Continuous glucose monitoring defined glucose variability is associated with cardiovascular autonomic neuropathy in type 1 diabetes

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.

Research progress on the association between glycemic variability index derived from CGM and cardiovascular disease complications

Currently, glycated hemoglobin A1c (HbA1c) has been widely used to assess the glycemic control of patients with diabetes. However, HbA1c has certain limitations in describing both short-term and long-term glycemic control. To more accurately evaluate the glycemic control of diabetes patients, the continuous glucose monitoring (CGM) technology has emerged. CGM technology can provide robust data on short-term glycemic control and introduce new monitoring parameters such as time in range, time above range, and time below range as indicators of glycemic fluctuation. These indicators are used to describe the changes in glycemic control after interventions in clinical research or treatment modifications in diabetes patient care. Recent studies both domestically and internationally have shown that these indicators are not only associated with microvascular complications of diabetes mellitus but also closely related to cardiovascular disease complications and prognosis. Therefore, this article aims to comprehensively review the association between CGM-based glycemic parameters and cardiovascular disease complications by analyzing a large number of domestic and international literature. The purpose is to provide scientific evidence and guidance for the standardized application of these indicators in clinical practice, in order to better evaluate the glycemic control of diabetes patients and prevent the occurrence of cardiovascular disease complications. This research will contribute to improving the quality of life for diabetes patients and provide important references for clinical decision-making.

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic condition characterized by glucose fluctuations. Laboratory studies suggest that cognition is reduced when glucose is very low (hypoglycemia) and very high (hyperglycemia). Until recently, technological limitations prevented researchers from understanding how naturally-occurring glucose fluctuations impact cognitive fluctuations. This study leveraged advances in continuous glucose monitoring (CGM) and cognitive ecological momentary assessment (EMA) to characterize dynamic, within-person associations between glucose and cognition in naturalistic environments. Using CGM and EMA, we obtained intensive longitudinal measurements of glucose and cognition (processing speed, sustained attention) in 200 adults with T1D. First, we used hierarchical Bayesian modeling to estimate dynamic, within-person associations between glucose and cognition. Consistent with laboratory studies, we hypothesized that cognitive performance would be reduced at low and high glucose, reflecting cognitive vulnerability to glucose fluctuations. Second, we used data-driven lasso regression to identify clinical characteristics that predicted individual differences in cognitive vulnerability to glucose fluctuations. Large glucose fluctuations were associated with slower and less accurate processing speed, although slight glucose elevations (relative to person-level means) were associated with faster processing speed. Glucose fluctuations were not related to sustained attention. Seven clinical characteristics predicted individual differences in cognitive vulnerability to glucose fluctuations: age, time in hypoglycemia, lifetime severe hypoglycemic events, microvascular complications, glucose variability, fatigue, and neck circumference. Results establish the impact of glucose on processing speed in naturalistic environments, suggest that minimizing glucose fluctuations is important for optimizing processing speed, and identify several clinical characteristics that may exacerbate cognitive vulnerability to glucose fluctuations.

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

24-h glucose level and variability in response to carbohydrate overfeeding assessed using continuous glucose monitoring system are associated with daily carbohydrate intake

BACKGROUND & AIMS

The quantity gap between daily and loaded carbohydrates may affects blood glucose response to carbohydrate intake; however, no study has investigated the difference in 24-h span. This study aimed to determine differences in the 24-h glucose levels and variability in response to single-day carbohydrate overfeeding based on daily carbohydrate intake in healthy Japanese men.

METHODS

Twenty male college students completed a 3-day dietary record and were divided into two groups based on whether their daily carbohydrate intake exceeded the median intake (H-CHO) or not (L-CHO). Thereafter, the participants consumed a high-carbohydrate diet (carbohydrate 8.1 g/kg/d) for 1 day. The 24-h glucose levels and glucose variability (CONGA1) were measured using a continuous glucose monitoring system.

RESULTS

The mean daily carbohydrate intakes in the L-CHO and H-CHO groups were 3.9 ± 0.5 and 5.8 ± 0.6 g/kg/d, respectively (p < 0.001). The peak 24-h glucose level was not differ between the L-CHO group and the H-CHO group (8.0 ± 0.8 vs. 8.0 ± 1.0; p = 0.886). The mean 24-h glucose level was higher in the L-CHO group than in the H-CHO group (6.0 ± 0.3 vs. 5.6 ± 0.3 mmol/L; p = 0.010). The CONGA1 was higher in the L-CHO group than in the H-CHO group (5.40 ± 0.41 vs. 4.95 ± 0.25; p = 0.008).

CONCLUSIONS

Mean glucose level and glucose variability in response to carbohydrate overfeeding were high in the individuals with small daily carbohydrate intake. These findings suggest that the large quantity gap between daily and loaded carbohydrates cause worse glucose control during carbohydrate overfeeding.

Serum Metabolomics Reveals a Potential Benefit of Methionine in Type 1 Diabetes Patients with Poor Glycemic Control and High Glycemic Variability

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.

Effect of repeated bolus and continuous glucose infusion on a panel of circulating biomarkers in healthy volunteers

HYPOTHESIS

Glycaemic variability (GV) refers to fluctuations in the blood glucose level and may contribute to complications in patients suffering from Diabetes. Several studies show negative effects of GV on the cardiovascular system, however there is still a lack of conclusive evidence. Using an explorative cardiovascular panel, it is possible to simultaneously measure the effects on proteins relevant for cardiovascular processes. The aim of this study was to investigate the effects of rapid glucose excursions on cardiovascular and metabolic parameters in healthy individuals.

METHODS

An explorative single-blinded cross-over study was performed in ten healthy men. Subjects received 3 times 20 grams of glucose i.v. over 5 minutes or 60 grams of glucose continuously over 3 hours. Blood was taken for repeated measurements of the cardiovascular panel over the following 6 hours and again after 24 and 48 hours.

RESULTS

We observed a significant elevation of 7 cardiovascular biomarkers (BMP6, SLAMF7, LOX-1, ADAMTS13, IL-1RA, IL-4RA, PTX3) at t = 360min after rapid glucose infusion compared to a continuous glucose infusion.

CONCLUSIONS

Intraday GV seems to have acute effects on cardiovascular proteins in healthy test persons. Rapid glucose administration compared to continuous administration showed significant changes in BMP6, SLAMF7, ADAMTS13, IL1RA, PTX3, IL-4RA and LOX-1.

CLINICAL TRIAL REGISTRATION

NCT04488848.

Continuous glucose monitoring reveals a novel association between duration and severity of hypoglycemia, and small nerve fiber injury in patients with diabetes

Objective

Continuous glucose monitoring (CGM) has revealed that glycemic variability and low time in range are associated with albuminuria and retinopathy. We have investigated the relationship between glucose metrics derived from CGM and a highly sensitive measure of neuropathy using corneal confocal microscopy in participants with type 1 and type 2 diabetes.

Methods

A total of 40 participants with diabetes and 28 healthy controls underwent quantification of corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), corneal nerve fiber length (CNFL) and inferior whorl length (IWL) and those with diabetes underwent CGM for four consecutive days.

Results

CNBD was significantly lower in patients with high glycemic variability (GV) compared to low GV (median (range) (25.0 (19.0-37.5) vs 38.6 (29.2-46.9); P = 0.007); in patients who spent >4% compared to <4% time in level 1 hypoglycemia (54-69 mg/dL) (25.0 (22.9-37.5) vs 37.5 (29.2-46.9); P = 0.045) and in patients who spent >1% compared to <1% time in level 2 hypoglycemia (<54 mg/dL) (25.0 (19.8-41.7) vs 35.4 (28.1-44.8); P = 0.04). Duration in level 1 hypoglycemia correlated with CNBD (r = -0.342, P = 0.031). Duration in level 1 (181-250 mg/dL) and level 2 (>250 mg/dL) hyperglycemia did not correlate with CNFD (P > 0.05), CNBD (P > 0.05), CNFL (P > 0.05) or IWL (P > 0.05).

Conclusions

Greater GV and duration in hypoglycemia, rather than hyperglycemia, are associated with nerve fiber loss in diabetes.

The continuous spectrum of glycaemic variability changes with pancreatic islet function: A multicentre cross-sectional study in China

AIMS

To investigate glycaemic variability (GV) patterns in patients with type 1 diabetes (T1D), type 2 diabetes (T2D), and latent autoimmune diabetes in adults (LADA).

MATERIALS AND METHODS

A total of 842 subjects (510 T1D, 105 LADA, 227 T2D) were enrolled and underwent 1 week of continuous glucose monitoring (CGM). Clinical characteristics and CGM parameters were compared among T1D, LADA, and T2D. LADA patients were divided into two subgroups based on glutamic acid decarboxylase autoantibody titres (≥180 U/mL [LADA-1], <180 U/mL [LADA-2]) and compared. The C-peptide cut-offs for predicting a coefficient of variation (CV) of glucose ≥36% and a time in range (TIR) > 70% were determined using receiver operating characteristic analysis.

RESULTS

Twenty-seven patients (9 T1D, 18 T2D) were excluded due to insufficient CGM data. Sex, diabetes duration and HbA1c were comparable among the three groups. Fasting and 2-h postprandial C-peptide (FCP, 2hCP) increased sequentially across T1D, LADA, and T2D. T1D and LADA patients had comparable TIR and GV, whereas those with T2D had much higher TIR and lower GV (p < 0.001). The GV of LADA-1 was close to that of T1D, while the GV of LADA-2 was close to that of T2D. CP exhibited the strongest negative correlation with GV. The cut-offs of FCP/2hCP for predicting a CV ≥ 36% and TIR >70% were 121.6/243.1 and 128.9/252.8 pmol/L, respectively.

CONCLUSIONS

GV presented a continuous spectrum across T1D, LADA-1, LADA-2, and T2D. More frequent glucose monitoring is suggested for patients with impaired insulin secretion.

CLINICAL TRAIL REGISTRATION

Chinese Clinical Trial Registration (ChiCTR) website approved by WHO; http://www.chictr.org.cn/ - ChiCTR2200065036.

Electrocardiography and heart rate variability in Göttingen Minipigs: Impact of diurnal variation, lead placement, repeatability and streptozotocin-induced diabetes

BACKGROUND

The Göttingen Minipig is widely used in preclinical research and safety pharmacology, but standardisation of porcine electrocardiography (ECG) is lacking. The aim of this study was to investigate diurnal effects, change over time and choice of lead on ECG morphology and heart rate variability (HRV) in healthy and streptozotocin (STZ) induced diabetic Göttingen Minipigs.

METHODS

Diabetes was experimentally induced using STZ in 11 Göttingen Minipigs (DIA). Seven controls (CON) were included. 24-h ECG was recorded at baseline and four months. Morphological parameters (QRS and T wave duration, P- and T-wave amplitude, PR and QT (Bazett's (QTcb) or Fridericia (QTcf) correction) intervals and ST segment), presence of cardiac arrhythmias, heart rate (HR) and HRV (time and frequency domain) were analysed.

RESULTS

Four months after induction, DIA had decreased P-wave amplitude (P < 0.0001) and T-wave duration (P = 0.017), compared to CON. QTcb was lower in DIA, but not in CON. Both groups had decreased HR (P < 0.0001) and QRS duration (lead II, P = 0.04) and length of PR-segment increased (lead I and II, P < 0.01) while selected HRV parameters also increased (all P < 0.01). Time of day influenced HR, QRS duration, PR segment, ST segment, T- and P-wave amplitude and some parameters of HRV. Inter- and intra-observer variability of morphological measurements was low (<6%).

CONCLUSION

ECG parameters were influenced by time setting, diurnal variation and lead. Some ECG and HRV changes were found in diabetic minipigs four months after STZ induction. The findings underline the need for standardisation of ECG and HRV in Göttingen Minipigs.

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

AIMS

To 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).

METHODS

In 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.

RESULTS

At baseline, participants had mean age of 60.9 years and mean HbA_1c 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.

CONCLUSIONS

Greater %CV was associated with increased risk for all-cause mortality even among patients with seemingly well-controlled glucose status.

Effects of curcumin and metformin on oxidative stress and apoptosis in heart tissue of type 1 diabetic rats

Introduction: Hyperglycemia enhances oxidative stress and apoptosis and induces damages in heart tissue. Based on antioxidant properties of curcumin and metformin, we hypothesized that these agents may exhibit cardioprotective effects by attenuating oxidative stress and modulating expression of the genes involved in apoptosis in type-1 diabetes.

Methods: Thirty-six male rats were randomly divided into six groups; (N): control; (D): streptozotocin-induced diabetic rats; (D+Cur50) and (D+Cur150): diabetic rats treated with 50 and 150 milligram of curcumin per kilogram of body weight (mg/kg.bw), respectively; (D+Met300) and (D+Met500): diabetic rats received 300 and 500 mg/kg.bw of metformin, respectively. Heart tissues were dissected and gene expression levels of Bax, Bcl-2, and caspase-3 were analyzed. Total anti-oxidant capacity (TAC), total oxidant status (TOS), and malondialdehyde (MDA) level, and activities of catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) were measured.

Results: Enhancement in TOS, OSI, and MDA levels as well as increased in the activity of CAT and reduction in SOD and GPx activities were observed in diabetic group (D) compared with control rats. Treatment of diabetic animals with either curcumin or metformin normalized TOS, OSI, and MDA levels and restored CAT, SOD, and GPx activities. Diabetes caused extensive damages in heart tissue of rats (group D) and increased expression of caspase-3 and Bax genes and enhanced ratio of Bax/Bcl-2 expression compared with controls. Treatment with curcumin or metformin mitigated histopathological changes and dampened apoptosis by normalizing Bax and caspase-3 expression.

Conclusion: Curcumin and metformin modulated diabetes-induced cardiac damage probably by reducing oxidative stress.

Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring

CONTEXT

Although glycated hemoglobin A1c is currently the best parameter used clinically to assess risk for the development of diabetes complications, it does not provide insight into short-term fluctuations in glucose levels. This review summarizes the relationship between continuous glucose monitoring (CGM)-derived metrics of glycemic variability and diabetes-related complications.

EVIDENCE ACQUISITION

PubMed and Embase databases were searched from January 1, 2010 to August 22, 2020, using the terms type 1 diabetes, type 2 diabetes, diabetes-related microvascular and macrovascular complications, and measures of glycaemic variability. Exclusion criteria were studies that did not use CGM and studies involving participants who were not diabetic, acutely unwell (post stroke, post surgery), pregnant, or using insulin pumps.

EVIDENCE SYNTHESIS

A total of 1636 records were identified, and 1602 were excluded, leaving 34 publications in the final review. Of the 20 852 total participants, 663 had type 1 diabetes (T1D) and 19 909 had type 2 diabetes (T2D). Glycemic variability and low time in range (TIR) showed associations with all studied microvascular and macrovascular complications of diabetes. Notably, higher TIR was associated with reduced risk of albuminuria, retinopathy, cardiovascular disease mortality, all-cause mortality, and abnormal carotid intima-media thickness. Peripheral neuropathy was predominantly associated with standard deviation of blood glucose levels (SD) and mean amplitude of glycemic excursions (MAGE).

CONCLUSION

The evidence supports the association between diabetes complications and CGM-derived measures of intraday glycemic variability. TIR emerged as the most consistent measure, supporting its emerging role in clinical practice. More longitudinal studies and trials are required to confirm these associations, particularly for T1D, for which there are limited data.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Association of glycemic variability and hypoglycemia with distal symmetrical polyneuropathy in adults with type 1 diabetes

Previous studies exploring the influence of glycemic variability (GV) on the pathogenesis of distal symmetrical polyneuropathy (DSPN) in type 1 diabetes (T1DM) produced conflicting results. The aim of this study was to assess the relationship between GV and DSPN in T1DM. Adults with T1DM were included in this cross-sectional study and asked to undergo 3-day CGM. GV quantified by coefficient of variation (CV) and mean amplitude of glucose excursions (MAGE) were obtained from CGM. Clinical characteristics and biochemical assessments were collected for analysis. The study comprised 152 T1DM patients (53.9% males) with mean age of 44.2 year. Higher levels of age and duration of diabetes and lower levels of total cholesterol, LDL, fasting C-peptide and postprandial C-peptide were observed in DSPN subjects. DSPN groups displayed a higher blood glucose between 00:00 and 12:59 according to the CGM profile. Higher MAGE and CV were associated with increased risk of DSPN in the fully adjusted model. Meanwhile, a significant association between measurements of hypoglycemia, especially nocturnal hypoglycemia, and DSPN was found after multiple tests. CGM parameters describing the glycemic variability and hypoglycemia were potential risk factors for DSPN in adults with T1DM.

A personalized blood glucose level prediction model with a fine-tuning strategy: A proof-of-concept study

BACKGROUND

The accurate prediction of blood glucose (BG) level is still a challenge for diabetes management. This is due to various factors such as diet, personal physiological characteristics, stress, and activities influence changes in BG level. To develop an accurate BG level predictive model, we propose a personalized model based on a convolutional neural network (CNN) with a fine-tuning strategy.

METHODS

We utilized continuous glucose monitoring (CGM) datasets from 1052 professional CGM sessions and split them into three groups according to type 1, type 2, and gestational diabetes mellitus (T1DM, T2DM, and GDM, respectively). During the preprocessing, only CGM data points were utilized, and future BG levels of four different prediction horizons (PHs, 15, 30, 45, and 60 min) were used as output. In training, we trained a general CNN and a multi-output random forest regressor using a hold-out method for each group. Next, we developed two personalized models: (1) by fine-tuning the general CNN on partial sample points of each CGM dataset, and (2) by learning a CNN from scratch on the points.

RESULTS

For all groups, the fine-tuned CNN showed the lowest average root mean squared error, average mean absolute percentage error, highest average time gain (PH = 15 and 60 min in T1DM) and highest percentage in region A of Clarke error grid analysis at all PHs. In the performance comparison between the fine-tuned CNN and other models, we found that the fine-tuned CNN improved the performance of the general CNN in most cases and outperformed the scratch CNN at all PHs in all groups, making the fine-tuning strategy was useful for accurate BG level prediction. We analyzed all cases of four predictive patterns in each group, and found that the input BG level trend and the BG level at the time of prediction were related to the future BG level trend.

CONCLUSIONS

We demonstrated the efficacy of the fine-tuning method in a large number of CGM datasets and analyzed the four predictive patterns. Therefore, we believe that the proposed method will significantly contribute to the development of an accurate personalized model and the analysis for its predictions.

Long-term complications of type 1 diabetes: what do we know and what do we need to understand?

Long-term complications of type 1 diabetes (T1D) include microvascular complications and macrovascular disease. Despite the important advances in the treatment of T1D of the last decades, these complications still represent the leading cause of morbidity and mortality in patients with T1D. Extensive evidence indicates that structural and functional alterations of the kidney, retina, nerves and large arteries occur already in the first years after the onset of diabetes. We performed a comprehensive review of the available evidence on screening, diagnosis, prevention and treatment of vascular complications of T1D. In particular, we focused on three major challenges related to long-term complications of T1D: 1) finding of new biomarkers and diagnostic methods able to identify early signs of complications; 2) identifying specific risk factors for the development of these complications; 3) identifying and implementing new therapeutic strategies able to prevent the development and progression of vascular complications.

Cardiovascular Risk Management in Type 1 Diabetes

PURPOSE OF REVIEW

Type 1 diabetes mellitus (T1DM) is associated with increased mortality, with premature cardiovascular disease (CVD) a major factor. To date, research has identified multiple risk factors for this excess CVD liability. However, gaps remain in our understanding of the underlying mechanisms.

RECENT FINDINGS

T1DM is generally diagnosed at a young age. Since cardiovascular complications often only manifest at a later stage of life, there is generally less focus in earlier years on reducing CVD risk for affected individuals. This is an area that requires improvement as risk factors might be managed from earlier age to reduce later development of CVD. In this review, we discuss the evidence for cardiovascular risk factors, risk prediction models, candidate surrogate measurements and CVD risk management.

The Association Between Continuous Glucose Monitoring-Derived Metrics and Cardiovascular Autonomic Neuropathy in Outpatients with Type 2 Diabetes

Background: Continuous glucose monitoring (CGM)-derived metrics, including time in range (TIR), are attracting attention as new indicators, beyond hemoglobin A1c, of glycemic control and diabetes complications. This study investigated the associations between CGM-derived TIR, hyperglycemia, and hypoglycemia metrics and cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes. Methods: A total of 284 patients with type 2 diabetes who underwent CGM using GOLD™ (Medtronic MiniMed) for 3 days or iPro™2 (Medtronic MiniMed) for 6 days and autonomic function tests within 3 months based on outpatient data were recruited. The definition of CGM-derived metrics was subject to the most recent international consensus. CAN was defined as an abnormal result in two or more parasympathetic test, and the severity of CAN was estimated as the sum of the scores of the five cardiovascular autonomic function tests. Results: A total of 84 patients (29.6%) were diagnosed with CAN, and the mean TIR was 57.0% ± 7.0%. A multiple logistic regression analysis revealed that the odds ratio (OR) of presence of CAN was 0.876 [95% confidence interval (CI): 0.79-0.98] per 10% increase in the TIR 70-180 mg/dL, after adjusting for age, sex, diabetes duration, any medications, and glycemic variability. A 10% increase in the TIR was significantly inversely associated with the severity of CAN (OR: 0.89, 95% CI: 0.81-0.98). Among the metrics of hyperglycemia, each 10% increase in a time above range (TAR) >180 mg/dL was also independently correlated with the presence of CAN (OR: 1.141, 97.5% CI: 1.01-1.29) and the severity of CAN (OR: 1.13, 97.5% CI: 1.01-1.26). Conclusion: A TIR 70-180 mg/dL and a TAR >180 mg/dL were significantly associated with CAN in outpatients with type 2 diabetes.

Association of Urinary N-Acetyl-β-D-Glucosaminidase with Cardiovascular Autonomic Neuropathy in Type 1 Diabetes Mellitus without Nephropathy

BACKGROUND

Cardiovascular autonomic neuropathy (CAN) is a common microvascular complication of diabetes and related to albuminuria in diabetic nephropathy (DN). Urinary N-acetyl-β-D-glucosaminidase (uNAG) is a renal tubular injury marker which has been reported as an early marker of DN even in patients with normoalbuminuria. This study evaluated whether uNAG is associated with the presence and severity of CAN in patients with type 1 diabetes mellitus (T1DM) without nephropathy.

METHODS

This cross-sectional study comprised 247 subjects with T1DM without chronic kidney disease and albuminuria who had results for both uNAG and autonomic function tests within 3 months. The presence of CAN was assessed by age-dependent reference values for four autonomic function tests. Total CAN score was assessed as the sum of the partial points of five cardiovascular reflex tests and was used to estimate the severity of CAN. The correlations between uNAG and heart rate variability (HRV) parameters were analyzed.

RESULTS

The association between log-uNAG and presence of CAN was significant in a multivariate logistic regression model (adjusted odds ratio, 2.39; 95% confidence interval [CI], 1.08 to 5.28; P=0.031). Total CAN score was positively associated with loguNAG (β=0.261, P=0.026) in the multivariate linear regression model. Log-uNAG was inversely correlated with frequency-domain and time-domain indices of HRV.

CONCLUSION

This study verified the association of uNAG with presence and severity of CAN and changes in HRV in T1DM patients without nephropathy. The potential role of uNAG should be further assessed for high-risk patients for CAN in T1DM patients without nephropathy.

Association between time in range, a novel measurement of glycemic control and islet secretory function in chinese patients with type 2 diabetes mellitus-An observational study

AIMS

To explore the association between dynamic islet secretory function and TIR (time in range), a new valuable metric of glycemic control in type 2 diabetes (T2D).

METHODS

In this observational study 256 patients with type 2 diabetes were included and continuous glucose monitoring system (CGMS) were applied to monitor blood glucose and also the calculation of TIR [the time spent in an individual's target glucose range (usually 3.9-10 mmol/L)]. The participants were divided into 3 groups according to the tertiles of TIR, 85 cases with TIR ≥ 65.05% (T1 group), 86 cases with 41.84 < TIR ≤ 65.05% (T2 group) and 85 cases with TIR < 41.84% (T3 group). Serum glucagon (GLA_0h, GLA_0.5h, GLA_1h, GLA_2h, GLA_3h), C-peptide (Cp_0h, Cp_0.5h, Cp_1h, Cp_2h, Cp_3h) concentration at different time points were measured after a 100 g standard steamed buns meal test to assess the pancreatic alpha cell and beta cell function. Spearman correlation analysis and multivariate linear stepwise regression analysis were adopted for statistical analysis.

RESULTS

The average age and diabetes duration of all the participants were separately 56.09 ± 13.8 years and 8.0 (4.0,15.0) years. Compared with patients in T1 group, participants in group T2 and T3 tend to have a lower concentration of C-peptide at all time points, as well as GLA_0h, GLA_2h and GLA_3h (p < 0.05). TIR was positively correlated with C-peptide at different time points, area under the curve of C-peptide in half an hour (AUC_Cp0.5h), GLA_0h, GLA_3h, area under the curve of glucagon in half an hour (AUC_GLA0.5h)(r_s = 0.263, 0.414, 0.510, 0.587, 0.528, 0.360, 0.259, 0.144 and 0.208, respectively, p < 0.05) and was negatively correlated with the increment of serum glucagon from baseline at 0.5 h, 1 h and 2 h after the standard energy loaded(△GLA_0.5h, △GLA_1h, △GLA_2h)(r_s = -0.152,-0.172 and -0.203, respectively, p < 0.05). Cp_2h, Cp_0h and GLA_0h were independent factors for TIR (β = 6.558,-6.930, 0.247, respectively, p < 0.01).

CONCLUSION

Both islet alpha cell and beta cell secretory function have important influence on TIR, a novel vital index of glycemic fluctuation.

The Role of Glucagon in Glycemic Variability in Type 1 Diabetes: A Narrative Review

Type 1 diabetes mellitus (T1DM) is a progressive disease as a result of the severe destruction of islet β-cell function, which leads to high glucose variability in patients. However, α-cell function is also compromised in patients with T1DM, characterized by aberrant fasting and postprandial glucagon secretion. According to recent studies, this aberrant glucagon secretion plays an increasing role in hyperglycemia, insulin-induced hypoglycemia and exercise-associated hypoglycemia in patients with T1DM. With application of continuous glucose monitoring system, dozens of metrics enable the assessment of glycemic variability, which is an integral component of glycemic control for patients with T1DM. There is growing evidences to illustrate the contribution of glucagon secretion to the glycemic variability in patients with T1DM, which may promote the development of new treatment strategies aiming to mitigate glycemic variability associated with aberrant glucagon secretion.

Evaluating Glycemic Control During Basalin or Lantus Administration in Adults With Controlled Type 2 Diabetes Mellitus Using Continuous Glucose Monitoring

AIM

This study aims at evaluating glycemic control during Basalin or Lantus administration in adults with controlled type 2 diabetes mellitus using continuous glucose monitoring system (CGM).

METHODS

47 patients with well-controlled T2DM using both Basalin and oral hypoglycemic drugs were recruited. CGM were applied from day 1 to day 3 with the unchanged dose of Basalin and then removed from day 4. A washout was performed with Lantus at the same dose as Basalin from day 4 to day 10. Then patients were continued to install the CGM under Lantus administration from day 11 to day 13. Variables of CGM, such as the area under the curve (AUC) for both hyperglycemia and hypoglycemia, 24h mean blood glucose (24h MBG), 24h standard deviation of blood glucose (24h SDBG), 24h mean amplitude of glycemic excursion (24h MAGE), PT (percentage of time), and time in range (TIR), were calculated and compared between Basalin group and Lantus group.

RESULTS

The group of Lantus showed lower 24h MBG (p<0.01), 24h MAGE (p<0.05), and lower 24h SDBG (p<0.01) than the Basalin group. Lantus-treated patients had a lower PT and AUC when the cut-off point for blood glucose was 10 mmol/L (p<0.05) and 13.9 mmol/L (p<0.05), respectively. In this study, no patient developed symptomatic hypoglycemia, few hypoglycemia was observed and there was no difference of hypoglycemia between the two groups.

CONCLUSION

In patients with well-controlled T2DM who were treated with insulin glargine, Lantus group showed lower MBG, GV, and lower PT (BG > 10.0 mmol/L, BG > 13.9 mmol/L) than Basalin group. In summary, for T2DM population with HbA1c ≤ 7%, Lantus may be a better choice compared with Basalin.

Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control

Glycosylated hemoglobin (HbA1c) has been the sole surrogate marker for assessing diabetic complications. However, consistently reported limitations of HbA1c are that it lacks detailed information on short-term glycemic control and can be easily interfered with by various clinical conditions such as anemia, pregnancy, or liver disease. Thus, HbA1c alone may not represent the real glycemic status of a patient. The advancement of continuous glucose monitoring (CGM) has enabled both patients and healthcare providers to monitor glucose trends for a whole single day, which is not possible with HbA1c. This has allowed for the development of core metrics such as time spent in time in range (TIR), hyperglycemia, or hypoglycemia, and glycemic variability. Among the 10 core metrics, TIR is reported to represent overall glycemic control better than HbA1c alone. Moreover, various evidence supports TIR as a predictive marker of diabetes complications as well as HbA1c, as the inverse relationship between HbA1c and TIR reveals. However, there are more complex relationships between HbA1c, TIR, and other CGM metrics. This article provides information about 10 core metrics with particular focus on TIR and the relationships between the CGM metrics for comprehensive understanding of glycemic status using CGM.

Cardiovascular disease in type 1 diabetes: A review of epidemiological data and underlying mechanisms

Cardiovascular disease (CVD) is highly prevalent in patients with type 1 diabetes (T1D) and a major cause of mortality. CVD arises earlier in life in T1D patients and is responsible for a significant reduction of at least 11 years' life expectancy. Also, the incidence of CVD is much more pronounced in patients with T1D onset at an earlier age. However, the factors responsible for increased atherosclerosis and CVD in T1D are not yet totally clarified. In addition to the usual cardiovascular (CV) risk factors, chronic hyperglycaemia plays an important role by promoting oxidative stress, vascular inflammation, monocyte adhesion, arterial wall thickening and endothelial dysfunction. Diabetic nephropathy and cardiac autonomic neuropathy are also associated with increased CVD in T1D. In fact, the CVD risk remains significantly increased even in well-controlled T1D patients who have no additional CV risk factors, indicating that other potential factors are likely to be involved. Hypoglycemia and glucose variability could enhance CV disease by promoting oxidative stress, vascular inflammation and endothelial dysfunction. Furthermore, even well-controlled T1D patients show significant qualitative and functional abnormalities of lipoproteins that are likely to be implicated in the development of atherosclerosis and premature CVD. In addition, recent data suggest that a dysfunctional immune system, which is typical of autoimmune T1D, might also promote CVD possibly through inflammatory pathways. Moreover, overweight and obese T1D patients can manifest additional CV risk through pathophysiological mechanisms resembling those observed in type 2 diabetes (T2D).

Flash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain

OBJECTIVE

Observations in real-world settings support and extend findings demonstrated in randomized controlled trials that show flash glucose monitoring improves glycemic control. In this study, Spain-specific relationships between testing frequency and glycemic parameters were investigated under real-world settings.

RESEARCH DESIGN AND METHODS

Deidentified glucose and user scanning data were analyzed and readers were rank ordered into 20 equal sized groups by daily scan frequency. Glucose parameters were calculated for each group: estimated HbA1c, time below range (<70 and ≤54 mg/dL), within range (70-180 mg/dL), and above range (>180 mg/dL). Glycemic variability (GV) metrics were described and data obtained from sensors in Spain and worldwide were compared.

RESULTS

Spanish users (n=22 949) collected 37.1 million glucose scans, 250 million automatically recorded glucose readings, and checked glucose values via a mean of 13 scans/day. Estimated HbA1c, time below 70 mg/dL, at or below 54 mg/dL, above 180 mg/dL, and GV metrics were significantly lower in the highest compared with lowest scan rate group (39.6 to 3.9 scans/day). Time-in-range was higher for the highest versus lowest scan rate group at 15.6 vs 11.5 hours/day, respectively. GV metrics correlated positively with time below 70 mg/dL, at or below 54 mg/dL, above 180 mg/dL, and negatively with time-in-range. The relationship between glucose metrics and scan rate was similar in Spain and worldwide. However, time in hypoglycemia in Spain was higher in the groups with lower scan rates.

CONCLUSIONS

As seen in clinical trials, flash glucose monitoring in real-world settings allows frequent glucose checks. High scan rates are associated with the favorable glycemic markers of increased time-in-range and reduced time in hyperglycemia and hypoglycemia, and GV. The same trends, with unique nuances, are observed in both Spanish and global data.

Time in Range, as a Novel Metric of Glycemic Control, Is Reversely Associated with Presence of Diabetic Cardiovascular Autonomic Neuropathy Independent of HbA1c in Chinese Type 2 Diabetes

OBJECTIVE

The objective of this study is to investigate the relationship between time in range (TIR), a new metric of continuous glucose monitoring (CGM) and cardiovascular autonomic neuropathy (CAN) in individuals with type 2 diabetes mellitus (T2DM).

METHODS

A total of 349 individuals with T2DM were enrolled in this study. Evaluating by the standard cardiac autonomic reflex tests (CARTs), there were 228 diabetic individuals without cardiovascular autonomic neuropathy (without confirmed CAN) including absent CAN (n = 83 cases) and early CAN (n = 83 cases) and early CAN (n = 83 cases) and early CAN (n = 83 cases) and early CAN (.

RESULTS

The total presence of CAN was 34.67% (definite CAN 31.23% and severe CAN 3.44%). Patients with more severe CAN had lower TIR (P < 0.001). With increasing quartiles of TIR, the presence of CAN by severity declined (P < 0.001). With increasing quartiles of TIR, the presence of CAN by severity declined (P < 0.001). With increasing quartiles of TIR, the presence of CAN by severity declined (P < 0.001). With increasing quartiles of TIR, the presence of CAN by severity declined (.

CONCLUSION

TIR is associated with the presence of CAN independent of HbA1c and GV metrics in Chinese type 2 diabetes.

Effect of Dapagliflozin on Glycemic Variability in Patients with Type 2 Diabetes under Insulin Glargine Combined with Other Oral Hypoglycemic Drugs

AIM

To evaluate the effect of an inhibitor of sodium-glucose cotransporter 2 (SGLT-2 inhibitor, dapagliflozin) on glycemic variability in type 2 diabetes mellitus (T2D) under insulin glargine combined with oral hypoglycemic drugs, using a continuous glucose monitoring system (CGMS).

METHODS

This prospective, self-controlled, single-center clinical trial recruited 36 patients with T2D under combined insulin glargine and oral hypoglycemic drugs. General clinical data were collected. Fasting blood glucose (FBG), postprandial blood glucose (PBG), glycosylated hemoglobin (HbA1c), and C-peptide levels were assessed before and four weeks of dapagliflozin (10 mg per day) treatment. Blood glucose was monitored for 72 hours before and after treatment using CGMS.

RESULTS

After treatment with dapagliflozin, FBG decreased from 6.74 ± 1.78 to 5.95 ± 1.13 mmol/L (p < 0.05); PBG decreased from 13.04 ± 2.99 to 10.92 ± 3.26 mmol/L (p < 0.05); HbA1c decreased from 7.37 ± 0.96% to 6.94 ± 0.80%. The proportion of patients with HbA1c < 7% increased from 27.8% to 58.3%, and the proportion of patients with HbA1c < 7% and without level 2 hypoglycemia increased from 27.8% to 55.6% (p < 0.05). CGMS data showed reduction of the 24 h MBG, MAGE, time-above-range (TAR, >10 mmol/L), high blood glucose index (HBGI), glucose management indicator (GMI), and incremental area under the curve of the glucose level more than 10 mmol/L (AUC > 10) and an increase of time-in-range (TIR, 3.9-10 mmol/L) with treatment. Homeostasis model assessment for pancreatic beta-cell function (HOMA-beta) increased significantly with treatment (p < 0.05), and fewer insulin doses were required after the treatment, without increasing in hypoglycemia and urinary tract infection. Further, a stratified analysis showed that patients with higher pretreatment HbA1c and waist-to-hip ratio (WHR) had greater improvement in glycemic control.

CONCLUSION

Dapagliflozin may reduce blood glucose levels, ameliorate glycemic variability, and improve pancreatic beta-cell function in patients with T2D under insulin glargine combined with other oral hypoglycemic drugs, especially in those with poor glucose control and abdominal obesity.

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.

Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care

Type 1 diabetes mellitus (T1DM) remains one of the most challenging long-term conditions to manage. Despite robust evidence to demonstrate that near normoglycaemia minimizes, but does not completely eliminate, the risk of complications, its achievement has proved almost impossible in a real-world setting. HbA1c to date has been used as the gold standard marker of glucose control and has been shown to reflect directly the risk of diabetes complications. However, it has been recognized that HbA1c is a crude marker of glucose control. Continuous glucose monitoring (CGM) provides the ability to measure and observe inter- and intraday glycaemic variability (GV), a more meaningful measure of glycaemic control, more relevant to daily living for those with T1DM. This paper reviews the relationship between GV and hypoglycaemia, and micro- and macrovascular complications. It also explores the impact on GV of CGM, insulin pumps, closed-loop technologies, and newer insulins and adjunctive therapies. Looking to the future, there is an argument that GV should become a key determinant of therapeutic success. Further studies are required to investigate the pathological and psychological benefits of reducing GV.

The early detection of atherosclerosis in type 1 diabetes: why, how and what to do about it

The major cause of morbidity and often premature mortality in people with type I diabetes (T1D) is cardiovascular disease owing to accelerated atherosclerosis. We review publications relating to the rationale behind, and clinical tests for, detecting and treating early atherosclerosis in people with T1D. Currently available tools for atherosclerosis assessment include risk equations using vascular risk factors, arterial intima-media thickness, the ankle-brachial index, coronary artery calcification and angiography, and for more advanced lesions, intravascular ultrasound and optical coherence tomography. Evolving research tools include risk equations incorporating novel clinical, biochemical and molecular tests; vascular MRI and molecular imaging. As yet there is little information available to quantify early atherosclerosis. With better means to control the vascular risk factors, such as hypertension, dyslipidaemia and glycaemic control, and emerging therapies to control novel risk factors, further epidemiologic and clinical trials are merited to facilitate the translation into clinical practice of robust means to detect, monitor and treat early atherosclerosis in those with T1D.

The Relationship of Hemoglobin A1C to Time-in-Range in Patients with Diabetes

BACKGROUND

There has been recent recognition of the limitations of hemoglobin A1C (HbA1C) in describing both short- and long-term glycemic control. Continuous glucose monitoring (CGM) provides robust data about short-term glycemic control and provides metrics such as percent time-in-range (%TIR) that are now routinely reported to describe the change in glycemic control after an intervention in a clinical study or a change in therapy in a patient's care. Recent studies have shown that %TIR may have similar associations with diabetes microvascular complications as does HbA1C. The relationship of %TIR to the long-standing metric of overall glycemic control has not been clearly defined to date.

METHODS

Articles that report paired HbA1C and %TIR metrics (n = 1137) or HbA1C and frequent self-monitoring of blood glucose (SMBG) (n = 1440) across a wide range of HbA1Cs, technologies, and subject demographics were reviewed to determine the correlation of these metrics.

RESULTS

Selected paired HbA1C and %TIR data from 18 articles were evaluated by linear regression analysis and Pearson's correlation coefficient. There was an excellent correlation between the two (R = -0.84; R² = 0.71). This relationship did not change after excluding one study that used SMBG or six studies with ≤7 days of CGM. For every absolute 10% change in %TIR, there was a 0.8% (9 mmol/mol) change in HbA1C.

CONCLUSIONS

There is a good correlation between HbA1C and %TIR that may permit the transition to %TIR as the preferred metric for determining the outcome of clinical studies, predicting of the risk of diabetes complications, and assessing of an individual patient's glycemic control.