Key result
OSAS linked to ~95% higher glycemic variability in T2D patients.
Why the study?
Obstructive sleep apnea syndrome in patients with type 2 diabetes mellitus may increase glycemic variability, affecting glycemic control and diabetes complications risk.
Does obstructive sleep apnea syndrome increase glycemic variability in patients with and without type 2 diabetes mellitus?
Cross-Sectional (n=60)
No
Does obstructive sleep apnea syndrome increase glycemic variability in patients with and without type 2 diabetes mellitus?
Absolute Event Rate: 5.2% vs 2.67%
p-value: p=<0.05
Obstructive sleep apnea syndrome significantly increases glycemic variability regardless of underlying diabetes status, with AHI showing a moderate positive correlation with glycemic variability indices.
OSAS linked to higher nocturnal GV in T2DM; hypothesis-generating observational data requiring prospective trials before clinical consideration.
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.
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Khaire et al. (2020) conducted a cross-sectional in Type 2 Diabetes Mellitus and Obstructive Sleep Apnea Syndrome (n=60). Obstructive sleep apnea syndrome vs. Without obstructive sleep apnea syndrome was evaluated on Mean Amplitude of Glycemic Excursion (MAGE) (p=<0.05). Obstructive sleep apnea syndrome was associated with significantly higher glycemic variability in patients with type 2 diabetes, demonstrating a Mean Amplitude of Glycemic Excursion of 5.20 mmol/L compared to 2.67 mmol/L in those without sleep apnea.
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