Short-term Glycemic Variability Research Articles

Short-term glycemic variability and intracranial atherosclerotic plaque stability assessed by high-resolution MR vessel wall imaging in type 2 diabetes mellitus

ObjectiveTo investigate the relationship between short-term glycemic variability in patients with T2DM and the vulnerability of intracranial atherosclerotic plaques using HR-MR-VWI. Materials and methodsIn total, 203 patients with acute ischemic stroke (AIS)/transient ischemia (TIA) combined with T2DM were enrolled. All of them underwent HR-MR-VWI during the period between July 2020 and July 2023. 203 patients were divided into groups with higher (1,5-AG≤30.7μmol/L) and lower (1,5-AG>30.7μmol/L) short-term glycemic variability. Patients were also divided into the T1WI and non-T1WI hyperintensity groups. Associated factors(FBG, HbA1c, and 1,5-AG)for the T1WI hyperintensity were analyzed by binary logistic regression. We used the area under the curve (AUC), while the sensitivity and specificity were calculated at the optimal threshold. The Delong test was employed to compare the quality of the AUC of the predictors. ResultsThe group with higher short-term glycemic variability had a higher incidence of the hyperintensity on T1WI, higher degree of enhancement, higher degree of stenosis and smaller lumen area (P<0.05). The T1WI hyperintensity group had higher HbA1c levels, higher hemoglobin levels and lower 1,5-AG levels(P<0.05). 1,5-AG (OR=0.971, 95% CI: 0.954∼0.988, P=0.001), HbA1c (OR=1.305, 95% CI: 1.065∼1.598, P=0.01) and male sex (OR=2.048, 95% CI: 1.016∼4.128, P=0.045)/(OR=2.102, 95% CI: 1.058∼4.177, P=0.034) were independent risk factors for the hyperintensity on T1WI. 1,5-AG demonstrated enhanced performance and yielded the highest AUC of the receiver operator characteristic curve (AUC=0.726), with sensitivity and specificity values of 0.727 and 0.635 respectively. Conclusion1,5-AG, HbA1c and male sex are independent predictors of intracranial plaques with T1WI hyperintensity, the greater short-term glycemic variability, the higher incidence of vulnerable plaques.

Correlation Between Blood Urea Nitrogen and Short- and Long-Term Glycemic Variability in Elderly Patients with Type 2 Diabetes Mellitus Who Were hospitalized:A Retrospective Study.

Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and progressively impaired insulin secretion resulting in dynamic fluctuations in glucose levels.High blood urea nitrogen (BUN) levels have been linked to decreased insulin sensitivity, suppressed insulin synthesis and increased risk of incident diabetes mellitus in humans as well as insulin use in patients with T2DM.This study characterize the association between BUN levels and short-term and long-term glycemic variability(GV) in the elderly patients with T2DM who were hospitalized. A total of 927 elderly patients with T2DM were included in the study. The short-term GV was quantified using parameters such as standard deviation (SD), coefficient of variation (CV), time in range (TIR), and mean amplitude of glycemic excursions (MAGE), based on multi-point fingertip blood glucose monitoring. The long-term GV was quantified using parameters such as SD, CV, variation independent of the mean (VIM), and average successive variability (ARV), based on fasting blood glucose(FPG). The relationship between BUN levels and short-term and long-term GV in elderly T2DM who were hospitalized was explored using methods such as Spearman correlation coefficient, linear regression analysis, logistic regression analysis, and interaction tests. In elderly patients with T2DM were hospitalized, there is a significant correlation between BUN levels and both short-term and long-term GV. BUN is negatively correlated with the GV parameter TIR (r=-0.12, P=0.000), and positively correlated with SD (r=0.12, P=0.000), CV (r=0.07, P=0.026), MAGE (r=0.11, P=0.001), FPG-SD (r=0.08, P=0.013), and FPG-CV (r=0.08, P=0.014).Furthermore, the association remains consistent across different age, gender, BMI, and haemoglobin A1c (HbA1c) subgroups (P interaction > 0.05). In elderly patients with T2DM were hospitalized, BUN levels were positively associated with GV.Therefore, monitoring BUN levels were beneficial in assessing the degree of GV.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes. Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome. Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease. GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Poor glycemic control is associated with more rapid kidney function decline after the onset of diabetic kidney disease.

The role of glycemic control and its variability on the rate of kidney function decline after the onset of diabetic kidney disease (DKD) remains unclear. The association between baseline HbA1c and rates of estimated GFR (eGFR) loss during follow-up was examined by mixed-effects linear regression in 530 individuals with type 1 diabetes and early-to-moderate DKD from the Preventing Early Renal Loss (PERL) trial, and 2,378 individuals with type 2 diabetes and established DKD from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. The benefit of intensive vs. standard glycemic control in slowing eGFR decline was examined in ACCORD. The associations between continuous glucose monitoring-derived short-term glycemic variability indices and rate of GFR decline were also evaluated in PERL. A higher baseline HbA1c was associated with a more negative eGFR slope in both PERL and ACCORD (-0.87 and -0.27 ml/min/1.73m2/year per Hba1c unit increment, p<0.0001 and p=0.0002, respectively). In both studies, the strength of this association progressively increased with increasing levels of albuminuria (p for interaction <0.05). Consistent with this, the benefit of intensive glycemic control on eGFR decline was greater in ACCORD participants with severe than in those with moderate albuminuria (+1.13 vs. +0.26 ml/min/1.73 m2/year, p=0.01). No independent associations were found in PERL between short-term glycemic variability indices and rate of eGFR decline. In both type 1 and type 2 diabetes, poor glycemic control is associated with a more rapid rate of GFR decline after DKD onset, especially in persons with severe albuminuria.

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs.This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine.Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation).A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively.The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

Serum 25-hydroxyvitamin D level is associated with short-term glycemic variability metrics derived from continuous glucose monitoring in T2DM

This study aims to investigate the association between 25-hydroxyvitamin D (25OHD) and continuous glucose monitoring-assessed short-term glycemic variability (GV) and HbA1c among patients with type 2 diabetes mellitus (T2DM). We conducted a cross-sectional study recruiting 325 patients. The association between 25OHD and GV metrics (mean amplitude of glycemic excursions [MAGE], coefficient of variation [CV], standard deviation of sensor glucose [SD], and TIR) and HbA1c were analyzed using multivariable linear and logistic regression analyses. The 25OHD level and GV metrics showed significant differences among HbA1c groups (P < 0.01). CV, MAGE, SD and HbA1c decreased, and TIR increased with ascending 25OHD tertiles (P < 0.05). Serum 25OHD was inversely associated with CV (β = − 0.211 [− 0.350 to − 0.071], P < 0.01) and HbA1c (β = − 0.061 [− 0.114 to − 0.031], P < 0.01), and further multivariable analyses confirmed these results (P < 0.05). However, no association of HbA1c and 25OHD was found with the highest tertile of CV. These findings revealed that increased GV and HbA1c were both associated with lower 25OHD, and the relationship between HbA1c and 25OHD was attenuated with higher glucose CV in T2DM. Taken together, the analyses suggest that increasing vitamin D status has effects on improvements in long-term glycemic control and low glycemic variability.

Association of Both Short-term and Long-term Glycemic Variability With the Development of Microalbuminuria in the ACCORD Trial.

The relative and potential additive effects of long- and short-term glycemic variability on the development of diabetic complications are unknown. We aimed to assess the individual and combined relationships of long-term visit-to-visit glycemic variability, measured as the coefficient of variation of fasting plasma glucose, and short-term glucose fluctuation, estimated by the biomarker 1,5-anhydroglucitol, with the development of proteinuria. Both estimates of glycemic variability were independently associated with microalbuminuria, but only long-term glycemic variability remained significant after adjusting for average hemoglobin A1c. Our findings suggest that longer-term visit-to-visit glucose variability improves renal disease prediction in type 2 diabetes.

Effects of Glycemic Variability on Regulatory T Cells in Patients with Type 2 Diabetes and Kidney Disease.

To investigate the pathogenesis of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM), we evaluated the effects of short-term glycemic variability (GV) on the profile of T cell subpopulations. A total of 47 T2DM patients with normoalbuminuria, 47 microalbuminuria, and 49 macroalbuminuria were enrolled. The continuous glucose monitoring (CGM) determined the GV of enrolled patients. Flow cytometry was used to determine the proportion of T cell subpopulations. The frequency of T helper (Th) 17 and Th1 cells significantly increased while regulatory T cells (Tregs) significantly decreased in the macroalbuminuria group compared to normoalbuminuria and microalbuminuria groups (P < 0.01). The suppressive function of Tregs was significantly lower in the macroalbuminuria group than the normoalbuminuria group (P < 0.05). Compared with the normoalbuminuria group, the mean amplitude of glucose excursions (MAGE) of the macroalbuminuria group was significantly higher (P<0.05). Furthermore, there were negative associations between the proportion of Tregs and MAGE. Increased GV could decrease the proportion of Tregs and may impair their function. This may lead to increases in Th1 and Th17 cells, and some inflammatory cytokines, which might contribute to the development and progression of DKD in T2DM.

1331-P: Association of Short-Term and Long-Term Glycemic Variability and the Renal Complications in the ACCORD Trial

Recent studies have shown that long-term and short-term glycemic variability (measured by low concentrations of the biomarker 1,5-anhydroglucitol (1,5-AG)) were associated with incident diabetes complications; yet, none have assessed their relative importance. Using the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we measured 1,5-AG 12 months from randomization (when HbA1c was stable) in 4,000 randomly selected samples and assessed the risk of short-term and long-term glycemic variability for incident renal complications. The coefficient of variation of fasting glucose (CV-FG) was used to measure long-term variability. Primary outcomes were time (after 12 months) to 1) microalbuminuria and 2) macroalbuminuria. Risks were estimated using Cox proportional hazard models. Both CV-FG and lower 1,5-AG were independently associated with microalbuminuria even after adjusting for other risk factors (Table). Only CV-FG was a significant risk factor for macroalbuminuria. After adjusting for average glycemic control, the CV-FG association remained. Subgroup analysis indicated that CV-FG and 1,5 AG were more harmful in those receiving less intensive glucose control. The secondary outcome analysis supported the main conclusion (Table). This post hoc analysis indicates that long-term and short-term glucose variability may play a role in developing renal diseases in T2D. Disclosure T.Okuno: None. P.Reaven: Research Support; Dexcom, Inc. J.Zhou: None. Funding National Institutes of Health (R01HG006139, DMS-2054253, IIS-2205441, R21HL150374)

Glycemic variability leads to higher levels of auto-oxidized oxysterol species in patients with type 1 diabetes mellitus.

Hyperglycemia and glycemic variability (GV) are associated with oxidative stress in patients with diabetes mellitus (DM). Oxysterol species, produced by the non-enzymatic oxidation of cholesterol, are potential biomarkers of oxidative stress. This study examined the relationship between auto-oxidizedoxysterols and GV in patients with type 1 DM. Thirty patients with type 1 DM using a continuous subcutaneous insulin infusion pump therapy and a healthy control group (n = 30) were included in this prospective study. A Continuous Glucose Monitoring System device was applied for 72h. Blood samples were taken for oxysterols produced by non-enzymatic oxidation [7-ketocholesterol (7-KC) and cholestane-3β, 5α, 6β-triol (Chol-Triol)] levels at 72h. Short-term glycemic variability parameters, mean amplitude of glycemic excursions (MAGE), the standard deviation of glucose measurements (Glucose-SD), and mean of daily differences (MODD) were calculated with continuous glucose monitoring data. HbA1c was used to evaluate glycemic control and HbA1c-SD (the SD of HbA1c over the past year) for long-term glycemic variability. 7-KC and Chol-triol levels were significantly higher in the study group than in the control group. Strong positive correlations were found between 7-KC with MAGE(24-48h) and Glucose-SD(24-48h). 7-KC was positively correlated with MAGE(0-72h) and Glucose-SD(0-72h). No significant correlation was found between HbA1c and HbA1c -SD with oxysterol levels. The regression models showed that SD(24-48h) and MAGE(24-48h) predicted 7-KC levels while HbA1c did not. Glycemic variability leads to higher levels of auto-oxidized oxysterol species in patients with type 1 DM independent of long-term glycemic control.

Short-term Glycemic Variability and Its Association With Macrovascular and Microvascular Complications in Patients With Diabetes.

The introduction of continuous glucose monitoring inaugurated a new era in clinical practice by shifting the characterization of glycemic control from HbA1c to novel metrics. The one that gained widespread attention over the past decades was glycemic variability (GV), which typically refers to peaks and nadirs of blood glucose measured over a given time interval. GV can be dichotomized into two main categories: short-term and long-term. Short-term GV reflects within-day and between-day glycemic oscillations, and its contribution to diabetic complications remains an enigma. In this review, we summarize the available data about short-term GV and its possible association with both microvascular and macrovascular complications, evaluating different pathogenic mechanisms and demonstrating nonpharmaceutical, as well as pharmaceutical, therapeutic interventions.

Albuminuria, glycemic variability and effect of flash glucose monitoring based decision making on short term glycemic variability in Indian type 2 diabetes patients: Indi-GlyVar study.

Glycemic variability (GV) denotes the fluctuations in the glucose values around the baseline. High glycemic variability is associated with a higher risk of diabetes-associated complications. In this study, we sought to determine the impact of therapeutic interventions based on flash glucose monitoring on rapid, short-term glycemic variability. We also studied the prevalent albuminuria in diabetic kidney disease and its effect on glycemic variability. In a 14-day, single-center, prospective intervention study, we measured the GV indices at baseline (days 1-4) and ten days after ambulatory glucose profile-based intervention using flash glucose monitoring (Abbott Libre Pro, Abbott Diabetes Care, Alameda, California, USA) in patients with type 2 diabetes. An EasyGV calculator was used to estimate the flash glucose monitoring (FGM)-derived measures of GV. The primary outcome was to assess the impact of FGMS-based therapeutic interventions on glycemic variability markers: SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD],Mvalue, and coefficient of variance [%CV], AUC below 70 mg/dl, low blood glucose index, AUC above 180 mg/dl [AUC >180], high blood glucose index [HBGI], and J index. Time-related matrices (time in range (%), time above range (%), and time below range (%) were also calculated from the ambulatory glucose profile. Renal function parameters (serum creatinine, estimated glomerular filtration rate, urine albumin excretion) were calculated. The GV with regard to albumin excretion rate was compared. Fifty-eight T2DM patients (63.8%, males) with a mean age of 51.5 ± 11.9 years were studied. When compared with baseline (days 1-4), on day 14, there was a significant improvement in mean sensor glucose (mg/dl) median (IQR) [155 (116-247) vs 131 (103-163)(p ≤0.001)], JINDEX [15,878 (7,706-28,298) vs 8,812 (5,545-14,130)(p ≤0.001)], HBGI [361 (304-492) vs 334 (280-379)(p ≤0.001)], MAGE (mg/dl) [112 (8-146) vs 82 (59-109)(p ≤0.001)], M-value [2,477 (1,883-3,848) vs 2,156 (1,667-2,656)(p ≤ 0.001)], MAG (mg/dl) [111 (88-132) vs 88 (69-102)(p ≤ 0.001)]. Patients with albuminuria at baseline had high mean sensor glucose (mg/dl) median (IQR) [190 (131-200) vs 131 (112-156)(p = 0.001)], CONGA (mg/dl) median (IQR) [155 (101-165) vs 108 (83-120) (p = 0.001)], JINDEX, HBGI, MAGE (mg/dl), and M-value are, median (IQR) [20,715 (10,970-26,217 vs 91,118 (6,504-15,445))(p ≤ 0.01)], [415 (338-423) vs 328 (292-354)(p = 0.001)], [125 (102-196) vs 103 (74-143)(p ≤ 0.01)], [3,014 (2,233-3,080) vs 2,132 (1,788-2,402)(p ≤0.01)], respectively. In type 2 diabetes, flash glucose monitoring-guided therapeutic interventions can reduce glycemic variability in a brief span (10 days) of time. Also, albuminuria in type 2 diabetes is associated with high glycemic variability. Reduced diabetes complications may ultimately result from this reduced glycemic variability.

Effects of perioperative glycemic indicators on outcomes of endovascular treatment for vertebrobasilar artery occlusion.

ObjectiveEndovascular treatment (EVT) is, to date, the most promising treatment of vertebrobasilar artery occlusion (VBAO). The study aimed to determine the influence of perioperative glucose levels on clinical outcomes in patients with acute VBAO treated with EVT.MethodsWe retrospectively collected consecutive VBAO patients received EVT in 21 stroke centers in China. The associations between perioperative glycemic indicators (including fasting blood glucose[FBG], admission hyperglycemia, stress hyperglycemia ratio [SHR] and short-term glycemic variability [GV]) and various clinical outcomes were analyzed in all patients and subgroups stratified by diabetes mellitus (DM).ResultsA total of 569 patients were enrolled. Admission hyperglycemia significantly correlated with increased risk of symptomatic intracranial hemorrhage (sICH) (odds ratio [OR] 3.24, 95% confidence interval [CI]: 1.40-7.46), poor functional outcomes at 90 days (OR 1.91, 95%CI: 1.15-3.18) and 1 year (OR 1.96, 95%CI: 1.20-3.22). Similar significant correlations exist between FBG, SHR, GV and all the adverse outcomes except higher levels GV was not associated with increased risk of sICH (OR 1.04, 95% CI: 0.97-1.12). Subgroup analyses showed that admission hyperglycemia, FBG and SHR were significantly associated with adverse outcomes in non-diabetic patients, but not in DM patients. While, GV was associated with poor functional outcomes regardless of diabetes history.ConclusionsAdmission hyperglycemia, FBG, SHR and short-term GV in VBAO patients treated with EVT were associated with adverse outcomes. The results suggested that comprehensive evaluation and appropriate management of perioperative glucose might be important for patients with VBAO and treatment with EVT.

Impact of short-term glycemic variability on risk of all-cause mortality in type 2 diabetes patients with well-controlled glucose profile by continuous glucose monitoring: A prospective cohort study

AimsTo investigate the association between short-term glycemic variability (GV) and all-cause mortality in type 2 diabetes with well-controlled glucose profile by continuous glucose monitoring (CGM). MethodsIn this prospective study, 1839 diabetes patients who reached percentage of time in the target glucose range of 3.9–10 mmol/L > 70%, percentage of time above range of 10 mmol/L < 25% and percentage of time below range of 3.9 mmol/L < 4% on CGM were enrolled and were classified into five groups by coefficient of variation for glucose (%CV) level: ≤20%, 20–25%, 25–30%, 30–35%, and > 35%. Cox proportional hazard models were used to estimate hazard ratios (HRs) of all-cause mortality risk associated with the different %CV categories. ResultsAt baseline, participants had mean age of 60.9 years and mean HbA1c of 7.3% (56 mmol/mol). A total of 165 deaths were identified during a median follow-up of 6.9 years. In multivariate Cox regression analysis, HRs associated with %CV categories were 1.00, 1.16 (95% CI 0.78–1.73), 1.38 (95% CI 0.89–2.15), 1.33 (95% CI 0.77–2.29) and 2.26 (95% CI 1.13–4.52) for all-cause mortality. ConclusionsGreater %CV was associated with increased risk for all-cause mortality even among patients with seemingly well-controlled glucose status.

Abstract 93: Ambulatory glucose profiles of children and adolescents with T1DM: Identification of factors affecting glycemic control

Background: Continuous glucose monitoring (CGM) provides information about glycemic control, beyond that provided by HbA1c and SMBG, including short-term glycemic variability, hypoglycemic and hyperglycemic events.Aims and Objectives: To describe ATTD endorsed CGM derived glycemic variables from a cohort of T1DM, assess possible factors influencing glycemic control and study the association between HbA1c and these variables.Results: 61 patients (age, 15.9 + 5), median duration of diabetes 6 years, wore the FreeStyle libre for a median of 14 days. Mean TIR was 46.3 + 15.5, TBR 16.7 + 13.5 and TAR 36.9 + 22.0. Mean glycemic variability (%CV) was 48% and 90% had high %CV. Those using NPH had a significantly higher %CV (P=0.007) compared to those on glargine. Females (n=34, P=0.05) and those using NPH (n=18, P=0.08) tended to spend a longer time in hypoglycemia. HbA1c showed a negative correlation with TIR (r=-0.441), TBR (r=-0.544), %CV (r=-0.335) and a positive correlation with TAR (r=0.656), mean glucose (r=0.714). On subgroup analysis, group with lowest HbA1c (<6.8%, n=16), although had highest TIR but also highest TBR.Conclusion: Our findings demonstrate wide gaps between the recommended versus real world glycemic variables in children with T1DM and highlight the need to implement interventions for better glycemic control.Acknowledgements: This work was supported by a grant from the Endocrine Society of India.

Pragmatic Clinic-Based Investigation of Glycemic Variability in Patients With Type 1 Diabetes in Routine Clinical Practice and Its Association With Cardiovascular Autonomic Neuropathy: A Pilot Study

ObjectiveTo evaluate the relationship between markers of glycemic variability (GV), assessed by blinded continuous glucose monitoring (CGM), and cardiovascular autonomic neuropathy (CAN) in patients with type 1 diabetes (T1D). MethodsGV indices, such as SD and coefficient of variation were obtained by blinded CGM through an electrode inserted into the subcutaneous tissue for at least 3 consecutive days. CAN was assessed by cardiovascular reflex tests and HRV. ResultsFifteen T1D patients were included: 7 (46.7%) women, aged 47.1 ± 11.6 years, with a diabetes duration of 26 years (20 to 29.5 years). Five patients (25%) were excluded from our study. The majority of our patients presented glycated hemoglobin (60%), SD (86.3%), and coefficient of variation (60%) above the established goals. Patients with defined CAN had a longer diabetes duration, higher glycated hemoglobin levels, lower glomerular filtration rate, lower prevalence of indices related to hypoglycemic stress, and short-term GV indices compared with patients without CAN. ConclusionOur study showed an inverse association between GV and CAN. The most important risk factors associated with CAN were age, diabetes duration, and markers of chronic hyperglycemia. Furthermore, the difficulty in the interpretation of data extracted from the blinded CGM system, which also requires a minimum of 3 capillary blood glucose measurements for calibration, should be carefully analyzed to ensure the accuracy and usefulness of the blinded CGM system as a tool for diabetes management in developing countries. Further studies are necessary to establish the role of GV in the development of CAN in patients with T1D.

Ranolazine Improves Glycemic Variability and Endothelial Function in Patients with Diabetes and Chronic Coronary Syndromes: Results from an Experimental Study.

Background Ranolazine is a second-line drug for the management of chronic coronary syndromes (CCS). Glucose-lowering and endothelial effects have also been reported with this agent. However, whether ranolazine may improve short-term glycemic variability (GV), strictly related to the prognosis of patients with type 2 diabetes (T2D), is unknown. Thus, we aimed to explore the effects of adding ranolazine to standard anti-ischemic and glucose-lowering therapy on long- and short-term GV as well as on endothelial function and oxidative stress in patients with T2D and CCS. Methods Patients starting ranolazine (n = 16) were evaluated for short-term GV, haemoglobin 1Ac (Hb1Ac) levels, endothelial-dependent flow-mediated vasodilation (FMD), and oxidative stress levels at enrolment and after 3-month follow-up. The same measurements were collected from 16 patients with CCS and T2D that did not receive ranolazine, matched for age, gender, and body mass index. Results A significant decline in Hb1Ac levels was reported after 3-month ranolazine treatment (mean change -0.60%; 2-way ANOVA p = 0.025). Moreover, among patients receiving ranolazine, short-term GV indexes were significantly improved over time compared with baseline (p = 0.001 for time in range; 2-way ANOVA p = 0.010). Conversely, no significant changes were reported in patients without ranolazine. Finally, greater FMD and lower oxidative stress levels were observed in patients on ranolazine at 3 months. Conclusions Ranolazine added to standard anti-ischemic and glucose-lowering therapy demonstrated benefit in improving the glycemic status of patients with T2D and CCS. How this improvement contributes to the overall myocardial benefit of ranolazine requires further studies.

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

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. 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). 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). 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.

Short-term glycemic variability and hemorrhagic transformation after successful endovascular thrombectomy.

Glycemic variability (GV) is a risk factor for poor outcomes after ischemic stroke. However, its effect on hemorrhagic transformation after endovascular recanalization therapy (ERT) remains to be elucidated. Patients with acute ischemic stroke due to large vessel occlusion with successful recanalization after ERT (modified thrombolysis in cerebral infarction 2b or 3) were enrolled between January 2013 and November 2019. Blood glucose level data were obtained during the first 36 h after ERT, and ten GV parameters including time rate (TR) of glucose variation were assessed. The TR of glucose variation reflects the speed of glucose fluctuations over time (mg/dL/hour) during the monitoring period. Symptomatic intracerebral hemorrhage (sICH) and unfavorable outcomes at 3 months after ERT were analyzed. The sICH was defined as parenchymal hematoma type 2 with a neurological deterioration of 4 points or more on the National Institute of Health Stroke Scale. Moreover, a modified Rankin Scale of 3-6 at 3 months was considered an unfavorable outcome. Among all patients (n = 176; mean age, 69.3 years; 47.7 % female), sICH developed after successful ERT in 16 (9.1%) patients. In addition, 54% (n = 95) patients had an unfavorable outcome at 3 months. Patients with sICH and unfavorable outcome had higher the TR of glucose variation. After adjusting for potential confounders, the TR of glucose (per 1 mg/dL/h increase) variation was independently associated with sICH (odds ratio, 1.17; 95% confidence interval [CI], 1.012-1.343) and 3-month unfavorable outcome (OR 1.14, 95% CI, 1.000-1.297). Time-related GV during the first 36 h after successful ERT has a stronger correlation with sICH and poor functional outcomes compared to any GV parameters. This suggests that maintaining stable glucose may be an important factor in the prevention of sICH after undergoing successful ERT.

Effects of Low-Intensity Resistance Exercise with Slow Movement and Tonic Force Generation on Short-Term Glycemic Variability in Healthy Subjects: A Randomized Controlled Study

Resistance exercise improves daily glycemic control. Low-intensity resistance exercise with slow movements and tonic force generation (LST) is a resistance exercise protocol that can increase skeletal muscle mass and strength without considerable physical burden; however, its effect on glycemic variability is unknown. The aim of this study was to investigate the effect of LST on short-term glycemic variability, expressed as the M-value, in healthy individuals. We recruited 20 healthy subjects (mean age: 27.9 ± 3.9 years) with normal glucose tolerance, and subjects were randomly assigned to the control (n = 10) or LST (n = 10) groups. Subjects in the LST group performed the resistance exercise with 40–50% of the one-repetition maximum for 40 min. All subjects wore a subcutaneous continuous glucose monitoring system and a triaxial accelerometer, and their daily glycemic variability and physical activity were measured. One hour after the intervention, in the LST group, the variability of blood glucose levels was significantly decreased compared to the control group (M-values in the LST group: pre 3.5 ± 6.2, post 2.7 ± 2.7, p = 0.575; M-values in the control group: pre 0.4 ± 0.7, post 2.7 ± 2.0, p = 0.017). However, there were no differences in total physical activity and daily glycemic control between groups. The findings of this study suggest that LST improves short-term glycemic variability in healthy subjects.