Suhas S Khaire, Jugal V Gada, Ketaki V Utpat, Nikita Shah, Premlata K Varthakavi, Nikhil M Bhagwat
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Patients were then divided into 4 groups: Group A (DM with OSAS, <i>n</i> = 20), Group B (DM without OSAS, <i>n</i> = 20), Group C (Non DM with OSAS, <i>n</i> = 10) and Group D (Non DM without OSAS, <i>n</i> = 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.</p><p><strong>Results: </strong>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 (<i>p</i> 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).</p><p><strong>Conclusions: </strong>OSAS has a significant impact on the glycemic variability irrespective of glycemic status. AHI has moderate positive correlation with the glycemic variability.</p>","PeriodicalId":56339,"journal":{"name":"Clinical Diabetes and Endocrinology","volume":"6 ","pages":"10"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40842-020-00098-0","citationCount":"7","resultStr":"{\"title\":\"A study of glycemic variability in patients with type 2 diabetes mellitus with obstructive sleep apnea syndrome using a continuous glucose monitoring system.\",\"authors\":\"Suhas S Khaire, Jugal V Gada, Ketaki V Utpat, Nikita Shah, Premlata K Varthakavi, Nikhil M Bhagwat\",\"doi\":\"10.1186/s40842-020-00098-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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, <i>n</i> = 20), Group B (DM without OSAS, <i>n</i> = 20), Group C (Non DM with OSAS, <i>n</i> = 10) and Group D (Non DM without OSAS, <i>n</i> = 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.</p><p><strong>Results: </strong>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 (<i>p</i> 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).</p><p><strong>Conclusions: </strong>OSAS has a significant impact on the glycemic variability irrespective of glycemic status. 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引用次数: 7
摘要
背景:阻塞性睡眠呼吸暂停综合征(OSAS)与2型糖尿病(DM)相关,可导致血糖变异性增加,影响血糖控制,从而增加糖尿病相关并发症的风险。我们决定评估伴有OSAS的2型糖尿病患者和对照组的血糖变异性(GV)。我们还将呼吸障碍指数与血糖变异性指数联系起来。方法:在满足纳入和排除标准后,对内分泌科和肺科门诊的患者进行改良睡眠呼吸暂停临床评分(SACS)并进行多导睡眠图(PSG)检查。将患者分为4组:A组(合并OSAS的DM, n = 20)、B组(不合并OSAS的DM, n = 20)、C组(非合并OSAS的DM, n = 10)、D组(非合并OSAS的DM, n = 10)。这些组的患者使用美敦力iPro2和重复PSG进行连续血糖监测。使用Easy GV软件计算GV参数:即平均葡萄糖、SD(标准差)、CV(变异系数)、Night SD、Night CV、MAGE和image。GV参数与呼吸指数有统计学相关性。定量数据以均数、标准差、中位数表示。不同组间GV指数的比较采用单因素方差分析(ANOVA)或Kruskal Wallis(对于不符合正态性的数据)进行。采用Pearson相关分析AHI与GV参数的相关性。结果:四组患者在年龄、性别、体重指数(BMI)、腰围(WC)、血压(BP)等指标均符合要求。我们发现,与B组相比,A组的GV参数Night CV、MAGE和image显著升高(p值p值)。结论:无论血糖状态如何,OSAS对血糖变异性都有显著影响。AHI与血糖变异性呈中度正相关。
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.
期刊介绍:
Clinical Diabetes and Endocrinology is an open access journal publishing within the field of diabetes and endocrine disease. The journal aims to provide a widely available resource for people working within the field of diabetes and endocrinology, in order to improve the care of people affected by these conditions. The audience includes, but is not limited to, physicians, researchers, nurses, nutritionists, pharmacists, podiatrists, psychologists, epidemiologists, exercise physiologists and health care researchers. Research articles include patient-based research (clinical trials, clinical studies, and others), translational research (translation of basic science to clinical practice, translation of clinical practice to policy and others), as well as epidemiology and health care research. Clinical articles include case reports, case seminars, consensus statements, clinical practice guidelines and evidence-based medicine. Only articles considered to contribute new knowledge to the field will be considered for publication.