Glycemic Variability In Patients Research Articles

Glycemic variability in patients with gastrointestinal cancer: An integrative review.

Glycemic variability is associated with risks for adverse events in patients with cancer. Several studies have evaluated the presence and impact of hyperglycemia and/or hypoglycemia in patients with cancer; however, few studies have evaluated glycemic variability. The purpose of this integrative review of studies in patients with gastrointestinal cancers was to investigate the presence and methods of reporting glycemic variability during and following treatments. A comprehensive review of the literature was conducted. PubMed, CINAHL, EMBASE, and Cochrane databases were searched for publications between 1/1/1969 and 7/24/2019. Studies of patients with gastrointestinal cancer following surgery, during treatment, and <5 years following treatment were included and evaluated by cancer type and method of glucose and glycemic variability measurement. Among 1526 patients with gastrointestinal cancer across 19 studies, gastric and pancreatic cancers were most prevalent. Timing of glucose testing and methods of analyzing glycemic variability varied. Most analyses used the standard deviation or interquartile range. Glycemic variability was more prevalent among patients with Type 2 Diabetes and among those with pancreatic cancer. In some patients glycemic variability remained notable>one year following surgery despite improvements in glycemic control. Patients with gastrointestinal cancer experience glycemic variability during and up to one year following treatment. There was heterogeneity in methods related to timing of testing and reporting glycemic variability among the 19 studies in this review. Future investigations need to identify the presence and define the methods of measuring glycemic variability in patients with gastrointestinal cancer.

Glycemic variability in patients with diabetes mellitus and different stages of diabetic nephropathy

The relevance of the study of glycemic variability in patients with diabetes mellitus and diabetic nephropathy is due to disability of the able-bodied population and high mortality against the background of the almost irreversible progression of diabetic nephropathy. The article highlights modern ideas about the influence of various factors on the occurrence of diabetic nephropathy and its course. The article is devoted to a review of current recommendations on diabetes mellitus and diabetic nephropathy; the etiopathogenesis of diabetic nephropathy was described in detail. The role of the kidneys in glucose homeostasis, renal gluconeogenesis, and glucose reabsorption by the kidneys in healthy and in pathology is described. Detailed expositions of glycemic variability parameters, their changes in patients with diabetes mellitus depending on the stage of diabetic kidney damage are presented. The role of the kidneys in maintaining energy homeostasis, impaired glucose homeostasis in conditions of chronic kidney disease is described. We analyzed different options for insulin therapy, their advantages, and disadvantages in patients with diabetes mellitus with diabetic nephropathy. The presented material is extremely relevant for the development and implementation in the clinical practice of glycemic control methods to optimize treatment tactics, prevent the formation of microvascular complications, and early disability of patients with diabetes mellitus.

A study of glycemic variability in patients with type 2 diabetes mellitus with obstructive sleep apnea syndrome using a continuous glucose monitoring system

BackgroundObstructive 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.MethodsAfter 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.ResultsAll 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).ConclusionsOSAS has a significant impact on the glycemic variability irrespective of glycemic status. AHI has moderate positive correlation with the glycemic variability.

1020-P: Effects of Insulin Degludec and Insulin Glargine U300 on Interday Glycemic Variability in Individuals with Type 1 Diabetes: A Multicenter, Randomized, Crossover Study

Background: Insulin degludec (IDeg) and insulin glargine U300 (IGla-300) are recently launched ultra-long-acting insulin formulations. It remains unclear whether or how their glucose-stabilizing effects differ. We now compared the effects of these insulins on parameters on glycemic variability in patients with type 1 diabetes (T1D). Methods: This multicenter, randomized, crossover, non-inferiority trial recruited 46 patients with T1D treated with basal-bolus insulin therapy at 14 facilities in Japan. Patients were randomly assigned 1:1 to IGla-300 (first period)/IDeg (second period) or IDeg (first period)/IGla-300 (second period) groups, in which subjects were treated with the corresponding basal insulin for 4-week periods. The last week of each treatment period constituted the data collection phase, during which the participants determined the plasma glucose level seven times a day using a SMBG devise and were also equipped with a CGM devise. The primary end point was comparison of day-to-day glycemic variability evaluated by the standard deviation (SD) of FPG during the last week of each treatment period. Results: The SD for FPG in the IDeg treatment period was not inferior to that in the IGla-300 treatment period (95% CI, -16.1-3.0 mg/dL). The levels (95% CI, -18.4-5.7 mg/dL) and the coefficient of variance (95% CI, -4.6-4.3 %) of FPG did not significantly differ between the two periods. Parameters obtained with CGM, including time-in-range, duration of nocturnal hypoglycemia, mean amplitude of glycemic excursion, and mean of the daily differences, as well as the doses of basal and bolus insulins also did not significantly differ between the two treatment periods. Conclusion: IDeg was not inferior to IGla-300 in terms of inter-day glycemic variability in the treatment of T1D, and the treatments with these two insulins yielded similar outcomes for various parameters related to blood glucose control. Disclosure H. Miura: None. K. Sakaguchi: Other Relationship; Self; AstraZeneca, Novartis AG, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi, Sanwa Kagaku Kenkyusho, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited. A. Kaneko: None. J. Ito: None. Y. Morita: None. T. Yamada: None. N. Otowa-Suematsu: None. A. So: None. T. Nakamura: None. H. Komada: None. Y. Okada: None. Y. Hirota: Other Relationship; Self; Sanofi. Y. Tamori: None. W. Ogawa: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharma GmbH &amp; Co.KG, Eli Lilly Japan K.K., Kowa Company, Ltd., Nippon Boehringer Ingelheim Co. Ltd., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd. Other Relationship; Self; Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited.

SUN-696 Effect of Night Shifts on Glycemic Variability in Patients with Type 2 Diabetes

The analysis of indicators of carbohydrate metabolism and variability of glycemia in patients with type 2 diabetes mellitus working in night shifts was carried out. As model patients with impaired circadian rhythm, the study included 34 patients, railway transport drivers, with shift mode and the presence of night shifts, with work experience of more than 5 years, the duration of type 2 diabetes mellitus (DM2) from 1 to 7 years, who are on oral therapy with hypoglycemic drugs. Simulation of different working conditions (day-night) was carried out in the simulator “driver’s cabin”. All patients underwent a study of the main indicators of carbohydrate metabolism (fasting glycemia, postprandial glycemia, glycated hemoglobin (HbA1c)), as well as continuous daily glucose monitoring (CGMS) using Medtronic MiniMed iPro2 system (from 3 to 7 days).Target glycemic levels were not achieved: fasting glycemia was 6.98±1.41 mmol/l; postprandial glycemia was 9.57±1.65 mmol/l; HbA1c was 7.23±1.62%.The analysis of CGMS revealed high variability of glycemic index MAGE-4.88±0.59 mmol/l. also calculated indicators SD - 1.52±0.63 mmol/l; Conga - 3.17±0.54 mmol/l; MODD - 2.27±0.12 mmol/l.The period of hyperglycemia (glucose value above 7.8 mmol/l) according to the results of CGMS was 43.5% (min 19-max 56). The duration of hypoglycemic States in patients without night shifts was 2.4% within 24 hours (min 0-max 6.6).Correlation analysis between carbohydrate metabolism and glucose variability revealed a significant strong relationship between the level of postprandial glycemia and modd (r=0.87, P=0.001) and MAGE (r=0.82, P=0.01), and also established a mean significant correlation between the level of postprandial glucose and Conga (r=0.52, P=0.01) and SD (r=0.61, P=0.05).Fasting glycemia and congas were moderately correlated (r=0.4, P=0.01).Weak reliable correlation was found only between HbA1c level and Conga variability index (r=0.27, P=0.04). Conclusions: the results of the study indicate the lack of adequate glycemic control in persons working night shifts, high variability of glycemia, which is an independent risk factor for cardiovascular disease in patients with DM2. The associative relationship of fasting glycemia and postprandial with the indicator of variability Conga shows that glucose fluctuations during the day in patients are constant.

The сardiometabolic assessment of the glycemic variability in patients with diabetes mellitus: the role of the glucocardiomonitoring

Aim To study quantitatively the two-way relationship between parameters of glycemic variability and development of cardiovascular events in patients with type 2 diabetes mellitus (DM) on chronic sulfonylurea (SM) therapy by synchronous, professional glucose and cardiac monitoring.Material and methods The study included 421 patients with type 2 DM on SM therapy. A 5-day synchronous glucose and cardiac monitoring was performed for these patients in a retrospective mode using an iPro2 (Medtronic, USA) continuous glycemia monitoring (CGM) system and Holter monitoring. Glycemic endpoints (CGM-parameters of glycemia variability and integral indexes) and cardiological endpoints (ventricular rhythm disorders (VRD), ST segment depression (dST), and corrected QT interval (QTc)) were evaluated.Results Clear correlations were found between the ST segment depression and the increase in TIR-HYPO index and the length of QTc. The strongest correlation was observed for VRD and the increase in TIR-HYPO. Moderate correlations were observed between VRD and the decrease in TIR-NORMO and between increased variabilities of glycemia (increases in SD and number of glycemia excursions &gt;4 mmol/l/h) and integral indexes (mean CGM-level of glycemia and HbA1c). Elongation of the QTc interval was associated with increased TIR-HYPO, decrease in maximum glycemia, and development of dST.Conclusion The glucose and cardiac monitoring confirmed the close interrelation between the quality of glycemic control and cardiovascular disorders and should be recommended for a wider use in real-life clinical practice for determining the cardiometabolic status of patients and personalization of hypoglycemic therapy.

Abstract #805111: Effects of Hydroxychloroquine 400 MG or Teneligliptin 20 MG on Glycemic Variability In Patients with Type 2 Diabetes Inadequately Controlledwith Insulin Plus Oral Therapy: A 12-Week, Randomised Pilot Study

Abstract #805111: Effects of Hydroxychloroquine 400 MG or Teneligliptin 20 MG on Glycemic Variability In Patients with Type 2 Diabetes Inadequately Controlledwith Insulin Plus Oral Therapy: A 12-Week, Randomised Pilot Study

Discussion on: Effective reduction in stress induced postoperative hyperglycemia in hyperglycemia in bariatric surgery by better carb loading

DR. BENJAMIN VEENSTRA (Chicago, Illinois): As a fellow bariatric surgeon, I can appreciate the usefulness of these enhanced recovery programs, especially this foray into the preoperative carbohydrate loading. Your paper examines this preop carb loading, specifically the two-drink compositions, the simple versus the more complex or GED, and finds that there is decreased post-op glycemic variability in patients with more complex drink. I have a few questions. Effective reduction in stress induced postoperative hyperglycemia in bariatric surgery by better carb loadingThe American Journal of SurgeryVol. 219Issue 3PreviewPreoperative carbohydrate loading is a recommended component of enhanced recovery protocols (ERP’s), however the impact on postoperative stress-induced insulin resistance remains poorly studied in both diabetics and non-diabetics. Full-Text PDF

Higher glycemic variability within the first day of ICU admission is associated with increased 30-day mortality in ICU patients with sepsis

BackgroundHigh glycemic variability (GV) is common in critically ill patients; however, the prevalence and mortality association with early GV in patients with sepsis remains unclear.MethodsThis retrospective cohort study was conducted in a medical intensive care unit (ICU) in central Taiwan. Patients in the ICU with sepsis between January 2014 and December 2015 were included for analysis. All of these patients received protocol-based management, including blood sugar monitoring every 2 h for the first 24 h of ICU admission. Mean amplitude of glycemic excursions (MAGE) and coefficient of variation (CoV) were used to assess GV.ResultsA total of 452 patients (mean age 71.4 ± 14.7 years; 76.7% men) were enrolled for analysis. They were divided into high GV (43.4%, 196/452) and low GV (56.6%, 256/512) groups using MAGE 65 mg/dL as the cut-off point. Patients with high GV tended to have higher HbA1c (6.7 ± 1.8% vs. 5.9 ± 0.9%, p < 0.01) and were more likely to have diabetes mellitus (DM) (50.0% vs. 23.4%, p < 0.01) compared with those in the low GV group. Kaplan–Meier analysis showed that a high GV was associated with increased 30-day mortality (log-rank test, p = 0.018). The association remained strong in the non-DM (log-rank test, p = 0.035), but not in the DM (log-rank test, p = 0.254) group. Multivariate Cox proportional hazard regression analysis identified that high APACHE II score (adjusted hazard ratio (aHR) 1.045, 95% confidence interval (CI) 1.013–1.078), high serum lactate level at 0 h (aHR 1.009, 95% CI 1.003–1.014), having chronic airway disease (aHR 0.478, 95% CI 0.302–0.756), high mean day 1 glucose (aHR 1.008, 95% CI 1.000–1.016), and high MAGE (aHR 1.607, 95% CI 1.008–2.563) were independently associated with increased 30-day mortality. The association with 30-day mortality remained consistent when using CoV to assess GV.ConclusionsWe found that approximately 40% of the septic patients had a high early GV, defined as MAGE > 65 mg/dL. Higher GV within 24 h of ICU admission was independently associated with increased 30-day mortality. These findings highlight the need to monitor GV in septic patients early during an ICU admission.

A case study of eight type 2 diabetic stage 4 chronic kidney disease patients showing lower glycemic variability with faster-acting insulin aspart as compared to insulin aspart

Background. Peaks and nadirs of blood glucose level varying daily in a person is referred to as glycemic variability (GV). GV associated with diabetics has been recently linked to cardiovascular disorders (CVD) or even chronic kidney disease (CKD) progression. Faster-acting insulin aspart is the latest ultra-rapid acting bolus insulin which has shown much lesser intra- and inter-patient variability as compared to conventional bolus insulin. Material and methods. However, inadequate data exist regarding GV in patients with advanced stages of CKD. Hence, with this objective, the present case study was undertaken with eight patients divided into two equal groups, wherein faster-acting insulin aspart and insulin aspart were used as the boluses, respectively. Continuous glucose monitoring data of the patients were taken for the initial four days to calculate mean amplitude of glycemic excursion (MAGE) of the total four days for each individual (mmol/L) to see the difference in GV. A value of > 3.607 mmol/L (65 mg/dL) was considered to be statistically significant. Results. In this case study of eight stage 4 CKD type 2 diabetic patients, statistically significant lower GV was observed in the faster-acting insulin aspart arm when compared with the insulin aspart arm. The pvalue was 0.0004 in unpaired t-test and < 0.05 for U in Mann-Whitney U test after ruling out the baseline confounding factors. Conclusions. This study confirms the stable pharmacokinetic and dynamic properties of faster-acting insulin aspart and subsequent studies with larger numer of patients are required for a conclusive outcome.

Relationship between daily and visit-to-visit glycemic variability in patients with type 2 diabetes.

The aim of the study was to explore the relationship between daily glycemic variability (GV) and visit-to-visit glycemic variability (VVV) in patients with type 2 diabetes (T2DM). A total of 156 outpatients with T2DM who had undergone continuous glucose monitoring (CGM) for 5 days were included in this study. Indices of GV, i.e., standard deviation and coefficient of variation (CV) of glucose, mean amplitude of glycemic excursion (MAGE) and mean of the daily differences (MODD) were calculated from the CGM data. VVV was calculated as CV of HbA1c or glycated albumin (GA) from HbA1c or GA measured for 3 years. Relationships among clinical parameters, GV and VVV were evaluated. Age was positively, and BMI and C-peptide index were inversely correlated with GV such as CV glucose and MAGE, while BMI was positively correlated with VVV. Mean glucose rather than GV was correlated with VVV. In contrast, time in range (TIR, 70-180 mg/dL) was correlated with both mean HbA1c or GA and VVV. In conclusion, GV and VVV were differently correlated with clinical parameters and were hardly correlated with each other. TIR was correlated with both mean HbA1c and VVV, suggesting that efforts to achieve optimal TIR are practical strategies to reduce VVV in patients with T2DM.

Analysis of the effect of liraglutide on glycemic variability in patients with type 2 diabetes.

The efficacy of liraglutide in the treatment of glycemic variability in type 2 diabetic patients remains to be fully elucidated. Some studies evaluated the efficacy and safety of liraglutide in glycemic variability, and this meta-analysis was performed to evaluate the accuracy of the results of existing studies on the efficacy of liraglutide. We conducted a comprehensive search for all relevant studies published in PubMed, EMBASE, Cochrane Library, and China Academic Journal Full-Text Database from the beginning of 2011 to October 31, 2019. The mean ± SD and 95% confidence interval were used for evaluation, and subgroup and sensitivity analysis were carried out. Publication bias was estimated by funnel plots and Egger's tests. A total of 16 studies were included in the meta-analysis involving 492 participants. MAGE (mean amplitude of glycemic excursion), LAGE (largest amplitude of glycemic excursions), SD (standard deviation of blood glucose), and MODD (mean of daily differences) were collected to reflect the variability of blood glucose. The glycemic variability indexes of patients before and after treatment with liraglutide were compared. Patients with treatment had lower glycemic variability compared with patients receiving treatment of liraglutide. Compared with the patients before the treatment, the patients after the treatment had a smaller glycemic variability (MAGE: I2 = 92%, p < 0.01, Z = 11.91, p < 0.01, MD = -2.78, 95%CI: -3.24 - -2.32; LAGE: I2 = 76%, p = 0.08, Z = 9.94, p < 0.01, MD = -2.20, 95%CI: -2.59 - -1.81; MODD: I2 = 74%, p = 0.002, Z = 14.03, p < 0.01, MD = -0.90, 95%CI: -1.02 - -0.77; SD: I2 = 93%, p < 0.01, Z = 3.62, p < 0.01, SMD = -1.77, 95%CI: -2.73 - -0.81). Sensitivity analysis showed that our results were reliable and no evidence of significant publication bias was detected. The results of this study suggest that patients with type 2 diabetes treated with liraglutide are associated with lower glycemic variability.

Вклад вариабельности гликемии и страха гипогликемических состояний в контроль сахарного диабета 1 типа

Background: currently, the level of HbA1c does not necessarily allow for the understanding of glycemic control. Considering this, glycemic variability (GV) is extensively studied in recent years. Psychological component has a significant impact on GV as the fear of hypoglycemia (FOH) and its consequences can reduce the motivation for achieving target glycemia and HbA1c as well as adherence to treatment.Aim: to assess GV in patients with type 1 diabetes with the elevated and target levels of HbA1c and to analyze the effect of FOH on diabetes control.Patients and Methods: 78 patients with type 1 diabetes who received insulin therapy (multiple daily injections or continuous subcutaneous insulin infusion) were enrolled. All patients were divided into two groups based on HbA1c levels, i.e., group 1 (patients who achieved individual HbA1c target level, n=13) and group 2 (patients who did not achieve individual HbA1c target level, n=65). HbA1c levels and GV were measured in all patients using iPRO™2 Professional CGM system (Medtronic, Ireland). In addition, FOH was evaluated using the Hypoglycemia Fear Survey II (HFS-II).Results: in group 1, time in-range was significantly higher (p=0.013) while daily GV was significantly lower (p=0.023) than in group 2. HbA1c levels, the rate of severe hypoglycemia, and diabetes duration were lower among the patients with lower levels of FOH.Conclusion: studying GV can provide better understanding of diabetes control in patients with any HbA1c level. Optimized treatment strategy using HFS-II can attenuate the effects of frequent hypoglycemia on GV and HbA1c levels and improve treatment efficacy.KEYWORDS: glycemic variability, glycated hemoglobin, diabetes, continuous glucose monitoring, hypoglycemia.FOR CITATION: Volkova A.R., Chernaya M.E., Mozgunova V.S. et al. Impact of glycemic variability and the fear of hypoglycemia on type 1 diabetes control. Russian Medical Inquiry. 2020;4(6):324–328. DOI: 10.32364/2587-6821-2020-4-6-324-328.

Skipping breakfast is associated with glycemic variability in patients with type 2 diabetes.

The association between skipping breakfast and glycemic parameters, including glycemic variability, in patients with type 2 diabetes is not well understood. Therefore, the aim of this study was to investigate the effects of skipping breakfast on glycemic parameters, including glycemic variability, in patients with type 2 diabetes. In this cross-sectional study, we assessed lifestyle factors, including skipping breakfast, using a questionnaire method. We calculated the average, SD, and coefficient of variation (CV) of hemoglobin (Hb)A1c levels. The CV of HbA1c was defined as follows: CV=(SD / average HbA1c)×100 (%). Among 317 patients, 22 (6.9%) skipped breakfast. Patients who did not eat breakfast were younger (58 [14.5] versus 67.4 [10.1] y, P < 0.001) than those who did. The proportion of current smokers among patients skipping breakfast was higher than the proportion of smokers among patients who did not (40.9 versus 11.5%, P < 0.001). Average (7.7 [1.3] versus 7.1 [0.8]%, P=0.003), SD (0.32 [0.17-0.85] versus 0.21 [0.14-0.35], P=0.024) and CV (0.04 [0.03-0.10] versus 0.03 [0.02-0.05], P=0.028) of HbA1c level were higher among patients who skipped breakfast than among those who did not. Multiple regression analysis revealed that skipping breakfast was associated with average HbA1c (β = 0.527, P=0.006) and CV of HbA1c (β=0.026, P=0.001) after adjusting for age, sex, body mass index, duration of diabetes, exercise, smoking, amount of alcohol consumption, total energy intake, carbohydrate intake, and medications for diabetes. Skipping breakfast is independently associated with poor glycemic control, including glycemic variability, in patients with type 2 diabetes.

A Randomized Pilot Study of the Effect of Trelagliptin and Alogliptin on Glycemic Variability in Patients with Type 2 Diabetes.

IntroductionThis open-label, parallel-group, exploratory study examined the effects of two dipeptidyl peptidase 4 (DPP4) inhibitors on glycemic variability (GV) in patients with type 2 diabetes.MethodsRandomized patients with glycated hemoglobin A1c of at least 6.5% to less than 8.5% received trelagliptin 100 mg (n = 13) once weekly or alogliptin 25 mg (n = 14) once daily for 29 days. Continuous glucose monitoring was performed before the start of the treatment period (baseline) and from day 21 to 29, inclusive. The primary endpoint was change from baseline in the standard deviation (SD) of 24-h blood glucose values, measured daily for 7 days (day 22–28) of the treatment period. Secondary and additional efficacy endpoints included changes in glycemic parameters and the rate of DPP4 inhibition, respectively. Adverse events (AEs) were monitored to assess safety.ResultsMean change from baseline in the SD of 24-h blood glucose (95% confidence interval) at day 28 was − 7.35 (− 15.13, 0.44) for trelagliptin and − 11.63 (− 18.67, − 4.59) for alogliptin. In both treatment groups, glycemic parameters improved and the rate of DPP4 inhibition was maintained. Three patients reported AEs; no severe treatment-emergent AEs were reported in either group.ConclusionOnce-weekly trelagliptin and once-daily alogliptin improved glycemic control and reduced GV without inducing hypoglycemia.Trial RegistrationClinicalTrials.gov (NCT02771093) and JAPIC (JapicCTI-163250).FundingTakeda Pharmaceutical Company, Ltd.

Effect of DPP-IV Inhibitors on Glycemic Variability in Patients with T2DM: A Systematic Review and Meta-Analysis

Glycemic variability (GV) has been an emerging target for preventing complications related to type 2 diabetes. For reducing GV, DPP-IV inhibitors have shown effectiveness compared to other oral anti-hyperglycemic drugs (OADs), but systematic evaluation has yet to be existed. A systematic review and meta-analysis of randomized controlled trials (RCTs) were performed to evaluate the effect of DPP-IV inhibitors compared with other OADs, on GV as measured by mean amplitude of glycemic excursions (MAGE). Searches were conducted using Pubmed, EMBASE, and the Cochrane Library, from which eligible studies were retrieved; seven RCTs were included in the analysis. DPP-IV inhibitors were found to significantly reduce MAGE compared to other OADs (mean difference = −14.61; 95% CI = −19.00 to −10.21; p < 0.0001) without significant heterogeneity among sulfonylureas (mean difference = −14.93; 95% CI = −21.60 to −8.26; p < 0.0001). Initial combination therapy with DPP-IV inhibitors more effectively reduced MAGE than stepwise add-on therapies (p = 0.006), although no differences in MAGE were found based on HbA1c values. These findings indicate that DPP-IV inhibitors are promising alternatives for reducing GV in type 2 diabetes patients. However, further studies utilizing larger numbers of patients and longer-term follow-ups are needed.

780-P: Effect of Partial Meal Replacement on Glycemic Variability in Patients with Type 2 Diabetes: A Randomized Clinical Trial

Background: Glycemic variability (GV) has been demonstrated to contribute to the development of diabetic complications. The current study sought to investigate the effect of partial meal replacement (PMR) on GV in type 2 diabetes. Methods: 123 individuals with newly diagnosed and untreated type 2 diabetes were randomized to receive either lifestyle intervention (LI) together with breakfast replacement with a liquid formula (LI+PMR, n=62) or LI alone (n=61) for 4 weeks. All participants underwent continuous glucose monitoring (CGM) for 72 hours both before and after intervention. GV metrics obtained from CGM included the incremental area under the curve of postprandial blood glucose (AUCpp), standard deviation of blood glucose (SDBG), glucose coefficient of variation (CV), and mean amplitude of glycaemic excursions (MAGE). Results: At the end of the study, patients in LI+PMR exhibited more pronounced improvements in systolic blood pressure (P=0.046) and time in range (P=0.033) than those in LI. More importantly, LI+PMR led to significantly greater improvements in all GV metrics including SDBG (P=0.005), CV (P=0.002), MAGE (P=0.016) and AUCpp (P&amp;lt;0.001) than did LI. In addition, multiple regression analysis revealed that LI+PMR (vs. LI) was independently associated with the improvements in GV after adjusting for covariates (all P&amp;lt;0.05). Conclusion: Our study showed that LI+PMR resulted in significantly greater improvements in GV than did LI, suggesting that LI+PMR could be an effective treatment to alleviate glucose excursions. Disclosure J. Zhou: None. J. Lu: None. X. Ma: None. J. Peng: None. L. Ying: None. W. Lu: None. W. Zhu: None. Y. Bao: None. Funding Shanghai Municipal Education Commission; Gaofeng Clinical Medicine Grant (20161430)

1163-P: Comparison of Glycemic Variability between Gemigliptin and Dapagliflozin in Patients with Type 2 Diabetes Mellitus: A Randomized, Active-Controlled, Multicenter, 12-Week Clinical Trial (STABLE II Study)

The aim of this study was to evaluate the effect of gemigliptin, a potent and selective dipeptidyl peptidase-4 inhibitor versus dapagliflozin, a sodium glucose co-transporter-2 inhibitor on glycemic variability in patients with type 2 diabetes mellitus (T2DM). This randomized, parallel group, blinded end-point, multicenter, 12-week clinical trial was conducted in 71 patients with T2DM inadequately controlled with metformin alone or drug naïve patients. Subjects were randomized to receive gemigliptin 50mg (N=35) or dapagliflozin 10mg (N=36) daily for 12 weeks. Glycemic variability parameters such as mean amplitude of glycemic excursions (MAGE), standard deviation (SD) and coefficient of variation (CV) were evaluated during 6-day continuous glucose monitoring at baseline and after 12 weeks of treatment. The primary efficacy endpoint was change in MAGE after 12 weeks compared to baseline. Measures of glycemic control, oxidative stress and inflammation, as well as safety profiles were assessed. Adjusted mean change ± standard error in MAGE after 12 weeks were -27.2 ± 4.4 mg/dL (P&amp;lt;0.001) in the gemigliptin group and -7.9 ± 4.9 mg/dL (P=0.160) in the dapagliflozin group. The between-group comparison showed that gemigliptin group had significantly larger reduction in MAGE compared to dapagliflozin group (-19.2 mg/dL, 95% CI -31.3 to -7.2, P=0.002). Other glycemic variability parameters including SD and CV also showed significantly larger reduction in the gemigliptin group. Glycemic control estimated by HbA1c, fasting glucose and safety profiles were comparable between the two groups. In conclusion, gemigliptin significantly improved glycemic variability than dapagliflozin with similar glycemic efficacy and safety profiles in patients with T2DM inadequately controlled with metformin alone or drug naïve patients. ClinicalTrials.gov Identifier: NCT03202563. Disclosure Y. Hwang: None. J. Kim: None. S. Kwak: None. S. Kim: None. J. Won: None. J. Bae: None. H. Kim: None. S. Suh: None. E. Lee: None. S. Lee: Employee; Self; LG Chem. Funding LG Chem, Ltd.

Glycemic Variability Is a Powerful Independent Predictive Factor of Midterm Major Adverse Cardiac Events in Patients With Diabetes With Acute Coronary Syndrome.

Acute glucose fluctuations are associated with hypoglycemia and are emerging risk factors for cardiovascular outcomes. However, the relationship between glycemic variability (GV) and the occurrence of midterm major cardiovascular events (MACE) in patients with diabetes remains unclear. This study investigated the prognostic value of GV in patients with diabetes and acute coronary syndrome (ACS). This study included consecutive patients with diabetes and ACS between January 2015 and November 2016. GV was assessed using SD during initial hospitalization. MACE, including new-onset myocardial infarction, acute heart failure, and cardiac death, were recorded. The predictive effects of GV on patient outcomes were analyzed with respect to baseline characteristics and cardiac status. A total of 327 patients with diabetes and ACS were enrolled. MACE occurred in 89 patients (27.2%) during a mean follow-up of 16.9 months. During follow-up, 24 patients (7.3%) died of cardiac causes, 35 (10.7%) had new-onset myocardial infarction, and 30 (9.2%) were hospitalized for acute heart failure. Multivariable logistic regression analysis showed that GV >2.70 mmol/L, a Synergy between PCI with Taxus and Cardiac Surgery (SYNTAX) score >34, and reduced left ventricular ejection fraction of <40% were independent predictors of MACE, with odds ratios (ORs) of 2.21 (95% CI 1.64-2.98; P < 0.001), 1.88 (1.26-2.82; P = 0.002), and 1.71 (1.14-2.54; P = 0.009), respectively, whereas a Global Registry of Acute Coronary Events (GRACE) risk score >140 was not (OR 1.07 [0.77-1.49]; P = 0.69). A GV cutoff value of >2.70 mmol/L was the strongest independent predictive factor for midterm MACE in patients with diabetes and ACS.

Breakfast replacement with a liquid formula improves glycaemic variability in patients with type 2 diabetes: a randomised clinical trial.

There is emerging evidence that glycaemic variability (GV) plays an important role in the development of diabetic complications. The current study aimed to compare the effects of lifestyle intervention (LI) with and without partial meal replacement (MR) on GV. A total of 123 patients with newly diagnosed and untreated type 2 diabetes (T2D) were randomised to receive either LI together with breakfast replacement with a liquid formula (LI+MR) (n 62) or LI alone (n 61) for 4 weeks and completed the study. Each participant was instructed to have three main meals per d and underwent 72-h continuous glucose monitoring (CGM) both before and after intervention. Measures of GV assessed by CGM included the incremental AUC of postprandial blood glucose (AUCpp), standard deviation of blood glucose (SDBG), glucose CV and mean amplitude of glycaemic excursions (MAGE). After a 4-week intervention, the improvements in systolic blood pressure (P=0·046) and time in range (P=0·033) were more pronounced in the LI+MR group than in the LI group. Furthermore, LI+MR caused significantly greater improvements in all GV metrics including SDBG (P=0·005), CV (P=0·002), MAGE (P=0·016) and AUCpp (P<0·001) than did LI. LI+MR (v. LI) was independently associated with improvements in GV after adjustment of covariates (all P<0·05). Our study showed that LI+MR led to significantly greater improvements in GV compared with LI, suggesting that LI+MR could be an effective treatment to alleviate glucose excursions.