Effect of Liraglutide on Times in Glycaemic Ranges as Assessed by CGM for Type 2 Diabetes Patients Treated With Multiple Daily Insulin Injections

Sofizadeh, Sheyda; Imberg, Henrik; Ólafsdóttir, Arndís F.; Ekelund, Magnus; Hirsch, Irl B.; Filipsson, Karin; Åhrén, Bo; Sjöberg, Stefan; Tuomilehto, Jaakko; Lind, Marcus

doi:10.1007/s13300-019-00692-1

Cited by 17 publications

(17 citation statements)

References 27 publications

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“…Similar to our results, the MDI Liraglutide trial 22 found that reductions in mean glucose levels and standard deviation estimated by masked continuous glucose monitoring were significantly greater in the liraglutide group than in the placebo group (−1.9 and −0.5 mmol/L (−34.2 and −9 mg/dL), respectively; P<0.001). The study of Lind et al 23 showed that liraglutide treatment lowers glucose levels and reduces time in hypoglycemia in T2DM patients treated with MDI.…”

Section: Discussionsupporting

confidence: 90%

Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes

Gomez¹,

Henao-Carillo²,

Taboada³

et al. 2021

MDER

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Background: High glycemic Variability (HGV) has become a stronger predictor of hypoglycemia. However, clinical factors associate with HGV still are unknown. Objective: To determine clinical variables that were associated with a coefficient of variation (CV) above 36% evaluated by continuous glucose monitoring (CGM) in a group of patients with diabetes mellitus. Methods: A cohort of patients with type 2 diabetes (T2D) was evaluated. Demographic variables, HbA1c, glomerular filtration rate (GFR) and treatment regimen were assessed. A bivariate analysis was performed, to evaluate the association between the outcome variable (CV> 36%) and each of the independent variables. A multivariate model was constructed to evaluate associations after controlling for confounding variables. Results: CGM data from 274 patients were analyzed. CV> 36% was present in 56 patients (20.4%). In the bivariate analysis, demographic and clinical variables were included, such as time since diagnosis, hypoglycemia history, A1c, GFR and treatment established. In the multivariate analysis, GFR <45 mL/min (OR 2.81; CI 1.27,6.23; p:0.01), A1c > 9% (OR 2.81; CI 1.05,7.51; p:0.04) and hypoglycemia history (OR 2.09; CI 1.02,4.32; p:0.04) were associated with HGV. Treatment with iDPP4 (OR 0.39; CI 0.19,0.82; p:0.01) and AGLP1 (OR 0.08; CI 0.01,0.68; p:0.02) was inversely associated with GV. Conclusion: Clinical variables such as GFR <45 mL/min, HbA1C>9% and a history of hypoglycemia are associated with a high GV. Our data suggest that the use of technology and treatments able to reduce glycemic variability could be useful in this population to reduce the risk of hypoglycemia and to improve glycemic control.

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Section: Discussionsupporting

confidence: 90%

Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes

Gomez¹,

Henao-Carillo²,

Taboada³

et al. 2021

MDER

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“…Furthermore, when novel glucose-lowering treatments are evaluated both in persons with type 1 and type 2 diabetes, 6,7,11,13 a certain amount of time-in-hypoglycemia registered via glucose sensor should be viewed as normal, at least for the 3.9 mmol/L cut point. However, none or very little time in hypoglycemia<3.0 mmol/L should exist where the median and mean time was 0.0% and 0.5%, respectively, in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…20,23 Earlier studies also did not use masked CGM, [21][22][23][24][25][26] which may influence glucose levels to some extent. 11,13 Others used earlier generation CGM sensors, which were generally less accurate than the Dexcom G4. 10,24 Finally, OGTT was not performed in some studies; thus, it is likely a significant number of persons with prediabetes or diabetes were included.…”

Section: Earlier Studiesmentioning

confidence: 99%

Evaluation of Reference Metrics for Continuous Glucose Monitoring in Persons Without Diabetes and Prediabetes

Sofizadeh

Pehrsson

Ólafsdóttir

et al. 2020

J Diabetes Sci Technol

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Background: Recent guidelines have been developed for continuous glucose monitoring (CGM) metrics in persons with diabetes. To understand what glucose profiles should be judged as normal in clinical practice and glucose-lowering trials, we examined the glucose profile of healthy individuals using CGM. Methods: Persons without diabetes or prediabetes were included after passing a normal oral glucose tolerance test, two-hour value <8.9 mmol/L (160 mg/dL), fasting glucose <6.1 mmol/L (110 mg/dL), and HbA1c <6.0% (<42 mmol/mol). CGM metrics were evaluated using the Dexcom G4 Platinum. Results: In total, 60 persons were included, mean age was 43.0 years, 70.0% were women, mean HbA1c was 5.3% (34 mmol/mol), and mean body mass index was 25.7 kg/m2. Median and mean percent times in hypoglycemia <3.9 mmol/L (70 mg/dL) were 1.6% (IQR 0.6-3.2), and 3.2% (95% CI 2.0; 4.3), respectively. For glucose levels <3.0 mmol/L (54 mg/dL), the corresponding estimates were 0.0% (IQR 0.0-0.4) and 0.5% (95% CI 0.2; 0.8). Median and mean time-in-range (3.9-10.0 mmol/L [70-180 mg/dL]) was 97.3% (IQR 95.4-98.7) and 95.4% (95% CI 94.0; 96.8), respectively. Median and mean standard deviations were 1.04 mmol/L (IQR 0.92-1.29) and 1.15 mmol/L (95% CI 1.05; 1.24), respectively. Measures of glycemic variability (standard deviation, coefficient of variation, mean amplitude of glycemic excursions) were significantly greater during daytime compared with nighttime, whereas others did not differ. Conclusions: People without prediabetes or diabetes show a non-negligible % time in hypoglycemia, median 1.6% and mean 3.2%, which needs to be accounted for in clinical practice and glucose-lowering trials. Glycemic variability measures differ day and night in this population.

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“…DM has become the third major disease threatening human health after cancer, cardiovascular, and cerebrovascular diseases (Fawzy et al, ; Xu et al, ). It is a group of clinical syndromes caused by the interaction of genetic and environmental factors (Sofizadeh et al, ) (El Husseny et al, ). The insufficient and relative lack of insulin secretion or the reduced insulin sensitivity of target tissue cells (the insulin resistance) leads to a series of nutrient metabolic disorders such as sugar, fat, protein, water, and electrolytes, resulting in hyperglycemia (Gupta et al, ).…”

Section: Discussionmentioning

confidence: 99%

Oxalomalate regulates the apoptosis and insulin secretory capacity in streptozotocin‐induced pancreatic β‐cells

Suping

2020

Drug Development Research

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Diabetes mellitus (DM) is a kind of metabolic disorder characterized by long-term hyperglycemia. Oxidative stress is involved in inducing the apoptosis of pancreatic β-cells and promoting the development of DM. Oxalomalate (OMA) is a competitive inhibitor of two classes of NADP+-dependent isocitrate dehydrogenase isoenzymes that are the main nicotinamide adenine dinucleotide phosphate (NADPH) producers to scavenge cellular reactive oxygen species (ROS). However, the role of OMA in DM remains unclear. The present study aimed to investigate the protective effects of OMA on streptozotocin (STZ)-induced β-cell damage and its underlying mechanisms. The viability of rat insulinoma cell line (INS-1) and the contents of ROS, nitric oxide and NAPDH were examined after cells being treated with STZ. After treatment with OMA in STZ-stimulated INS-1, the cell viability, apoptosis, and apoptosis-related proteins were measured. Meanwhile, the levels of oxidative stress-related factors and the changes of insulin secretion were determined. The results revealed that OMA significantly increased the cell viability (p < .05), reduced the apoptotic rate (p < .001), and altered the expression levels of Bcl-2, Bax, cleaved caspase3, and cleaved-caspase9 (p < .05 or p < .01) in STZ-induced INS-1 cells. Moreover, OMA enhanced the activities of superoxide dismutase, catalase, glutathione peroxidase (p < .01), whereas reduced the levels of ROS, malondialdehyde and lactic dehydrogenase (p < .001). Furthermore, OMA improved the ability of insulin secretion. These results indicated that OMA might have antioxidative stress and anti-apoptosis effects to protect INS-1 cells from STZ-induced cell damage. K E Y W O R D S cell apoptosis, diabetes mellitus, Oxalomalate, oxidative stress, reactive oxygen species

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Effect of Liraglutide on Times in Glycaemic Ranges as Assessed by CGM for Type 2 Diabetes Patients Treated With Multiple Daily Insulin Injections

Cited by 17 publications

References 27 publications

Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes

Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes

Evaluation of Reference Metrics for Continuous Glucose Monitoring in Persons Without Diabetes and Prediabetes

Oxalomalate regulates the apoptosis and insulin secretory capacity in streptozotocin‐induced pancreatic β‐cells

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