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

Chao-sheng, Peng; Yue-an, Cao; Tian, Yange; Zhang, Wen-luo; Xia, Jian; Yang, Lu

doi:10.1016/j.diabres.2017.08.010

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…The parameters of Night CV, MAGE and NMAGE were significantly higher in group C compared to group D. These finding were similar to those with the study done by Peng et al [26] who had recruited OSAS (n-80) and non OSAS (n-40) patients but both without diabetes. Our patients in Group C (Non-diabetes with OSAS) had lower MAGE (2.47 mmol/L vs. 4.00 mmol/L) and comparable NMAGE (2.85 mmol/L vs. 1.99 mmol/ L) with respect to their study group.…”

Section: Discussionsupporting

confidence: 91%

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

Khaire

Gada

Utpat

et al. 2020

Clin Diabetes Endocrinol

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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).

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Section: Discussionsupporting

confidence: 91%

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

Khaire

Gada

Utpat

et al. 2020

Clin Diabetes Endocrinol

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“…Obstructive sleep apnoea is a common comorbidity of type 1 diabetes [5]. A few reports have suggested higher glycaemic variability in non-diabetic adults with obstructive sleep apnoea [27,28]. Being overweight and obesity are major risk factors for obstructive sleep apnoea.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…A continuous glucose monitoring (CGM) device allows the measurement of the blood glucose concentration and the monitoring of dynamic glucose changes during sleep 11,12 . A CGM study involving patients with OSA showed higher glycemic variability in the OSA patients than in the controls; glycemic variability may lead to increased organ damage 13 .…”

mentioning

confidence: 99%

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

Byun

Jun

et al. 2020

Sci Rep

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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.

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

Cited by 14 publications

References 45 publications

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

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

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

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

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