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

Abstract

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).

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

“…Kurosawa et al found a significant and positive correlation between AHI and NREM-AHI with SD [23], another index of GV. Khaire et al reported that night CV% and MAGE between 10 pm and 6 am were significantly higher in 10 patients with OSA compared with those in 10 patients without OSA [24]. In the present study, the values of various markers of GV (e.g., SD and CV) during sleep, in addition to TIR, HBGI, and MDP, were higher in patients with OSA than those in the healthy control group.…”

“…Kurosawa et al found a significant and positive correlation between AHI and NREM-AHI with SD [ 23 ], another index of GV. Khaire et al reported that night CV% and MAGE between 10 pm and 6 am were significantly higher in 10 patients with OSA compared with those in 10 patients without OSA [ 24 ].…”

Objective sleep characteristics and continuous glucose monitoring profiles of type 2 diabetes patients in real‐life settings

“…Although we did not find a significant association between sleep midpoint and mean glucose value or percentage of TIR/TAR/TBR, attention must be paid to circadian‐related variation of glycaemia because greater glycaemic variability has been related to cardiovascular complications in T2D patients 33 . Preliminary evidence shows that severity of obstructive sleep apnoea, a possible trigger of nocturnal glycaemic fluctuations, has a U‐shaped association with chronotype 34,35 . Moreover, a later timing of food consumption in the nights with a postponed sleep midpoint might be another reason behind greater glucose variation during sleep.…”

Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model

“…Pathologically, OSAS is characterized by intermittent hypoxia and consequent activated inflammation, oxidative stress, and sympathetic system, all of which are involved in the pathogenesis of CAD [ 7 , 8 ]. Previous reports have shown that OSAS confers an increased risk of CAD, particularly in the high-risk population, such as those with hyperlipidemia, diabetes, and hypertension [ 9 – 11 ]. Indeed, the prevalence of OSAS in patients with CAD is estimated 40–60% [ 12 , 13 ].…”