Glycemic Variability Is Associated with Markers of Vascular Stress in Adolescents

The clinical importance of measuring glycaemic variability: Utilising new metrics to optimise glycaemic control

With the widespread use of continuous glucose monitoring (CGM), glycaemic variability (GV) is a glucose metric that has been gaining increasing attention. However, unlike other glucose metrics that are easily defined and have clear targets, GV has a large number of different measures given the complexity involved in assessment. While variabilities in HbA1c, fasting and postprandial glucose have been incorporated under the GV banner, short-term variability in glucose, within day and between days, is more in keeping with the correct definition of GV. This review is focused on short-term GV, as assessed by CGM data, although studies calculating GV from capillary glucose testing are also mentioned as appropriate. The different measures of GV are addressed, and their potential role in microvascular and macrovascular complications, as well as patient-related outcomes, discussed. It should be noted that the independent role of GV in vascular pathology is not always clear, given the inconsistent findings in different populations and the close association between GV and hypoglycaemia, itself an established risk factor for adverse outcomes. Therefore, this review attempts, where possible, to disentangle the contribution of GV to diabetes complications from other glycaemic parameters, particularly hypoglycaemia. Evidence to date strongly suggests an independent role for GV in vascular pathology but future large-scale outcome studies are required to fully understand the exact contribution of this metric to vascular complications. This can be followed by setting appropriate GV measures and targets in different diabetes subgroups, in order to optimise glycaemic management and limit the risk of complications.

Association between glycemia risk index and arterial stiffness in type 2 diabetes

AIM

This study aims to investigate the association of glycemia risk index (GRI), a novel composite metric derived from continuous glucose monitoring (CGM), with arterial stiffness in patients with type 2 diabetes.

MATERIALS AND METHODS

A total of 342 adults with type 2 diabetes were enrolled between April and June 2023 from 11 communities in Shanghai, China. Medical examinations, including measurements of anthropometric parameters, blood pressure, and venous blood samples were conducted. Brachial-ankle pulse wave velocity (baPWV) was examined to evaluate arterial stiffness. All the participants underwent a 14 day CGM recording and GRI was calculated from the CGM data.

RESULTS

The mean age was 70.3 ± 6.8 years, and 162 (47.4%) were male. Participants with a higher baPWV had significantly higher levels of GRI and hyperglycemia component (both P for trend < 0.05). Linear regression revealed the significant positive linear associations of the GRI with baPWV in unadjusted or adjusted models (All P < 0.05). In the multivariable logistic analysis, each increase in the GRI quartile was associated with a 1.30-fold (95% CI 1.01-1.68, P for trend < 0.05) higher prevalence of increased arterial stiffness after adjustment for age, sex, BMI, diabetes duration, current smoking status, blood pressure, and lipid profile. Subgroup analyses showed that the association between the GRI quartiles and increased arterial stiffness was stronger among participants with a diabetes duration ≥15 years (P for interaction = 0.014).

CONCLUSION

Glycemia risk index assessed by continuous glucose monitoring is associated with increased arterial stiffness in type 2 diabetes.

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

BACKGROUND & AIMS

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.

METHODS

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 ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

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.

CONCLUSION

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.

Metformin improves glycemic variability in adults with type 1 diabetes mellitus: an open-label randomized control trial

INTRODUCTION

Metformin has been demonstrated to enhance cardioprotective benefits in type 1 diabetes (T1DM). Although glycemic variability (GV) is associated with increased risk of CVD in diabetes, there is a scarcity of research evaluating the effect of metformin on GV in T1DM.

OBJECTIVES

In the present study, the effects of adjuvant metformin therapy on GV and metabolic control in T1DM were explored.

PATIENTS AND METHODS

A total of 65 adults with T1DM were enrolled and subjected to physical examination, fasting laboratory tests, and continuous glucose monitoring, and subsequently randomized 1:1 to 3 months of 1000-2000 mg metformin daily add-on insulin (MET group, n = 34) or insulin (non-MET group, n = 31). After, baseline measurements were repeated.

RESULTS

The mean amplitude of glycemic excursions was substantially reduced in MET group, compared with non-MET group (-1.58 (-3.35, 0.31) mmol/L vs 1.36 (-1.12, 2.24) mmol/L, P = 0.004). In parallel, the largest amplitude of glycemic excursions (-2.83 (-5.47, -0.06) mmol/L vs 0.45 (-1.29, 4.48) mmol/L, P = 0.004), the s.d. of blood glucose (-0.85 (-1.51, 0.01) mmol/L vs -0.14 (-0.68, 1.21) mmol/L, P = 0.015), and the coefficient of variation (-6.66 (-15.00, 1.50)% vs -1.60 (-6.28, 11.71)%, P = 0.012) all demonstrated improvement in the MET group, compared with the non-MET group. Significant reduction in insulin dose, BMI, and body weight was observed in patients in MET, not those in non-MET group.

CONCLUSION

Additional metformin therapy improved GV in adults with T1DM, as well as improving body composition and reducing insulin requirement. Hence, metformin as an adjunctive therapy has potential prospects in reducing the CVD risk in patients with T1DM in the long term.

Interstitial glucose and subsequent affective and physical feeling states: A pilot study combining continuous glucose monitoring and ecological momentary assessment in adolescents

OBJECTIVE

Circulating glucose may relate to affective and physical feeling states reflective of emotional disorder symptoms. No prior studies have investigated within-day associations between glucose and subsequent affective and physical feeling states (positive affect, negative affect, and fatigue) as they occur naturally among healthy adolescents; this pilot study assessed these associations by combining data collected from ecological momentary assessment (EMA) and continuous glucose monitors (CGM).

METHODS

Participants (N = 15, mean age = 13.1[±1.0] years, 66.7% female, 40.0% Hispanic, 66.7% healthy weight) wore a CGM for 7-14 days. Simultaneously, participants reported on their current positive affect, negative affect, and fatigue randomly during specified windows up to 7 times daily via EMA. CGM-measured mean interstitial glucose was calculated during the time windows (mean minutes = 122.5[±47.3]) leading up to each EMA prompt. Multilevel models assessed within-subject (WS) associations between mean interstitial glucose since the previous EMA prompt and EMA-reported affective and physical feeling states at the current prompt.

RESULTS

Participants provided 532 interstitial glucose-matched EMA reports of affective and physical feeling states. During intervals when interstitial glucose was higher than one's usual, higher positive affect (WS β = 0.01, p < .0001, f² = 0.02) and lower fatigue (WS β = -0.01, p < .0001, f² = 0.09) were subsequently reported. Interstitial glucose was unrelated to negative affect (WS β = -0.002, p = .10, f² = 0.01). Associations were weakened, but remained significant following further adjustment for time of day.

CONCLUSIONS

Though effect sizes were small, within-person variations in interstitial glucose may relate to subsequent affective and physical feeling states among healthy youth. Investigations using similar methodologies in larger, more diverse samples are warranted.

Hyperglycemia and Glucose Variability Are Associated with Worse Brain Function and Seizures in Neonatal Encephalopathy: A Prospective Cohort Study

OBJECTIVES

To investigate how glucose abnormalities correlate with brain function on amplitude-integrated electroencephalography (aEEG) in infants with neonatal encephalopathy.

STUDY DESIGN

Neonates born at full term with encephalopathy were enrolled within 6 hours of birth in a prospective cohort study at a pediatric academic referral hospital. Continuous interstitial glucose monitors and aEEG were placed soon after birth and continued for 3 days. Episodes of hypoglycemia (≤50 mg/dL; ≤2.8 mmol/L) and hyperglycemia (>144 mg/dL; >8.0 mmol/L) were identified. aEEG was classified in 6-hour epochs for 3 domains (background, sleep-wake cycling, electrographic seizures). Generalized estimating equations assessed the relationship of hypo- or hyperglycemia with aEEG findings, adjusting for clinical markers of hypoxia-ischemia (Apgar scores, umbilical artery pH, and base deficit).

RESULTS

Forty-five infants (gestational age 39.5 ± 1.4 weeks) were included (24 males). During aEEG monitoring, 16 episodes of hypoglycemia were detected (9 infants, median duration 77.5, maximum 220 minutes) and 18 episodes of hyperglycemia (13 infants, median duration 237.5, maximum 3125 minutes). Epochs of hypoglycemia were not associated with aEEG changes. Compared with epochs of normoglycemia, epochs of hyperglycemia were associated with worse aEEG background scores (B 1.120, 95% CI 0.501-1.738, P < .001), less sleep-wake cycling (B 0.587, 95% CI 0.417-0.757, P < .001) and more electrographic seizures (B 0.433, 95% CI 0.185-0.681, P = .001), after adjusting for hypoxia-ischemia severity.

CONCLUSIONS

In neonates with encephalopathy, epochs of hyperglycemia were temporally associated with worse global brain function and seizures, even after we adjusted for hypoxia-ischemia severity. Whether hyperglycemia causes neuronal injury or is simply a marker of severe brain injury requires further study.

Effect of exercise on glucose variability in healthy subjects: randomized crossover trial

The aim of this study was to evaluate the acute effect of aerobic (AER) and eccentric (ECC) exercise on glucose variability, correlating it with circulating markers of inflammation and oxidative stress in healthy subjects. Sixteen healthy subjects (32 ± 12 years old) wore a continuous glucose monitoring system for three days. Participants randomly performed single AER and ECC exercise sessions. Glucose variability was evaluated by glucose variance (VAR), glucose coefficient of variation (CV%) and glucose standard deviation (SD). Blood samples were collected to evaluate inflammatory and oxidative stress markers. When compared with the pre-exercise period of 0-6 h, all the indices of glucose variability presented comparable reductions 12-18 h after both exercises (∆AER: VAR= 151.5, ∆CV% = 0.55 and ∆SD = 3.1 and ECC: ∆VAR = 221.2 , ∆CV% = 3.7 and ∆SD = 6.5). Increased interleukin-6 (IL-6) levels after AER (68.5%) and ECC (30.8%) (P<0.001) were observed, with no differences between sessions (P = 0.459). Uric acid levels were increased after exercise sessions (3% in AER and 4% in ECC, P = 0.001). In conclusion, both AER and ECC exercise sessions reduced glucose variability in healthy individuals. Inflammatory cytokines, such as IL-6, and stress oxidative markers might play a role in underlying mechanisms modulating the glucose variability responses to exercise (clinicalTrials.gov NCT02262208).

Effects of a lifestyle intervention on markers of cardiometabolic risk and oxidized lipoproteins among obese adolescents with prediabetes

OBJECTIVE

Obesity and hyperglycaemia contribute to the atherosclerotic process in part through oxidative modifications to lipoprotein particles. The present study aimed to evaluate the effects of a lifestyle intervention on markers of oxidized lipoproteins in obese Latino adolescents with prediabetes.

DESIGN

Pre-post design.

SETTING

Participants were enrolled into a 12-week lifestyle intervention. Measurements pre- and post-intervention included anthropometrics and body composition, lipid panel, oxidized LDL (oxLDL), oxidized HDL (oxHDL), intake of fresh fruits and vegetables, and cardiorespiratory fitness.

PARTICIPANTS

Thirty-five obese Latino adolescents (seventeen females, eighteen males; mean age 15·5 (sd 1·0) years; mean BMI percentile 98·5 (sd 1·2)) with prediabetes.

RESULTS

Intervention participation resulted in significant reductions in weight (-1·2 %, P = 0·042), BMI and BMI percentile (-2·0 and -0·4 %, respectively, P < 0·001), body fat (-7·0 %, P = 0·025), TAG (-11·8 %, P = 0·032), total cholesterol (-5·0 %, P = 0·002), VLDL-cholesterol (-12·5 %, P = 0·029), and non-HDL-cholesterol (-6·7 %, P = 0·007). Additionally, fitness (6·4 %, P < 0·001) and intake of fruits and vegetables (42·4 %, P = 0·025) increased significantly. OxLDL decreased significantly after the intervention (51·0 (sd 14·0) v. 48·7 (sd 12·8) U/l, P = 0·022), while oxHDL trended towards a significant increase (395·2 (sd 94·6) v. 416·1 (sd 98·4) ng/ml, P = 0·056).

CONCLUSIONS

These data support the utility of lifestyle intervention to improve the atherogenic phenotype of Latino adolescents who are at high risk for developing premature CVD and type 2 diabetes.

Resistance to Data Loss of Glycemic Variability Measurements in Long-Term Continuous Glucose Monitoring

Background: Continuous glucose monitoring (CGM) is a method of estimating blood glucose values from those recorded in the interstitial fluid. Because increasingly longer CGM measurements are possible, errors and data loss become more and more likely and potentially more damaging to accurate calculations of glycemic variability (GV) indices. Our research investigates the resistance of the CGM recording to data loss. Methods: We collected 71 CGM recordings (duration of min: 2, max: 265, median: 42 days) from patients with type 1 diabetes and used three algorithms to introduce missing data. We calculated mean and standard deviation (SD) of absolute percentage error of 12 variability indices and correlated those with the percentage of missing data and duration of the measurements. Results: Mean absolute percentage error of variability indices increased linearly with the percentage of missing data along with SD of absolute percentage error. Except for mean amplitude of glycemic excursions and time spent in hypoglycemia, all absolute errors never exceeded 25%, while mean absolute errors stayed below 5%. The gradient removal algorithm introduced errors larger than the single datapoint and block removal algorithms. The absolute percentage error of variability indices correlated negatively with the duration of the CGM measurements. Conclusions: Standard GV measurements in long-term glucose monitoring are robustly resistant to data loss.

Impact of different timing of consuming sweet snack on postprandial glucose excursions in healthy women

AIMS

Our aim was to evaluate the acute effect of eating sweet snacks at different times of day on glycaemic parameters in young women without diabetes.

METHODS

In this randomized controlled three-treatment crossover study, 17 women [(means ± SD) age: 21.2 ± 0.8 years, BMI: 20.7 ± 2.5 kg/m2, HbA_1c: 36 ± 2 mmol/mol (5.1 ± 0.2%)] wore flash (continuous) glucose monitoring systems for 7 days. Each participant consumed identical test meals on days 4, 5 and 6, but consumed sweet snacks (baked cake: 498 kcal; 53.6 g of carbohydrate, 8.0 g of protein, 28.0 g of fat) at 12:30 (post-lunch), 15:30 (mid-afternoon) and 19:30 (post-dinner), respectively, on each of those days. Daily glycaemic parameters on those 3 days of snacking at different times of day were compared within-participant.

RESULTS

The mean amplitude of glycaemic excursions (3.54 ± 0.32 vs. 2.73 ± 0.20 mmol/L; P < 0.05), standard deviation of glucose (1.20 ± 0.11 vs. 0.92 ± 0.07 mmol/L; P < 0.05), incremental area under the curve (IAUC) for glucose at 12:00-07:00 (986 ± 89 vs. 716 ± 88 mmol/L × min; P < 0.05) and IAUC at 07:00-10:00 the next day (141 ± 17 vs. 104 ± 12 mmol/L × min; P < 0.05) when the snack was eaten post-dinner were all significantly higher than with mid-afternoon snacking.

CONCLUSION

Eating sweet snacks post-dinner should be avoided because it worsens glucose excursions as well as postprandial glucose levels after both dinner and the following day's breakfast in young healthy (non-diabetic) women.

Hemoglobin A1c Accurately Predicts Continuous Glucose Monitoring-Derived Average Glucose in Youth and Young Adults With Cystic Fibrosis

OBJECTIVE

In cystic fibrosis (CF), hemoglobin A_1c (HbA_1c) is thought to underestimate glycemia. However, few studies have directly assessed the relationship between HbA_1c and average glucose in CF. We determined the relationships among glycemic markers-HbA_1c, fructosamine (FA), glycated albumin (%GA), and 1,5-anhydroglucitol (1,5-AG)-and continuous glucose monitoring (CGM) in CF, hypothesizing that alternate markers would better predict average sensor glucose (ASG) than HbA_1c.

RESEARCH DESIGN AND METHODS

CF participants and a group of healthy control subjects (HCs), ages 6-25 years, wore CGM for up to 7 days. Pearson correlations assessed the relationships between CGM variables and HbA_1c, FA, %GA, and 1,5-AG. The regression line between HbA_1c and ASG was compared in CF versus HC. Linear regressions determined whether alternate markers predicted ASG after adjustment for HbA_1c.

RESULTS

CF (n = 93) and HC (n = 29) groups wore CGM for 5.2 ± 1 days. CF participants were 14 ± 3 years of age and 47% were male, with a BMI z score -0.1 ± 0.8 and no different from HCs in age, sex, or BMI. Mean HbA_1c in CF was 5.7 ± 0.8% (39 ± 9 mmol/mol) vs. HC 5.1 ± 0.2% (32 ± 2 mmol/mol) (P < 0.0001). All glycemic markers correlated with ASG (P ≤ 0.01): HbA_1c (r = 0.86), FA (r = 0.69), %GA (r = 0.83), and 1,5-AG (r = -0.26). The regression line between ASG and HbA_1c did not differ in CF versus HC (P = 0.44). After adjustment for HbA_1c, %GA continued to predict ASG (P = 0.0009) in CF.

CONCLUSIONS

HbA_1c does not underestimate ASG in CF as previously assumed. No alternate glycemic marker correlated more strongly with ASG than HbA_1c. %GA shows strong correlation with ASG and added to the prediction of ASG beyond HbA_1c. However, we are not advocating use of HbA_1c for diabetes screening in CF based on these results. Further study will determine whether glycemic measures other than ASG differ among different types of diabetes for a given HbA_1c.

Association of glycaemic variability evaluated by continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients

PURPOSE

Diabetic peripheral neuropathy (DPN), a common microvascular complication of diabetes, is linked to glycaemic derangements. Glycaemic variability, as a pattern of glycaemic derangements, is a key risk factor for diabetic complications. We investigated the association of glycaemic variability with DPN in a large-scale sample of type 2 diabetic patients.

METHODS

In this cross-sectional study, we enrolled 982 type 2 diabetic patients who were screened for DPN and monitored by a continuous glucose monitoring (CGM) system between February 2011 and January 2017. Multiple glycaemic variability parameters, including the mean amplitude of glycaemic excursions (MAGE), mean of daily differences (MODD), standard deviation of glucose (SD), and 24-h mean glucose (24-h MG), were calculated from glucose profiles obtained from CGM. Other possible risks for DPN were also examined.

RESULTS

Of the recruited type 2 diabetic patients, 20.1% (n = 197) presented with DPN, and these patients also had a higher MAGE, MODD, SD, and 24-h MG than patients without DPN (p < 0.001). Using univariate and multiple logistic regression analyses, MAGE and conventional risks including diabetic duration, HOMA-IR, and hemoglobin A1c (HbA1c) were found to be independent contributors to DPN, and the corresponding odds ratios (95% confidence interval) were 4.57 (3.48-6.01), 1.10 (1.03-1.17), 1.24 (1.09-1.41), and 1.33 (1.15-1.53), respectively. Receiver operating characteristic analysis indicated that the optimal MAGE cutoff value for predicting DPN was 4.60 mmol/L; the corresponding sensitivity was 64.47%, and the specificity was 75.54%.

CONCLUSIONS

In addition to conventional risks including diabetic duration, HOMA-IR and HbA1c, increased glycaemic variability assessed by MAGE is a significant independent contributor to DPN in type 2 diabetic patients.

Effect of obesity and type 2 diabetes, and glucose ingestion on circulating spexin concentration in adolescents

Spexin is a novel peptide that has been reported to be down regulated in obese adults and children and in normoglycemic adults following glucose ingestion. Spexin may therefore have a role in metabolic regulation. The purpose of the current study was to determine the effect of obesity and type 2 diabetes (T2DM), and the effect of glucose ingestion on circulating spexin concentration in adolescents. Boys and girls (mean age 16 years old) classified as healthy normal weight (NW, n = 22), obese (Ob, n = 10), or obese with T2DM (n = 12) completed measurements of body composition, blood pressure, cardiorespiratory fitness, and blood concentrations of glucose, insulin, and lipids. The median fasting serum spexin concentration did not differ between groups (NW: 0.35; Ob: 0.38, T2DM: 0.34 ng/mL, respectively). In 10 NW participants who completed a standard oral glucose tolerance test, spexin concentration was unchanged at 30 and 120 minutes relative to the fasting baseline. Finally, spexin was not significantly correlated with any of the body composition, fitness, or blood biochemical measurements. These data do not support the proposed role of spexin as a metabolic regulator or biomarker of glucose control in adolescents.

Alternate glycemic markers reflect glycemic variability in continuous glucose monitoring in youth with prediabetes and type 2 diabetes

OBJECTIVE

To determine whether the alternate glycemic markers, fructosamine (FA), glycated albumin (GA), and 1,5-anhydroglucitol (1,5AG), predict glycemic variability captured by continuous glucose monitoring (CGM) in obese youth with prediabetes and type 2 diabetes (T2D).

STUDY DESIGN

Youth with BMI ≥85th%ile, 10-18 years, had collection of fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), FA, GA, and 1,5AG and 72 hours of CGM. Participants with HbA1c ≥5.7% were included. Relationships between glycemic markers and CGM variables were determined with Spearman correlation coefficients. Linear models were used to examine the association between alternate markers and CGM measures of glycemic variability-standard deviation (SD) and mean amplitude of glycemic excursions (MAGE)-after controlling for HbA1c.

RESULTS

Total n = 56; Median (25th%ile, 75th%ile) age = 14.3 years (12.5, 15.9), 32% male, 64% Hispanic, 20% black, 13% white, HbA1c = 5.9% (5.8, 6.3), FA=211 mmol/L (200, 226), GA= 12% (11%, 12%), and 1,5AG = 22mcg/mL (19, 26). HbA1c correlated with average sensor glucose, AUC, SD, MAGE, and %time > 140 mg/dL. FA and GA correlated with average and peak sensor glucose, %time >140 and >200 mg/dL, and MAGE. GA also correlated with SD and AUC180. 1,5AG correlated with peak glucose, AUC180, SD, and MAGE. After adjusting for HbA1c, all 3 markers independently predicted MAGE; FA and GA independently predicted SD.

CONCLUSIONS

Alternate glycemic markers predict glycemic variability as measured by CGM in youth with prediabetes and T2D. After adjusting for HbA1c, these alternate markers continued to predict components of glycemic variability detected by CGM.

Use of Continuous Glucose Monitoring in Youth-Onset Type 2 Diabetes

PURPOSE OF REVIEW

Continuous glucose monitoring (CGM) technology has long been accepted as a tool for managing glycemia in type 1 diabetes (T1D) and is receiving increased attention as a tool for monitoring glucose patterns in patients with other forms of diabetes, in particular type 2 diabetes (T2D). Recent studies in adults with T2D have shown benefits of CGM in the investigation of glycemic variability, as well as utility as a tool for improving glycemic control. The literature on CGM use in youth-onset T2D, however, is sparse. This paper reviews the various roles for CGM in T2D, with a focus on published reports of CGM use in youth-onset T2D. The gaps in knowledge are highlighted, along with a discussion regarding need for future studies of potential applications for CGM in this younger population.

RECENT FINDINGS

CGM systems provide insight into glycemic abnormalities in obese youth with and at risk for T2D. This technology has enabled examination of the relationship between free-living glycemic profiles and traditional diabetes screening tests, as well as markers of cardiometabolic risk in this high-risk population. Investigators are incorporating CGM technology into the study of T2D in youth, but interventional studies of CGM as a tool for glycemic control in youth-onset T2D are limited. Youth with T2D face a more aggressive disease than adults with T2D, and further studies utilizing advances in glucose monitoring technology to improve outcomes in this population are needed.

The relationship between fasting blood glucose variability and coronary artery collateral formation in type 2 diabetes patients with coronary artery disease

BACKGROUND

Coronary collaterals are an alternative source of blood supply to ischemic myocardium. Well-developed coronary collateral arteries in patients with coronary artery disease (CAD) limit the size of acute myocardial infarction and improves survival. The aim of this study was to investigate the relationship between glycemic variability and coronary collateral formation in patients with type 2 diabetes mellitus and CAD.

METHODS

Consecutive patients undergoing percutaneous coronary intervention or coronary artery bypass grafting procedures were studied. Multivariate logistic regression models were used to examine the association between coronary artery collateral formation graded by Rentrope classification and glycemic variability, measured by coefficient variation of fasting blood glucose.

RESULTS

In our study, we retrospectively enrolled 300 patients, of whom 239 were diabetic (age: 70.1±11.9, 56% men) and 61 were nondiabetic (age: 71.5±11.5, 72% men). Diabetic patients were further stratified as follows: those with poor coronary collateral artery development (n=171, age: 69.7±12.4, 55% men) and those with good coronary collateral artery development (n=68, age 71.1±10.8, 59% men) according to the Rentrope classification. Our findings did not show association between glycemic variability and coronary collateral vessels development after controlling for potential confounders (odds ratio: 2.51; 95% confidence interval: 0.57-11.03; P=0.22). The culprit lesion (≥75% stenosis) in the left anterior descending artery and the right coronary artery was more frequent in the good collateral group compared with the poor collateral group (66 vs. 50%, P=0.02; 63 vs. 45%, P=0.01 respectively).

CONCLUSION

Glycemic variability is not associated with coronary collateral artery formation in patients with type 2 diabetes mellitus and CAD.

Glucose Outcomes with the In-Home Use of a Hybrid Closed-Loop Insulin Delivery System in Adolescents and Adults with Type 1 Diabetes

BACKGROUND

The safety and effectiveness of the in-home use of a hybrid closed-loop (HCL) system that automatically increases, decreases, and suspends insulin delivery in response to continuous glucose monitoring were investigated.

METHODS

Adolescents (n = 30, ages 14-21 years) and adults (n = 94, ages 22-75 years) with type 1 diabetes participated in a multicenter (nine sites in the United States, one site in Israel) pivotal trial. The Medtronic MiniMed^® 670G system was used during a 2-week run-in phase without HCL control, or Auto Mode, enabled (Manual Mode) and, thereafter, with Auto Mode enabled during a 3-month study phase. A supervised hotel stay (6 days/5 nights) that included a 24-h frequent blood sample testing with a reference measurement (i-STAT) occurred during the study phase.

RESULTS

Adolescents (mean ± standard deviation [SD] 16.5 ± 2.29 years of age and 7.7 ± 4.15 years of diabetes) used the system for a median 75.8% (interquartile range [IQR] 68.0%-88.4%) of the time (2977 patient-days). Adults (mean ± SD 44.6 ± 12.79 years of age and 26.4 ± 12.43 years of diabetes) used the system for a median 88.0% (IQR 77.6%-92.7%) of the time (9412 patient-days). From baseline run-in to the end of study phase, adolescent and adult HbA_1c levels decreased from 7.7% ± 0.8% to 7.1% ± 0.6% (P < 0.001) and from 7.3% ± 0.9% to 6.8% ± 0.6% (P < 0.001, Wilcoxon signed-rank test), respectively. The proportion of overall in-target (71-180 mg/dL) sensor glucose (SG) values increased from 60.4% ± 10.9% to 67.2% ± 8.2% (P < 0.001) in adolescents and from 68.8% ± 11.9% to 73.8% ± 8.4% (P < 0.001) in adults. During the hotel stay, the proportion of in-target i-STAT^® blood glucose values was 67.4% ± 27.7% compared to SG values of 72.0% ± 11.6% for adolescents and 74.2% ± 17.5% compared to 76.9% ± 8.3% for adults. There were no severe hypoglycemic or diabetic ketoacidosis events in either cohort.

CONCLUSIONS

HCL therapy was safe during in-home use by adolescents and adults and the study phase demonstrated increased time in target, and reductions in HbA_1c, hyperglycemia and hypoglycemia, compared to baseline.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT02463097.

Vildagliptin reduces plasma stromal cell-derived factor-1α in patients with type 2 diabetes compared with glimepiride

AIMS/INTRODUCTION

Dipeptidyl peptidase-4 inhibitors might have pleiotropic protective effects on cardiovascular disease (CVD), in contrast to sulfonylureas. Therefore, we compared various CVD risk factors between vildagliptin and glimepiride.

MATERIALS AND METHODS

We carried out a randomized, prospective and crossover trial. A total of 16 patients with type 2 diabetes whose glycated hemoglobin was >7% were randomized to add vildagliptin or glimepiride. After 12-week treatment, each drug was replaced with the other for another 12 weeks. Before and after each treatment, glucose homeostasis and CVD risk factors were assessed, and the continuous glucose monitoring system was applied to calculate glycemic variability.

RESULTS

The mean age of the participants was 60 years, 31% were men, body mass index 25.5 kg/m² and HbA1c 8.41%. Both vildagliptin and glimepiride significantly decreased glycated hemoglobin and glycemic variability indices. Despite the improved glucose homeostasis, favorable change of CVD markers was not prominent in both the arms, along with significant weight gain. Only plasma stromal cell-derived factor (SDF)-1α decreased by 30% in the vildagliptin arm. According to regression analyses, the reduction of SDF-1α was independently associated with vildagliptin usage and serum interleukin-6 changes, but white blood cells were not related with the SDF-1α changes.

CONCLUSION

Compared with glimepiride, vildagliptin arrestingly decreased plasma SDF-1α, and its clinical implications should be further investigated.