of awakenings) based on the National Sleep Foundation's consensus criteria. Glycaemic variability was quantified using SD and CV of overnight glucose values based on overnight CGM profiles.

RESULTS

A total of 170 nights were analysed. Overall, 86 (51%) nights were categorised as good sleep quality, and 84 (49%) nights were categorised as poor sleep quality. Using linear mixed-effects models, poor sleep quality was significantly associated with greater glycaemic variability as quantified by SD (coefficient: 0.39 [95% CI 0.10, 0.67], p = 0.009) and CV (coefficient: 4.35 [95% CI 0.8, 7.9], p = 0.02) of overnight glucose values, after accounting for age, sex, BMI and overnight insulin dose. There was large inter- and intra-individual variability in sleep and glycaemic characteristics. Both pooled and individual-level data revealed that the nights with poor sleep quality had larger SDs and CVs, and, conversely, the nights with good sleep quality had smaller SDs and CVs. No associations were found between sleep quality and time spent in the target glucose range, or above or below the target glucose range, where CGM alarms are most likely to occur.

CONCLUSIONS/INTERPRETATION

Objectively measured sleep quality is associated with overnight glycaemic variability in adults with type 1 diabetes. These findings highlight an important role of sleep quality in overnight glycaemic control in type 1 diabetes. They also provide a strong incentive to both patients and healthcare providers for considering sleep quality in personalised type 1 diabetes glycaemic management plans. Future studies should investigate the mechanisms linking sleep quality to glycaemic variability.

Blood glucose dynamics during sleep in patients with obstructive sleep apnea and normal glucose tolerance: effects of CPAP therapy

Purpose

Glycemic variability (GV) and hypoglycemia during nighttime are presumed to be associated with fatal bradycardia. The aim of this prospective study was to evaluate blood glucose dynamics during sleep in patients with obstructive sleep apnea syndrome (OSA) and normal glucose tolerance.

Methods

Patients with OSA and no diabetes who underwent type 1 overnight polysomnography from December 2018 to May 2020 participated in this study. GV was evaluated in all participants for 14 days using a flash glucose monitoring device. Correlations were examined between GV indexes and indexes related to sleep breathing disorders, the effects of treatment with continuous positive airway pressure (CPAP) on these GV indexes, and the characteristics of glucose dynamics in different OSA subtypes classified by sleep stage.

Results

Among 42 patients with OSA and no diabetes, the standard deviation of GV during sleep correlated significantly with sleep time spent with oxygen saturation <90% (r=0.591, p=0.008). High blood glucose index during sleep correlated significantly with stage N1% (r=0.491, p=0.032) and negatively with stage N2% (r=−0.479, p=0.038). High blood glucose index correlated significantly with sleep time spent with oxygen saturation <90% (r=0.640, p=0.003). The rapid eye movement–related OSA group had a higher incidence of hypoglycemia. One-week with CPAP treatment significantly improved GV during sleep, standard deviation of GV (from 12.1 to 9.0 mg/dL, p<0.001), and high blood glucose index (from 0.7 to 0.4, p=0.006).

Conclusions

To evaluate GV during sleep in patients with OSA may be useful for clinical risk management. CPAP treatment for 1 week may have an improving GV and high blood glucose index.

Clinical trial registration

UMIN000038489 2019/11/04, UMIN 000025433 2016/12/27

Supplementary Information

The online version contains supplementary material available at 10.1007/s11325-021-02442-9.

Postprandial hyperglycemia in type 2 diabetes and obstructive sleep apnea

OBJECTIVES

Postprandial hyperglycemia is common in type 2 diabetes even in those with acceptable glycemic control and conveys an increased risk of cardiovascular morbidity and mortality. Although obstructive sleep apnea (OSA) has been associated with altered glucose metabolism, data regarding its association with postprandial hyperglycemia in type 2 diabetes are limited. Thus, the current study sought to characterize the association between OSA and postprandial hyperglycemia in adults with type 2 diabetes.

METHODS

A cross-sectional study of adults with type 2 diabetes was conducted. Home sleep testing was used to assess OSA severity as determined by the oxygen desaturation index (ODI). Self-monitoring of blood glucose (SMBG) was performed before and 2-h after breakfast, lunch, and dinner for three days. The association between OSA and glucose levels before and after each meal was examined using multivariable logistic regression.

RESULTS

The study sample consisted of 195 adults with 52% being men. OSA severity, as assessed by ODI quartiles, was associated with higher postprandial glucose values after dinner but not after breakfast or lunch. The adjusted odds ratios (95% confidence intervals) for a higher post-dinner glucose level for four ODI quartiles were 1.00 (Reference), 2.16 (0.96, 4.87), 2.23 (1.03, 4.83), and 2.58 (1.18, 5.94). Stratified analyses showed that this association was present in men but not women.

CONCLUSIONS

Increasing OSA severity is associated with postprandial hyperglycemia in type 2 diabetes and may contribute to impaired glycemic control. Future studies examining the impact of OSA treatment on glucose metabolism should consider meal-related glycemic excursions as a potential outcome.

Dynamic changes in nocturnal blood glucose levels are associated with sleep-related features in patients with obstructive sleep apnea

Obstructive sleep apnea (OSA) has a bidirectional relationship with insulin resistance conditions; however, the mechanism remains unclear. This study aimed to compare dynamic nocturnal glucose changes among patients with OSA of varying levels of severity and evaluate temporal changes associated with the cardinal features of OSA (sympathetic hyperactivation, intermittent hypoxemia, and sleep fragmentation) in nondiabetic subjects. Nocturnal glucose was measured with a continuous glucose monitoring device every 5 min during polysomnography (PSG). The OSA features were evaluated using heart rate variability (HRV), minimum saturation, and electroencephalography. Eleven subjects with moderate to severe OSA and 12 subjects with no or mild OSA were evaluated. Those with moderate to severe OSA showed an increasing trend in blood glucose levels after sleep onset, whereas those without or with mild OSA showed a decreasing trend (F = 8.933, p < 0.001). Delta band power also showed different trends during sleep between the two groups (F = 2.991, p = 0.009), and minimum saturation remained lower in the moderate to severe OSA group than in the no or mild OSA group. High degrees of coupling between nocturnal glucose levels and each OSA feature were observed. Altered trends in nocturnal glucose in moderate to severe OSA may reflect glucose intolerance and result in metabolic consequences. Managing the features of sleep-related OSA may have implications for metabolic management in the future.

A study of glycemic variability in patients with type 2 diabetes mellitus with obstructive sleep apnea syndrome using a continuous glucose monitoring system

Background

Obstructive sleep apnea syndrome (OSAS) in association with Type 2 Diabetes Mellitus (DM) may result in increased glycemic variability affecting the glycemic control and hence increasing the risk of complications associated with diabetes. We decided to assess the Glycemic Variability (GV) in patients with type 2 diabetes with OSAS and in controls. We also correlated the respiratory disturbance indices with glycemic variability indices.

Methods

After fulfilling the inclusion and exclusion criteria patients from the Endocrinology and Pulmonology clinics underwent modified Sleep Apnea Clinical Score (SACS) followed by polysomnography (PSG). Patients were then divided into 4 groups: Group A (DM with OSAS, n = 20), Group B (DM without OSAS, n = 20), Group C (Non DM with OSAS, n = 10) and Group D (Non DM without OSAS, n = 10). Patients in these groups were subjected to continuous glucose monitoring using the Medtronic iPro2 and repeat PSG. Parameters of GV: i.e. mean glucose, SD (standard Deviation), CV (Coefficient of Variation), Night SD, Night CV, MAGE and NMAGE were calculated using the Easy GV software. GV parameters and the respiratory indices were correlated statistically. Quantitative data was expressed as mean, standard deviation and median. The comparison of GV indices between different groups was performed by one-way analysis of variance (ANOVA) or Kruskal Wallis (for data that failed normality). Correlation analysis of AHI with GV parameters was done by Pearson correlation.

Results

All the four groups were adequately matched for age, sex, Body Mass Index (BMI), waist circumference (WC) and blood pressure (BP). We found that the GV parameters Night CV, MAGE and NMAGE were significantly higher in Group A as compared to Group B (p values < 0.05). Similarly Night CV, MAGE and NMAGE were also significantly higher in Group C as compared to Group D (p value < 0.05). Apnea-hypopnea index (AHI) correlated positively with Glucose SD, MAGE and NMAGE in both diabetes (Group A plus Group B) and non- diabetes groups (Group C plus Group D).

Conclusions

OSAS has a significant impact on the glycemic variability irrespective of glycemic status. AHI has moderate positive correlation with the glycemic variability.