Glycemic Variability and Diabetes Complications: Does It Matter? Simply Put, There Are Better Glycemic Markers!

U-shaped association between the glycemic variability and prognosis in hemorrhagic stroke patients: a retrospective cohort study from the MIMIC-IV database

Background

Elevated glycemic variability (GV) is commonly observed in intensive care unit (ICU) patients and has been associated with clinical outcomes. However, the relationship between GV and prognosis in ICU patients with hemorrhagic stroke (HS) remains unclear. This study aims to investigate the association between GV and short- and long-term all-cause mortality.

Methods

Clinical data for hemorrhagic stroke (HS) patients were obtained from the MIMIC-IV 3.1 database. GV was quantified using the coefficient of variation (CV), calculated as the ratio of the standard deviation to the mean blood glucose level. The association between GV and clinical outcomes was analyzed using Cox proportional hazards regression models. Additionally, restricted cubic spline (RCS) curves were employed to examine the nonlinear relationship between GV and short- and long-term all-cause mortality.

Results

A total of 2,240 ICU patients with HS were included in this study. In fully adjusted models, RCS analyses revealed a U-shaped association between the CV and both short- and long-term all-cause mortality (P for nonlinearity < 0.001 for all outcomes). Two-piecewise Cox regression models were subsequently applied to identify CV thresholds. The thresholds for all-cause mortality in ICU, during hospitalization, and at 30, 90, and 180 days were determined to be 0.14, 0.16, 0.155, 0.14, and 0.14, respectively. These findings were consistent in sensitivity and subgroup analyses.

Conclusions

In HS patients, higher GV is associated with an increased risk of both short- and long-term all-cause mortality. Our findings suggest that stabilizing GV may improve the prognosis of HS patients.

Glucose control and variability assessed by continuous glucose monitoring in patients with type 1 diabetes and diabetic kidney disease

Continuous glucose monitoring (CGM) has emerged as a superior method to glycated hemoglobin (HbA1c) monitoring for glycemic control assessment in type 1 diabetes (T1D). The association between CGM parameters and diabetic kidney disease (DKD) has not been extensively researched. The aim of the present study was to compare CGM metrics between patients with stable and progressive DKD and T1D. A cross-sectional study was performed with 75 patients with T1D, of which 28 had progressive DKD, defined as an estimated glomerular filtration rate decrease of ≥3 ml/min/year or an increased albuminuria stage over the median follow-up time of 7.46 (6.50-8.16) years. FreeStyle Libre ProiQ Sensors were used for CGM. Insulin sensitivity was calculated according to the estimated glucose disposal rate (eGDR) formula. The results revealed that as compared with subjects with stable DKD, individuals with progressive DKD exhibited a higher average glucose level (P=0.03), spent more time above the target range (P=0.05), less time in time in range (TIR; P=0.03), had a higher median estimated HbA1c (P=0.02) and glucose management indicator (P=0.03), as well as a longer duration of hypoglycemic events (P=0.03). There were no differences in compliance levels and recognition of hypoglycemia between the DKD study groups. Differences in correlation patterns between CGM parameters in patients with stable and progressive DKD were observed. For example, glucose variability was significantly positively correlated with TIR in subjects with DKD (Ρ=0.390; P=0.04) but not in individuals without DKD. The progression of DKD was statistically significantly associated with several CGM parameters in multivariate logistic regression models. Collectively, associations between CGM metrics and DKD status were demonstrated in patients with T1D. The findings of the present study indicate the necessity for regular CGM in patients with progressive DKD for improvement of their glycemic control and DKD outcomes but also call for the development of a personalized approach to CGM data interpretation and establishing therapeutic targets in these subjects.

Slowly progressive subtype of childhood-onset type 1 diabetes as a high-risk factor for end-stage renal disease: A cohort study in Japan

AIM

To compare the incidence of end-stage renal disease (ESRD) between slowly progressive type 1 diabetes and acute-onset type 1 diabetes.

METHODS

This cohort study enrolled all 521 patients with childhood-onset type 1 diabetes with the year of onset from 1959 to 1996 in Hokkaido Prefecture, Japan. We calculated the ESRD incidence rate per 100,000 person-years by sex, onset year, onset age, and type 1 diabetes subtype (slowly progressive or acute-onset). We also constructed a Kaplan-Meier curve for ESRD by these risk factors.

RESULTS

The data of 391 patients were gathered, among whom 66 developed ESRD. The ESRD incidence rate per 100,000 person-years was 525 among all patients; 538 and 503 among women (n = 235) and men (n = 156); 893, 413, and 225 for onset year of 1959-1979 (n = 107), 1980-1989 (n = 201), and 1990-1996 (n = 83); 420 and 715 for onset before (n = 243) and after (n = 148) puberty; and 1388 and 432 for the slowly progressive (n = 41) and acute-onset (n = 350) subtypes, respectively. The Kaplan-Meier curve also indicated a significantly higher incidence of ESRD in slowly progressive than in acute-onset type 1 diabetes.

CONCLUSION

The incidence of ESRD was higher in slowly progressive than acute-onset type 1 diabetes.

Association of glycemic variability and prognosis in patients with traumatic brain injury: A retrospective study from the MIMIC-IV database

BACKGROUND

Elevated glycemic variability (GV) often occurs in intensive care unit (ICU) patients and is associated with patient prognosis. However, the association between GV and prognosis in ICU patients with traumatic brain injury (TBI) remains unclear.

METHOD

Clinical data of ICU patients with TBI were obtained from the Medical Information Mart for Intensive Care (MIMIC) -IV database. The coefficient of variation (CV) was utilized to quantify GV, while the Glasgow Coma Scale (GCS) was employed to evaluate the consciousness status of TBI patients. Pearson linear correlation analysis, linear regression, COX regression and restricted cubic spline (RCS) were used to investigate the relationship between CV and consciousness impairment, as well as the risk of in-hospital mortality.

RESULT

A total of 1641 ICU patients with TBI were included in the study from the MIMIC-IV database. Pearson linear correlation and restricted cubic spline (RCS) analysis results showed a negative linear relationship between CV and the last GCS (P = 0.002) with no evidence of nonlinearity (P for nonlinear = 0.733). Multivariable linear regression suggested a higher CV was associated with a lower discharge GCS [β (95 %CI) = -1.86 (-3.08 ∼ -0.65), P = 0.003]. Furthermore, multivariable COX regression indicated that CV ≥ 0.3 was a risk factor for in-hospital death in TBI patients [HR (95 %CI) = 1.74 (1.15-2.62), P = 0.003], and this result was also consistent across sensitivity and subgroup analyses.

CONCLUSION

Higher GV is related to poorer consciousness outcomes and increased risk of in-hospital death in ICU patients with TBI. Additional research is needed to understand the logical relationship between GV and TBI progression.

The Contribution of Postprandial Glucose Levels to Hyperglycemia in Type 2 Diabetes Calculated from Continuous Glucose Monitoring Data: Real World Evidence from the DIALECT-2 Cohort

BACKGROUND/OBJECTIVES

Traditional glycemic monitoring in type 2 diabetes is limited, whereas continuous glucose monitoring (CGM) offers better insights into glucose fluctuations. This study aimed to determine the correlations and relative contributions of fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels to hyperglycemia.

METHODS

We utilized CGM and recorded carbohydrate intake data from lifestyle diaries of 59 patients enrolled in the Diabetes and Lifestyle Cohort Twente (DIALECT-2). Correlations between FPG and the glucose management indicator (GMI), FPG and Time Above Range (TAR), PPG and GMI, and PPG and TAR were conducted. Daily and mealtime relative contributions of PPG and FPG to glycated hemoglobin (HbA1c) and GMI were determined, considering two ranges: on target (<7.0%, 53 mmol/mol) and not on target (≥7.0%, 53 mmol/mol). Correlations between mealtime PPG and carbohydrate consumption were examined.

RESULTS

FPG and PPG correlated with GMI (r = 0.82 and 0.41, respectively, p < 0.05). The relative contribution of PPG in patients with HbA1c, GMI, and TAR values not on target was lower than in patients with HbA1c, GMI, and TAR values on target. When analyzing different mealtimes, patients with target GMI values had a higher PPG (73 ± 21%) than FPG after breakfast (27 ± 21%, p < 0.001). Individuals with elevated GMI levels had lower PPG after lunch (30 ± 20%), dinner (36 ± 23%), and snacks (34 ± 23%) than FPG. PPG after breakfast positively correlated (r = 0.41, p < 0.01) with breakfast carbohydrate intake.

CONCLUSIONS

Both PPG and FPG contribute to hyperglycemia, with PPG playing a larger role in patients with better glycemic control, especially after breakfast. Targeting PPG may be crucial for optimizing glucose management.

Potential Benefits of Continuous Glucose Monitoring for Predicting Vascular Outcomes in Type 2 Diabetes: A Rapid Review of Primary Research

(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides valuable insights into GV. Unlike in type 1 diabetes mellitus, the use of CGM-derived GV indices has not been widely adopted in the management of T2DM due to the limited evidence of their effectiveness in predicting clinical outcomes. This study aimed to explore the associations between GV metrics and short- or long-term vascular and clinical complications in T2DM. (2) Methods: A rapid literature review was conducted using the Cochrane Library, MEDLINE, and Scopus databases to seek high-level evidence. Lower-quality studies such as cross-sectional studies were excluded, but their content was reviewed. (3) Results: Six studies (five prospective cohort studies and one clinical trial) reported associations between GV indices (coefficient of variation (CV), standard deviation (SD), Mean Amplitude of Glycaemic Excursions (MAGE), Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR)), and clinical complications. However, since most evidence came from moderate to low-quality studies, the results should be interpreted with caution. (4) Conclusions: Limited but significant evidence suggests that GV indices may predict clinical compilations in T2DM both in the short term and long term. There is a need for longitudinal studies in larger and more diverse populations, longer follow-ups, and the use of numerous CGM-derived GV indices while collecting information about all microvascular and macrovascular complications.

Expert Opinion on Optimising Type 2 Diabetes Treatment Using Fixed-Ratio Combination of Basal Insulin and GLP-1 RA for Treatment Intensification and Simplification

The management of type 2 diabetes (T2D) often necessitates treatment intensification, and sometimes simplification to achieve glycaemic targets and mitigate complications. This expert opinion paper evaluates the use and positioning of the fixed-ratio combinations (FRCs) of basal insulin (BI) and glucagon-like peptide 1 receptor agonists (GLP-1 RAs) in optimising T2D management. On the basis of the evidence presented and discussions, these FRCs offer a promising approach for both treatment intensification and simplification in people with suboptimal glucose control despite receiving various therapies. In treatment intensification, FRCs provide a synergistic effect by addressing multiple pathophysiological defects contributing to hyperglycaemia. These FRCs effectively control both fasting and postprandial glucose (PPG) excursions, offering significantly improved glycaemic control with a lower hypoglycaemia risk and weight neutrality compared to traditional or complex insulin regimens. Moreover, the reduced injection frequency (once daily) and flexibility in the dosing schedule (with any major meal of the day) help mitigate patient resistance to insulin initiation or titration. This further reduces treatment burden, facilitating treatment adherence and enhancing patient convenience. These key benefits of FRCs over complex insulin regimens play a crucial role in long-term glycaemic management and overall treatment outcomes. Hence, the timely use of FRCs in the treatment algorithm for people with T2D represents a valuable strategy for optimising glycaemic control, addressing treatment barriers and enhancing patient-reported outcomes.

The effect of different times of day for exercise on blood glucose fluctuations

AIMS

This study aims to explore blood glucose variations before and after short-term intensive exercise in the morning or afternoon of a day and the trend of blood glucose fluctuations during exercise in patients with T2DM (type 2 diabetes, T2DM).

METHODS

Blood glucose variations of Fouty during morning exercise 8:00-12:00 hours and twenty during afternoon exercise 14:30-18:30 hours). Patients with T2DM discharged from the hospital were analyzed retrospectively, with the baseline data checked through the medical record system before intervention. We were asked to perform seven times of treadmill aerobic exercise, which lasted for 30 minutes with incremental intensity for each time, for two weeks under the supervision of the Continuous Glucose Monitor (CGM) and the heart rate armband. The exercise intensity has been adjusted by the clinicians and specialist nurses from the Department of Diabetes Mellitus according to the blood glucose levels and heart rate curves during exercise; data including the height, weight, body mass index (BMI), waist-to-hip ratio, fasting blood glucose, glycosylated hemoglobin, in-exercise CGM-measured blood glucose value/min, and after-exercise fingertip blood glucose value of patients with T2DM were collected after the intensive exercise (2 weeks). SPSS 22.0 and GraphPad Prism 7 were adopted for statistical analysis using the T-test and ANOVA.

RESULT

No difference was observed in the baseline data between the morning and afternoon exercise groups before intervention; compared to the morning exercise group, the fasting C-peptide value (2.15±0.97 vs. 1.53±0.46) in the afternoon exercise group was higher than that in the morning exercise group, with a superior (p=0.029) effect after two weeks of intervention, exhibiting a significant difference in the results. According to the results of repeated variance ANOVA analysis, the time for the appearance of significant improvement in blood glucose in the afternoon exercise group was 5 minutes earlier (11th minute vs 1 minute)than that in the morning exercise group (15th minute vs 1 min); significant differences were observed in both time (p=0.048 vs p<0.01) between the two groups on exercise days, as revealed by the results of bivariate ANOVA; in comparison to the morning exercise group (7.42±1.68), there was a significant difference (p=0.049)in the mean blood glucose between the two groups 25 min after patients with T2DM in the afternoon exercise group (6.25±1.53) started to exercise; in addition, a significant statistical difference (p=0.021) was revealed in the CGM-measured hourly the mean blood glucose on exercise days between the morning(8.18±1.88) and afternoon exercise (6.75±1.40)groups at 4:00 pm in week one and two w.

CONCLUSIONS

Glycaemic improvement in the short-term intensive afternoon exercise group may be superior to that of the morning exercise group, which may be related to greater fasting C-peptide secretion and longer effective exercise duration. The time to exercise is a factor affecting blood glucose variations during exercise. However, significant variations in the level of blood glucose during exercise must be further observed through exercise intervention over a more extended period.

Increased frequency of microalbuminuria in patients with type 3 autoimmune polyglandular syndrome (APS) compared to isolated autoimmune type 1 diabetes mellitus: A real-life study

AIM OF THE STUDY

The primary aim of the study was to evaluate the differences in metabolic control and chronic microvascular complications in patients with type 3 autoimmune polyglandular syndrome (APS3), compared to type 1 diabetes mellitus (T1DM) alone. Secondary aims were to evaluate the age of autoimmune thyroid disease (AIT) onset and the effects of levothyroxine treatment on metabolic control in patients with APS3.

MATERIAL AND METHODS

We retrospectively reviewed 276 patients with T1DM alone and 214 patients with APS3 and evaluated clinical and metabolic parameters and microvascular complications.

RESULTS

Patients with T1DM showed a longer duration of diabetes (p = 0.001) and lower age of diabetes onset (p = 0.020) compared to patients with APS3. Female gender (p = 0.001) and microalbuminuria (p = 0.006) were significantly more frequent in patients with APS3 compared to T1DM. In addition, patients with APS3 showed higher AIT onset frequency in the 16-30 quartile age-range. Furthermore, APS3 patients treated with levothyroxine showed significantly better HbA1c values than non-treated patients (p = 0.001).

CONCLUSIONS

We found that patients with APS3 showed positive microalbuminuria, earlier than T1DM. Patients with APS3 showed higher frequency of AIT age of onset in the 16-30 age-range and those treated with levothyroxine had better metabolic control, than untreated ones.

The Relationship Between Hemoglobin A1c, Time in Range, and Glycemic Management Indicator in Patients With Type 1 and Type 2 Diabetes in a Tertiary Care Hospital in Saudi Arabia

OBJECTIVES

This study aimed to assess the correlation between glycated hemoglobin A1 (HbA1c), time in range (TIR), and glycemic management indicator (GMI) in patients with both type 1 diabetes (T1D) and type 2 diabetes (T2D) who were using a flash glucose monitoring (FGM) device (FreeStyle Libre; Abbott Diabetic Care, Witney, UK).

METHODS

This was a retrospective study that looked at T1D and T2D FreeStyle Libre users' LibreView database in the period between January 2020 to June 2022. The study was conducted at the diabetes department at the King Fahad Medical City (KFMC) in Riyadh, Saudi Arabia, following Institutional Review Board (IRB) approval. Data were collected from the LibreView website, as well as from the electronic privacy information center (EPIC) hospital records.

RESULTS

Data were available for 327 patients, mean age of 33.08(±17.1) years old, and 55.7% were females. HbA1c had a statistically significant correlation with both TIR and GMI with coefficient of correlation (r) values of 0.78 (p<0.001) and 0.82 (p<0.001), respectively. A linear regression model between TIR and Hb1Ac was also developed and found to be statistically significant (p<0.001) with an acceptable R2 value (0.60).

CONCLUSION

Study findings revealed that the %TIR could be a reliable predictor of Hb1Ac. Thus, Freestyle Libre was able to determine Hb1Ac as close to the lab results as possible. Therefore, it is necessary to encourage diabetes patients to achieve at least 70% TIR in order to keep Hb1Ac within the desired range.

Tight versus liberal blood-glucose control in the intensive care unit: special considerations for patients with diabetes

Stress hyperglycaemia, hypoglycaemia, and diabetes are common in critically ill patients and related to clinical endpoints. To avoid complications related to hypoglycaemia and hyperglycaemia, it is recommended to start insulin therapy for the majority of critically ill patients with persistent blood glucose concentrations higher than 10·0 mmol/L (>180 mg/dL), targeting a range of 7·8-10·0 mmol/L (140-180 mg/dL). However, management and evidence-based targets for blood glucose control are under debate, particularly for patients with diabetes. Recent randomised controlled clinical trials now challenge current recommendations. In this Personal View, we aim to highlight these developments and the important differences between critically ill patients with and without diabetes, taking into account the considerable heterogeneity in this patient group. We critically discuss evidence from prospective randomised controlled trials and observational studies on the safety and efficacy of glycaemic control, specifically in the context of patients with diabetes in intensive care units.

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.

Association of glycemic variability with death and severe consciousness disturbance among critically ill patients with cerebrovascular disease: analysis of the MIMIC-IV database

BACKGROUND

The association of glycemic variability with severe consciousness disturbance and in-hospital all-cause mortality in critically ill patients with cerebrovascular disease (CVD) remains unclear, This study aimed to investigate the association of glycemic variability with cognitive impairment and in-hospital death.

METHOD

We extracted all blood glucose measurements of patients diagnosed with CVD from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Glycemic variability was defined as the coefficient of variation (CV), which was determined using the ratio of standard deviation and the mean blood glucose levels. Cox hazard regression models were applied to analyze the link between glycemic variability and outcomes. We also analyzed non-linear relationship between outcome indicators and glycemic variability using restricted cubic spline curves.

RESULTS

The present study included 2967 patients diagnosed with cerebral infarction and 1842 patients diagnosed with non-traumatic cerebral hemorrhage. Log-transformed CV was significantly related to cognitive impairment and in-hospital mortality, as determined by Cox regression. Increasing log-transformed CV was approximately linearly with the risk of cognitive impairment and in-hospital mortality.

CONCLUSION

High glycemic variability was found to be an independent risk factor for severe cognitive decline and in-hospital mortality in critically ill patients with CVD. Our study indicated that enhancing stability of glycemic variability may reduced adverse outcomes in patients with severe CVD.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

The Role of Telemedicine in Type 1 Diabetes Children during COVID-19 Pandemic Era: A Systematic Review and Meta-analysis

BACKGROUND: The COVID-19 pandemic determined a profound impact on the routine follow-up of type 1 diabetes (T1D) children. Telemedicine represents a critical tool to guarantee regular care for these patients in this form. AIM: The purpose of this study was to assess the impact of telemedicine programs during the COVID-19 pandemic era on T1D children. PATIENTS AND METHODS: Studies from PubMed, Cochrane, and Directory of Open Access Journals from December 2021, to February 18, 2022, were conducted to calculate the pooled mean difference using either a random or fixed-effect model in Review Manager version 5.3. Our study has applied to ensure that our procedures, including record collection, extraction of data, quality evaluation, and statistical analysis, adhere to the Preferred Reporting Items for Systematic Examination and Meta-Analysis guidelines. RESULTS: Three articles relevant to the current study (436 children). Our pooled analysis found that there was an impact of telemedicine in reducing the HbA1c (mean diff: 5.64 [95% confidence interval (CI) 3.71–7.57], p < 0.00001). However, the physical activity was not affected by the telemedicine program (mean diff: −37.25 [95% CI −317.53–243.02], p = 0.79). CONCLUSION: Our findings suggest that telemedicine has a role in T1D children controlling HbA1c during the COVID-19 pandemic. Meanwhile, telehealth has emerged as a promising alternate mode of health-care delivery. Its utility during the pandemic warrants further investigation.

Endothelial Dysfunction and Platelet Hyperactivation in Diabetic Complications Induced by Glycemic Variability

The development and progression of the complications of chronic diabetes mellitus are attributed not only to increased blood glucose levels but also to glycemic variability. Therefore, a deeper understanding of the role of glycemic variability in the development of diabetic complications may provide more insight into targeted clinical treatment strategies in the future. Previously, the mechanisms implicated in glycemic variability-induced diabetic complications have been comprehensively discussed. However, endothelial dysfunction and platelet hyperactivation, which are two newly recognized critical pathogenic factors, have not been fully elucidated yet. In this review, we first evaluate the assessment of glycemic variability and then summarise the roles of endothelial dysfunction and platelet hyperactivation in glycemic variability-induced complications of diabetes, highlighting the molecular mechanisms involved and their interconnections.

A Power-Harvesting CGM Chiplet Featuring Silicon-Based Enzymatic Glucose Sensor

Diabetes has become a leading cause of death and disability in the past decades. Continuous glucose monitoring (CGM) is a prevailing technique to determine the glucose level and provide in-time treatment. However, conventional CGM systems combine an electrochemical sensor with a CMOS chip, suffering from bulky size and interface issues. Integrating the CGM sensor on silicon is potential to miniaturize the CGM system and reduce the cost, while the recent silicon-based sensors show limited detection range and sensitivity. In this work, we present a silicon-based CGM chip let with wireless power transfer (WPT) and real-time wireless telemetry. Fabricated on a single silicon substrate, the chiplet consists of a silicon-based CGM sensor, a power-harvesting wireless-telemetry chip, and a silicon-based antenna. Measured results show that the chip let achieves a sensitivity of 4 μA.mM.cm-2 and a linear detection range of 0-10 mM. Based on WPT and backscattering communication, the chip let consumes 18.8 μ W power in glucose telemetry.

Tim-3 Expression Causes NK Cell Dysfunction in Type 2 Diabetes Patients

Type 2 diabetes mellitus (T2DM) is characterized by high blood glucose levels and chronic low-grade inflammation. It shows a strong association with obesity and immune dysfunction, which makes T2DM patients more susceptible to infectious diseases. NK cells play an important role in pathogen control and tumor surveillance. However, whether NK cell distribution and functional status are altered in T2DM is unclear. To address this issue, we compared surface receptor expression and cytokine production between peripheral blood NK cells from 90 T2DM patients and 62 age- and sex-matched healthy controls. We found a significantly lower frequency and absolute number of NK cells in patients than in controls. Interestingly, the expression of inhibitory receptor Tim-3 was significantly increased, while the expression of the activating receptor NKG2D was significantly decreased, in T2DM NK cells. Both TNF-α secretion and degranulation capacity (evidenced by CD107a expression) were dampened in NK cells from patients. The expression of Tim-3 on NK cells correlated positively with both HbA1c and fasting blood glucose levels and negatively with the percentage and absolute number of total NK cells and was associated with increased NK cell apoptosis. In addition, Tim-3 expression on NK cells negatively correlated with TNF-α production, which could be restored by blocking Galectin-9/Tim-3 pathway. Our results suggest that NK cell dysfunction secondary to augmented Tim-3 expression occurs in T2DM patients, which may partly explain their increased susceptibility to cancer and infectious disease.

Inhibition of SGLT1 Alleviates the Glycemic Variability-Induced Cardiac Fibrosis via Inhibition of Activation of Macrophage and Cardiac Fibroblasts

Glycemic variability has been considered one of the predictors of diabetes complications in patients with diabetes mellitus (DM). In this work, we evaluated whether glycemic variability induces cardiac fibrosis through regulating cardiac fibroblast activation and macrophage polarization. Moreover, we determined whether glucose transporter sodium-glucose cotransporter 1 (SGLT1) plays an important role in this process. Glycemic variability-induced mice were established using DM mice (GVDM mice), and intermittent high-glucose (IHG) treatment was used to simulate glycemic variability in RAW264.7 macrophages and cardiac fibroblasts. The short hairpin RNA for SGLT1 was used to knock down SGLT1. The results showed that glycemic variability aggravated the cardiac fibrosis in GVDM mice. Additionally, glycemic variability promoted the expression of fibrogenic cytokine and the extracellular matrix proteins in left ventricular tissues and cardiac fibroblasts. GVDM mice showed a higher incidence of macrophage infiltration and M1 polarization in left ventricular tissues. Moreover, IHG-promoted RAW264.7 macrophages tended to differentiate to M1 phenotype. SGLT1 knockdown alleviated cardiac fibrosis in GVDM mice and inhibited activations of cardiac fibroblast and macrophage M1 polarization. Our results indicated that glycemic variability aggravates cardiac fibrosis through activating cardiac fibroblast and macrophage M1 polarization, which could be partially inhibited by SGLT1 knockdown.

Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring

BACKGROUND

Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM.

METHODS

Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months.

RESULTS

The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA_1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV.

CONCLUSION

The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.

Maternal glucose variability during pregnancy & birthweight percentile in women with pre-gestational diabetes

INTRODUCTION

Pre-gestational diabetes mellitus (PGDM) is a major risk factor for fetal overgrowth. Interestingly, even in relatively well controlled PGDM women, as determined by average glucose indices such HbA1c, there is an increased rate of LGA (large for gestational age). Glucose variability (GV) has emerged as an important independent risk factor for several diabetes complications. The aim of this study was to determine the relationship between maternal GV indices and neonatal birth percentile.

METHODS

This was a historical cohort study that included all consecutive PGDM women monitored in a single tertiary care center. Clinical and demographic variables, as well as data regarding glucose control, were prospectively recorded. Mean, standard deviation (SD) and coefficient of variance (CV) of glucose values were calculated. Pearson correlations coefficient was used to determine the correlation between glucose indices and birth percentile. The analysis was repeated after adjustment for several confounders.

RESULTS

Mean birthweight and birthweight percentile were 3212 ± 532 g and 66.9%, respectively. There was a statistically significant correlation between birthweight percentile and maternal glucose SD (β = 0.28, p = .002) and maternal glucose CV (β = 0.21, p = .019). There was no significant correlation between birthweight percentile and mean glucose values. The association between the maternal glucose SD and birthweight percentile remained statistically significant after adjustment for maternal age, pre-pregnancy BMI and duration of diabetes.

CONCLUSION

There is an association between maternal glucose variability indices (SD and CV) during pregnancy and neonatal birth percentile. Larger studies are needed to confirm these results.

T1DMicro: A Clinical Risk Calculator for Type 1 Diabetes Related Microvascular Complications

Development of complications in type 1 diabetes patients can be reduced by modifying risk factors. We used a cross-sectional cohort of 1646 patients diagnosed with type 1 diabetes (T1D) to develop a clinical risk score for diabetic peripheral neuropathy (DPN), autonomic neuropathy (AN), retinopathy (DR), and nephropathy (DN). Of these patients, 199 (12.1%) had DPN, 63 (3.8%) had AN, 244 (14.9%) had DR, and 88 (5.4%) had DN. We selected five variables to include in each of the four microvascular complications risk models: age, age of T1D diagnosis, duration of T1D, and average systolic blood pressure and HbA1C over the last three clinic visits. These variables were selected for their strong evidence of association with diabetic complications in the literature and because they are modifiable risk factors. We found the optimism-corrected R2 and Harrell’s C statistic were 0.39 and 0.87 for DPN, 0.24 and 0.86 for AN, 0.49 and 0.91 for DR, and 0.22 and 0.83 for DN, respectively. This tool was built to help inform patients of their current risk of microvascular complications and to motivate patients to control their HbA1c and systolic blood pressure in order to reduce their risk of these complications.

Continuous glucose monitoring in persons with type 2 diabetes not using insulin

INTRODUCTION

CGM is an evidence-based intervention to improve glycemic control in persons with T1D and T2D using insulin. Use of CGM in persons with T2D not using insulin is not well studied.

AREAS COVERED

Existing clinical evidence for the use of CGM in persons with T2D is reviewed with a focus on persons with T2D not using insulin. Additional perspective and consideration are provided on the role and rationale for using CGM in persons with T2D not using insulin.

EXPERT OPINION

On the basis of available evidence, persons with T2D not using insulin benefit clinically through reduction in HbA1c, and improvement in time in range. Additional benefits include improvement in behavior modification, satisfaction, quality of life, empowerment, and diabetes distress. Drivers of these benefits are independent of insulin use in persons with T2D and may include an improved understanding of how diet, lifestyle, and exercise impact diabetes through CGM use. Clinical benefits from CGM independent of medication use include ability to modify health behavior and subsequently improve self-management.

Glycemic Gap as a Useful Surrogate Marker for Glucose Variability and Progression of Diabetic Retinopathy

(1) Background: Recent studies have reported that the glucose variability (GV), irrespective of glycosylated hemoglobin (HbA1c), could be an additional risk factor for the development of diabetic retinopathy (DR). However, measurements for GV, such as continuous glucose monitoring (CGM) and fasting plasma glucose (FPG) variability, are expensive and time consuming. (2) Methods: This present study aims to explore the correlation between the glycemic gap as a measurement of GV, and DR. In total, 2565 patients were included in this study. We evaluated the effect of the different types of glycemic gaps on DR progression. (3) Results: We found that the area under the curve (AUC) values of both the glycemic gap and negative glycemic gap showed an association with DR progression. (4) Conclusions: On eliminating the possible influences of chronic blood glucose controls, the results show that GV has deleterious effects that are associated with the progression of DR. The glycemic gap is a simple measurement of GV, and the predictive value of the negative glycemic gap in DR progression shows that GV and treatment-related hypoglycemia may cause the development of DR. Individual treatment goals with a reasonable HbA1c and minimal glucose fluctuations may help in preventing DR.

The Role of Glycemic Variability in Cardiovascular Disorders

Diabetes mellitus (DM) is one of the most common and costly disorders that affect humans around the world. Recently, clinicians and scientists have focused their studies on the effects of glycemic variability (GV), which is especially associated with cardiovascular diseases. In healthy subjects, glycemia is a very stable parameter, while in poorly controlled DM patients, it oscillates greatly throughout the day and between days. Clinically, GV could be measured by different parameters, but there are no guidelines on standardized assessment. Nonetheless, DM patients with high GV experience worse cardiovascular disease outcomes. In vitro and in vivo studies showed that high GV causes several detrimental effects, such as increased oxidative stress, inflammation, and apoptosis linked to endothelial dysfunction. However, the evidence that treating GV is beneficial is still scanty. Clinical trials aiming to improve the diagnostic and prognostic accuracy of GV measurements correlated with cardiovascular outcomes are needed. The present review aims to evaluate the clinical link between high GV and cardiovascular diseases, taking into account the underlined biological mechanisms. A clear view of this challenge may be useful to standardize the clinical evaluation and to better identify treatments and strategies to counteract this DM aspect.

Time in range correlates glycated albumin measured immediately after 2 weeks of continuous glucose monitoring

AIMS

Time in range (TIR), an index of glycemic control and also blood glucose fluctuation, obtained from continuous glucose monitoring (CGM), has been increasing its importance along with the spread of CGM in recent years. For a while, glycated albumin (GA) has been also used as a glycemic control index during about 2-weeks in routine clinical practice. It has not yet been confirmed under optimal condition whether TIR and GA correlates. Clarification of the correlation between TIR and GA, which was measured immediately after 2-weeks of CGM, might be a finding that further supports the utility of TIR.

METHODS

GA was measured at the conclusion of 2-week CGM in 71 diabetes outpatients at our hospital, and the correlation between GA and indices such as TIR obtained from CGM was statistically analyzed.

RESULTS

It was found that TIR and time above range (TAR) were significantly correlated with GA. Upon performing multiple regression analysis, TIR, TAR and BMI. indicated a significant regression coefficient with respect to GA.

CONCLUSIONS

These findings further support the utility of TIR as a marker of glycemic control that it might also be correlated with GA, and also suggest a relation between GA and blood glucose fluctuation.

T-Cell Expression and Release of Kidney Injury Molecule-1 in Response to Glucose Variations Initiates Kidney Injury in Early Diabetes

Half of the mortality in diabetes is seen in individuals <50 years of age and commonly predicted by the early onset of diabetic kidney disease (DKD). In type 1 diabetes, increased urinary albumin-to-creatinine ratio (uACR) during adolescence defines this risk, but the pathological factors responsible remain unknown. We postulated that early in diabetes, glucose variations contribute to kidney injury molecule-1 (KIM-1) release from circulating T cells, elevating uACR and DKD risk. DKD risk was assigned in youth with type 1 diabetes (n = 100; 20.0 ± 2.8 years; males/females, 54:46; HbA_1c 66.1 [12.3] mmol/mol; diabetes duration 10.7 ± 5.2 years; and BMI 24.5 [5.3] kg/m²) and 10-year historical uACR, HbA_1c, and random blood glucose concentrations collected retrospectively. Glucose fluctuations in the absence of diabetes were also compared with streptozotocin diabetes in apolipoprotein E ^-/- mice. Kidney biopsies were used to examine infiltration of KIM-1-expressing T cells in DKD and compared with other chronic kidney disease. Individuals at high risk for DKD had persistent elevations in uACR defined by area under the curve (AUC; uACR_AUC0-10yrs, 29.7 ± 8.8 vs. 4.5 ± 0.5; P < 0.01 vs. low risk) and early kidney dysfunction, including ∼8.3 mL/min/1.73 m² higher estimated glomerular filtration rates (modified Schwartz equation; P_adj < 0.031 vs. low risk) and plasma KIM-1 concentrations (∼15% higher vs. low risk; P < 0.034). High-risk individuals had greater glycemic variability and increased peripheral blood T-cell KIM-1 expression, particularly on CD8⁺ T cells. These findings were confirmed in a murine model of glycemic variability both in the presence and absence of diabetes. KIM-1⁺ T cells were also infiltrating kidney biopsies from individuals with DKD. Healthy primary human proximal tubule epithelial cells exposed to plasma from high-risk youth with diabetes showed elevated collagen IV and sodium-glucose cotransporter 2 expression, alleviated with KIM-1 blockade. Taken together, these studies suggest that glycemic variations confer risk for DKD in diabetes via increased CD8⁺ T-cell production of KIM-1.

Time-in-range for monitoring glucose control: Is it time for a change?

The HbA1c value has been the gold standard for evaluating glucose control for decades. However, it has limitations such as the lack of information on glycemic variability or the risk of hypoglycemia. The increasing use of continuous glucose monitoring has provided patients and healthcare professionals with a range of useful metrics for the management of diabetes. Among them, Time in Range (TIR) is a simple and intuitive metric that gives information regarding the quality of glucose control. It is defined as the time spent in an individual's target glucose range. TIR is strongly correlated with HbA1c, and it has been linked to the risk of developing microvascular and macrovascular complications. The International Consensus on Time in Range has recently set targets for different diabetes populations. For the majority of people with type 1 or type 2 diabetes, a TIR (70-180 mg/dL or 3.9-10.0 mmol/L) of >70%, a time below range (TBR) <70 mg/dL (<3.9 mmol/L) of <4% and a TBR <54 (<3.0 mmol/L) of <1% are recommended. In this review, we address the latest evidence for the use of TIR as an essential parameter in the management of diabetes.

Levothyroxine and insulin requirement in autoimmune polyglandular type 3 syndrome: a real-life study

PURPOSE

To evaluate factors influencing the insulin and levothyroxine requirement in patients with autoimmune polyglandular syndrome type 3 (APS-3) vs. patients with type 1 diabetes mellitus (T1DM) and autoimmune hypothyroidism (AH) alone, respectively.

METHODS

Fifty patients with APS-3, 60 patients with T1DM and 40 patients with AH were included. Anthropometric, clinical and biochemical parameters were evaluated in all patients. Insulin requirement was calculated in patients with APS-3 and T1DM, while levothyroxine requirement was calculated in APS-3 and AH.

RESULTS

Patients with APS-3 showed higher age (p = 0.001), age of onset of diabetes (p = 0.006) and TSH (p = 0.004) and lower total insulin as U/day (p < 0.001) and U/Kg (p = 0.001), long-acting insulin as U/day (p = 0.030) and U/kg (p = 0.038) and irisin (p = 0.002) compared to T1DM. Patients with APS-3 had higher waist circumference (p = 0.008), duration of thyroid disease (p = 0.020), levothyroxine total daily dose (p = 0.025) and mcg/kg (p = 0.006), triglycerides (p = 0.007) and VAI (p = 0.010) and lower age of onset of thyroid disease (p = 0.007) than AH. At multivariate analysis, levothyroxine treatment and VAI were associated with insulin and levothyroxine requirement in APS-3, respectively. VAI was independently associated with insulin requirement in T1DM. Circulating irisin levels were independently associated with levothyroxine requirement in AH.

CONCLUSION

Patients with APS-3 show lower insulin requirement and higher levothyroxine requirement than T1DM and AH alone, respectively. Levothyroxine treatment and VAI affect insulin and levothyroxine requirement, respectively, in APS-3. In T1DM, adipose tissue dysfunction, indirectly expressed by high VAI, is associated with an increased insulin requirement, while circulating irisin levels influence the levothyroxine requirement in AH.

The Effects of Glucagon-Like Peptide-1 Receptor Agonists and Dipeptydilpeptidase-4 Inhibitors on Blood Pressure and Cardiovascular Complications in Diabetes

Glucagon-like peptide-1 receptor (GLP-1R) agonists are a class of newly introduced antidiabetic medications that potentially lower blood glucose by several molecular pathways. DPP-4 inhibitors are the other type of novel antidiabetic medications which act by preventing GLP-1 inactivation and thereby increasing the activity levels of GLP-1, leading to more glucose-induced insulin release from islet β-cells and suppression of glucagon release. Most patients with diabetes have concurrent hypertension and cardiovascular disorder. If antihyperglycemic agents can attenuate the risk of hypertension and cardiovascular disease, they will amplify their overall beneficial effects. There is conflicting evidence on the cardiovascular benefits of GLP-1R induction in laboratory studies and clinical trials. In this study, we have reviewed the main molecular mechanisms by which GLP-1R induction may modulate the cardiovascular function and the results of cardiovascular outcome clinical trials.

Intestinal Microbiota in Common Chronic Inflammatory Disorders Affecting Children

The incidence and prevalence rate of chronic inflammatory disorders is on the rise in the pediatric population. Recent research indicates the crucial role of interactions between the altered intestinal microbiome and the immune system in the pathogenesis of several chronic inflammatory disorders in children, such as inflammatory bowel disease (IBD) and autoimmune diseases, such as type 1 diabetes mellitus (T1DM) and celiac disease (CeD). Here, we review recent knowledge concerning the pathogenic mechanisms underlying these disorders, and summarize the facts suggesting that the initiation and progression of IBD, T1DM, and CeD can be partially attributed to disturbances in the patterns of composition and abundance of the gut microbiota. The standard available therapies for chronic inflammatory disorders in children largely aim to treat symptoms. Although constant efforts are being made to maximize the quality of life for children in the long-term, sustained improvements are still difficult to achieve. Additional challenges are the changing physiology associated with growth and development of children, a population that is particularly susceptible to medication-related adverse effects. In this review, we explore new promising therapeutic approaches aimed at modulation of either gut microbiota or the activity of the immune system to induce a long-lasting remission of chronic inflammatory disorders. Recent preclinical studies and clinical trials have evaluated new approaches, for instance the adoptive transfer of immune cells, with genetically engineered regulatory T cells expressing antigen-specific chimeric antigen receptors. These approaches have revolutionized cancer treatments and have the potential for the protection of high-risk children from developing autoimmune diseases and effective management of inflammatory disorders. The review also focuses on the findings of studies that indicate that the responses to a variety of immunotherapies can be enhanced by strategic manipulation of gut microbiota, thus emphasizing on the importance of proper interaction between the gut microbiota and immune system for sustained health benefits and improvement of the quality of life of pediatric patients.

Glucodensities: A new representation of glucose profiles using distributional data analysis

Biosensor data have the potential to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state-of-the-art analysis methods. In particular, our findings demonstrate that (i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes; (ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information and; (iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics and provides more in-depth insight into assessing glucose metabolism.

Stress Induced Hyperglycemia in the Context of Acute Coronary Syndrome: Definitions, Interventions, and Underlying Mechanisms

Elevation of glucose level in response to acute coronary syndrome (ACS) has been recognized as stress induced hyperglycemia (SIH). Plenty of clinical studies have documented that SIH occurs very common in patients hospitalized with ACS, even in those without previously known diabetes mellitus. The association between elevated blood glucose levels with adverse outcome in the ACS setting is well-established. Yet, the precise definition of SIH in the context of ACS remains controversial, bringing confusions about clinical management strategy. Several randomized trials aimed to evaluate the effect of insulin-based therapy on outcomes of ACS patients failed to demonstrate a consistent benefit of intensive glucose control. Mechanisms underlying detrimental effects of SIH on patients with ACS are undetermined, oxidative stress might play an important role in the upstream pathways leading to subsequent harmful effects on cardiovascular system. This review aims to discuss various definitions of SIH and their values in predicting adverse outcome in the context of ACS, as well as the effect of intensive glucose control on clinical outcome. Finally, a glimpse of the underlying mechanisms is briefly discussed.

Glycemic Variability in Type 1 Diabetes Mellitus Saudis Using Ambulatory Glucose Profile

Background

Glucose variability (GV) is a common and challenging clinical entity in the management of people with type 1 diabetes (T1DM). The magnitude of GV in Saudi people with T1DM was not addressed before. Therefore, we aimed to study GV in a consecutive cohort of Saudis with T1DM.

Methods

We prospectively assessed interstitial glucose using FreeStyle^® Libre flash glucose monitoring in people with TIDM who attended follow-up in the diabetes clinics at King Fahad Medical City between March and June 2017. Glycemia profile, standard deviation (SD), coefficient of variation (CV), mean of daily differences (MODD), and mean amplitude of glucose excursion (MAGE) were measured using the standard equations over a period of 2 weeks.

Results

Fifty T1DM subjects (20 males) with mean age 20.2 ± 6.1 years and mean fortnight glucose 192 ± 42.3 mg/dl were included. The mean SD of 2-week glucose readings was 100.4 ± 36.3 mg/dl and CV was 52.1% ± 13%. Higher levels of glucose excursions were also observed. MODD and MAGE were recorded as 104.5 ± 51.7 and 189 ± 54.9 mg/dl, respectively which is 2 to 4 times higher than the international standards. Higher MODD and MAGE were observed on weekends compared to weekdays (111.3 ± 62.1 vs 98.6 ± 56.2 mg/dl and 196.4 ± 64.6 vs 181.7 ± 52.4 mg/dl, respectively; P ⩽ .001).

Conclusion

Higher degree of glycemic variability was observed in this cohort of TIDM Saudis. Weekends were associated with higher glucose swings than weekdays. More studies are needed to explore these findings further.

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 (GLA0h, GLA0.5h, GLA1h, GLA2h, GLA3h), C-peptide (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h) 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 GLA0h, GLA2h and GLA3h (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 (AUCCp0.5h), GLA0h, GLA3h, area under the curve of glucagon in half an hour (AUCGLA0.5h)(rs = 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(△GLA0.5h, △GLA1h, △GLA2h)(rs = -0.152,-0.172 and -0.203, respectively, p < 0.05). Cp2h, Cp0h and GLA0h 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.

Kolb L. An Effective Model of Diabetes Care and Education: The ADCES7 Self-Care Behaviors™

PURPOSE

The ADCES7 Self-Care Behaviors^™ (ADCES7) is a robust framework for self-management of diabetes and other related conditions, such as prediabetes and cardiometabolic diseases. It is the position of the Association of Diabetes Care and Education Specialists (ADCES) that at the cornerstone of diabetes self-management education and support, the ADCES7 is the framework for achieving behavior change that leads to effective self-management through improved behavior and clinical outcome measures. The ADCES7 model guides the health care team in effective person-centered collaboration and goal setting to achieve health-related outcomes and improved quality of life. Continued research and evidence are critical to expand this model and broaden its application to other chronic conditions. Given the advances in the science of diabetes management as well as diabetes self-management education and support, ADCES has evaluated the ADCES7 within the framework of these advances, including the digital and dynamic health care landscape.

CONCLUSION

This revised position statement blends the updates in research and ADCES's vision and expansion beyond diabetes to refresh the ADCES7 framework. This revision reflects the perspectives of all members of the health care team as they problem solve with individuals who are at risk for or who have diabetes and related conditions to achieve healthier outcomes.

Ramadan fasting in insulin-treated patients is associated with potentially unfavourable changes in glucose metrics: A flash glucose monitoring (FGM) study

AIM(S)

Ramadan fasting (RF) can represent various challenges to glycaemic control especially in insulin-treated patients with diabetes. We aimed to assess the effect of RF on several glucose metrics using flash glucose monitoring (FGM).

METHODS

Complete FGM data for 29-30 days before, during and after Ramadan were available for 40 patients with type 1 (n = 13) and type 2 diabetes (n = 27) on insulin (with or without oral hypoglycaemic) treatment. Indicators of mean glucose, glucose variability (GV) and time in different glycaemic ranges were analysed.

RESULTS

RF was associated with increase in time in hyperglycaemia (38.5 ± 18.2 vs 48.7 ± 20.7%; P < 0.001) and decrease in time in hypoglycaemia (3.2 ± 2.8 vs 2.1 ± 2.1%; P = 0.003), and time in target range (56.3 ± 17.2 vs 47.9 ± 19.7%, P < 0.001). There were no significant differences in markers of GV with RF; however, RF was associated with a significant reduction in GV during the day but not night time with an increase in the ensuing non-fasting period.

CONCLUSIONS

In insulin-treated patients, RF is associated with an increase in time in hyperglycaemia, a reduced time in target range and nocturnal increase in GV, indicating a need for more refined management algorithms.

Comprehensive elaboration of glycemic variability in diabetic macrovascular and microvascular complications

Diabetes mellitus is the major risk factor for the development of macrovascular and microvascular complications. It is increasingly recognized that glycemic variability (GV), referring to oscillations in blood glucose levels and representing either short-term or long-term GV, is involved in the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for them. In this review, we summarize the metrics and measurement of GV in clinical practice, as well as comprehensively elaborate the role and related mechanisms of GV in diabetic macrovascular and microvascular complications, aiming to provide the mechanism-based therapeutic strategies for clinicians to manage diabetes mellitus.

Clinical, Laboratory, Instrumental, Anamnestic Characteristics in Children With Type I Diabetes Mellitus and Early Stage of Diabetic Nephropathy

Type 1 diabetes (T1D) is mainly a disease of children and young adults. Diabetic nephropathy (DN) is a common finding in diabetic patients. Microalbuminuria is the earliest clinical evidence of DN. Aim of the study was analysis of clinical, laboratory, instrumental, anamnestic examinations data in pediatric patients with T1D and early stage of DN in order to evaluate possible factors associated with early stage of DN and predictors of DN development and progression. A survey of 105 children (62 males, 43 females) with T1D and DN aged 5 to 17 years in Endocrinology unit on Clinical Pediatric Hospital №6 (Kyiv, Ukraine) done. Following clinical and biochemical characteristics found associated with an early DN: inflammatory phenotype (increased ESR, decreased albumin/globulin ratio), functional cardiovascular disorders (increased systolic blood pressure, “minor” ECG changes), signs of secondary metabolic disorders (high HbA1c, increased serum cholesterol level, increase ALAT and ASAT levels). Kidney function impairment at early stage of DN shows: higher MAU grade, GFR decline, rise in serum creatinine level as compared to T1D group. Presence of concomitant kidney and endocrine disease; positive family history found in a bigger number of patients with DN. DKA episodes number found as a factor associated with higher levels of MAU in children with DN. Patients who had microalbuminuria and more than 5 episodes of DKA/year (poorly controlled T1D) have higher progression rate to macroalbuminuria as compared to those who have less than 5 episodes of DKA/year after a 6-year follow-up study.

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

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

Continuous glucose monitoring in cystic fibrosis - A practical guide

Our ability to monitor blood glucose levels has become increasingly accurate over the last few decades. Continuous glucose monitoring (CGM) technology now allows providers and patients the ability to monitor glucose levels retrospectively as well as in real-time for diabetes management. CGM also provides the ability to study glucose patterns and trends for insight into the pathophysiology and natural history of disease. CGM captures a more complete picture of glucose profiles than traditional measures of glycemia such as the hemoglobin A1c or self-monitoring of blood glucose levels. This article provides a review of the history of glucose monitoring, a review of the literature pertaining to CGM with a focus on studies in patients with cystic fibrosis, and discusses practical uses of CGM technology and its application for the evaluation and management of cystic fibrosis related diabetes.

Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes

BACKGROUND AND AIMS

Self-monitoring blood glucose (SMBG) remains a widespread tool to monitor blood glucose. The development of diabetes management systems (DMS) allows SMBG to provide additional information as time spent in target range (TIR). This study evaluates the association between HbA1c and TIR, evaluated through DMS, over 2 months, and 2 weeks.

METHODS AND RESULTS

Type 1 (T1D) and Type 2 (T2D) insulin-treated patients with diabetes were enrolled. We used the term PIR (Points in Range) instead of TIR, since SMBG provides point-in-time glucose values rather than a continuous trend over time. PIR was calculated in 2-month and 2-week time ranges before available HbA1c measurement. One-hundred ninety-seven patients with T1D and 36 with T2D were recruited. HbA1c and PIR were inversely associated (2 months: R -0.72, 2 weeks R -0.70; p < 0.0001) in all subjects. The relationship did not change when T1D and T2D patients were analyzed separately. For every 10% change of PIR, there was a change of HbA1c by 0.4%.

CONCLUSIONS

Our study, for the first time, demonstrates a significant correlation between HbA1c and PIR calculated by DMS. DMS offers additional information useful in disease management of patients with T1D and T2D performing SMBG.

Rationale for Timely Insulin Therapy in Type 2 Diabetes Within the Framework of Individualised Treatment: 2020 Update

Type 2 diabetes is characterised by chronic hyperglycaemia and variable degrees of insulin deficiency and resistance. Hyperglycaemia and elevated fatty acids exert harmful effects on β-cell function, regeneration and apoptosis (gluco-lipotoxicity). Furthermore, chronic hyperglycaemia triggers a vicious cycle of insulin resistance, low-grade inflammation and a cascade of pro-atherogenic processes. Thus, timely near to normal glucose control is of utmost importance in the management of type 2 diabetes and prevention of micro- and macroangiopathy. The majority of patients are multimorbid and obese, with critical comorbidities such as cardiovascular disease, heart failure and chronic kidney disease. Recently published guidelines therefore recommend patient-centred risk/benefit-balanced use of oral glucose-lowering drugs or a glucagon-like peptide 1 (GLP-1) receptor agonist, or switching to insulin with glycated haemoglobin (HbA1c) out of target. This article covers the indications of early insulin treatment to prevent diabetes-related complications, particularly in subgroups with severe insulin deficit, and to achieve recovery of residual β-cell function. Furthermore, the individualised, risk/benefit-balanced, timely initiation of insulin as second and third option is analysed. Timely insulin initiation may prevent diabetes progression, reduce diabetes-related complications and has less serious adverse effects. Basal insulin is the preferred option in most clinical situations with consequences of undertreatment of chronic hyperglycaemia.

Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes

BACKGROUND

To assess the associations of various HbA1c measures, including a single baseline HbA1c value, overall mean, yearly updated means, standard deviation (HbA1c-SD), coefficient of variation (HbA1c-CV), and HbA1c variability score (HVS), with microvascular disease (MVD) risk in patients with type 2 diabetes.

METHODS

Linked data between National Cheng Kung University Hospital and Taiwan's National Health Insurance Research Database were utilized to identify the study cohort. The primary outcome was the composite MVD events (retinopathy, nephropathy, or neuropathy) occurring during the study follow-up. Cox model analyses were performed to assess the associations between HbA1c measures and MVD risk, with adjustment for patients' baseline HbA1c, demographics, comorbidities/complications, and treatments.

RESULTS

In the models without adjustment for baseline HbA1c, all HbA1c variability and mean measures were significantly associated with MVD risk, except HVS. With adjustment for baseline HbA1c, HbA1c-CV had the strongest association with MVD risk. For every unit of increase in HbA1c-CV, the MVD risk significantly increased by 3.42- and 2.81-fold based on the models without and with adjustment for baseline HbA1c, respectively. The associations of HbA1c variability and mean measures with MVD risk in patients with baseline HbA1c < 7.5% (58 mmol/mol) were stronger compared with those in patients with baseline HbA1c ≥ 7.5% (58 mmol/mol).

CONCLUSIONS

HbA1c variability, especially HbA1c-CV, can supplement conventional baseline HbA1c measure for explaining MVD risk. HbA1c variability may play a greater role in MVD outcomes among patients with relatively optimal baseline glycemic control compared to those with relatively poor baseline glycemic control.

Glycemic variability: adverse clinical outcomes and how to improve it?

Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.

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

Background

Obstructive sleep apnea syndrome (OSAS) in association with Type 2 Diabetes Mellitus (DM) may result in increased glycemic variability affecting the glycemic control and hence increasing the risk of complications associated with diabetes. We decided to assess the Glycemic Variability (GV) in patients with type 2 diabetes with OSAS and in controls. We also correlated the respiratory disturbance indices with glycemic variability indices.

Methods

After fulfilling the inclusion and exclusion criteria patients from the Endocrinology and Pulmonology clinics underwent modified Sleep Apnea Clinical Score (SACS) followed by polysomnography (PSG). Patients were then divided into 4 groups: Group A (DM with OSAS, n = 20), Group B (DM without OSAS, n = 20), Group C (Non DM with OSAS, n = 10) and Group D (Non DM without OSAS, n = 10). Patients in these groups were subjected to continuous glucose monitoring using the Medtronic iPro2 and repeat PSG. Parameters of GV: i.e. mean glucose, SD (standard Deviation), CV (Coefficient of Variation), Night SD, Night CV, MAGE and NMAGE were calculated using the Easy GV software. GV parameters and the respiratory indices were correlated statistically. Quantitative data was expressed as mean, standard deviation and median. The comparison of GV indices between different groups was performed by one-way analysis of variance (ANOVA) or Kruskal Wallis (for data that failed normality). Correlation analysis of AHI with GV parameters was done by Pearson correlation.

Results

All the four groups were adequately matched for age, sex, Body Mass Index (BMI), waist circumference (WC) and blood pressure (BP). We found that the GV parameters Night CV, MAGE and NMAGE were significantly higher in Group A as compared to Group B (p values < 0.05). Similarly Night CV, MAGE and NMAGE were also significantly higher in Group C as compared to Group D (p value < 0.05). Apnea-hypopnea index (AHI) correlated positively with Glucose SD, MAGE and NMAGE in both diabetes (Group A plus Group B) and non- diabetes groups (Group C plus Group D).

Conclusions

OSAS has a significant impact on the glycemic variability irrespective of glycemic status. AHI has moderate positive correlation with the glycemic variability.

Evaluation of the relationship between cardiac autonomic function and glucose variability and HOMA-IR in prediabetes

AIMS

The present study aims to investigate the relationship between cardiac autonomic function (CAF) and glucose variability (GV) and HOMA-IR in subjects with prediabetes and normal glucose tolerance (NGT).

MATERIAL AND METHODS

Ninety-two subjects (59 with prediabetes and 33 with NGT), of mean age 50.3 ± 11.5 years, mean BMI 30.4 ± 6.0 kg/m², were included in this cross-sectional study. Glucose tolerance was assessed by OGTT according to WHO 2006 criteria. Glucose, HbA1c, insulin, oxLDL, and 3-Nitrotyrosine were measured. CGM was performed with a blinded sensor (FreeStyle Libre Pro). CAF was assessed by ANX-3.0 technology.

RESULTS

GV indices were increased in prediabetes. CAF was suppressed in subjects with any stage of dysglycemia. The prevalence of cardiac autonomic dysfunction was higher in prediabetes -20.3% as compared to NGT -3.0%, p = 0.028. HOMA-IR [OR 1.5 (95% CI: 1.1-2.1), p = 0.010] and time in target range [OR 0.8 (95% CI: 0.67-0.97), p = 0.021] were found to be predictive variables for impaired CAF. Sympathetic and parasympathetic activity negatively correlated with mean glycemia and GV indices and were independently related to JINDEX in prediabetes (F[1, 47] = 5.76, p = 0.021 and F[1, 47] = 5.94, p = 0.019, respectively); and to time above target range in NGT (F[1, 18] = 4.48, p = 0.049 and F[1, 18] = 4.65, p = 0.046, respectively).

CONCLUSION

CAF is declined in prediabetes and seems to be related to GV and HOMA-IR at early stages of dysglycemia.

Association of glucose variability at the last day of hospitalization with 30-day readmission in adults with diabetes

OBJECTIVE

To evaluate whether increased glucose variability (GV) during the last day of inpatient stay is associated with increased risk of 30-day readmission in patients with diabetes.

RESEARCH DESIGN AND METHODS

A comprehensive list of clinical, pharmacy and utilization files were obtained from the Veterans Affairs (VA) Central Data Warehouse to create a nationwide cohort including 1 042 150 admissions of patients with diabetes over a 14-year study observation period. Point-of-care glucose values during the last 24 hours of hospitalization were extracted to calculate GV (measured as SD and coefficient of variation (CV)). Admissions were divided into 10 categories defined by progressively increasing SD and CV. The primary outcome was 30-day readmission rate, adjusted for multiple covariates including demographics, comorbidities and hypoglycemia.

RESULTS

As GV increased, there was an overall increase in the 30-day readmission rate ratio. In the fully adjusted model, admissions with CV in the 5th-10th CV categories and admissions with SD in the 4th-10th categories had a statistically significant progressive increase in 30-day readmission rates, compared with admissions in the 1st (lowest) CV and SD categories. Admissions with the greatest CV and SD values (10th category) had the highest risk for readmission (rate ratio (RR): 1.08 (95% CI 1.05 to 1.10), p<0.0001 and RR: 1.11 (95% CI 1.09 to 1.14), p<0.0001 for CV and SD, respectively).

CONCLUSIONS

Patients with diabetes who exhibited higher degrees of GV on the final day of hospitalization had higher rates of 30-day readmission.

TRIAL REGISTRATION NUMBER

NCT03508934, NCT03877068.

Positioning time in range in diabetes management

Recent upswings in the use of continuous glucose monitoring (CGM) technologies have given people with diabetes and healthcare professionals unprecedented access to a range of new indicators of glucose control. Some of these metrics are useful research tools and others have been welcomed by patient groups for providing insights into the quality of glucose control not captured by conventional laboratory testing. Among the latter, time in range (TIR) is an intuitive metric that denotes the proportion of time that a person's glucose level is within a desired target range (usually 3.9-10.0 mmol/l [3.5-7.8 mmol/l in pregnancy]). For individuals choosing to use CGM technology, TIR is now often part of the expected conversation between patient and healthcare professional, and consensus recommendations have recently been produced to facilitate the adoption of standardised TIR targets. At a regulatory level, emerging evidence linking TIR to risk of complications may see TIR being more widely accepted as a valid endpoint in future clinical trials. However, given the skewed distribution of possible glucose values outside of the target range, TIR (on its own) is a poor indicator of the frequency or severity of hypoglycaemia. Here, the state-of-the-art linking TIR with complications risk in diabetes and the inverse association between TIR and HbA_1c are reviewed. Moreover, the importance of including the amount and severity of time below range (TBR) in any discussions around TIR and, by inference, time above range (TAR) is discussed. This review also summarises recent guidance in setting 'time in ranges' goals for individuals with diabetes who wish to make use of these metrics. For most people with type 1 or type 2 diabetes, a TIR >70%, a TBR <3.9 mmol/l of <4%, and a TBR <3.0 mmol/l of <1% are recommended targets, with less stringent targets for older or high-risk individuals and for those under 25 years of age. As always though, glycaemic targets should be individualised and rarely is that more applicable than in the personal use of CGM and the data it provides.