Targeting Predialysis Glucose up to 180 mg/dl Reduces Glycemic Variability in End Stage Diabetic Nephropathy.

Context: Glycemic variability plays a major role in the development as well as the progression of cardiovascular disease in diabetes. Aims: We compared the mean plasma glucose and glycemic variability (GV) parameters on and off hemodialysis (HD) in patients with End-Stage Diabetic Nephropathy (ESDN) and End-Stage Renal Disease (ESRD). Settings and Design: Cross-sectional study. Methods and Material: We included 23 ESDN and 6 ESRD patients who underwent continuous glucose monitoring (CGM) (iPro2) for 6 days and a glucose-free dialysate for 4 hours thrice weekly. EasyGV software was used to calculate the variability parameters {mean glucose, Time in range (TIR), Time above and below range (TAR/TBR), CV (Coefficient of Variation) and MAGE}. Statistical Analysis Used: The quantitative data variables were expressed by using mean and SD. Unpaired t-test was used to compare the two groups. P value <0.05 was considered significant. Results: In the ESDN group, TIR was significantly lower whereas TAR and TBR were significantly higher on HD day. MAGE (101.88 ± 40.5 v/s 89.46 ± 30.0, P < 0.007) and CV (29.41% v/s 21.67%) were higher on HD day. Subjects with pre-HD glucose values ≥180 mg/dl (Group B, n = 24) had a rapid drop with a delayed higher rise in glucose values than those with pre-HD glucose values <180 mg/dl (Group A, n = 27). Ten patients had 13 episodes of hypoglycemia. The CGM parameters were not different in the ESRD group. Conclusions: Targeting a pre- HD glucose value <180 mg/dl could be a good strategy to prevent larger fluctuation during and post HD.

Prevalence and predictors of abnormal glucose tolerance and its resolution in acromegaly: Single Centre retrospective study of 90 cases.

Aims The aim of the study was to evaluate the prevalence and predictors of abnormal glucose tolerance (Diabetes + Prediabetes) and its resolution in Acromegaly. SETTINGS AND DESIGN: Retrospective observational study. METHODS AND MATERIAL: Ninety patients with acromegaly and followed up post operatively for 1 year were included. The study cohort was divided into two groups: Group A: abnormal glucose tolerance [AGT: Diabetes + prediabetes (n = 40)] and Group B: normal glucose tolerance (NGT) (n = 50).The impact of the following parameters: age, sex, Waist Circumference(WC), Body Mass Index (BMI), duration of acromegaly, Growth Hormone (GH) levels, Insulin like Growth Factor 1 (IGF1) levels, pituitary tumour size, hypertension, and family history of diabetes as predictors for diabetes were studied pre surgery and post-surgery at 3 months and 1 year affecting glycaemia. Unpaired t-test, chi-square test and binary logistic regression analysis were used for statistical analysis. RESULTS: The prevalence of AGT in our cohort was 44.44% (Diabetes 37.77%, prediabetes 6.66%).Patients with AGT were older (44.2 ± 12.21 years vs. 34.92 ± 11.62 years; p = 0.00040) and had higher WC (in cm) (91.35 ± 7.87 vs.87.12 ± 6.07; p = 0.005) than NGT. Hypertension and family history of diabetes were significantly more frequent in patients with AGT. GH and IGF1 levels were not significantly different between the groups. On binary logistic regression, Sex (p = 0.0105) (OR = 6.0985), waist circumference (p = 0.0023) (OR = 1.2276) and hypertension (p = 0.0236) (OR = 1.632) were found to be significant predictors of AGT in acromegaly. After surgery 42.5% and 62.5% patients became normoglycemic at 3 months and 1 year respectively. On binary logistic regression there were no predictors for achieving normoglycemia at 3 months or 1 year, however the delta change in GH, BMI and tumour size were significant. CONCLUSIONS: The prevalence of AGT was 44.44%. Female sex, WC and hypertension were found to be significant predictors of AGT in acromegaly. Post-surgery normoglycemia was achieved in 42.5% at 3 months and 62.5% at 1 year with no predictors for normalisation of AGT.

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.