Search

Background As a new indicator of glycemic management, time in range (TIR) is significantly related to short-term glycemic variability, but whether it is also associated with long-term glycemic variability is still unclear. Objective To investigate the relationship of TIR with coefficient of variability (CV) of HbA1c and HbA1c variability score (HVS) during a long-term follow-up in elderly male patients with type 2 diabetes. Methods Two hundred elderly male type 2 diabetic inpatients from the Second Medical Center of PLA General Hospital were enrolled from January 2007 to January 2011. All of them underwent continuous glucose monitoring (CGM) during hospitalization, and by the baseline TIR derived from CGM, they were divided into TIR≥85% group (n=141) and TIR

Objective To investigate the effects of insulin-mediated lipohypertrophy (LH) on short-term glycemic variability of hospitalized diabetic patients treated with insulin pump. Methods A total of 232 inpatients with diabetes who received both short-term insulin pump therapy and dynamic blood glucose monitor were retrospectively analyzed, including 52 cases of type 1 diabetes mellitus (T1DM) and 180 cases of type 2 diabetes mellitus (T2DM). The incidence of insulin injection-related LH in patients with different types of diabetes was counted, and the differences in short-term glucose fluctuation indicators between patients with LH and those without LH were compared. The relationship between mean glucose fluctuation amplitude (MAGE) and clinical indicators was analysed. Binary Logistic regression was used to analyse the factors influencing glucose fluctuations. Results The overall incidence of LH was 61.2% in 232 patients, with 57.7% (30/52) in T1DM patients and 62.2% (112/180) in T2DM patients. In both T1DM and T2DM patients, MAGE was positively correlated with LH and negatively correlated with BMI, years of insulin use, and duration of diabetes (P < 0.05). Multi-factor Logistic analysis showed that LH was a risk factor for blood glucose fluctuations in both T1DM (OR= 4.665, 95%CI: 1.134-19.200) and T2DM (OR=6.659, 95%CI: 3.248-13.654) patients. Conclusion Insulin-mediated lipohypertrophy is identified as a vital risk factor for short-term glycemic fluctuations in diabetes. [ABSTRACT FROM AUTHOR]

Objective To assess differences in the reduction of blood glucose fluctuations (GV), glucose target time (TIR) and oxidative stress levels in liraglutide plus insulin degludec and multiple daily insulin injections (MDI) using scanning glucose monitoring (FGM) system. Methods In this randomized parallel-design study, subjects who were treated with MDI for at least one month were randomly assigned (through a computer-generated, random order) 1∶1 to either MDI treatment (control group, n=26) or liraglutide combined with insulin degludec treatment (experimental group, n=26). The primary endpoint of this study was the change in GV and TIR (3.9-10 mmol/L) from baseline to 14 days. Secondary endpoints included hypoglycemia, oxidative stress levels and body weight. Results The serum 1,5-anhydroglucitol (1,5- AG) was significantly increased in both groups (P＜0.05). After treatment, the MAGE and eHbA1c were significantly lower in the experimental group than those of the control group (P＜0.05), and 1,5-AG was significantly higher in the experimental group than that of the control group (P＜0.05). The TIR was significantly prolonged after treatment in both groups, and the time above range (TAR) (＞10 mmol/L) was significantly shortened. The time below range (TBR) (＜3.9 mmol/L) was significantly lower in the experimental group than that in the control group (P＜0.05). After treatment, 8-iso-PGF2α and 8- OHdG were significantly decreases in the experimental group than those of the control group (P＜0.05). After treatment, the body weights decreased significantly in the experimental group, while no significant changes in the control group (P＜0.05). Conclusion Liraglutide combined with insulin degludec treatment can achieve good glycemic control in patients with type 2 diabetes and which is superior to MDI in improving GV, reducing hypoglycemia risk, oxidative stress levels and body weight. [ABSTRACT FROM AUTHOR]

Copyright of Practical Pharmacy & Clinical Remedies is the property of Editorial Department of Practical Pharmacy & Clinical Remedies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore the correlation between the different glycemic variability (GV) indices calculated by self-monitoring of blood glucose (SMBG) and mean amplitude of glycemic excursion (MAGE) obtained from the continuous glucose monitor (CGM) system in patients with type 2 diabetes mellitus (T2DM). Methods This was a retrospective study. We analyzed the data of T2DM patients who received 48-72 h CGM and 7-point SMBG (pre and post-breakfast, lunch, dinner and prior to bedtime) simultaneously in Department of Endocrinology and Metabolic Disease, the Third Affiliated Hospital of Sun-Yat-sen University from January 2018 to October 2019. The GV indices calculated from the 7-point SMBG data included the standard deviation (SDBG) of the 7-point glucose profiles, the largest amplitude of glycemic excursions (LAGE) and the postprandial glucose excursion (PPGE), coefficient of variation of blood glucose (CV) and mean amplitude of glucose excursion (MAGE', calculated by SMBG profile). Spearman's correlation analysis, simple linear regression and multiple stepwise regression analysis were used to analyze the relationship between the different GV indices calculated from 7-point SMBG and MAGE obtained by CGM, and the receiver operator characteristic (ROC) curve was drawn to evaluate the ability of the former to predict the latter. Results Among 105 patients with T2DM, the SDBG, PPGE, LAGE and CV calculated by 7-point glucose profiles of SMBG were (2.02±0.77) mmol/L, (2.75±1.13) mmol/L, (5.62±2.13) mmol/L and (25.92±0.77)%, respectively, while the level of MAGE' and median MAGE were 4.11 (2.84, 5.92) mmol/L and 4.00 (2.65, 5.00) mmol/L, respectively. SDBG, PPGE, LAGE, CV and MAGE' were significantly correlative with MAGE (r=0.614, 0.499, 0.588, 0.533 and 0.473, respectively, all P<0.01). Multiple stepwise regression analysis was performed with MAGE as dependent variable and SDBG, PPGE, LAGE, CV and MAGE' as independent variables, and only SDBG entered the equation (P<0.01). The areas under ROC curve for SDBG [0.795, 95% confidence interval (CI) was 0.708-0.882], LAGE (0.782, 95%CI was 0.692-0.872) and CV (0.769, 95%CI was 0.677-0.846) were larger than those for PPGE (0.718, 95%CI was 0.620-0.816) and MAGE' (0.704, 95%CI was 0.607-0.789, all P<0.01) in reflecting the unqualified MAGE. Conclusions GV indices calculated from 7-point SMBG data including SDBG, LAGE, PPGE, CV and MAGE' are positively correlated with MAGE obtained from CGM. And SDBG has higher accuracy than the other four parameters. [ABSTRACT FROM AUTHOR]

Objective To investigate the relationship betweenglycemic variability and erectile dysfunction (ED) in patients with type 2 diabetes (T2DM). Methods Eight hundred and thirteen T2DM outpatientsin Henan Provincial People′s Hospital were enrolled in this study from December 2017 to December 2018. All these patients accepted blood glucose monitoring with flash glucose-sensing technology and answered the International Questionnaire on Erectile Function (IIEF-5) to assess ED severity. At last, they were divided into non-ED group (n=148), mild ED group (n=319), moderate ED group (n=144) and severe ED group (n=202) according to the score of IIEF-5. The time in range (TIR), glucose coefficient of variation (CV), standard deviation (SD) and the mean amplitude of glycemic excursions (MAGE) weregenerated by FGM. Correlation between glycemic variability and ED was analyzed with Pearson correlation analysis, multiple linear regression and logistic regression. Results (1) With the development of ED, patients′ age, course of disease, glycated hemoglobin (HbA1c), mean glucose (MBG), CV, SD and MAGE showed an increasing trend, and the difference between the groups was statistically significant (F=5.683 to 108.791, All P<0.05); meanwhile, TIR showed a significant downward trend, and the difference between the groups was statistically significant (F=563.078, P<0.05). (2)Pearson correlation analysis showed that TIR was significantly positively correlated with IIEF-5 (r=0.881, P<0.05). At the same time, CV, SD and MAGE were negatively correlated with IIEF-5 (r=-0.153, -0.370, -0.288, all P<0.05). (3)Multiple linear regression analysis showed that TIR was a relevant factor of ED severity (β′=0.736, P<0.05) and had the greatest impact among the factors included in the analysis. Multivariate logistic regression analysis showed significant correlation between TIR and ED after various confounding factors corrected (all P<0.05). Conclusions Glycemic variability is related to ED in T2DM, and low TIR is independently and positively associated with ED. All these indexes may be important risk factors for diabetic patients with ED. [ABSTRACT FROM AUTHOR]

Objective To investigate the status of blood glucose control in children with type 1 diabetes mellitus (T1DM) in Henan Province, and the safety of flash glucose monitoring system（FGMS）in children with T1DM. Methods Between August 2017 and February 2018, 116 children with TIDM from the Department of Endocrinology in 15 hospitals were selected, including the First Affiliated Hospital of Henan University of Science and Technology and other tertiary hospitals in Henan province. All patients were younger than 14 years old and treated with either continuous subcutaneous insulin infusion (CSII) or multiple daily insulin injection (MDI). All patients wore FGMS for 2 weeks after the selection. Various glucose monitoring indicators and adverse events were recorded during the application of FGMS, and the effects of diabetes duration, insulin injection model and other factors on blood glucose control were also analyzed. The t test, analysis of variance and rank sum test were used to compare the differences between groups, the correlation was analyzed by partial correlation. Results The baseline glycated hemoglobin A1c (HbA1c) was (8.3±1.7)%. During the application of FCMS, the estimated HbA1c was (7.8±1.3)%, the mean blood glucose was (9.8±2.1) mmol/L, and the percentage of time of blood glucose reaching the target (3.9-7.8 mmol/L) was (37±14)%. The percentage of time of hypoglycemia (<3.9 mmol/L) was 4% (3, 8)%. The standard deviation of blood glucose (SDBG) was (4.1±1.2) mmol/L and the mean amplitude of glycemic excursion (MAGE) was (8.4±2.5) mmol/L. The SDBG and MAGE in CSII group were decreased compared with MDI group (t=2.65, 2.51, both P<0.05). The partial correlation analysis showed that the SDBG as well as the MAGE was positively correlated with the baseline HbA1c (r=0.401, 0.357, both P<0.05). No adverse events related to FGMS was recorded during the study period. The drop rate of FGMS sensors was decreased significantly in winter. Conclusion T1DM patients under 14 years old in Henan province have a high HbA1c level, a high risk of hypoglycemia and a large fluctuation of blood glucose. The application of FGMS in children is safe. It may be helpful to reduce the drop of the sensor by avoiding the exposure of FGMS sensors. [ABSTRACT FROM AUTHOR]

Objectives: Patients with classical type 1 diabetes mellitus (T1DM) require lifelong dependence on exogenous insulin therapy due to pancreatic beta-cell destruction and absolute insulin deficiency. T1DM accounts for about 90% of children with diabetes in China, with a rapid increase in incidence and a younger-age trend. Epidemiological studies have shown that the overall glycated haemoglobin (HbA1c) and compliance rate are low in Chinese children with T1DM. Optimal glucose control is the key for diabetes treatment, and maintaining blood glucose within the target range can prevent or delay chronic vascular complications in patients with T1DM. Therefore, this study aims to investigate the glycemic control of children with T1DM from Hunan and Henan Province with flash glucose monitoring system (FGMS), and to explore factors associated with glycemic variability., Methods: A total of 215 children with T1DM under 14 years old were enrolled continuously in 16 hospitals from August 2017 to August 2020. All subjects wore a FGMS device to collect glucose data. Correlation of HbA1c, duration of diabetes, or glucose scan rates with glycemic variability was analyzed. Glucose variability was compared according to the duration of diabetes, HbA1c, glucose scan rates and insulin schema., Results: HbA1c and duration of diabetes were positively correlated with mean blood glucose, standard deviation of glucose, mean amplitude of glucose excursions (MAGE), and coefficient of variation (CV) of glucose (all P <0.01). The glucose scan rates during FGMS wearing was significantly positively correlated with time in range (TIR) ( P =0.001) and negatively correlated with MAGE and mean duration of hypoglycemia (all P <0.01). Children with duration ≤1 year had lower time below range (TBR) and MAGE when compared with those with duration >1 year (all P <0.05). TIR and TBR in patients with HbA1c ≤7.5% were higher (TIR: 65% vs 45%, TBR: 5% vs 4%, P <0.05), MAGE was lower (7.0 mmol/L vs 9.4 mmol/L, P <0.001) than those in HbA1c >7.5% group. Compared to the multiple daily insulin injections group, TIR was higher (60% vs 52%, P =0.006), MAGE was lower ( P =0.006) in the continuous subcutaneous insulin infusion group. HbA1c was lower in the high scan rates (≥14 times/d) group (7.4% vs 8.0%, P =0.046), TIR was significantly higher (58% vs 47%, P <0.001), and MAGE was lower ( P <0.001) than those in the low scan rate (<14 times/d) group., Conclusions: The overall glycemic control of T1DM patients under 14 years old in Hunan and Henan Province is under a high risk of hypoglycemia and great glycemic variability. Shorter duration of diabetes, targeted HbA1c, higher glucose scan rates, and CSII are associated with less glycemic variability.