Continuous Glucose Monitoring Captures Glycemic Variability After Roux-en-Y Gastric Bypass in Patients with and Without Type 2 Diabetes Mellitus: A Prospective Cohort Study.

PURPOSE: To evaluate glycemic variability (GV) using continuous glucose monitoring (CGM) in individuals with and without type 2 diabetes mellitus (T2DM) undergoing Roux-en-Y gastric bypass (RYGB). METHODS: This prospective cohort study compared the CGM data of fourteen patients with T2DM (n = 7) and without T2DM (n = 7) undergoing RYGB. After 6 months, these patients were compared to a non-operative control group (n = 7) matched by BMI, sex, and age to the T2DM group. RESULTS: Fourteen patients underwent RYGB, with a mean BMI of 46.9 ± 5.3 kg/m2 and an average age of 47.9 ± 8.9 years; 85% were female. After 6 months post-surgery, the total weight loss (TWL) was 27.1 ± 6.3%, with no significant differences between the groups. Patients without diabetes had lower mean interstitial glucose levels (81 vs. 94 and 98 mg/dl, p < 0.01) and lower glucose management indicator (GMI) (5.2 vs. 5.6 and 5.65%, p = 0.01) compared to the control and T2DM groups, respectively. The coefficient of variation (CV) significantly increased only in patients with diabetes (17% vs. 26.7%, p < 0.01). Both groups with (0% vs. 2%, p = 0.03) and without (3% vs. 22%, p = 0.03) T2DM experienced an increased time below range with low glucose (54-69 mg/dL). However, patients without T2DM had significantly less time in rage (70-180 mg/dL) (97% vs. 78%, p = 0.04). CONCLUSION: Significant differences in CGM metrics among RYGB patients suggest an increase in glycemic variability after surgery, with a longer duration of hypoglycemia, especially in patients without T2DM.

Association between inpatient glycemic variability and COVID-19 mortality: a prospective study.

BACKGROUND: This study aimed to determine the association between glycemic variability (GV) and mortality in hospitalized patients with coronavirus disease 2019 (COVID-19). METHODS: We prospectively analyzed data from inpatients (> 18 years old) with RT-PCR confirmed COVID-19 admitted between March 2020 and July 2021. All patients were hospitalized for more than 48 h and had at least six point-of-care capillary glucose tests obtained three times daily in the pre-prandial period during hospitalization. GV was measured using the glucose standard deviation (SD) and coefficient of variation (CV). ROC curve was adjusted to determine the SD and CV cutoff values associated with mortality (44.7 mg/dL and 27.5%, respectively); values above these were considered indicative of high GV. Logistic regression models were fitted to explore the association between GV and mortality in patients with and without diabetes. RESULTS: A total of 628 patients were stratified into SD < 44.7 mg/dL (n = 357) versus ≥ 44.7 mg/dL (n = 271) and CV < 27.5% (n = 318) versus ≥ 27.5% (n = 310) groups. After controlling for age, sex, presence of diabetes mellitus (DM) and cardiovascular disease, we found a significant association between high GV and mortality (odds ratio 2.99 [1.88-4.77] for SD and 2.43 [1.54-3.85] for CV; p values < 0.001). The mortality rate was higher with SD ≥ 44.7 mg/dL and CV ≥ 27.5% compared to that with SD < 44.7 mg/dL and CV < 27.5%, regardless of DM (p < 0.001 for all). CONCLUSION: High glycemic variability was independently associated with mortality in patients with and without DM, who were hospitalized with COVID-19.

Glycemic Variability in Patients with Type 2 Diabetes Mellitus (T2DM): The Role of Melatonin in a Crossover, Double-Blind, Placebo-Controlled, Randomized Study.

BACKGROUND: Glycemic variability in patients with type 2 diabetes mellitus (T2DM) may be associated with chronic complications of the disease. Melatonin is a hormone that plays a crucial role in biological rhythms. Previous studies have indicated that individuals with T2DM often exhibit reduced melatonin production. In this study, our objective was to investigate whether nighttime melatonin supplementation could mitigate glycemic variability in these patients. METHODS: Crossover, double-blind, placebo-controlled, randomized study. A total of 30 patients were enrolled in this study. The study included 15 participants who followed the intervention sequence of placebo (7 days)-washout (7 days)-melatonin (3 mg) (7 days), and another 15 participants who followed the sequence of melatonin (3 mg) (7 days)-washout (7 days)-placebo (7 days). During the final three days of the first and third weeks, the participants measured their pre- and postprandial capillary blood glucose levels. This study was reported according to the CONSORT 2010 statement: extension to randomized crossover trials. RESULTS: There was a significant absolute difference in the breakfast blood glucose levels (p = 0.016) on Day 7. The use of melatonin determined a greater positive variation between pre- and postprandial glycemia than the placebo. The difference in glycemic amplitude between post-dinner Day 6 and pre-breakfast Day 7 was also significantly higher in the melatonin group (p = 0.032). CONCLUSIONS: Melatonin increased glycemic variability in individuals with type 2 diabetes mellitus (T2DM). These results can be attributed to the residual daytime effects of melatonin, prospective proximal effects, and damage to the prospective distal effects of exogenous melatonin. Therefore, caution should be exercised when administering melatonin supplementation to patients with T2DM, taking into consideration factors such as dosage, duration of use and genetic considerations.

Effects of Melatonin on Glycemic Variability in Type 2 Diabetes Mellitus: Protocol for a Crossover, Double-Blind, Placebo-Controlled Trial.

BACKGROUND: Glycemic variability is recognized as a significant factor contributing to the development of micro- and macrovascular complications in individuals with type 2 diabetes mellitus (T2DM). Numerous studies have shown that melatonin, a hormone involved in regulating various biological rhythms, including those related to glucose regulation, such as hunger, satiety, sleep, and circadian hormone secretion (ie, cortisol, growth hormone, catecholamines, and insulin), is deficient in individuals with T2DM. This raises an important question: Could melatonin replacement potentially reduce glycemic variability in these patients? This warrants investigation as a novel approach to improving glycemic control and reducing the risk of complications associated with T2DM. OBJECTIVE: We aimed to investigate whether melatonin replacement in individuals with T2DM who supposedly have melatonin deficiency can positively impact the regulation of insulin secretion rhythms and improve insulin sensitivity, ultimately resulting in a reduction in glycemic variability. METHODS: This study will use a crossover, randomized, double-blind, placebo-controlled trial design. Patients with T2DM in group 1 will receive 3 mg of melatonin at 9:00 PM in the first week, undergo a washout period in the second week, and receive a placebo in the third week (melatonin-washout-placebo). Group 2 will be randomized to receive a placebo-washout-melatonin sequence (3 mg). Capillary blood glucose levels will be measured at 6 different times before and after meals during the last 3 days of the first and third weeks. The study aims to compare the mean differences in blood glucose levels and the coefficient of glycemic variability in patients receiving melatonin and placebo during the first and third weeks. After analyzing the initial results, the number of needed patients will be recalculated. If the recalculated number is higher than 30, new participants will be recruited. Thirty patients with T2DM will be randomized into the 2 groups: melatonin-washout-placebo or placebo-washout-melatonin. RESULTS: Participant recruitment took place between March 2023 to April 2023. In all, 30 participants were eligible and completed the study. We expect that patients will show different glycemic variability on the days they receive placebo or melatonin. Studies on melatonin and glycemic control have shown both positive and negative results. We hope that there will be a positive outcome regarding glycemic variability (ie, a reduction in glycemic variability), as melatonin has a well-described chronobiotic effect in the literature. CONCLUSIONS: This study aims to determine whether melatonin supplementation can effectively reduce glycemic variability in patients with T2DM. The crossover design is necessary due to the multiple variables involved in the circadian variations of glucose, including diet, physical activity, sleep parameters, and pharmacological treatments. The relatively low cost of melatonin and its potential role in reducing the severe complications associated with T2DM have motivated this research effort. Furthermore, the indiscriminate use of melatonin in current times makes conducting this study essential to evaluate the effect of this substance in patients with T2DM. TRIAL REGISTRATION: Brazilian Registry of Clinical Trials RBR-6wg54rb; https://ensaiosclinicos.gov.br/rg/RBR-6wg54rb. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47887.

Impact on Glycemic Variation Caused by a Change in the Dietary Intake Sequence.

This work presents an analysis of the effect on glycemic variation caused by modifying the macronutrient intake sequence in a person without a diagnosis of diabetes. In this work, three types of nutritional studies were developed: (1) glucose variation under conditions of daily intake (food mixture); (2) glucose variation under conditions of daily intake modifying the macronutrient intake sequence; (3) glucose variation after a modification in the diet and macronutrient intake sequence. The focus of this research is to obtain preliminary results on the effectiveness of a nutritional intervention based on the modification of the sequence of macronutrient intake in a healthy person during 14-day periods. The results obtained corroborate the positive effect on the glucose of consuming vegetables, fiber, or proteins before carbohydrates, decreasing the peaks in the postprandial glucose curves (vegetables: 113-117 mg/dL; proteins: 107-112 mg/dL; carbohydrates: 115-125 mg/dL) and reducing the average levels of blood glucose concentrations (vegetables: 87-95 mg/dL; proteins: 82-99 mg/dL; carbohydrates: 90-98 mg/dL). The present work demonstrates the preliminary potential of the sequence in the macronutrient intake for the generation of alternatives of prevention and solution of chronic degenerative diseases, improving the management of glucose in the organism and permeating in the reduction of weight and the state of health of the individuals.

Maintenance of plasma glucose variability after an acute session of aerobic exercise despite changes in insulin and glucagon-like peptide-1 levels in type 2 diabetes

ABSTRACT Objective: The present study aimed to evaluate glucose variability and hormonal responses during and after an aerobic exercise session performed after breakfast in type 2 diabetes patients treated with metformin. Materials and methods: In this quasi-experimental study individuals underwent clinical and laboratory evaluations and maximal exercise test. After two weeks an aerobic exercise session (30 minutes at 60%-70% of the peak heart rate) was performed. At rest, during and after the exercise session, glucose variability (mean amplitude glucose excursions, glucose coefficient of variation, and glucose standard deviation) and levels of plasma glucose, insulin, glucagon, and glucagon-like-peptide-1 were evaluated. Results: Thirteen patients were enrolled in the study. Plasma glucose increased at 15 minutes during the exercise session (244.6 ± 61.9 mg/dL), and decreased at 60 min after exercise (195.6 ± 50.0 mg/dL). Glucose variability did not show any difference before and after exercise. Insulin levels at 15 min [27.1 µU/mL (14.2-42.1)] and 30 min [26.3 µU/mL (14.6-37.4)] during the exercise were higher than those at fasting [11.2 µU/mL (6.7-14.9)] but decreased 60 minutes after exercise (90 minutes) [16.6 µU/mL (8.7-31.7)]. Glucagon levels did not show any difference. GLP-1 levels increased at 30 min [7.9 pmol/L (7.1-9.2)] during exercise and decreased 60 min after exercise (90 minutes) [7.7 pmol/L (6.8-8.5)]. Conclusion: Subjects with type 2 diabetes presented expected changes in insulin, glucagon and GLP-1 levels after breakfast and a single aerobic exercise session, not accompanied by glycemic variability changes.

Maintenance of plasma glucose variability after an acute session of aerobic exercise despite changes in insulin and glucagon-like peptide-1 levels in type 2 diabetes.

Objective: The present study aimed to evaluate glucose variability and hormonal responses during and after an aerobic exercise session performed after breakfast in type 2 diabetes patients treated with metformin. Methods: In this quasi-experimental study individuals underwent clinical and laboratory evaluations and maximal exercise test. After two weeks an aerobic exercise session (30 minutes at 60%-70% of the peak heart rate) was performed. At rest, during and after the exercise session, glucose variability (mean amplitude glucose excursions, glucose coefficient of variation, and glucose standard deviation) and levels of plasma glucose, insulin, glucagon, and glucagon-like-peptide-1 were evaluated. Results: Thirteen patients were enrolled in the study. Plasma glucose increased at 15 minutes during the exercise session (244.6 ± 61.9 mg/dL), and decreased at 60 min after exercise (195.6 ± 50.0 mg/dL). Glucose variability did not show any difference before and after exercise. Insulin levels at 15 min [27.1 µU/mL (14.2-42.1)] and 30 min [26.3 µU/mL (14.6-37.4)] during the exercise were higher than those at fasting [11.2 µU/mL (6.7-14.9)] but decreased 60 minutes after exercise (90 minutes) [16.6 µU/mL (8.7-31.7)]. Glucagon levels did not show any difference. GLP-1 levels increased at 30 min [7.9 pmol/L (7.1-9.2)] during exercise and decreased 60 min after exercise (90 minutes) [7.7 pmol/L (6.8-8.5)]. Conclusion: Subjects with type 2 diabetes presented expected changes in insulin, glucagon and GLP-1 levels after breakfast and a single aerobic exercise session, not accompanied by glycemic variability changes.

Resistant Starch Consumption Effects on Glycemic Control and Glycemic Variability in Patients with Type 2 Diabetes: A Randomized Crossover Study.

We previously observed beneficial effects of native banana starch (NBS) with a high resistant starch (RS) content on glycemic response in lean and obese participants. Here, we aimed to determine the effects of NBS and high-amylose maize starch (HMS) on glycemic control (GC) and glycemic variability (GV) in patients with type 2 diabetes (T2D) when treatments were matched for digestible starch content. In a randomized, crossover study, continuous glucose monitoring (CGM) was performed in 17 participants (aged 28-65 years, BMI ≥ 25 kg/m2, both genders) consuming HMS, NBS, or digestible maize starch (DMS) for 4 days. HMS and NBS induced an increase in 24 h mean blood glucose during days 2 to 4 (p < 0.05). CONGA, GRADE, and J-index values were higher in HMS compared with DMS only at day 4 (p < 0.05). Yet, NBS intake provoked a reduction in fasting glycemia changes from baseline compared with DMS (p = 0.0074). In conclusion, under the experimental conditions, RS from two sources did not improve GC or GV. Future longer studies are needed to determine whether these findings were affected by a different baseline microbiota or other environmental factors.

Daily glucose variation influenced by the use of corticosteroids in COVID-19 patients treated in Lima-Peru.

AIM: The pandemic has generated the need for COVID-19 patients to be treated as best as possible; however, the effect of these treatments on glycemic control has not yet been taken into account. This article aims to determine whether the daily variation of glucose is influenced by the use of corticosteroids in COVID-19 patients treated in Lima-Peru. METHODOLOGY: A prospective cohort study was undertook, in which glucose was measured four times a day in 53 patients hospitalized due to COVID-19. These values were associated with the use of corticosteroids and adjusted for other socio-educational variables, all by means of PA-GEE models. RESULTS: Nested multivariate analysis of daily glucose variation found that those using corticosteroids increased the daily average glucose as well as the first and last glucose measurements, this is, at 6am and 10pm, respectively (all p-values <0.026). An increase in glucose levels was also observed in those with diabetes (all p-values <0.001). In contrast, we found that there was a decrease in the last glucose measurement of the day in obese patients (p-value = 0.044). CONCLUSIONS: The patients who used corticosteroids for the treatment of COVID-19 increased the average glucose per day, especially in the first and last measurement.

Association of specialized enteral nutrition with glycemic control and clinical outcomes in critically ill patients: A meta-analysis of randomized controlled trials.

OBJECTIVE: To evaluate the association of glycemic-control formulae (GCF) with measurements of glycemic control and clinical outcomes compared to standard enteral formulae (SF) in critically ill patients. DATA SOURCES: MEDLINE, EMBASE, Scopus and the Cochrane Central Register of Controlled Trials were searched from inception up to January, 2021. STUDY SELECTION: RCTs that assessed the effects of GCF relative to SF in adult critically ill patients. DATA EXTRACTION: Measurements of glycemic control were the primary outcomes. Secondary outcomes included insulin requirements, mechanical ventilation (MV), length of intensive care unit (ICU) stay and mortality. Two authors independently extracted data and assessed risk of bias using the Cochrane's RoB 2 tool and the GRADE approach was used to assess the quality of evidence. DATA SYNTHESIS: Ten studies (12 reports, 685 patients) were included. The use of GCFs was associated with lower blood glucose (WMD, -16.06 mg/dL; 95% CI -23.48 to -8.63; I2 = 47%) and lower daily administered insulin (WMD, -7.20 IU; 95% CI -13.92 to -0.48; I2 = 53%). Glycemic variability, measured by the coefficient of variation, was also associated with the use of GCFs (WMD, -6.84%; 95% CI, -13.57 to -0.11; I2 = 95%). In contrast, analyses for length of ICU stay (WMD, -0.12, 95% CI -1.77 to 1.52; I2 = 0%), duration of MV (WMD, -0.34 days; 95% CI, -1.72 to 1.04; I2 = 0%) and mortality (RR, 1.13; 95% CI 0.82 to 1.56; I2 = 0%) were not statistically significant. Quality of evidence ranged from low to very low, and only one study was judged as at low risk of bias. CONCLUSIONS: In this meta-analysis, GCFs were significantly associated with lower insulin requirements and improved glycemic control. Although results for clinical outcomes were not statistically significant, there is insufficient evidence to confirm or exclude important differences due to serious imprecision in the effect estimates and overall low quality of evidence. The effects of GCFs on clinical outcomes require confirmation in larger randomized trials.

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

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.

Relationship between short and long-term glycemic variability and oxidative stress in type 1 diabetes mellitus under daily life insulin treatment.

OBJECTIVE: The purpose of this study was to investigate the heterogeneity of the association between glycemic variability and oxidative stress markers in T1DM patients under daily life insulin treatment. METHODS: We studied, in a cross-sectional analysis, 76 T1DM patients without clinical chronic diabetes complications and 22 healthy individuals. Were evaluated the short-term glycemic variability (STGV), long-term glycemic variability (LTGV), oxidative stress markers [8-isoprostaglandin-F2α (Ur-8-iso-PGF2α), nitric oxide (NO), thiobarbituric acid reactive substances (TBARS) and erythrocytes reduced/oxidized glutathione (GSH/GSSG)] and biochemical dosages (glycaemia, HbA1c, lipidogram, albuminuria). RESULTS: Plasmatic NO was positively associated with LTGV (last year average of HbA1c) (8.7 ± 1.6% or 71 ± 18 mmol) (rS: 0.278; p: 0.042). Plasmatic TBARS, erythrocytes GSH/GSSH and Ur-8-iso-PGF-2α didn't show correlation with glycemic variability. GSH/GSSG was inversely correlated with LDL-cholesterol (rS: - 0.417; p: 0.047) and triglycerides (rS: -0.521; p: 0.013). Albuminuria was positive correlated with age (rS: 0.340; p: 0.002), plasmatic NO (rS: 0.267; p 0.049) and TBARS (rS: 0.327; p: 0.015). CONCLUSION: In daily life insulin treatment, young T1DM patients have higher plasmatic NO than healthy subjects. However, the correlation between glycemic variability and oxidative stress markers is heterogeneous. Lipid profile and albuminuria are associated with different oxidative stress markers. These data collaborate to explain the controversial results in this issue.

Association Between Intra-Hospital Uncontrolled Glycemia and Health Outcomes in Patients with Diabetes: A Systematic Review of Observational Studies.

BACKGROUND: Many people are still getting affected by uncontrolled glycemic events during hospital admission, which encompasses hypoglycemia, hyperglycemia, and high glycemic variability. INTRODUCTION: Primary studies have shown an association of glycemic dysregulation with increased length of hospital stay and mortality among overall patients, however, there is no systematic review of current evidence on the association between uncontrolled in-hospital glycemia in patients with diabetes and health outcomes. This study aimed to systematically review the current evidence on the association between uncontrolled in-hospital glycemia in patients with diabetes and health outcomes. METHODS: The association between glycemic dysregulation and health outcomes for inpatients with diabetes was systematically reviewed. PubMed, Embase, and LILACS databases were searched. Two independent reviewers were involved in each of the following steps: screening titles, abstracts, and fulltexts; assessing the methodological quality; and extracting data from included reviews. Descriptive analysis method was used. RESULTS: Seven cohort studies were included, and only two had a prospective design, consisting of 7,174 hospitalized patients with diabetes. In-hospital occurrence of hypoglycemia, hyperglycemia, and glycemic variability were assessed, and outcomes were mortality, infections, renal complications, and adverse events. Among the exposure and outcomes, an association was observed between severe hypoglycemia and mortality, hyperglycemia and infection, and hyperglycemia and adverse events. CONCLUSION: In-hospital uncontrolled glycemia in patients with diabetes is associated with poor health outcomes. More studies should be conducted for proper investigation because diabetes is a complex condition. Effects of glycemic dysregulation should be investigated on the basis of overall health of a patient instead from only organ-target perspective, which makes the investigation difficult.

Glycemic Variability, Oxidative Stress, and Impact on Complications Related to Type 2 Diabetes Mellitus.

Chronic hyperglycemia is an established risk factor for the development of complications in both type 1 and type 2 diabetes, but glycemic variability has emerged as a possible independent risk factor for diabetes complications, possibly through oxidative stress. In this review, methods to access glycemic variability and oxidative stress, as well as their correlations, are discussed. Non-pharmacological and pharmacological strategies are also debated to achieve better glycemic control, not only by HbA1c target but also with reduced glycemic fluctuations, possibly minimizing the risk of diabetes complications.

Relationship between short and long-term glycemic variability and oxidative stress in type 1 diabetes mellitus under daily life insulin treatment

ABSTRACT Objectives: The purpose of this study was to investigate the heterogeneity of the association between glycemic variability and oxidative stress markers in T1DM patients under daily life insulin treatment. Subjects and methods: We studied, in a cross-sectional analysis, 76 T1DM patients without clinical chronic diabetes complications and 22 healthy individuals. Were evaluated the short-term glycemic variability (STGV), long-term glycemic variability (LTGV), oxidative stress markers [8-isoprostaglandin-F2α (Ur-8-iso-PGF2α), nitric oxide (NO), thiobarbituric acid reactive substances (TBARS) and erythrocytes reduced/oxidized glutathione (GSH/GSSG)] and biochemical dosages (glycaemia, HbA1c, lipidogram, albuminuria). Results: Plasmatic NO was positively associated with LTGV (last year average of HbA1c) (8.7 ± 1.6% or 71 ± 18 mmol) (rS: 0.278; p: 0.042). Plasmatic TBARS, erythrocytes GSH/GSSH and Ur-8-iso-PGF-2α didn't show correlation with glycemic variability. GSH/GSSG was inversely correlated with LDL-cholesterol (rS: - 0.417; p: 0.047) and triglycerides (rS: −0.521; p: 0.013). Albuminuria was positive correlated with age (rS: 0.340; p: 0.002), plasmatic NO (rS: 0.267; p 0.049) and TBARS (rS: 0.327; p: 0.015). Conclusion: In daily life insulin treatment, young T1DM patients have higher plasmatic NO than healthy subjects. However, the correlation between glycemic variability and oxidative stress markers is heterogeneous. Lipid profile and albuminuria are associated with different oxidative stress markers. These data collaborate to explain the controversial results in this issue.

Randomized Study Comparing Vildagliptin vs Glibenclamide on Glucose Variability and Endothelial Function in Patients with Type 2 Diabetes Mellitus and Hypertension.

BACKGROUND: Glucose variability (GV) is considered an important factor for cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM). High GV causes endothelial dysfunction and increased oxidative stress. Dipeptidyl peptidase-4 (DPP-4) inhibitors may improve endothelial function and decrease GV. The aim of this study was to investigate the effects of vildagliptin, a DPP-4 inhibitor, compared with glibenclamide in GV and endothelial function in patients with T2DM and arterial hypertension. METHODS: This is a prospective, randomized, open and drug-controlled study. Fifty patients older than 35 years with T2DM and hypertension without CVD were randomized to receive vildagliptin (n=25) or glibenclamide (n=25), both in added-on metformin. Laboratory tests and analysis of endothelial function were performed before and 12 weeks after treatment. Endothelial function, defined by reactive hyperemia index (RHI), was analyzed by peripheral artery tonometry (endo-PAT2000). GV was evaluated by capillary glucose with intermittent monitoring device, six measurements per day, for three days, before and after treatment. The median of standard deviation (SD) of mean blood glucose (MBG) was used to evaluate GV. RESULTS: GV decreased in the vildagliptin group (35.2 to 30.7, P=0.037) but did not change with glibenclamide (37.6 to 37.5, P=0.765). Glycated hemoglobin was similar in both groups. MBG decreased only in glibenclamide group, without difference with vildagliptin group (P=0.374). There were no changes in the RHI in both groups and there was no correlation between GV and RHI (P=0.658). CONCLUSION: Vildagliptin reduces GV; however, the action on endothelial function was not demonstrated. In addition, there was no correlation between GV and endothelial function.

Different amounts of moderate to vigorous physical activity and change in glycemic variability in adolescents with type 1 diabetes: is there dose-response relationship?

ABSTRACT Objective To identify the level of physical activity and glycemic variability of adolescents with type 1 diabetes mellitus and to compare glycemic variability on days with different amounts of moderate to vigorous physical activity (MVPA). Subjects and methods A sample of 34 subjects aged 10 to 15 years, 18 (52.94%) female; age: 13.04 ± 1.94; HbA1c: 9.76 ± 1.51. Physical activity was measured by wGT3X accelerometer. The glucose data were obtained using continuous glucose monitoring, and the following glycemic variability measures were calculated: standard deviation (SD), low blood glucose index (LBGI), high blood glucose index (HBGI), mean amplitude of glycemic excursions (MAGE), glycemic risk assessment in diabetes equation (GRADE) and coefficient of variation (CV). The most and least active days (the days with greater and lesser time dedicated to physical activities of moderate to vigorous intensity, respectively) were identified. In addition, based on the whole period of accelerometer use, daily means of time spent in MVPA were identified among participants, who were then divided into three groups: up to 100 minutes; from 101 to 200 minutes and above 201 minutes. Then, the measures of glycemic variability were compared among the most and least active days and among the groups too. Results The amount of MVPA was significantly different between the days evaluated (237.49 ± 93.29 vs. 125.21 ± 58.10 minutes), but glycemic variability measures did not present a significant difference. Conclusion Despite the significant differences in the amount of MVPA between the two days evaluated, the glycemic variability did not change significantly. Arch Endocrinol Metab. 2020;64(3):312-8

Impact of Glycemic Variability and Hypoglycemia on the Mortality and Length of Hospital Stay among Elderly Patients in Brazil.

BACKGROUND: Glycemic variability (GV) is an alternative diabetes-related parameter that has been associated with mortality and longer hospitalization periods. There is no ideal method for calculating GV. In this study, we used standard deviation and coefficient of variation due to their suitability for this sample and ease of use in daily clinical practice. OBJECTIVE: This study aimed to investigate the association between GV, hypoglycemia, and the 90-day mortality and length of hospital stay (LOS) among non-critically ill hospitalized elderly patients. METHODS: The medical records of 2,237 elderly patients admitted to the Zilda Arns Elderly Hospital over a 2.5-year period were reviewed. Hypoglycemia was defined as a glucose level <70 mg/dL (hypoglycemia alert value) and represented by the proportion of days in which the patient presented with this condition relative to the LOS. The Charlson comorbidity index was used to evaluate prognosis. Data were analyzed using multiple linear and logistic multivariate regression analyses. RESULTS: Adjusted analysis of 687 patients (305 men [44.4%] and 382 women [55.6%], mean age of 77.86±9.25 years) revealed that GV was associated with a longer LOS (p=0.048). Mortality was associated with hypoglycemia (p=0.005) and mean patient-day blood glucose level (p=0.036). Variables such as age (p<0.001), Charlson score (p<0.001), enteral diet (p<0.001), and corticosteroid use (p=0.007) were also independently associated with 90-day mortality. CONCLUSION: Increased GV during hospitalization is independently associated with a longer LOS and hypoglycemia in non-critically ill elderly patients, while the mean patient-day blood glucose is associated with increased mortality.

HbA1c variability and long-term glycemic control are linked to diabetic retinopathy and glomerular filtration rate in patients with type 1 diabetes and multiethnic background.

AIM: To evaluate the associations between HbA1c variability and long-term glycemic control with microvascular complications in type 1 diabetes (T1D) patients and multiethnic background. METHODS: T1D adults with ≥10 years of follow-up and ≥ 2 HbA1c measurements were included. Glycemic variability was evaluated by the standard deviation (HbA1c-SD), and coefficient of variation (HbA1c-CV), and glycemic control by mean HbA1c over 10 years. Diabetic retinopathy (DR), increased urinary albumin excretion rate (UAER) and reduced glomerular filtration rate (eGFR) were diagnosed. Cardiac autonomic neuropathy (CAN) was diagnosed by cardiac reflex tests. Associations between glycemic parameters with complications were assessed by multivariate logistic regressions. RESULTS: 220 patients were included. Simultaneously adjusted for each other, mean HbA1c was independently associated with DR (OR: 2.82; 95%CI: 1.45-5.50), increased UAER (OR: 1.97; 95%CI: 1.14-3.09) and CAN (OR: 4.42; 95%CI: 1.45-13.51); whereas HbA1c-CV was independently associated with DR (OR: 8.93; 95%CI: 1.86-42.87) and reduced eGFR (OR: 7.02; 95%CI: 1.47-35.55). CONCLUSIONS: Long-term glycemic control was associated with DR, increased UAER and CAN, while glycemic variability was additionally associated with DR and impaired renal function; suggesting that both good and stable glycemic status might be important to prevent microvascular complications in T1D patients and multiethnic background.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia.

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.